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Study Protocol

Assessing the effect of the COVID-19 pandemic, shift to online learning, and social media use on the mental health of college students in the Philippines: A mixed-method study protocol

Roles Funding acquisition, Writing – original draft

Affiliation College of Medicine, University of the Philippines, Manila, Philippines

Roles Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

Affiliations Department of Clinical Epidemiology, College of Medicine, University of the Philippines, Manila, Philippines, Institute of Clinical Epidemiology, National Institutes of Health, University of the Philippines, Manila, Philippines

ORCID logo

Roles Methodology

Affiliation Department of Psychiatry, College of Medicine, University of the Philippines, Manila, Philippines

Roles Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

  • Leonard Thomas S. Lim, 
  • Zypher Jude G. Regencia, 
  • J. Rem C. Dela Cruz, 
  • Frances Dominique V. Ho, 
  • Marcela S. Rodolfo, 
  • Josefina Ly-Uson, 
  • Emmanuel S. Baja


  • Published: May 3, 2022
  • Peer Review
  • Reader Comments

Fig 1


The COVID-19 pandemic declared by the WHO has affected many countries rendering everyday lives halted. In the Philippines, the lockdown quarantine protocols have shifted the traditional college classes to online. The abrupt transition to online classes may bring psychological effects to college students due to continuous isolation and lack of interaction with fellow students and teachers. Our study aims to assess Filipino college students’ mental health status and to estimate the effect of the COVID-19 pandemic, the shift to online learning, and social media use on mental health. In addition, facilitators or stressors that modified the mental health status of the college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning will be investigated.

Methods and analysis

Mixed-method study design will be used, which will involve: (1) an online survey to 2,100 college students across the Philippines; and (2) randomly selected 20–40 key informant interviews (KIIs). Online self-administered questionnaire (SAQ) including Depression, Anxiety, and Stress Scale (DASS-21) and Brief-COPE will be used. Moreover, socio-demographic factors, social media usage, shift to online learning factors, family history of mental health and COVID-19, and other factors that could affect mental health will also be included in the SAQ. KIIs will explore factors affecting the student’s mental health, behaviors, coping mechanism, current stressors, and other emotional reactions to these stressors. Associations between mental health outcomes and possible risk factors will be estimated using generalized linear models, while a thematic approach will be made for the findings from the KIIs. Results of the study will then be triangulated and summarized.

Ethics and dissemination

Our study has been approved by the University of the Philippines Manila Research Ethics Board (UPMREB 2021-099-01). The results will be actively disseminated through conference presentations, peer-reviewed journals, social media, print and broadcast media, and various stakeholder activities.

Citation: Lim LTS, Regencia ZJG, Dela Cruz JRC, Ho FDV, Rodolfo MS, Ly-Uson J, et al. (2022) Assessing the effect of the COVID-19 pandemic, shift to online learning, and social media use on the mental health of college students in the Philippines: A mixed-method study protocol. PLoS ONE 17(5): e0267555.

Editor: Elisa Panada, UNITED KINGDOM

Received: June 9, 2021; Accepted: April 11, 2022; Published: May 3, 2022

Copyright: © 2022 Lim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This project is being supported by the American Red Cross through the Philippine Red Cross and Red Cross Youth. The funder will not have a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

The World Health Organization (WHO) declared the Coronavirus 2019 (COVID-19) outbreak as a global pandemic, and the Philippines is one of the 213 countries affected by the disease [ 1 ]. To reduce the virus’s transmission, the President imposed an enhanced community quarantine in Luzon, the country’s northern and most populous island, on March 16, 2020. This lockdown manifested as curfews, checkpoints, travel restrictions, and suspension of business and school activities [ 2 ]. However, as the virus is yet to be curbed, varying quarantine restrictions are implemented across the country. In addition, schools have shifted to online learning, despite financial and psychological concerns [ 3 ].

Previous outbreaks such as the swine flu crisis adversely influenced the well-being of affected populations, causing them to develop emotional problems and raising the importance of integrating mental health into medical preparedness for similar disasters [ 4 ]. In one study conducted on university students during the swine flu pandemic in 2009, 45% were worried about personally or a family member contracting swine flu, while 10.7% were panicking, feeling depressed, or emotionally disturbed. This study suggests that preventive measures to alleviate distress through health education and promotion are warranted [ 5 ].

During the COVID-19 pandemic, researchers worldwide have been churning out studies on its psychological effects on different populations [ 6 – 9 ]. The indirect effects of COVID-19, such as quarantine measures, the infection of family and friends, and the death of loved ones, could worsen the overall mental wellbeing of individuals [ 6 ]. Studies from 2020 to 2021 link the pandemic to emotional disturbances among those in quarantine, even going as far as giving vulnerable populations the inclination to commit suicide [ 7 , 8 ], persistent effect on mood and wellness [ 9 ], and depression and anxiety [ 10 ].

In the Philippines, a survey of 1,879 respondents measuring the psychological effects of COVID-19 during its early phase in 2020 was released. Results showed that one-fourth of respondents reported moderate-to-severe anxiety, while one-sixth reported moderate-to-severe depression [ 11 ]. In addition, other local studies in 2020 examined the mental health of frontline workers such as nurses and physicians—placing emphasis on the importance of psychological support in minimizing anxiety [ 12 , 13 ].

Since the first wave of the pandemic in 2020, risk factors that could affect specific populations’ psychological well-being have been studied [ 14 , 15 ]. A cohort study on 1,773 COVID-19 hospitalized patients in 2021 found that survivors were mainly troubled with fatigue, muscle weakness, sleep difficulties, and depression or anxiety [ 16 ]. Their results usually associate the crisis with fear, anxiety, depression, reduced sleep quality, and distress among the general population.

Moreover, the pandemic also exacerbated the condition of people with pre-existing psychiatric disorders, especially patients that live in high COVID-19 prevalence areas [ 17 ]. People suffering from mood and substance use disorders that have been infected with COVID-19 showed higher suicide risks [ 7 , 18 ]. Furthermore, a study in 2020 cited the following factors contributing to increased suicide risk: social isolation, fear of contagion, anxiety, uncertainty, chronic stress, and economic difficulties [ 19 ].

Globally, multiple studies have shown that mental health disorders among university student populations are prevalent [ 13 , 20 – 22 ]. In a 2007 survey of 2,843 undergraduate and graduate students at a large midwestern public university in the United States, the estimated prevalence of any depressive or anxiety disorder was 15.6% and 13.0% for undergraduate and graduate students, respectively [ 20 ]. Meanwhile, in a 2013 study of 506 students from 4 public universities in Malaysia, 27.5% and 9.7% had moderate and severe or extremely severe depression, respectively; 34% and 29% had moderate and severe or extremely severe anxiety, respectively [ 21 ]. In China, a 2016 meta-analysis aiming to establish the national prevalence of depression among university students analyzed 39 studies from 1995 to 2015; the meta-analysis found that the overall prevalence of depression was 23.8% across all studies that included 32,694 Chinese university students [ 23 ].

A college student’s mental status may be significantly affected by the successful fulfillment of a student’s role. A 2013 study found that acceptable teaching methods can enhance students’ satisfaction and academic performance, both linked to their mental health [ 24 ]. However, online learning poses multiple challenges to these methods [ 3 ]. Furthermore, a 2020 study found that students’ mental status is affected by their social support systems, which, in turn, may be jeopardized by the COVID-19 pandemic and the physical limitations it has imposed. Support accessible to a student through social ties to other individuals, groups, and the greater community is a form of social support; university students may draw social support from family, friends, classmates, teachers, and a significant other [ 25 , 26 ]. Among individuals undergoing social isolation and distancing during the COVID-19 pandemic in 2020, social support has been found to be inversely related to depression, anxiety, irritability, sleep quality, and loneliness, with higher levels of social support reducing the risk of depression and improving sleep quality [ 27 ]. Lastly, it has been shown in a 2020 study that social support builds resilience, a protective factor against depression, anxiety, and stress [ 28 ]. Therefore, given the protective effects of social support on psychological health, a supportive environment should be maintained in the classroom. Online learning must be perceived as an inclusive community and a safe space for peer-to-peer interactions [ 29 ]. This is echoed in another study in 2019 on depressed students who narrated their need to see themselves reflected on others [ 30 ]. Whether or not online learning currently implemented has successfully transitioned remains to be seen.

The effect of social media on students’ mental health has been a topic of interest even before the pandemic [ 31 , 32 ]. A systematic review published in 2020 found that social media use is responsible for aggravating mental health problems and that prominent risk factors for depression and anxiety include time spent, activity, and addiction to social media [ 31 ]. Another systematic review published in 2016 argues that the nature of online social networking use may be more important in influencing the symptoms of depression than the duration or frequency of the engagement—suggesting that social rumination and comparison are likely to be candidate mediators in the relationship between depression and social media [ 33 ]. However, their findings also suggest that the relationship between depression and online social networking is complex and necessitates further research to determine the impact of moderators and mediators that underly the positive and negative impact of online social networking on wellbeing [ 33 ].

Despite existing studies already painting a picture of the psychological effects of COVID-19 in the Philippines, to our knowledge, there are still no local studies contextualized to college students living in different regions of the country. Therefore, it is crucial to elicit the reasons and risk factors for depression, stress, and anxiety and determine the potential impact that online learning and social media use may have on the mental health of the said population. In turn, the findings would allow the creation of more context-specific and regionalized interventions that can promote mental wellness during the COVID-19 pandemic.

Materials and methods

The study’s general objective is to assess the mental health status of college students and determine the different factors that influenced them during the COVID-19 pandemic. Specifically, it aims:

  • To describe the study population’s characteristics, categorized by their mental health status, which includes depression, anxiety, and stress.
  • To determine the prevalence and risk factors of depression, anxiety, and stress among college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning.
  • To estimate the effect of social media use on depression, anxiety, stress, and coping strategies towards stress among college students and examine whether participant characteristics modified these associations.
  • To estimate the effect of online learning shift on depression, anxiety, stress, and coping strategies towards stress among college students and examine whether participant characteristics modified these associations.
  • To determine the facilitators or stressors among college students that modified their mental health status during the COVID-19 pandemic, quarantine, and subsequent shift to online learning.

Study design

A mixed-method study design will be used to address the study’s objectives, which will include Key Informant Interviews (KIIs) and an online survey. During the quarantine period of the COVID-19 pandemic in the Philippines from April to November 2021, the study shall occur with the population amid community quarantine and an abrupt transition to online classes. Since this is the Philippines’ first study that will look at the prevalence of depression, anxiety, and stress among college students during the COVID-19 pandemic, quarantine, and subsequent shift to online learning, the online survey will be utilized for the quantitative part of the study design. For the qualitative component of the study design, KIIs will determine facilitators or stressors among college students that modified their mental health status during the quarantine period.

Study population

The Red Cross Youth (RCY), one of the Philippine Red Cross’s significant services, is a network of youth volunteers that spans the entire country, having active members in Luzon, Visayas, and Mindanao. The group is clustered into different age ranges, with the College Red Cross Youth (18–25 years old) being the study’s population of interest. The RCY has over 26,060 students spread across 20 chapters located all over the country’s three major island groups. The RCY is heterogeneously composed, with some members classified as college students and some as out-of-school youth. Given their nationwide scope, disseminating information from the national to the local level is already in place; this is done primarily through email, social media platforms, and text blasts. The research team will leverage these platforms to distribute the online survey questionnaire.

In addition, the online survey will also be open to non-members of the RCY. It will be disseminated through social media and engagements with different university administrators in the country. Stratified random sampling will be done for the KIIs. The KII participants will be equally coming from the country’s four (4) primary areas: 5–10 each from the national capital region (NCR), Luzon, Visayas, and Mindanao, including members and non-members of the RCY.

Inclusion and exclusion criteria

The inclusion criteria for the online survey will include those who are 18–25 years old, currently enrolled in a university, can provide consent for the study, and are proficient in English or Filipino. The exclusion criteria will consist of those enrolled in graduate-level programs (e.g., MD, JD, Master’s, Doctorate), out-of-school youth, and those whose current curricula involve going on duty (e.g., MDs, nursing students, allied medical professions, etc.). The inclusion criteria for the KIIs will include online survey participants who are 18–25 years old, can provide consent for the study, are proficient in English or Filipino, and have access to the internet.

Sample size

A continuity correction method developed by Fleiss et al. (2013) was used to calculate the sample size needed [ 34 ]. For a two-sided confidence level of 95%, with 80% power and the least extreme odds ratio to be detected at 1.4, the computed sample size was 1890. With an adjustment for an estimated response rate of 90%, the total sample size needed for the study was 2,100. To achieve saturation for the qualitative part of the study, 20 to 40 participants will be randomly sampled for the KIIs using the respondents who participated in the online survey [ 35 ].

Study procedure

Self-administered questionnaire..

The study will involve creating, testing, and distributing a self-administered questionnaire (SAQ). All eligible study participants will answer the SAQ on socio-demographic factors such as age, sex, gender, sexual orientation, residence, household income, socioeconomic status, smoking status, family history of mental health, and COVID-19 sickness of immediate family members or friends. The two validated survey tools, Depression, Anxiety, and Stress Scale (DASS-21) and Brief-COPE, will be used for the mental health outcome assessment [ 36 – 39 ]. The DASS-21 will measure the negative emotional states of depression, anxiety, and stress [ 40 ], while the Brief-COPE will measure the students’ coping strategies [ 41 ].

For the exposure assessment of the students to social media and shift to online learning, the total time spent on social media (TSSM) per day will be ascertained by querying the participants to provide an estimated time spent daily on social media during and after their online classes. In addition, students will be asked to report their use of the eight commonly used social media sites identified at the start of the study. These sites include Facebook, Twitter, Instagram, LinkedIn, Pinterest, TikTok, YouTube, and social messaging sites Viber/WhatsApp and Facebook Messenger with response choices coded as "(1) never," "(2) less often," "(3) every few weeks," "(4) a few times a week," and “(5) daily” [ 42 – 44 ]. Furthermore, a global frequency score will be calculated by adding the response scores from the eight social media sites. The global frequency score will be used as an additional exposure marker of students to social media [ 45 ]. The shift to online learning will be assessed using questions that will determine the participants’ satisfaction with online learning. This assessment is comprised of 8 items in which participants will be asked to respond on a 5-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree.’

The online survey will be virtually distributed in English using the Qualtrics XM™ platform. Informed consent detailing the purpose, risks, benefits, methods, psychological referrals, and other ethical considerations will be included before the participants are allowed to answer the survey. Before administering the online survey, the SAQ shall undergo pilot testing among twenty (20) college students not involved with the study. It aims to measure total test-taking time, respondent satisfaction, and understandability of questions. The survey shall be edited according to the pilot test participant’s responses. Moreover, according to the Philippines’ Data Privacy Act, all the answers will be accessible and used only for research purposes.

Key informant interviews.

The research team shall develop the KII concept note, focusing on the extraneous factors affecting the student’s mental health, behaviors, and coping mechanism. Some salient topics will include current stressors (e.g., personal, academic, social), emotional reactions to these stressors, and how they wish to receive support in response to these stressors. The KII will be facilitated by a certified psychologist/psychiatrist/social scientist and research assistants using various online video conferencing software such as Google Meet, Skype, or Zoom. All the KIIs will be recorded and transcribed for analysis. Furthermore, there will be a debriefing session post-KII to address the psychological needs of the participants. Fig 1 presents the diagrammatic flowchart of the study.


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Data analyses

Quantitative data..

Descriptive statistics will be calculated, including the prevalence of mental health outcomes such as depression, anxiety, stress, and coping strategies. In addition, correlation coefficients will be estimated to assess the relations among the different mental health outcomes, covariates, and possible risk factors.

online learning research paper philippines

Several study characteristics as effect modifiers will also be assessed, including sex, gender, sexual orientation, family income, smoking status, family history of mental health, and Covid-19. We will include interaction terms between the dichotomized modifier variable and markers of social media use (total TSSM and global frequency score) and shift to online learning in the models. The significance of the interaction terms will be evaluated using the likelihood ratio test. All the regression analyses will be done in R ( ). P values ≤ 0.05 will be considered statistically significant.

Qualitative data.

After transcribing the interviews, the data transcripts will be analyzed using NVivo 1.4.1 software [ 50 ] by three research team members independently using the inductive logic approach in thematic analysis: familiarizing with the data, generating initial codes, searching for themes, reviewing the themes, defining and naming the themes, and producing the report [ 51 ]. Data familiarization will consist of reading and re-reading the data while noting initial ideas. Additionally, coding interesting features of the data will follow systematically across the entire dataset while collating data relevant to each code. Moreover, the open coding of the data will be performed to describe the data into concepts and themes, which will be further categorized to identify distinct concepts and themes [ 52 ].

The three researchers will discuss the results of their thematic analyses. They will compare and contrast the three analyses in order to come up with a thematic map. The final thematic map of the analysis will be generated after checking if the identified themes work in relation to the extracts and the entire dataset. In addition, the selection of clear, persuasive extract examples that will connect the analysis to the research question and literature will be reviewed before producing a scholarly report of the analysis. Additionally, the themes and sub-themes generated will be assessed and discussed in relevance to the study’s objectives. Furthermore, the gathering and analyzing of the data will continue until saturation is reached. Finally, pseudonyms will be used to present quotes from qualitative data.

Data triangulation.

Data triangulation using the two different data sources will be conducted to examine the various aspects of the research and will be compared for convergence. This part of the analysis will require listing all the relevant topics or findings from each component of the study and considering where each method’s results converge, offer complementary information on the same issue, or appear to contradict each other. It is crucial to explicitly look for disagreements between findings from different data collection methods because exploration of any apparent inter-method discrepancy may lead to a better understanding of the research question [ 53 , 54 ].

Data management plan.

The Project Leader will be responsible for overall quality assurance, with research associates and assistants undertaking specific activities to ensure quality control. Quality will be assured through routine monitoring by the Project Leader and periodic cross-checks against the protocols by the research assistants. Transcribed KIIs and the online survey questionnaire will be used for recording data for each participant in the study. The project leader will be responsible for ensuring the accuracy, completeness, legibility, and timeliness of the data captured in all the forms. Data captured from the online survey or KIIs should be consistent, clarified, and corrected. Each participant will have complete source documentation of records. Study staff will prepare appropriate source documents and make them available to the Project Leader upon request for review. In addition, study staff will extract all data collected in the KII notes or survey forms. These data will be secured and kept in a place accessible to the Project Leader. Data entry and cleaning will be conducted, and final data cleaning, data freezing, and data analysis will be performed. Key informant interviews will always involve two researchers. Where appropriate, quality control for the qualitative data collection will be assured through refresher KII training during research design workshops. The Project Leader will check through each transcript for consistency with agreed standards. Where translations are undertaken, the quality will be assured by one other researcher fluent in that language checking against the original recording or notes.

Ethics approval.

The study shall abide by the Principles of the Declaration of Helsinki (2013). It will be conducted along with the Guidelines of the International Conference on Harmonization-Good Clinical Practice (ICH-GCP), E6 (R2), and other ICH-GCP 6 (as amended); National Ethical Guidelines for Health and Health-Related Research (NEGHHRR) of 2017. This protocol has been approved by the University of the Philippines Manila Research Ethics Board (UPMREB 2021-099-01 dated March 25, 2021).

The main concerns for ethics were consent, data privacy, and subject confidentiality. The risks, benefits, and conflicts of interest are discussed in this section from an ethical standpoint.


The participants will be recruited to answer the online SAQ voluntarily. The recruitment of participants for the KIIs will be chosen through stratified random sampling using a list of those who answered the online SAQ; this will minimize the risk of sampling bias. In addition, none of the participants in the study will have prior contact or association with the researchers. Moreover, power dynamics will not be contacted to recruit respondents. The research objectives, methods, risks, benefits, voluntary participation, withdrawal, and respondents’ rights will be discussed with the respondents in the consent form before KII.

Informed consent will be signified by the potential respondent ticking a box in the online informed consent form and the voluntary participation of the potential respondent to the study after a thorough discussion of the research details. The participant’s consent is voluntary and may be recanted by the participant any time s/he chooses.

Data privacy.

All digital data will be stored in a cloud drive accessible only to the researchers. Subject confidentiality will be upheld through the assignment of control numbers and not requiring participants to divulge the name, address, and other identifying factors not necessary for analysis.


No monetary compensation will be given to the participants, but several tokens will be raffled to all the participants who answered the online survey and did the KIIs.

This research will pose risks to data privacy, as discussed and addressed above. In addition, there will be a risk of social exclusion should data leaks arise due to the stigma against mental health. This risk will be mitigated by properly executing the data collection and analysis plan, excluding personal details and tight data privacy measures. Moreover, there is a risk of psychological distress among the participants due to the sensitive information. This risk will be addressed by subjecting the SAQ and the KII guidelines to the project team’s psychiatrist’s approval, ensuring proper communication with the participants. The KII will also be facilitated by registered clinical psychologists/psychiatrists/social scientists to ensure the participants’ appropriate handling; there will be a briefing and debriefing of the participants before and after the KII proper.

Participation in this study will entail health education and a voluntary referral to a study-affiliated psychiatrist, discussed in previous sections. Moreover, this would contribute to modifications in targeted mental-health campaigns for the 18–25 age group. Summarized findings and recommendations will be channeled to stakeholders for their perusal.


The results will be actively disseminated through conference presentations, peer-reviewed journals, social media, print and broadcast media, and various stakeholder activities.

This study protocol rationalizes the examination of the mental health of the college students in the Philippines during the COVID-19 pandemic as the traditional face-to-face classes transitioned to online and modular classes. The pandemic that started in March 2020 is now stretching for more than a year in which prolonged lockdown brings people to experience social isolation and disruption of everyday lifestyle. There is an urgent need to study the psychosocial aspects, particularly those populations that are vulnerable to mental health instability. In the Philippines, where community quarantine is still being imposed across the country, college students face several challenges amidst this pandemic. The pandemic continues to escalate, which may lead to fear and a spectrum of psychological consequences. Universities and colleges play an essential role in supporting college students in their academic, safety, and social needs. The courses of activities implemented by the different universities and colleges may significantly affect their mental well-being status. Our study is particularly interested in the effect of online classes on college students nationwide during the pandemic. The study will estimate this effect on their mental wellbeing since this abrupt transition can lead to depression, stress, or anxiety for some students due to insufficient time to adjust to the new learning environment. The role of social media is also an important exposure to some college students [ 55 , 56 ]. Social media exposure to COVID-19 may be considered a contributing factor to college students’ mental well-being, particularly their stress, depression, and anxiety [ 57 , 58 ]. Despite these known facts, little is known about the effect of transitioning to online learning and social media exposure on the mental health of college students during the COVID-19 pandemic in the Philippines. To our knowledge, this is the first study in the Philippines that will use a mixed-method study design to examine the mental health of college students in the entire country. The online survey is a powerful platform to employ our methods.

Additionally, our study will also utilize a qualitative assessment of the college students, which may give significant insights or findings of the experiences of the college students during these trying times that cannot be captured on our online survey. The thematic findings or narratives from the qualitative part of our study will be triangulated with the quantitative analysis for a more robust synthesis. The results will be used to draw conclusions about the mental health status among college students during the pandemic in the country, which will eventually be used to implement key interventions if deemed necessary. A cross-sectional study design for the online survey is one of our study’s limitations in which contrasts will be mainly between participants at a given point of time. In addition, bias arising from residual or unmeasured confounding factors cannot be ruled out.

The COVID-19 pandemic and its accompanying effects will persistently affect the mental wellbeing of college students. Mental health services must be delivered to combat mental instability. In addition, universities and colleges should create an environment that will foster mental health awareness among Filipino college students. The results of our study will tailor the possible coping strategies to meet the specific needs of college students nationwide, thereby promoting psychological resilience.

The impact of extreme weather on student online learning participation

  • Ezekiel Adriel D. Lagmay 1 &
  • Maria Mercedes T. Rodrigo   ORCID: 1  

Research and Practice in Technology Enhanced Learning volume  17 , Article number:  26 ( 2022 ) Cite this article

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In March 2020, the COVID-19 pandemic forced over 1 billion learners to shift from face-to-face instruction to online learning. Seven months after it began, this transition became even more challenging for Filipino online learners. Eight typhoons struck the Philippines from October to November 2020. Two of these typhoons caused widespread flooding, utilities interruptions, property destruction, and loss of life. We examine how these severe weather conditions affected online learning participation of Filipino students pursuing their undergraduate and graduate studies. We used CausalImpact analysis to explore September 2020 to January 2021 data collected from the Moodle Learning Management System data of one university in the Philippines. We found that overall student online participation was significantly negatively affected by typhoons. However, the effect on participation in Assignments and Quizzes was not significant. These findings suggested that students continued to participate in activities that have a direct bearing on their final grades, rather than activities that had no impact on their course outcomes.

Context of the study

The shift to online learning because of COVID-19 offered us a unique opportunity to quantify the impact of extreme weather on the online learning participation of Filipino students. In prior years, the majority of education in the Philippines, as in most countries, took place in person. While some institutions made use of Learning Management Systems (LMSs), most instruction was face to face. LMSs were repositories for materials, submission sites, or test platforms, but were typically not used to replace class time. The onset of the pandemic forced 1 billion students (UNESCO, 2021 ), including Filipinos, to shift to an online mode. The struggle to teach and learn online worsened when eight typhoons entered the Philippine Area of Responsibility (PAR) from October 11 to November 12, 2020 (Lalu, 2020 ). Two of them, Typhoons Goni and Vamco, were particularly destructive, causing widespread destruction, utilities disruptions, and loss of life. The migration of all instruction to digital platforms thus enabled us to capture a greater variety of instructional activities, data that were previously unavailable, and to use this data to study the effects of these typhoons on student learning behaviors.

Effects of extreme weather on academic achievement

The immediate effects of extreme weather events such as severe typhoons and heat waves include property destruction, crop failure, and human casualties. On November 8, 2013, for example, Typhoon Haiyan made landfall in the Philippines. A Category 5 storm, it was one of the most powerful typhoons of all time. It displaced 4.1 million people, killed 6,000, damaged 1.1 million homes, and destroyed 33 million coconut trees, a major cash crop (World Vision, 2021 ). In total, Typhoon Haiyan caused damages estimated at US$5.8 billion. Typhoon Goni made landfall in the Philippines on October 27, 2020, seven months into the COVID-19 pandemic. Like Haiyan, Goni was a Category 5 storm, the strongest of 2020, with maximum sustained winds of 255 km per hour. It left 25 dead and damaged over 280,000 houses. Damage to crops, livestock, fisheries, and agriculture was estimated at P5 billion, while damage to infrastructure such as roads and bridges was estimated at P12.8 billion (International Federation of Red Cross & Red Crescent Societies, 2020a ). Typhoon Vamco made landfall in the Philippines on November 11, 2020. Vamco was weaker than Goni, with maximum sustained winds at 155 km per hour (International Federation of Red Cross & Red Crescent Societies, 2020b ). However, Vamco brought historically high levels of flooding in parts of the country—the worst in 45 years. The storm killed 101 people and left over P20 billion in damages to livelihoods and infrastructure.

The longer-term consequences of these events are far-reaching and complex. In the developing world specifically, limited savings among less wealthy households and the lack of social supports such as access to credit and insurance make it difficult for poorer families to recover from shocks caused by extreme weather (Groppo & Kraehnert, 2017 ; Marchetta et al., 2018 ). Parents are forced to shift their investments from their children’s schooling, e.g., uniforms, books, transportation, tuition (Joshi, 2019 ), instead directing their resources to recovery from the economic consequences of the typhoon’s damage (Deuchert & Felfe, 2015 ). Post-typhoon enrollment decreases. Parents spend less time on their children’s learning and care (Joshi, 2019 ). Children spend less time in school and more time helping at home. Teens and young adults who are transitioning from school to work are particularly vulnerable to these shocks. They are likely to drop out of school and join the workforce in order to mitigate the impact of extreme weather. Poor young women in particular are susceptible to being pushed into the labor market (Marchetta et al., 2018 ).

These necessary choices cause an immediate gap in learning that grows over time. When Typhoon Mike hit Cebu in 1990, the children whose houses suffered typhoon damage lagged 0.13 years behind in school. The lag grew to 0.27 years in 1998, 0.52 years in 2002, and 0.67 years in 2005. By the time children are 22 years old, the gap in educational attainment is approximated at one year (Deuchert & Felfe, 2015 ).

The work of Bernabe et al. ( 2021 ) agrees. They found that storms have a disruptive impact on education. In areas severely affected by winds, children are 9% more likely to accumulate an educational delay and 6.5% less likely to complete secondary education. Individuals severely affected by storms between the ages of 23 and 33 are less likely to complete higher education, reducing their ability to obtain regular salaried jobs.

One might ask: Is it not possible for these children and young adults to return to school to make up for these gaps? Cunha and Heckman ( 2007 ) argue that different stages of childhood are more receptive to certain types of inputs than others. Secondary language learning, for example, is best before 12. They also find that public training programs for adults that try to bridge learning gaps from childhood do not produce substantial gains for most of their participants and tend to be more costly than remediation provided at earlier ages.

In summary, the physical and economic damage wrought by extreme weather events has an adverse impact on educational achievement. The education of young children who come from economically disadvantaged homes receives less financial support and parental attention, resulting in an achievement gap that increases with time. Adolescents and young adults, on the other hand, are sometimes forced to discontinue their studies and to enter the workforce to help mitigate the effects of the event. Resuming studies after an interruption is challenging because oftentimes an optimal window for learning has passed and attempts at remediation are costly and generally produce fewer gains.

Research questions

For this study, we ask two main research questions:

RQ1: To what extent was student participation affected by Typhoons Goni and Vamco?

RQ2: Was student participation able to return to pre-typhoon levels, or did the typhoons dampen participation for the rest of the post-typhoon period? If participation did return to pre-typhoon levels, how long did it take for participation to recover?

Time series analysis in education

We use CausalImpact analysis (Brodersen et al., 2015 ) to analyze the ways in which student participation in an online learning environment was affected by Typhoons Goni and Vamco. CausalImpact is a type of causal inference analysis method for time series data.

Time series analysis methods

Causal inference refers to a family of analysis methods that enable researchers to draw conclusions about the effect of a causal variable or treatment on some outcome or phenomenon of interest (Hill & Stuart, 2015 ). These methods have the same general approach: They take time series data prior to an interruption or intervention, create a model from this data, use the model to predict counterfactual post-intervention trends, and then compare the counterfactual against the actual data to check for differences. They differ in terms of their underlying modeling approach. Examples of these methods are as follows (Kuromiya et al., 2020 ; Moraffah et al., 2021 ):

CausalImpact—It is developed to evaluate the impact of a market intervention using difference-in-difference to infer the causality from observational data. Under the hood, “…it builds a Bayesian structural time series model based on multiple comparable control groups (or markets) and uses the model to project (or forecast) a series of baseline values for the time period after the event.” (Brodersen et al., 2015 ; Nishida, 2017 )

Interrupted Time Series (ITS) Model—Uses segmented regression model with dummy variables representing the period of the intervention for evaluating the effectiveness of population-level interventions. It is simple in terms of interpreting the results. (Bernal et al., 2017 )

Prophet—A type of generalized additive model consisting of trend, seasonality, and holidays. There is no need to interpolate missing values since the model handles time series analysis as a curve fitting problem and can predict future values at a very high accuracy. (Taylor & Letham, 2018 )

CausalTransfer—An improvement to CausalImpact which estimates treatment effects from experiments spanning multiple time points by using a state-space model. The main issue with CausalImpact is that it “treats every time point as a separate experiment and does not pool information over time”; hence, one is “only able to observe the outcomes under the treatment for one time series and under the control for the treatment for another one, but not the potential outcome under control for the former and under treatment for the latter.” CausalTransfer “combines regression to adjust for confounding with time series modelling to learn the effect of the treatment and how it evolves over time” and does not assume that data is stationary. (Li & Bühlmann, 2020 )

Several methods based on neural networks and deep learning have been introduced in recent years (Moraffah et al., 2021 ):

Recurrent Marginal Structural Network (R-MSN)—A sequence-to-sequence recurrent neural network (RNN)-based architecture for forecasting responses to a series of planned treatments. In contrast to other marginal structural models (MSMs) which model “the potential outcomes associated with each possible treatment trajectory with the Inverse Probability of Treatment Weighted (IPTW),” which in turn is “dependent on a correct specification of the conditional probability of treatment assignment,” R-MSN directly learns “time-dependent treatment responses from observational data, based on the marginal structural modeling framework.” (Lim et al., 2018 )

Time Series Deconfounder—This method “uses a novel recurrent neural network architecture with multitask output to build a factor model over time and infer latent variables that render the assigned treatments conditionally independent” prior to performing causal inference with the aforementioned latent variables being used in place of the multi-cause unobserved confounders. To further ensure that the factor model is able to estimate the distribution of the assigned causes, “a validation set of subjects were considered in order to compare the similarity of the two test statistics.” This overcomes the problem of having to ensure that all the confounders are observed, which may lead to biased results otherwise. (Bica et al., 2020 )

Deep Sequential Weighting—It is used for estimating individual treatment effects with time-varying confounders by using a deep recurrent weighting neural network for inferring the hidden confounders using a combination of the current treatment assignments and historical information. The learned representations of hidden confounders combined with current observed data are then utilized for obtaining potential outcome and treatment predictions. For re-weighting the population, the time-varying inverse probabilities of treatment are computed. (Liu et al., 2020 )

For their own study, Kuromiya et al. ( 2020 ) first considered ITS and Prophet as possible approaches. They found that ITS had weak predictive power and limited flexibility. Prophet was better than ITS at predicting future values. In determining the impact of an event, though, Prophet was more difficult to interpret. They therefore decided to use a method called CausalImpact instead. As this was the study that we emulated, we used CausalImpact as well. We were not able to consider using CausalTransfer nor any of the neural network/deep learning methods.

Prior Studies using CausalImpact analysis

CausalImpact is a specific type of causal inference that enables researchers to estimate the impact of an intervention such as an ad campaign on an outcome variable such as additional clicks (Brodersen, 2014 ; Brodersen, et al., 2015 ). Given time series data, we first identify predictor variables, the outcome variable, and the pre- and post-intervention time segments. CausalImpact uses the pre-intervention data to model the relationship between the predictor variables and the outcome variable. It then uses the model to estimate the post-intervention counterfactual. The impact of the intervention is the difference between the counterfactual and the observed post-intervention data. While many algorithms may be used to model the counterfactual, CausalImpact made use of Bayesian structural time series models, explained in detail in (Brodersen, et al., 2015 ). The CausalImpact R package (Brodersen, 2014 ; Brodersen, et al., 2015 ) is publicly available at .

CausalImpact was created within a commercial context and was intended for use on marketing data and clickstream traffic (Brodersen, 2014 ). Since its release in 2014, the method has also been used to model the effects of product modularity on bus manufacturing (Piran et al., 2017 ), US cyber policies on cyberattacks (Kumar et al., 2016 ), Arab uprisings and tourism (Perles-Ribes et al., 2018 ), and the performance of app store releases (Martin, 2016 ).

In 2020, Kuromiya and colleagues applied CausalImpact to estimate the effects of school closures on student use of the LMS Moodle and the electronic book reader BookRoll (Kuromiya et al., 2020 ). They performed this analysis for all courses in aggregate and for one specific English course. In their analysis, they found that student traffic in Moodle and BookRoll increased significantly during the COVID-19 pandemic. For all courses in aggregate, Moodle traffic increased by 163%, while BookRoll traffic increased by 77%. With the English course, Moodle traffic increased by 2227%, while BookRoll traffic increased by 875%. Note that Kuromiya and colleagues use the term “intervention” to refer to school closures rather than a new teaching strategy. They therefore expanded the definition of “intervention” to include external events that may affect a system, rather than deliberate actions from researchers, educators, or other persons that are intended to influence how the system behaves. In this study, we use this expanded definition of “intervention” to refer to the typhoons that affected online learning.

In 2021, Lagmay and Rodrigo began the analysis of Typhoons Goni and Vamco’s effects on student participation in online classes and published initial results at an international conference (Lagmay & Rodrigo, 2021 ). While this paper drew inspiration from Kuromiya et al. ( 2020 ), it differed in its choice of predictor variables. Lagmay and Rodrigo ( 2021 ) made use of teacher and non-editing teacher activity to predict student activity. In contrast, Kuromiya et al. ( 2020 ; personal communications, 26 January 2021) used number of logs per day as both the input variable and the outcome variable.

Lagmay and Rodrigo ( 2021 ) analyzed Moodle activity from September 9, 2020, to January 9, 2021. The pre-intervention period was defined as the pre-typhoon period from September 9, 2020, to October 28, 2020. The intervention period were the days disrupted by the typhoon, October 29 to November 13. Finally, the post-intervention period was November 14 to December 23, the period after the typhoon to just before the Christmas break. The paper found a statistically significant decrease in all LMS activity but a non-statistically significant difference in activities related to assessment. The paper we present here expands the Lagmay and Rodrigo ( 2021 ) paper by experimenting with the time periods.

While much educational research makes use of causal inference in general, as of the time of this writing, the works of Kuromiya et al. ( 2020 ) and Lagmay and Rodrigo ( 2021 ) were the only applications of CausalImpact on educational data that our survey of the literature could find.

The dataset was composed of a time series of log data from the Moodle of a privately owned university in Metro Manila, Philippines. Prior to the study, the researchers conferred with the University Data Protection Office and the University Counsel to determine whether we needed to seek informed consent from faculty and students to access their Moodle data. Since the data that we received were anonymized and because we did not have the ability to re-identify the same, there was no need to seek informed consent from the Moodle users (J. Jacob, personal communication, 25 September 2020; P. Sison-Arroyo, personal communication, 25 September 2020). Furthermore, the University Research Ethics Office determined that our research protocol was considered exempt from institutional ethics review because it was research conducted in educational settings involving normal educational practices, and that the information was processed such that participants could not be identified (L. Alampay, personal communication, 11 October 2020).

We collected data from 11,736 students, 925 teachers, and 38 non-editing teachers beginning September 9, 2020, and ending on January 9, 2021. The students were undergraduate and graduate students. Undergraduate students were from 18 to 22 years old, while graduate students were 23 and older. Students generally came from middle- to upper-class families. Teachers had at least a bachelor’s degree in the subject area that they were teaching. Most had master’s degrees or higher. Both students and teachers were a mix of males and females, though the exact distribution was not included in the Moodle data.

This time period of data collection represented two distinct academic terms: the first quarter (September 9 to October 24) and second quarter (October 28 to January 9). The dataset contained a total of 2,641,461 logs from 12,699 users. Each transaction was composed of the complete set of the following columns available from Moodle:

Time—timestamp of the of the action, up to the minute.

User ID—numerical identifier (ID) of the user performing the action.

Affected user—numerical identifier of the user affected by the action; When Teacher T sends a notification to Student S, the User ID would be that of Teacher T whereas the Affected user would be Student S.

Event context—teacher-given name of the module or activity within which the action took place, e.g., “Classroom Exercise 1 Module 1.”

Component—one of 43 Moodle-defined categories under which various events take place, e.g., Quiz.

Event name—one of 244 Moodle-defined names for actions that can be performed by the user, e.g., Quiz attempt viewed.

Description—narrative description of the action performed by the user, e.g., The user with id '1603' has viewed the attempt with id '20202' belonging to the user with id “1603” for the quiz with course module id “18804.”

Origin—The method used to access Moodle (examples: web, cli (Client), etc.).

IP address—If Moodle is accessed via the web, this gives the originating IP address (this was anonymized or deleted to ensure data privacy concerns).

The users of Moodle fell into three categories: teachers , non-editing teachers (e.g., a teaching assistant; non-editing teachers may view and grade work but may not edit or delete course content), and students . Because the logs did not include the user category, the university’s systems administrators provided the researchers with each user’s type.

We used transaction log volume, i.e., counts, as the indicator of participation. A transaction is defined as any interaction with Moodle. Each time a student performs an action such as accessing course materials or answering a quiz within Moodle, that action is logged as a transaction. The more the student works within Moodle, the more transaction Moodle logs for that student. While we were interested in broad types of transactions such as quizzes, we did not examine the actual content of course activities and resources. We did not read lectures, discussion postings, exams, quizzes, etc. To answer our research questions, an examination of transaction categories and volumes was sufficient.

Data preprocessing

The raw data consisted of 3 files of User IDs and User Types (each file representing a user type), and one transaction log file for each of the 123 days of the academic term under study. To preprocess the data, we first merged the list of User IDs and User Types with the transaction logs. We eliminated identifying features such as IP addresses, user full names, and ID numbers. We also had to parse and separate the Time column into separate Date and Time features. The log file was then aggregated according to the Date, User Type, and Component, and the rows that fall under each category were counted. All preprocessed files were then appended to a single file of transactions.

The second phase of the data preprocessing procedure, just prior to the CausalImpact analysis, was to normalize the data (See Table 1 ). We first aggregated the data frame according to User Type and Component columns (305 for 2020-10-10 and 1133 for 2020-12-23). We took the maximum possible Total for each group across all dates (6146). Then, the items in the Total column were divided by their respective maximum possible value according to the User Type and Component, normalizing the data between 0 and 1 for each User Type and Component (0.05 and 0.18).

We then decided to model three of the top ten most frequently occurring components overall: System, Quiz, and Assignment which, together, represented over 88% of all transactions (See Table 2 ). System refers to all actions related to communication and course management. Quizzes in Moodle are activities that are completed online and are often automatically graded. Assignment in Moodle is usually file uploads of work completed outside of the LMS.

CausalImpact analysis

We performed a CausalImpact analysis for four outcome variables: overall student LMS activity, the System component, the Assignment component, and the Quiz component. In this section, we discuss the analysis in three sections: predictor variable selection, time period definition, and CausalImpact results.

Predictor variable selection

We opted to use teacher and non-editing teacher transactions as predictor variables. Our theoretical grounding for this choice is the teacher expectancy effect (TEE), also known as the Pygmalion Effect. The Pygmalion Effect stems from research on how interpersonal expectations shape reality (Szumski & Karwowski, 2019 ). It is a form of self-fulfilling prophecy, asserting that teacher expectations have an impact on students’ academic progress. Through verbal and non-verbal behaviors, teachers signal their expectations to students about how the students will (not should) behave or how they will succeed or fail academically (Niari et al., 2016 ). Students then enact the behaviors or achievement levels that meet teachers’ expectations. Pygmalion effects have been observed at the individual and class level for both achievement outcomes and self-concept (see Friedrich et al., 2015 ; Szumski & Karwowski, 2019 ). These effects have been shown to persist over time (see Szumski & Karwowski, 2019 ). On this basis, we speculate that what teachers signal as their expectations for the online classes will serve as cues to the student about what they will deliver in order to pass the course.

Since teacher and non-editing teacher transactions were categorized under various components, it was necessary to determine which of these components were most predictive. We used Dynamic Time Warping (DTW) to arrive at a parsimonious set of predictor variables. As explained in (Larsen, 2021 ), the usual approach to finding the relationship between a predictor and a response variable in time series data is to use the Euclidean distance. However, this penalizes instances where the relationships between data have shifted. DTW finds the distance along the warping curve, as opposed to the raw data, to arrive at the best alignment between two time series. We used the MarketMatching R implementation of the DTW algorithm (Larsen, 2021 ). It should be noted, however, that MarketMatching will only work on predictor variables with a complete set of values and with a variance or standard deviation not equal to 0. To guarantee this, we trimmed the dataset to the top 10 most frequently used components across users. The result of this algorithm was a set of predictor variables with the closest relationship with the response variable (See Table 3 ).

Time period definition

The definitions of the pre- and post-intervention periods required some consideration. As mentioned in Sect.  2 , we collected data from the first quarter (September 9 to October 24) and second quarter (October 28 to January 9) of the academic year. The start of the second quarter was immediately disrupted by Typhoons Goni and Vamco. This led the university to suspend second quarter classes from November 16–21. The university mandated asynchronous-only classes from November 23–28 and resumed synchronous classes, if teachers chose to hold them, from November 29 onward (Vilches, 2020 ). Furthermore, the second quarter included a Christmas break from December 24 to January 3. To factor in the possible impacts of the class suspension and the Christmas break, we decided to run CausalImpact on four different time periods (See Fig.  1 ). The pre-intervention period was from September 9 to October 28, the days before the typhoons entered the Philippine Area of Responsibility (PAR). We included October 25 to 27, the period in-between the quarters, because it was during this time that teachers began contacting students to send them links to the online classroom where they would meet on the first meeting day. The intervention period was the period in which the two typhoons struck. We considered two possible endings to this period: November 13, the day Typhoon Vamco left the PAR, and November 21, the last day of the post-typhoon break. The post-intervention period followed and, like the intervention period, had two possible end dates: December 23, before the Christmas break, and January 9. Hence, we created four time periods:

Intervention period that does not include the post-typhoon break; post-intervention period that includes the Christmas break (NB-WC)

Intervention period that does not include the post-typhoon break; post-intervention period that does not include the Christmas break (NB-NC)

Intervention period that includes the post-typhoon break; post-intervention period that includes the Christmas break (WB-WC)

Intervention period that includes the post-typhoon break; post-intervention period that does not include the Christmas break (WB-NC)

figure 1

Time period definitions

Note that we were working with the same dataset reported in Lagmay and Rodrigo ( 2021 ). In this current paper, though, the end date of the intervention period and the start date of the post-intervention periods in time periods WB-WC and WB-NC are different. The dates in Fig.  1 are consistent with the university memo regarding the post-typhoon period (Vilches, 2020 ). These same time period definitions in Lagmay and Rodrigo ( 2021 ) were off by 2 days.

CausalImpact results

Tables 4 and 5 show the results of the analysis for each of the time periods.

All LMS Activity

During time period NB-WC, all LMS activity decreased significantly ( p  = 0.02). The response variable had an average value of 0.17. The counterfactual prediction was 0.21. The typhoons therefore had an estimated effect of − 0.041 with a 95% confidence interval of [− 0.077, − 0.0063]. When the data points during the intervention period are summed, the response variable had an overall value of 9.61. The counterfactual prediction was 11.98 with a 95% confidence interval of [9.97, 14.10]. This means that overall student participation decreased by − 20% with a 95% confidence interval of [− 37%, − 3%].

Figure  2 a shows the CausalImpact graph of all LMS activity for time period NB-WC. Each unit on the x -axis represents one day in the time period. The topmost graph labeled “original” shows a solid line representing the actual observed data, i.e., the number of transactions per day. The broken line represents the prediction. The light blue band represents the confidence interval of the prediction. The middle graph labeled “pointwise” shows the difference between the predicted number of transactions and the actual number of transactions per day. If the predicted number of transactions for day 1 was 100 and the actual number of transactions was 80, the pointwise difference was 20. Finally, the cumulative graph at the bottom shows the accumulated difference between the predicted number of transactions and the actual number of transactions. If the pointwise difference on day 2 was 10, the accumulated difference of days 1 and 2 is 30. If the pointwise difference on day 3 was 12, the accumulated difference of days 1, 2, and 3 is 42. The gap in the pointwise and cumulative graphs is the intervention period. There is no accumulated difference during the pre-intervention period. The differences are accumulated post-intervention. Note that the cumulative graph shows a downward trend during the post-intervention period and that there was indeed a slump in the week or so following the typhoons.

figure 2

CausalImpact graphs for all LMS activity

During time period NB-NC, all LMS activity also decreased significantly ( p  = 0.01). Student participation had an average value of 0.18. The counterfactual prediction was 0.23. The typhoons therefore had an estimated effect of − 0.045 with a 95% interval of [− 0.082, − 0.010]. When the data points during the intervention period are summed, the response variable had an overall value of 7.41. The counterfactual prediction was 9.25 with a 95% confidence interval of [7.83, 10.77]. Like time period NB-WC, student participation decreased by − 20% with a 95% confidence interval of [− 36%, − 5%]. Figure  2 b shows the CausalImpact graph for time period NB-NC.

The results for all LMS activity during time periods WB-WC and WB-NC were insignificant. Time period WB-WC yielded a p value of 0.044, while time period WB-NC yielded a p value of 0.033. However, in both cases, the signs of the 95% CI fluctuated, which means that even if the p value implies significance, the results cannot be meaningfully interpreted. Since time periods WB-WC and WB-NC included the class suspension, it is possible that the definition of the intervention period was too long and the effect of the typhoons had already worn off. Figure  2 c, d shows a visualization of this scenario. We trim off the slump that follows immediately after the typhoons. Although the cumulative graph still follows a decreasing trajectory, the difference between the predicted and actual data is no longer significant. Note that the graph shape does not change, regardless of time period. What changes is the size of the intervention period from the end of October to around the middle of November and the length of the graph’s tail.

During time periods NB-WC (Fig.  3 a) and WB-WC (Fig.  3 c), System activity decreased, but not significantly. Although the p value of time period NB-WC was 0.03 and student participation showed a decrease of − 26%, the 95% interval of this percentage was [− 52%, + 1%]. The p value of time period WB-WC was 0.04 and the response variable showed a decrease of − 25% with a 95% interval of [− 52%, + 5%]. These fluctuations of the sign during the post-periods of the two time periods meant that the effect is not significant and cannot be meaningfully interpreted (Coqueret & Guida, 2020 ).

figure 3

CausalImpact graphs for System component

System activity during period NB-NC (Fig.  3 b) significantly decreased ( p  = 0.01). Student participation averaged 0.11 as opposed to a counterfactual prediction of 0.15 with a 95% interval of [0.12, 0.19]. The effect of the typhoons is estimated at − 0.046 with a 95% interval of [− 0.080, − 0.013]. The sum of student participation data points during the post-intervention period was 4.43 in contrast to a predicted 6.30 with a 95% interval of [4.96, 7.71].

The results of time period WB-NC (Fig.  3 d) were also statistically significant ( p  = 0.01). Student participation averaged 0.12 as opposed to the predicted 0.17 with a 95% interval of [0.13, 0.20]. The effect of the typhoons was therefore estimated at − 0.046 with a 95% interval of [− 0.081, − 0.0072]. The sum of the student participation data points was 4.04 in contrast to a predicted 5.57 with a 95% interval of [4.28, 6.70].


The effects of the typhoons on student behavior on Assignments were not significant across any of the time periods. p values were 0.14, 0.348, and 0.195 for time periods NB-WC, WB-WC, and WB-NC, respectively. Although time period NB-NC had a p value of 0.04, student participation’s sign fluctuated. It showed a decrease of − 18% with a 95% interval of [− 37%, + 2%]. This meant that the result could not be meaningfully interpreted.

The effects of the typhoons on student behavior on Quizzes were not statistically significant for any of the four time periods. p values were 0.29, 0.39, 0.45, and 0.14 for time periods NB-WC, NB-NC, WB-WC, and WB-NC, respectively.

The purpose of this paper was to determine (1) the extent to which extreme weather affected student participation during online classes and (2) whether and at what point they were able to return to pre-typhoon levels of participation. It extends the earlier work by Lagmay and Rodrigo ( 2021 ) in several ways: The earlier work only included one time period definition, which we labelled in this paper as NB-NC, while this paper experiments with four different time period definitions. Furthermore, Lagmay and Rodrigo ( 2021 ) limited the discussion of the findings to the significance of the decrease, the standard deviation, and the confidence interval. This paper also discusses the absolute and relative effects which were not discussed in the prior paper. Despite these differences in scope, the findings were consistent: Student participation decreased as a whole but those certain components of participation remained at pre-typhoon levels. These findings need to be unpacked for greater nuance.

While student participation as a whole decreased, we found that the significance of the decrease varied, first depending on the definition of the intervention period and second depending on component. When the post-intervention time period excluded the Christmas break (time periods NB-NC and WB-NC), post-typhoon participation as measured in System component significantly decreased, while when the intervention time period excluded the additional week of post-Vamco class suspensions (time periods NB-WC and NB-NC), all LMS Logs significantly decreased.

What was most interesting was that participation in the Assignments and Quizzes components was not significantly different from their predicted behavior, regardless of time period. Because we did not examine the details of actual learning design, course activities, or relative weights of assessments, these findings suggest that students continued to comply with academic assessments as assignments and quizzes make measurable contributions to their grades. System behavior, on the other hand, refers to actions such as checking the course for announcements. These activities are generally not graded. This implies that students were able to continue complying with academic requirements despite the setbacks brought on by the typhoons.

The findings from this study are consistent with findings from prior work on the negative effects of interruptions on academic outcomes. Short-term, small-scale interruptions from social media use, family and friends, sleepiness, and computer malfunctions can derail concentration and throw learning off-course (Zhang et al., 2022 ; Zureick et al., 2018 ). Hence, students who experience these interruptions tend to have lower assessment scores than peers who do not. Larger-scale interruptions such as extreme weather and other natural disasters have adverse long-term effects on educational outcomes, especially among marginalized groups (Bernabe et al., 2021 ; Groppo & Kraehnert, 2017 ; Marchetta et al., 2018 ). It is therefore unsurprising that overall student participation dropped following Typhoons Goni and Vamco.

That students were able to continue engaging with Assignments and Quizzes calls for further reflection. How did students still have the capacity to work on assessments when it seemed most logical, under the circumstances, for them to deprioritize their studies in general? The work of Lai et al. ( 2019 ) offers some insight in this regard. They found two trajectories of school recovery after a disaster: low-interrupted and high-stable. The low-interrupted trajectory referred to school performance levels that dropped following a disaster, while the high-stable trajectory referred to relatively unchanged performance levels. Schools that had higher levels of attendance in general were more likely to have high-stable trajectories, while schools with a high percentage of economically disadvantaged students were more likely to have low-interrupted trajectories. Sustained engagement with assessments despite the typhoons implies that the university examined in this study had a high-stable trajectory and that its students, by and large, were not economically disadvantaged.

There are solutions available to mitigate the effects of inclement weather. Herrera-Almanza and Cas ( 2017 ) studied the long-term academic outcomes of Filipino public school students whose schools were built as part of the Typhoon-Resistant School Building Program of the Philippine government and the Government of Japan. The project made use of Japanese pre-fabrication construction methods and materials to build more structures that were less prone to storm damage, increasing post-typhoon access to schools. The researchers found that students from these beneficiary schools accumulated more years of schooling and were more likely to complete secondary school. Programs such as this illustrate ways in which policy makers can increase the resilience of economically disadvantaged communities.


The generalizability of these findings is subject to at least five limitations. Firstly, CausalImpact analysis requires that the predictor variables should not be affected by the same intervention as the response variable (Brodersen & Hauser, 2014 –2017). In this case, it was the likely case that the teachers and non-editing teachers were affected by the typhoons, just as their students were. To this point, we offer two counterarguments: First, we used DTW to find the teacher and non-editing teacher features that were most predictive of student behaviors. The algorithm eliminated the features with no predictive power, leaving only those that could give us a reasonable estimate of student behavior. Multicollinearity was not an issue of concern because CausalImpact’s underlying model “uses spike and slab priors to combat collinearity” (K. Larsen, personal communications, June 29, 2021). The methodology is provided in (Larsen, 2016 ).

Second, we return to our theoretical framework regarding the Pygmalion Effect (Szumski & Karwowski, 2019 ). Teacher expectations have been shown to affect student behavior, achievement, and self-concept. Since teachers continued to provide learning materials and assessments after the typhoons and throughout the second quarter, this may have signaled to the students that they were still expected to fulfill their academic obligations.

Our second limitation has to do with the population from which the data were taken. Prior research cited in the “ Effects of extreme weather on academic achievement ” section showed that extreme weather has detrimental, long-term effects on student achievement, and yet these students seemed to have flourished despite these two typhoons. One possible explanation for this is that the students in this sample were among the best in the country. They generally came from well-to-do socioeconomic backgrounds, and their families had the economic stability to withstand the typhoon’s shocks. Their resilience may not be indicative of the resilience of the Philippines or any developing country as a whole. It may, at best, serve as validation of prior findings that the impact of extreme weather varies along socioeconomic lines. Those who are more financially able will survive, possibly flourish. While it would have been revelatory to perform this analysis on data from an LMS used by less economically fortunate people, such data were not available.

Third, the university had two LMSs working in parallel, Moodle and Canvas. We were only able to capture Moodle data for this study, and the classes using the Moodle server were generally the Computer Science and Management Information Systems classes. The students were therefore technology-savvy and adept at online modes of communication. Students from other courses might have encountered greater challenges.

Fourth, the data captured here represent LMS participation but not other important outcomes such as assessment results, the quality of the educational experience, or the mental health consequences of online learning coupled with severe weather. While students and faculty evidently powered through their requirements, it would be best to triangulate these results with findings and observations from other constituency checks, for a more complete reading of our community.

Finally, as mentioned in the “ Time series analysis methods ” section, we were not able to consider using CausalTransfer, a more updated version of CausalImpact, nor any of the neural network or deep learning approaches. Future studies may consider experimenting with these other approaches to determine if they yield better results.

Despite these limitations, this paper contributes to technology-enhanced education research and practice. For education researchers, this paper adds to the literature by applying CausalImpact analysis on LMS data to determine the effects of severe weather on students. It contributes to what is quantitatively known about how Philippine students cope with online learning. In the context of severe weather, quantitative research on this subject is still scarce.

This paper serves also as a possible model for researchers who wish to determine the effects of an intervention on a system. They can consider the use of CausalImpact as a possible approach if they have sufficient pre-intervention data for CausalImpact to draw an accurate model, a clearly defined intervention period, and sufficient post-intervention data to serve as a comparison. Future researchers should also be careful with their choice of predictor variables as the behavior of predictor variables should not be affected by the intervention.

For education practitioners, this paper provides evidence that schools and their students can be resilient, and that academic continuity is possible even in the face of difficult circumstances. However, evidence of resilience for some students should not be interpreted as resilience for all. Markers of resilience such as hope and confidence must be grounded in reality (Mahdiani & Ungar, 2021 ). Resilience should not be used as an excuse for social inequalities and should not shift the responsibility to survive and thrive on people who may lack the power or resources to do so. As extreme weather events that are characteristic to the Philippines, policy makers have to invest in typhoon-resistant infrastructure (Herrera-Almanza & Cas, 2017 ) and practitioners will have to provide marginalized students with more support in order to achieve desired educational outcomes.

Availability of data and materials

The dataset(s) supporting the conclusions of this article are available in the RPTEL_CausalImpact_Lagmay_Rodrigo repository, .

Project name: RPTEL_CausalImpact_Lagmay_Rodrigo.

Project home page: .

Archived version: N/A.

Operating system(s): Windows 10 or later (with PowerShell), Windows 8.1 or later (with CMD), macOS 10.13 High Sierra or later (with BASH), macOS 10.14 Mojave or later (with ZSH), or Ubuntu 20.04 or later (with BASH) .

Programming language: R, Python, Jupyter Notebook, Shell, PowerShell, and Batch.

Other requirements: Anaconda with Python 3.6 or higher + Pyro5, pandas, import_ipynb, netifaces, and dateutil; R with CausalImpact, MarketMatching, dplyr, ggplot2, zoo, tidyr, reshape2, mctest, ppcor, and fsMTS libraries.

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We thank Hiroyuki Kuromiya and Hiroaki Ogata of Kyoto University for their advice. Finally, we thank the Ateneo Laboratory for the Learning Sciences for its constant support.

Funding for this project was provided by Ateneo Research Institute for Science and Engineering (ARISE) of Ateneo de Manila University through the grant entitled: Analysis of Student and Faculty Behavior within Canvas and Moodle.

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Original research article, teaching and learning continuity amid and beyond the pandemic.

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The study explored the challenges and issues in teaching and learning continuity of public higher education in the Philippines as a result of the COVID-19 pandemic. The study employed the exploratory mixed-method triangulation design and analyzed the data gathered from 3, 989 respondents composed of students and faculty members. It was found out that during school lockdowns, the teachers made adjustments in teaching and learning designs guided by the policies implemented by the institution. Most of the students had difficulty complying with the learning activities and requirements due to limited or no internet connectivity. Emerging themes were identified from the qualitative responses to include the trajectory for flexible learning delivery, the role of technology, the teaching and learning environment, and the prioritization of safety and security. Scenario analysis provided the contextual basis for strategic actions amid and beyond the pandemic. To ensure teaching and learning continuity, it is concluded that higher education institutions have to migrate to flexible teaching and learning modality recalibrate the curriculum, capacitate the faculty, upgrade the infrastructure, implement a strategic plan and assess all aspects of the plan.


The COVID-19 pandemic has created unprecedented challenges economically, socially, and politically across the globe. More than just a health crisis, it has resulted in an educational crisis. During lockdowns and quarantines, 87% of the world’s student population was affected and 1.52 billion learners were out of school and related educational institutions ( UNESCO Learning Portal, 2020 ). The suddenness, uncertainty, and volatility of COVID-19 left the education system in a rush of addressing the changing learning landscape.

The disruption of COVID-19 in the educational system is of great magnitude that universities have to cope with at the soonest possible time. The call is for higher education institutions to develop a resilient learning system using evidence-based and needs-based information so that responsive and proactive measures can be instituted. Coping with the effects of COVID-19 in higher education institutions demands a variety of perspectives among stakeholders. Consultation needs to include the administration who supports the teaching-learning processes, the students who are the core of the system, the faculty members or teachers who perform various academic roles, parents, and guardians who share the responsibility of learning continuity, the community, and the external partners who contribute to the completion of the educational requirements of the students. These complicated identities show that an institution of higher learning has a large number of stakeholders ( Illanes et al., 2020 ; Smalley, 2020 ). In the context of the pandemic, universities have to start understanding and identifying medium-term and long-term implications of this phenomenon on teaching, learning, student experience, infrastructure, operation, and staff. Scenario analysis and understanding of the context of each university are necessary to the current challenges they are confronted with (Frankki et al., 2020). Universities have to be resilient in times of crisis. Resiliency in the educational system is the ability to overcome challenges of all kinds–trauma, tragedy, crises, and bounce back stronger, wiser, and more personally powerful ( Henderson, 2012 ). The educational system must prepare to develop plans to move forward and address the new normal after the crisis. To be resilient, higher education needs to address teaching and learning continuity amid and beyond the pandemic.

Teaching and Learning in Times of Crisis

The teaching and learning process assumes a different shape in times of crisis. When disasters and crises (man-made and natural) occur, schools and colleges need to be resilient and find new ways to continue the teaching–learning activities ( Chang-Richards et al., 2013 ). One emerging reality as a result of the world health crisis is the migration to online learning modalities to mitigate the risk of face-to-face interaction. Universities are forced to migrate from face-to-face delivery to online modality as a result of the pandemic. In the Philippines, most universities including Cebu Normal University have resorted to online learning during school lockdowns. However, this sudden shift has resulted in problems especially for learners without access to technology. When online learning modality is used as a result of the pandemic, the gap between those who have connectivity and those without widened. The continuing academic engagement has been a challenge for teachers and students due to access and internet connectivity.

Considering the limitation on connectivity, the concept of flexible learning emerged as an option for online learning especially in higher institutions in the Philippines. Flexible learning focuses on giving students choice in the pace, place, and mode of students’ learning which can be promoted through appropriate pedagogical practice ( Gordon, 2014 ). The learners are provided with the option on how he/she will continue with his/her studies, where and when he/she can proceed, and in what ways can the learners comply with the requirements and show evidences of learning outcomes. Flexible learning and teaching span a multitude of approaches that can meet the varied needs of diverse learners. These include “independence in terms of time and location of learning, and the availability of some degree of choice in the curriculum (including content, learning strategies, and assessment) and the use of contemporary information and communication technologies to support a range of learning strategies” ( Alexander, 2010 ).

One key component in migrating to flexible modality is to consider how flexibility is integrated into the key dimensions of teaching and learning. One major consideration is leveraging flexibility in the curriculum. The curriculum encompasses the recommended, written, taught or implemented, assessed, and learned curriculum ( Glatthorn, 2000 ). Curriculum pertains to the curricular programs, the teaching, and learning design, learning resources as assessment, and teaching and learning environment. Adjustment on the types of assessment measures is a major factor amid the pandemic. There is a need to limit requirements and focus on the major essential projects that measure the enduring learning outcomes like case scenarios, problem-based activities, and capstone projects. Authentic assessments have to be intensified to ensure that competencies are acquired by the learners. In the process of modifying the curriculum amid the pandemic, it must be remembered that initiatives and evaluation tasks must be anchored on what the learners need including their safety and well-being.

Curriculum recalibration is not just about the content of what is to be learned and taught but how it is to be learned, taught, and assessed in the context of the challenges brought about by the pandemic. A flexible curriculum design should be learner-centered; take into account the demographic profile and circumstances of learners–such as access to technology, technological literacies, different learning styles and capabilities, different knowledge backgrounds and experiences - and ensure varied and flexible forms of assessment ( Ryan and Tilbury, 2013 ; Gachago et al., 2018 ). The challenge during the pandemic is how to create a balance between relevant basic competencies for the students to acquire and the teachers’ desire to achieve the intended outcomes of the curriculum.

The learners’ engagement in the teaching-learning process needs to be taken into consideration in the context of flexibility. This is about the design and development of productive learning experiences so that each learner is exposed to most of the learning opportunities. Considering that face-to-face modality is not feasible during the pandemic, teachers may consider flexible distant learning options like correspondence teaching, module-based learning, project-based, and television broadcast. For learners with internet connectivity, computer-assisted instruction, synchronous online learning, asynchronous online learning, collaborative e-learning may be considered.

The Role of Technology in Learning Continuity

Technology provides innovative and resilient solutions in times of crisis to combat disruption and helps people to communicate and even work virtually without the need for face-to-face interaction. This leads to many system changes in organizations as they adopt new technology for interacting and working ( Mark and Semaan, 2008 ). However, technological challenges like internet connectivity especially for places without signals can be the greatest obstacle in teaching and learning continuity especially for academic institutions who have opted for online learning as a teaching modality. Thus, the alternative models of learning during the pandemic should be supported by a well-designed technical and logistical implementation plan ( Edizon, 2020 ).

The nationwide closure of educational institutions in an attempt to contain the spread of the virus has impacted 90% of the world’s student population ( UNESCO, 2020 ). It is the intent of this study to look into the challenges in teaching and learning continuity amidst the pandemic. The need to mitigate the immediate impact of school closures on the continuity of learning among learners from their perspectives is an important consideration ( Edizon, 2020 ; Hijazi, 2020 ; UNESCO, 2020 ). Moreover, the teachers' perspectives are equally as important as the learners since they are the ones providing and sustaining the learning process. Teachers should effectively approach these current challenges to facilitate learning among learners, learner differentiation, and learner-centeredness and be ready to assume the role of facilitators on the remote learning platforms ( Chi-Kin Lee, 2020 ; Edizon, 2020 ; Hijazi, 2020 ).

Statement of Objective

This study explores the issues and challenges in teaching and learning amid the pandemic from the lenses of the faculty members and students of a public university in the Philippines as the basis for the development of strategic actions for teaching and learning continuity. Specifically, this study aimed to:

a.1. Preferred flexible learning activities.

a.2. Problems completing Requirements due to ICT Limitation

a.3. Provision of alternative/additional requirement.

a.4. Receipt of learning feedback.

a.5. Learning environment.

Objective 2: determine the profile of faculty and students in terms of online capacity as categorized into:

b.1. Access to Information Technology.

b.2. Access to Internet/Wi-fi.

b.3. Stability of internet connection.

Objective 3: develop emerging themes from the experiences and challenges of teaching and learning amidst the pandemic.


The design used in the study is an exploratory mixed-method triangulation design. It was utilized to obtain different information but complementary data on a common topic or intent of the study, bringing together the differing strengths non-overlapping weaknesses of quantitative methods with those of qualitative methods ( Creswell, 2006 ). The use of the mixed method provided the data used as a basis for the analysis and planning perspective of the study.

This study was conducted in the context of a state university funded by the Philippine government whose location was once identified as having one of the highest COVID19 cases in the country. With this incidence, the sudden suspension of classes and the immediate need to shift the learning platform responsive to the needs of the learners lend a significant consideration in this study. This explored the perspectives of the learners in terms of their current capacity and its implications in the learning continuity using online learning. These were explored based on the availability of gadgets, internet connectivity, and their learning experiences with their teachers. These perspectives were also explored on the part of the teachers as they were the ones who provided learning inputs to the students. These are necessary information to identify strategic actions for the teaching and learning continuity plan of the university.

After getting the quantitative and qualitative findings, these data were reviewed to provide a clear understanding of teachers’ and learners’ context and their experiences. From this information, a scenario analysis through scenario building was conducted which led to the development of the strategic actions for teaching and learning continuity. Scenario analysis is a method used in predicting the possible occurrences of consequences of a situation assuming the phenomenon will be continued in the future ( Kishita et al., 2016 ). This approach is considered a useful way for exploring plausible events that may or may not happen in the future ( Bekessy and Selinske, 2017 ). This approach was used to analyze the behavior of both teachers and students as part of the whole system in response to an unexpected event such as the pandemic which creates a theoretical scenario of best -case (optimistic) or worse case (pessimistic) scenario to enable the university to develop a holistic strategic plan for the teaching and learning continuity ( Balaman, 2019 ).

Both quantitative and qualitative approaches were used simultaneously. In this study, objectives 1 and 2 require data on the profile of the teachers and learners which can best be acquired using a descriptive quantitative design. This was done through an online structured survey was conducted to identify the challenges in teaching and learning using google forms. Choices were provided in the Google form which the respondents can choose from. The surveys were done by the Cebu Normal University - Center for Research and Development and Federation of Supreme Student Council.

The qualitative approach was utilized to answer objective number 3 which looked into the experiences and challenges of the teachers and the learners. The narratives which the respondents submitted were done through online open-ended questions to allow them to share their experiences and challenges. These were analyzed using a thematic approach to best provide a clear description of the experiences and challenges.

After the analysis of the quantitative and qualitative data, the team of researchers developed the possible scenarios that will take place as the basis for the flexible strategic actions that the university will adapt depending on the classification of community quarantine and the health situation of the locale where the university is located. In the analysis of the current status of Cebu Normal University, parameters are reviewed and outcomes are utilized through scenario building. Scenario building provides the contextual basis for the development of the new normal in the university. Scenario building as explained by Wilkinson (1995) is a good strategy to use on how current observations play their role in future situations. Each scenario is constructed about the future, modeling a distinct, plausible world. Scenarios are plausible alternative futures of what might happen under particular assumptions by focusing on key drivers, complex interactions, and irreducible uncertainties ( Polcyznski, 2009 ).

The prospective scenarios created are the best, probable scenarios, and worse scenarios. Current or existing situations/conditions of CNU served as the probable scenario, while the ideal case situation served as the best scenario. From the scenario built, key problems and challenges are developed as a basis for the model developed ( Imperial, 2020 ). This provided the strategic long-term and short-term strategies for CNU’s academic operations. The best scenario is based on the perspective that the university allows limited face-to-face classes in the remaining months of the semester. The probable scenario is with the current enhanced community quarantine (ECQ) status of the city or province where the university is located, at least six (6) months, after, face-to-face interactions will be allowed with the opening of the new school year will. Worse Scenario happens when the locale is placed under sustained community quarantine and face-to-face classes will never be allowed at the start of the new school year. The strategic actions of the university are inclusive of the three (3) scenarios to allow flexibility of the responses of the university in this pandemic.

There were 3,646 student respondents (85% of the student population) and 252 (97% of the teaching personnel) teaching personnel who responded to the survey. To determine accessibility and reach of communication transmission related to the teaching-learning process, the location of the respondents was also identified. The majority of the student respondents (67%) are located in Cebu province; 17% in Cebu City, and 12% in other provinces. The 63% or 157 faculty members are residing in Cebu province while 32% or 81 of them reside in Cebu City; other provinces 5%. Qualitative feedback was also gathered to explore further the challenges experienced and clarify information about open-ended online messaging. Data was gathered from March-April 2020 in a state-funded university in the Philippines with the campus located in the center of the city. To comply with the ethical guidelines, strict adherence to data privacy protocols and data use restrictions were followed. The data were analyzed and were considered in identifying emerging themes scenarios in teaching and learning.

The data gathered were reviewed and analyzed by looking into the challenges that need to be addressed and the ideal perspectives that should have been implemented to generate different scenarios. Scenario building provides the contextual basis for the development of the new normal in the university. Scenario building as explained by Wilkinson (1995) is a good strategy to use on how current observations play their role in future situations. Each scenario is constructed about the future modeling a distinct, plausible world. Scenarios are plausible alternative futures of what might happen under particular assumptions by focusing on key drivers, complex interactions, and irreducible uncertainties ( Polcyznski, 2009 ). The prospective scenarios created are the best and probable scenarios. Current or existing situations/conditions of the university served as the probable scenario, while the ideal case situation served as the best scenario. From the scenario built, key problems and challenges are developed as a basis for the model developed ( Imperial, 2020 ). The model will provide the strategic long-term and short-term strategies for the university’s academic operations Figure 1 .

FIGURE 1 . Schematic diagram of the conceptual analysis.

Results and Discussion

Challenges on teaching and learning amid the pandemic.

In the quantitative data gathered through an online survey, the students reported their concerns related to their learning experiences during the suspension of physical classes. Most of the student respondents reported that adjustments were made by the teachers in terms of course outcomes and syllabi. However, most of them claimed that the learning activities were not flexible enough to be done either offline or online as they could not as shown in Table 1 comply with the requirements within the expected schedule.

TABLE 1 . The profile of flexibility of the learning activities for offline or online learning among students (n = 1,689).

Moreover, as shown in Table 2 , students reported that the majority of them were unable to accomplish the tasks assigned by the teachers due to their inability to access the internet or use suitable gadgets to finish the tasks.

TABLE 2 . Number of students who reported if they have problems. Completing requirements due to ICT limitation (n = 1952).

Part of the survey for students focused on how students reacted to home-based tasks assigned to them to complete the learning competencies of the course. Teachers provided alternative tasks online through electronic mails and an online portal Table 3 .

TABLE 3 . Provision of alternative/additional requirement (n=1952).

Students confirmed that some online classes and additional requirements were still provided to them by the faculty ( Table 4 ) The majority of the students responded that the alternative tasks were adequate. The nature and content of the alternative tasks provided were suited to the remaining concepts to be addressed in their coursework ( Table 4 ). Despite that, several students still reported that these alternative tasks are not sufficient to enable them to acquire the remaining competencies required of them at the end of the semester.

TABLE 4 . Adequacy of alternative tasks for learning attainment (n=74).

Students in one college were surveyed on the receipt of feedback from their respective teachers. A comparable response from students claimed they received and didn’t receive immediate feedback as to whether what they submitted to the professors is okay or what aspect they still need to improve more. As teaching continuity was made possible through online modality and other home-based tasks, they still had difficulty complying with the requirements of the course. The survey included the type of home environment the students have to assess factors that influence their difficulty. Students were asked whether their home learning environment is conducive to learning or not.

Data in Table 5 show that learners believed that their home environment is not conducive for learning when schools were closed and physical contact was discontinued as there were many disruptions including internet connectivity. On the part of the faculty, there were challenges met as evidenced by the feedbacks of the students. The teaching-learning process requires an active engagement of the faculty. They are the drivers of the learning process and the success of the learning outcomes would partially depend on their extent of active participation as facilitators, mentors, or coaches to the learners.

TABLE 5 . Students learning environment.

In the teaching-learning process, students need feedback on the progress of their outputs and whether they did well in their tasks. As shown in Table 6 , the majority of the students reported receiving no feedback from their teachers on the online module while a majority hope to get immediate feedback. Further exploration is required to determine why teachers are unable to provide immediate feedback for students.

TABLE 6 . Feedback from teachers (n = 154).

Faculty and Students’ Access to Technology

One of the modalities in teaching and learning that gained popularity amid COVID-19 was online learning. When classes were suspended, universities migrated from the face to face interaction to the online modality. Hence, this survey was conducted to determine the capability of the students and teachers in terms of available information technology gadgets and connections.

The profile of both the faculty and students’ access to internet-based information showed that the majority can access this information ( Table 7 ). Moreover, the majority of the students (82.61%) and faculty (94.4%) have internet access Table 8 . However, most of them reported unstable internet connections which makes their home environment less conducive to sustain learning facilitated by the online readings and activities given Table 9 . The majority of the students used mobile phones for online learning which is not capable of addressing online tasks and submission of requirements. On top of this, concerns for limited internet access of students and faculty emanate from external service providers most especially when using cellular data in areas where satellite signals are limited.

TABLE 7 . Faculty and students’ access to information technology (n = 4,072).

TABLE 8 . Faculty, staff and students’ access to internet/Wi-fi.

TABLE 9 . Stability of internet connection (n = 1952).

Emerging Themes in Teaching and Learning

A qualitative survey was also conducted to substantiate the quantitative data gathered. The narrative comments of the respondents in the survey were analyzed and were grouped into emerging themes and scenarios of teaching and learning.

The Trajectory Towards Flexibility in Teaching Design, Delivery, and Assessment

The sudden cancellation of classes in the middle of the semester placed both faculty and students unprepared. Questions on how to continue their classes, the learning modality, the appropriate assessment, and access to learning materials were foremost in the mind of both teachers and students. The narratives of the respondents became the basis for identifying the emerging scenarios in teaching and learning amid and beyond the pandemic.

For many years, students have been exposed to traditional, face-to-face classroom-based teaching. Outcomes-based education has been integrated into the curriculum and its implementation, but the learning delivery is still under the actual supervision of teachers. Due to ECQ students have to shift to independent learning through the home-based tasks assigned to them by their teachers. Ordinarily, many students have trouble making the transition to the more independent learning required at university compared with their secondary years .

“It’s very difficult for me to learn on my own in the confines of my home, but I don’t have a choice ,” narrated one student.

This shows that this pandemic has created a new platform in teaching and learning delivery that students are compelled to accept. In this situation, students have to take responsibility for their learning, be more self-directed, make decisions about what they will focus on how much time they will spend on learning outside the classroom ( The Higher Education Academy, 2014 ; Camacho and Legare, 2016 ). In the new setting, students are expected to read, understand and comply with the tasks without the guidance of the teachers. They are forced to assume self-directed independent learning.

The teachers on the other hand affirmed that the use of face-to-face delivery would not work anymore in the new learning environment.

“ One thing that I have learned is to adjust my materials to ensure that learners can still acquire the competencies without the face-to-face interaction with my students ” narrated one teacher.

With the concerns on access to online services, faculty members considered the use of a non-online approach and explored the necessary modifications that can be applied in the future. Hence, in the narrative, several faculty members said they have prepared modules as an option for pure online learning delivery.

Assessment of student learning outcomes is very important. A concern on how to assess learning outcomes and how to answer assessment tasks emerged as a major concern as reflected in the narratives of the teacher and student respondents. The assessment measures are essential as an assurance that learners have attained various knowledge and skills and that they are ready for employment or further study ( Coates, 2015 ). There is a need to address the teachers’ concern on how to conduct off-classroom performance evaluation and the bulk of submissions that they have to evaluate which are submitted online or offline. The design and planning are important factors to consider not only in the assessment per se but also in the parameters on how students will be graded ( Osborn, 2015 ). For the teachers, the following concerns emerged,

“Difficulty assessing performance-based tasks (RLE) , ” “Difficulty tracking, checking of students’ outputs” and “Concerns on failing due to non-submission of requirements online and low midterm Performance”

In the assessment of learning, the teacher respondents agreed that they have to think of innovative ways of assessing students in the context of their situation and home environment so the outcomes expected of the course will be manifested by the students.

One of the challenges of online or distance learning is the difficulty in participating in groupwork activities. The challenge is how the schedule or availability of group members be accommodated within the group ( Gillett-Swan, 2017 ; Kebritchi, Lipschuetz, and Santiague, 2017 ). More particularly when online assessments are done with certain deadlines or time limits.

“Difficulty complying group activities”

“Time-based online exams”

The challenges seen in this phase are to determine the flexible learning system most applicable for CNU learners, the readiness of the students and faculty to handle the tasks to assign and to be complied by the students, the appropriateness of the learning delivery vis-à-vis learning outcome, and the preparation of the learning materials fit for self-directed independent learning.

In times of disaster, the educational system takes on a different route for effective learning continuity. The learning curriculum requires it to be more responsive to the current needs of the learners and the teachers.

“ Concerns in completing OJT”

“Dissertation/Thesis defense scheduled”

“Concerns on when the academic year ends”

The flexibility that the curriculum has to adopt requires the offering of choices on the current reality of the educational environment and customizing a given course to meet the needs of the learners. It is therefore crucial in considering the provision of the possibility of making learning choices to learners. These learning choices can cover class times, course content, instructional ( Huang et al., n.d. ).

It is a challenge for the university to consider the restructuring of the curriculum to address the gaps in the learning outcomes left when classes were suspended and the re-scheduling of the mid-semester On-the-Job Training of some programs. Amidst this crisis, flexibility in the next academic calendar has to be considered while it is uncertain when the COVID-19 crisis will be contained.

The Role of Technology

In the overall narratives concerning teaching-learning delivery and assessment, the role of information technology particularly on internet connection has been repetitively mentioned by both teachers and students. In the crisis scenario, faculty and students could eventually bounce forward to the usual teaching-learning activities outside the classrooms had this concern been made available to all. Per survey results, most of the students and some faculty members are residing outside the city and are experiencing unstable if no internet connection at all.

“ No internet connectivity/unstable connectivity”

“Occasional power interruptions”

In designing for online or distance learning, there is a need to understand the role of technology to attain the success of the engagement ( Kerka, 2020 ). Internet is not the only factor to consider but also the equipment that is needed for the teachers and the learners to engage effectively. If these are not available, there is a need to evaluate the approach used in the teacher-learner interaction.

“Limited gadgets (one laptop shared with other siblings/no laptop or PC only phone)”

“No printer for completion of a requirement to be submitted”

With the current health crisis with the shifting of learning delivery, the challenge would be on how to provide an inclusive IT infrastructure to provide quality education for all learners ( Internet access and education: Key considerations for policy makers, 2017 ).

The Learner’s and the Teacher’s Learning Environment

In an attempt to address the disruption of classes and promote continuity of learning, teachers immediately resort to online learning as the most workable way of delivery of the lessons. In this new learning setup, students are forced to stay at home and transfer their classrooms to the same location. In most cases, it is often overlooked that learners come from different home settings and have different home arrangements.

“Not appropriate learning environment (congested home setting)”

“Lack of support from parents (assigning home tasks when a student is supposed to be work on learning tasks)”

“Overlapping of home activities and academic activities”

In most cases, families frequently engaged their children in learning activities, however, different patterns were observed across different social groups. Families in low socio-economic position households, and those living in disadvantaged neighborhoods provided fewer learning experiences. This may in part be due to the challenges that families living in socially and economically disadvantaged circumstances face in accessing the financial and social resources needed to provide a rich early home learning environment for learning. The findings reveal that education is still pursued in economically challenging settings but with more challenges. A home learning environment has a positive “direct association” with a child’s academic performance ( Australian Institute of Family Studies, 2015 ). The findings require a three-helix platform in education that is the partnership between academe, industry, and the stakeholders.

Maslow Before Bloom Orientation: Safety and Security

Prevailing sentiments among employees and students are their concern for their safety and security. The basic needs of humans according to Maslow’s Hierarchy of needs are foremost in the minds of the university’s clients and workers. As reported by the students and employees, their foremost concern is safety and the psychological manifestations of the anxiety of being infected.

“Foremost concern is safety and security even after ECQ is lifted”

“Fear of being infected with COVID”

“With PUI/PUM family members or the students themselves”

“Psychological and emotional reactions (anxiety, panic, fear, loneliness, a feeling of helplessness, mood swings, anger)”

The second category of concerns is on security and the possibility of sustaining their education due to loss of jobs, loss of family members, and the uncertainty of traveling to the university.

“Family financial crisis–no budget to buy loads, sustain needs”

“Unable to go home”

“Transportation concerns”

The concerns raised by the participants of the study require the university to provide access to considerable support to deal with the struggles, challenges, and even trauma because of the pandemic. There is a need to help manage mental health, self-esteem, and relationships after the quarantine which left some of the students isolated for quite a time ( Sweeney, 2020 ). Mental health programs have to be in place in formal learning settings. Because of the unprecedented challenges that students and teachers experienced in the pandemic, the ability to successfully hurdle through formal learning may be limited if the overall well-being is compromised.

Strategic Scenario Analysis

This section presents the analysis of the possible scenarios that might take place in the university based on the following components: the planned curriculum, instruction (teaching-learning process), assessment, student engagement, and technology and infrastructure. The probable scenario is the current enhanced community quarantine (ECQ) status of the City or province where the university is located. During ECQ, no face-to-face interaction is allowed and province-wide lockdowns are implemented. The best scenario allows the limited face-to-face class and the worse scenario happens when the locale is under ECQ and placed on a lockdown due to the increasing COVID-19 cases.

In the area of curriculum and instruction, the action points revolved around the identification of courses that can be flexibly offered, rescheduling offerings when health measures permit it and providing interventions for competencies that were not met. The additional action points would refer to the creation of materials that would meet the needs of the students in the different scenarios and the provision of access to all resources that aid learning. Lastly, plans for assessment delivery are laid out to ensure the validity of means and with consideration to quarantine measures. Laying down the scenarios provide options for the educational institution to be able to meet the demands of the changes enforced by the pandemic to the delivery of learning to students. Reviewing these options reveal that the differences in the plan of action for this area of concern are a matter of granting access to students for resources needed for learning continuity.

The next area of concern is student engagement which reveals the different levels of engagement of parents and guardians, the means of communication with students, and an investment in the capability-building of faculty members to facilitate the teaching-learning process amid the pandemic. The focus on the trainings for the faculty members in the area implies that flexible learning in this health crisis requires a particular skill set to heighten student engagement without diminishing the role of support systems in the students’ homes and the need for appropriate technology to facilitate the needed interactions. This leads to the last area of concern on technology and infrastructure. The University has to take into account and facilitate the provision of needed equipment, materials, systems, software, and physical structures to support flexible learning. The complete scenario matrix is reflected in Table 10 .

TABLE 10 . Scenario matrix.

Migrating to Flexible Teaching and Learning: The University’s Strategic Response for Academic Continuity

After exploring the perspectives of the respondents and the analysis of the emerging scenarios in teaching and learning, the University implemented the proactive response to ensure academic continuity in times of crisis. It is evident that for universities to thrive and lead, the flexible teaching-learning modality needs to be adopted taking into consideration the best and worst-case scenarios. Migrating to flexi learning means recalibrating the written curriculum, capacitating the faculty, and upgrading technological infrastructure to respond to the changing scenarios amid and beyond the pandemic. Outlined in the paragraphs that follow were the ways forward pursued by the university as a response for academic continuity.

Recalibrate the Curriculum

To address the competencies which were left at the time of the class suspension, discipline-based course mapping was conducted. A series of cluster meetings by faculty members teaching similar courses teaching load were done for the revision of the unified syllabus, integration of the outcomes-based teaching and learning strategies using flexible learning platforms such as distance and online learning options, and the learning assessment strategies suitable for individual student needs. A syllabi repurposing is conducted and the revisiting of the syllabi focusing on the essential course outcomes. This strategy enables the faculty to revise the activities/course work/tasks/experiences that can be delivered through blended learning. This also enabled them in designing the instructional strategies, activities, and assessments that will achieve the learning objectives. The modification of the syllabi incorporated the development of modules, assessment tasks that can be delivered using differentiated instruction/in class or off class.

A program-based curriculum review was also conducted to identify courses that would need to be re-scheduled in its offering due to its nature and requirement such as swimming courses. Moreover, On-the- Job (OJT) which was supposedly offered during summer or mid-year was transferred to a later semester as industry partners are limiting its personnel at the height of the pandemic.

Reconfiguring the OJT, practice teaching and Related Learning Experience based on simulation set-up with scenario-based activities with assigned equivalency hours was also developed. The Practice teaching using blended learning or online approach, Nursing used alternative Related learning simulation.

The strategic actions included short-term plans of possible limited physical classes and long-term plans of pure online classes. Embedded in the plans are the in-class and off-class mode, re-structuring and retrofitting requirement for limited face-to-face classes, and the upgrading of internet-based facilities for pure online classes. On top of this, they need to cater to learners who have no access to the internet includes the translation of online learning modules to printed modules.

Capacitate the Faculty

Flexible learning capacitation of faculty was also addressed as online learning was new to the university. The university conducted an upskilling and rewiring through series of online trainings on module development for flexible learning distance education and the use of an online learning management system for faculty members. Reskilling and reconfiguring of faculty through webinar series on laboratory teaching using simulation learning for teachers handling laboratory, RLE, OJT. And a cross-skilling and reimagining using series of online webinars on developing counseling skills of faculty members concerning the COVID crisis. The university initiated the Higher Education Connect webinar series by discipline which served as an avenue of sharing and exchanging best practices during the pandemic-induced suspension of physical classes. The series of online for and webinars provided the teachers’ professional development including information sharing platform, Online learning platform, Hands-on training platform, Repository of web tools, and Laboratory for data analytics.

Safe learning infrastructure for Reframing Teaching and Learning was addressed through Telecounseling Services with mobile hotline numbers to cater to the needs of the clients and Student Communication Center with hotline numbers accessible by phone or online to cater to the academic concerns of the students. The university also initiated the Adopt-a-Student program for stranded students during the Enhanced Community Quarantine and assisted in the process of going back to their provinces.

Upgrade the Infrastructure

The university’s priority is to ensure that technology is sustainable and feasible. The ICT focal persons of the university were mobilized to Determine basic computer configuration and minimum Operating System requirements and provide alternative solutions to learners with technological/location-related challenges. For example, provide small learning activity packages for learners with slow internet connections. Ensure changes to the learning activity that can be made with internal resources. Determine the characteristics, possibilities, and limitations of the learning management system (LMS) to be used and ensure consistency of access across platforms (if applicable).

An Organizational Structures as a support system was also created which was the Center for Innovative Flexible Learning to provide assistance and monitoring so that the existing Information Technology Office of the university will not be overwhelmed.

It is also strategic to develop collaboration with stakeholders (Local Government Units (LGU), Alumni, Partner agencies). The forging of partnerships with LGU provides avenues where students during off-class students will go to the learning hub in the LGU complete with internet connectivity for students to work on their tasks in case they don’t have connectivity at home, so students will not go to the internet café and pay. This will also provide opportunities for resource sharing for the benefit of the students.

ICT Infrastructure in teaching and learning and student services was also addressed through Online enrollment, full utilization of Google Classroom as the learning management system, and the fully online delivery of classes. The university also changed its internet subscription to higher bandwidth and subscription to zoom for online meetings and conferences. Internet Connectivity of faculty members has assisted a monthly internet allowance. Gadget on loan for students in coordination with Student Supreme Council. Library online services through Document Delivery Services (DDS) and Modern Information Assistant in the New Normal Innovative Education.

Implementation and On-Going Assessment of the Strategic Response

The implementation of the strategic response entails the collaborative engagement of all stakeholders in the university. The process requires the involvement of the administration, faculty, staff, students, parents, and other stakeholders that enables the institution to move forward, managing and mitigating risks successfully. Hence, the university is implementing the continuous process of consultation, feedbacking, and intensive monitoring as important ingredients for the plans to be successfully implemented. The regular conduct of dialogues and discussions among stakeholders, capacity building of students and faculty, open communication through hotline centers, and continuous quality assurance monitoring mechanisms enable the university to enhance and implement successfully the strategic programs and activities amid the pandemic.

Anchored on the initial success of the evidenced-based strategic plans, the university at present has institutionalized the flexible learning system with the establishment of the Center for Flexible Learning that manages, capacitates, and assists the students and the faculty members in the continuing implementation of the flexible learning modality. Technology support has been provided by increasing the internet bandwidth to ensure uninterrupted connectivity in the campus and providing internet allowance to the faculty. Students with limited or no connectivity are given printed modules as instructional resources. In anticipation of the limited face-to-face classes as safety and health protocols may allow, the curricular offerings, teaching-learning processes, and assessment tools have been enhanced by applying best practices that maximize quality teaching and learning. On-going trainings and webinars for the faculty, students, and stakeholders to thrive in the new educational landscape have been conducted. The university has also established professional learning communities which become avenues for the sharing of resources and practices that continuously support and enhance teaching and learning continuity amid and beyond the pandemic.

Teaching and learning continuity amid the pandemic requires an analysis of the parameters by which the university operates from the perspective of the stakeholders to include the students, faculty, curriculum, and external stakeholders. Grounded on data, higher education institutions have to conduct strategic scenario analysis for best, possible and worse scenarios in the areas of curriculum and instruction, student engagement, and technology and infrastructure. To ensure teaching and learning continuity amid and beyond the pandemic, higher education institutions need to migrate to flexible teaching and learning modality by recalibrating the curriculum, capacitating the faculty, and upgrading the infrastructure. These strategic actions have to be continuously assessed, modified, and enhanced to respond to the volatile, uncertain, and changing scenarios in times of crisis.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

FD, DP, LG, and MO contributed to the conception and design of the study. DP and LG organized the data and facilitated the initial analysis. FD and DP wrote the first draft of the manuscript. All authors wrote sections of the manuscript and contributed to the manuscript revision. MO ran the final plagiarism test and grammar check prior to submission.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: teaching and learning continuity, flexible learning, pandemic, higher education, scenario–analysis

Citation: Dayagbil FT, Palompon DR, Garcia LL and Olvido MMJ (2021) Teaching and Learning Continuity Amid and Beyond the Pandemic. Front. Educ. 6:678692. doi: 10.3389/feduc.2021.678692

Received: 10 March 2021; Accepted: 06 July 2021; Published: 23 July 2021.

Reviewed by:

Copyright © 2021 Dayagbil, Palompon, Garcia and Olvido. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Michelle Mae J. Olvido, [email protected]

Header Jurnal Ilmiah Peuradeun

Exploring the Online Learning Experience of Filipino College Students During Covid-19 Pandemic

  • Louie Giray College of Education, Polytechnic University of the Philipines, Taguig City, Philippines
  • Daxjhed Gumalin College of Education, Polytechnic University of the Philipines, Taguig City, Philippines
  • Jomarie Jacob College of Education, Polytechnic University of the Philipines, Taguig City, Philippines
  • Karl Villacorta College of Education, Polytechnic University of the Philipines, Taguig City, Philippines

This study was endeavored to understand the online learning experience of Filipino college students enrolled in the academic year 2020-2021 during the COVID-19 pandemic. The data were obtained through an open-ended qualitative survey. The responses were analyzed and interpreted using thematic analysis. A total of 71 Filipino college students from state and local universities in the Philippines participated in this study. Four themes were classified from the collected data: (1) negative views toward online schooling, (2) positive views toward online schooling, (3) difficulties encountered in online schooling, and (4) motivation to continue studying. The results showed that although many Filipino college students find online learning amid the COVID-19 pandemic to be a positive experience such as it provides various conveniences, eliminates the necessity of public transportation amid the COVID-19 pandemic, among others, a more significant number of respondents believe otherwise. The majority of the respondents shared a general difficulty adjusting toward the new online learning setup because of problems related to technology and Internet connectivity, mental health, finances, and time and space management. A large portion of students also got their motivation to continue studying despite the pandemic from fear of being left behind, parental persuasion, and aspiration to help the family.

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Online and face‐to‐face learning: Evidence from students’ performance during the Covid‐19 pandemic

Carolyn chisadza.

1 Department of Economics, University of Pretoria, Hatfield South Africa

Matthew Clance

Thulani mthembu.

2 Department of Education Innovation, University of Pretoria, Hatfield South Africa

Nicky Nicholls

Eleni yitbarek.

This study investigates the factors that predict students' performance after transitioning from face‐to‐face to online learning as a result of the Covid‐19 pandemic. It uses students' responses from survey questions and the difference in the average assessment grades between pre‐lockdown and post‐lockdown at a South African university. We find that students' performance was positively associated with good wifi access, relative to using mobile internet data. We also observe lower academic performance for students who found transitioning to online difficult and who expressed a preference for self‐study (i.e. reading through class slides and notes) over assisted study (i.e. joining live lectures or watching recorded lectures). The findings suggest that improving digital infrastructure and reducing the cost of internet access may be necessary for mitigating the impact of the Covid‐19 pandemic on education outcomes.


The Covid‐19 pandemic has been a wake‐up call to many countries regarding their capacity to cater for mass online education. This situation has been further complicated in developing countries, such as South Africa, who lack the digital infrastructure for the majority of the population. The extended lockdown in South Africa saw most of the universities with mainly in‐person teaching scrambling to source hardware (e.g. laptops, internet access), software (e.g. Microsoft packages, data analysis packages) and internet data for disadvantaged students in order for the semester to recommence. Not only has the pandemic revealed the already stark inequality within the tertiary student population, but it has also revealed that high internet data costs in South Africa may perpetuate this inequality, making online education relatively inaccessible for disadvantaged students. 1

The lockdown in South Africa made it possible to investigate the changes in second‐year students' performance in the Economics department at the University of Pretoria. In particular, we are interested in assessing what factors predict changes in students' performance after transitioning from face‐to‐face (F2F) to online learning. Our main objectives in answering this study question are to establish what study materials the students were able to access (i.e. slides, recordings, or live sessions) and how students got access to these materials (i.e. the infrastructure they used).

The benefits of education on economic development are well established in the literature (Gyimah‐Brempong,  2011 ), ranging from health awareness (Glick et al.,  2009 ), improved technological innovations, to increased capacity development and employment opportunities for the youth (Anyanwu,  2013 ; Emediegwu,  2021 ). One of the ways in which inequality is perpetuated in South Africa, and Africa as a whole, is through access to education (Anyanwu,  2016 ; Coetzee,  2014 ; Tchamyou et al.,  2019 ); therefore, understanding the obstacles that students face in transitioning to online learning can be helpful in ensuring more equal access to education.

Using students' responses from survey questions and the difference in the average grades between pre‐lockdown and post‐lockdown, our findings indicate that students' performance in the online setting was positively associated with better internet access. Accessing assisted study material, such as narrated slides or recordings of the online lectures, also helped students. We also find lower academic performance for students who reported finding transitioning to online difficult and for those who expressed a preference for self‐study (i.e. reading through class slides and notes) over assisted study (i.e. joining live lectures or watching recorded lectures). The average grades between pre‐lockdown and post‐lockdown were about two points and three points lower for those who reported transitioning to online teaching difficult and for those who indicated a preference for self‐study, respectively. The findings suggest that improving the quality of internet infrastructure and providing assisted learning can be beneficial in reducing the adverse effects of the Covid‐19 pandemic on learning outcomes.

Our study contributes to the literature by examining the changes in the online (post‐lockdown) performance of students and their F2F (pre‐lockdown) performance. This approach differs from previous studies that, in most cases, use between‐subject designs where one group of students following online learning is compared to a different group of students attending F2F lectures (Almatra et al.,  2015 ; Brown & Liedholm,  2002 ). This approach has a limitation in that that there may be unobserved characteristics unique to students choosing online learning that differ from those choosing F2F lectures. Our approach avoids this issue because we use a within‐subject design: we compare the performance of the same students who followed F2F learning Before lockdown and moved to online learning during lockdown due to the Covid‐19 pandemic. Moreover, the study contributes to the limited literature that compares F2F and online learning in developing countries.

Several studies that have also compared the effectiveness of online learning and F2F classes encounter methodological weaknesses, such as small samples, not controlling for demographic characteristics, and substantial differences in course materials and assessments between online and F2F contexts. To address these shortcomings, our study is based on a relatively large sample of students and includes demographic characteristics such as age, gender and perceived family income classification. The lecturer and course materials also remained similar in the online and F2F contexts. A significant proportion of our students indicated that they never had online learning experience before. Less than 20% of the students in the sample had previous experience with online learning. This highlights the fact that online education is still relatively new to most students in our sample.

Given the global experience of the fourth industrial revolution (4IR), 2 with rapidly accelerating technological progress, South Africa needs to be prepared for the possibility of online learning becoming the new norm in the education system. To this end, policymakers may consider engaging with various organizations (schools, universities, colleges, private sector, and research facilities) To adopt interventions that may facilitate the transition to online learning, while at the same time ensuring fair access to education for all students across different income levels. 3

1.1. Related literature

Online learning is a form of distance education which mainly involves internet‐based education where courses are offered synchronously (i.e. live sessions online) and/or asynchronously (i.e. students access course materials online in their own time, which is associated with the more traditional distance education). On the other hand, traditional F2F learning is real time or synchronous learning. In a physical classroom, instructors engage with the students in real time, while in the online format instructors can offer real time lectures through learning management systems (e.g. Blackboard Collaborate), or record the lectures for the students to watch later. Purely online courses are offered entirely over the internet, while blended learning combines traditional F2F classes with learning over the internet, and learning supported by other technologies (Nguyen,  2015 ).

Moreover, designing online courses requires several considerations. For example, the quality of the learning environment, the ease of using the learning platform, the learning outcomes to be achieved, instructor support to assist and motivate students to engage with the course material, peer interaction, class participation, type of assessments (Paechter & Maier,  2010 ), not to mention training of the instructor in adopting and introducing new teaching methods online (Lundberg et al.,  2008 ). In online learning, instructors are more facilitators of learning. On the other hand, traditional F2F classes are structured in such a way that the instructor delivers knowledge, is better able to gauge understanding and interest of students, can engage in class activities, and can provide immediate feedback on clarifying questions during the class. Additionally, the designing of traditional F2F courses can be less time consuming for instructors compared to online courses (Navarro,  2000 ).

Online learning is also particularly suited for nontraditional students who require flexibility due to work or family commitments that are not usually associated with the undergraduate student population (Arias et al.,  2018 ). Initially the nontraditional student belonged to the older adult age group, but with blended learning becoming more commonplace in high schools, colleges and universities, online learning has begun to traverse a wider range of age groups. However, traditional F2F classes are still more beneficial for learners that are not so self‐sufficient and lack discipline in working through the class material in the required time frame (Arias et al.,  2018 ).

For the purpose of this literature review, both pure online and blended learning are considered to be online learning because much of the evidence in the literature compares these two types against the traditional F2F learning. The debate in the literature surrounding online learning versus F2F teaching continues to be a contentious one. A review of the literature reveals mixed findings when comparing the efficacy of online learning on student performance in relation to the traditional F2F medium of instruction (Lundberg et al.,  2008 ; Nguyen,  2015 ). A number of studies conducted Before the 2000s find what is known today in the empirical literature as the “No Significant Difference” phenomenon (Russell & International Distance Education Certificate Center (IDECC),  1999 ). The seminal work from Russell and IDECC ( 1999 ) involved over 350 comparative studies on online/distance learning versus F2F learning, dating back to 1928. The author finds no significant difference overall between online and traditional F2F classroom education outcomes. Subsequent studies that followed find similar “no significant difference” outcomes (Arbaugh,  2000 ; Fallah & Ubell,  2000 ; Freeman & Capper,  1999 ; Johnson et al.,  2000 ; Neuhauser,  2002 ). While Bernard et al. ( 2004 ) also find that overall there is no significant difference in achievement between online education and F2F education, the study does find significant heterogeneity in student performance for different activities. The findings show that students in F2F classes outperform the students participating in synchronous online classes (i.e. classes that require online students to participate in live sessions at specific times). However, asynchronous online classes (i.e. students access class materials at their own time online) outperform F2F classes.

More recent studies find significant results for online learning outcomes in relation to F2F outcomes. On the one hand, Shachar and Yoram ( 2003 ) and Shachar and Neumann ( 2010 ) conduct a meta‐analysis of studies from 1990 to 2009 and find that in 70% of the cases, students taking courses by online education outperformed students in traditionally instructed courses (i.e. F2F lectures). In addition, Navarro and Shoemaker ( 2000 ) observe that learning outcomes for online learners are as effective as or better than outcomes for F2F learners, regardless of background characteristics. In a study on computer science students, Dutton et al. ( 2002 ) find online students perform significantly better compared to the students who take the same course on campus. A meta‐analysis conducted by the US Department of Education finds that students who took all or part of their course online performed better, on average, than those taking the same course through traditional F2F instructions. The report also finds that the effect sizes are larger for studies in which the online learning was collaborative or instructor‐driven than in those studies where online learners worked independently (Means et al.,  2010 ).

On the other hand, evidence by Brown and Liedholm ( 2002 ) based on test scores from macroeconomics students in the United States suggest that F2F students tend to outperform online students. These findings are supported by Coates et al. ( 2004 ) who base their study on macroeconomics students in the United States, and Xu and Jaggars ( 2014 ) who find negative effects for online students using a data set of about 500,000 courses taken by over 40,000 students in Washington. Furthermore, Almatra et al. ( 2015 ) compare overall course grades between online and F2F students for a Telecommunications course and find that F2F students significantly outperform online learning students. In an experimental study where students are randomly assigned to attend live lectures versus watching the same lectures online, Figlio et al. ( 2013 ) observe some evidence that the traditional format has a positive effect compared to online format. Interestingly, Callister and Love ( 2016 ) specifically compare the learning outcomes of online versus F2F skills‐based courses and find that F2F learners earned better outcomes than online learners even when using the same technology. This study highlights that some of the inconsistencies that we find in the results comparing online to F2F learning might be influenced by the nature of the course: theory‐based courses might be less impacted by in‐person interaction than skills‐based courses.

The fact that the reviewed studies on the effects of F2F versus online learning on student performance have been mainly focused in developed countries indicates the dearth of similar studies being conducted in developing countries. This gap in the literature may also highlight a salient point: online learning is still relatively underexplored in developing countries. The lockdown in South Africa therefore provides us with an opportunity to contribute to the existing literature from a developing country context.


South Africa went into national lockdown in March 2020 due to the Covid‐19 pandemic. Like most universities in the country, the first semester for undergraduate courses at the University of Pretoria had already been running since the start of the academic year in February. Before the pandemic, a number of F2F lectures and assessments had already been conducted in most courses. The nationwide lockdown forced the university, which was mainly in‐person teaching, to move to full online learning for the remainder of the semester. This forced shift from F2F teaching to online learning allows us to investigate the changes in students' performance.

Before lockdown, classes were conducted on campus. During lockdown, these live classes were moved to an online platform, Blackboard Collaborate, which could be accessed by all registered students on the university intranet (“ClickUP”). However, these live online lectures involve substantial internet data costs for students. To ensure access to course content for those students who were unable to attend the live online lectures due to poor internet connections or internet data costs, several options for accessing course content were made available. These options included prerecorded narrated slides (which required less usage of internet data), recordings of the live online lectures, PowerPoint slides with explanatory notes and standard PDF lecture slides.

At the same time, the university managed to procure and loan out laptops to a number of disadvantaged students, and negotiated with major mobile internet data providers in the country for students to have free access to study material through the university's “connect” website (also referred to as the zero‐rated website). However, this free access excluded some video content and live online lectures (see Table  1 ). The university also provided between 10 and 20 gigabytes of mobile internet data per month, depending on the network provider, sent to students' mobile phones to assist with internet data costs.

Sites available on zero‐rated website

Note : The table summarizes the sites that were available on the zero‐rated website and those that incurred data costs.

High data costs continue to be a contentious issue in Africa where average incomes are low. Gilbert ( 2019 ) reports that South Africa ranked 16th of the 45 countries researched in terms of the most expensive internet data in Africa, at US$6.81 per gigabyte, in comparison to other Southern African countries such as Mozambique (US$1.97), Zambia (US$2.70), and Lesotho (US$4.09). Internet data prices have also been called into question in South Africa after the Competition Commission published a report from its Data Services Market Inquiry calling the country's internet data pricing “excessive” (Gilbert,  2019 ).


We use a sample of 395 s‐year students taking a macroeconomics module in the Economics department to compare the effects of F2F and online learning on students' performance using a range of assessments. The module was an introduction to the application of theoretical economic concepts. The content was both theory‐based (developing economic growth models using concepts and equations) and skill‐based (application involving the collection of data from online data sources and analyzing the data using statistical software). Both individual and group assignments formed part of the assessments. Before the end of the semester, during lockdown in June 2020, we asked the students to complete a survey with questions related to the transition from F2F to online learning and the difficulties that they may have faced. For example, we asked the students: (i) how easy or difficult they found the transition from F2F to online lectures; (ii) what internet options were available to them and which they used the most to access the online prescribed work; (iii) what format of content they accessed and which they preferred the most (i.e. self‐study material in the form of PDF and PowerPoint slides with notes vs. assisted study with narrated slides and lecture recordings); (iv) what difficulties they faced accessing the live online lectures, to name a few. Figure  1 summarizes the key survey questions that we asked the students regarding their transition from F2F to online learning.

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Summary of survey data

Before the lockdown, the students had already attended several F2F classes and completed three assessments. We are therefore able to create a dependent variable that is comprised of the average grades of three assignments taken before lockdown and the average grades of three assignments taken after the start of the lockdown for each student. Specifically, we use the difference between the post‐ and pre‐lockdown average grades as the dependent variable. However, the number of student observations dropped to 275 due to some students missing one or more of the assessments. The lecturer, content and format of the assessments remain similar across the module. We estimate the following equation using ordinary least squares (OLS) with robust standard errors:

where Y i is the student's performance measured by the difference between the post and pre‐lockdown average grades. B represents the vector of determinants that measure the difficulty faced by students to transition from F2F to online learning. This vector includes access to the internet, study material preferred, quality of the online live lecture sessions and pre‐lockdown class attendance. X is the vector of student demographic controls such as race, gender and an indicator if the student's perceived family income is below average. The ε i is unobserved student characteristics.


4.1. descriptive statistics.

Table  2 gives an overview of the sample of students. We find that among the black students, a higher proportion of students reported finding the transition to online learning more difficult. On the other hand, more white students reported finding the transition moderately easy, as did the other races. According to Coetzee ( 2014 ), the quality of schools can vary significantly between higher income and lower‐income areas, with black South Africans far more likely to live in lower‐income areas with lower quality schools than white South Africans. As such, these differences in quality of education from secondary schooling can persist at tertiary level. Furthermore, persistent income inequality between races in South Africa likely means that many poorer black students might not be able to afford wifi connections or large internet data bundles which can make the transition difficult for black students compared to their white counterparts.

Descriptive statistics

Notes : The transition difficulty variable was ordered 1: Very Easy; 2: Moderately Easy; 3: Difficult; and 4: Impossible. Since we have few responses to the extremes, we combined Very Easy and Moderately as well as Difficult and Impossible to make the table easier to read. The table with a full breakdown is available upon request.

A higher proportion of students reported that wifi access made the transition to online learning moderately easy. However, relatively more students reported that mobile internet data and accessing the zero‐rated website made the transition difficult. Surprisingly, not many students made use of the zero‐rated website which was freely available. Figure  2 shows that students who reported difficulty transitioning to online learning did not perform as well in online learning versus F2F when compared to those that found it less difficult to transition.

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Transition from F2F to online learning.

Notes : This graph shows the students' responses to the question “How easy did you find the transition from face‐to‐face lectures to online lectures?” in relation to the outcome variable for performance

In Figure  3 , the kernel density shows that students who had access to wifi performed better than those who used mobile internet data or the zero‐rated data.

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Object name is AFDR-33-S114-g001.jpg

Access to online learning.

Notes : This graph shows the students' responses to the question “What do you currently use the most to access most of your prescribed work?” in relation to the outcome variable for performance

The regression results are reported in Table  3 . We find that the change in students' performance from F2F to online is negatively associated with the difficulty they faced in transitioning from F2F to online learning. According to student survey responses, factors contributing to difficulty in transitioning included poor internet access, high internet data costs and lack of equipment such as laptops or tablets to access the study materials on the university website. Students who had access to wifi (i.e. fixed wireless broadband, Asymmetric Digital Subscriber Line (ADSL) or optic fiber) performed significantly better, with on average 4.5 points higher grade, in relation to students that had to use mobile internet data (i.e. personal mobile internet data, wifi at home using mobile internet data, or hotspot using mobile internet data) or the zero‐rated website to access the study materials. The insignificant results for the zero‐rated website are surprising given that the website was freely available and did not incur any internet data costs. However, most students in this sample complained that the internet connection on the zero‐rated website was slow, especially in uploading assignments. They also complained about being disconnected when they were in the middle of an assessment. This may have discouraged some students from making use of the zero‐rated website.

Results: Predictors for student performance using the difference on average assessment grades between pre‐ and post‐lockdown

Coefficients reported. Robust standard errors in parentheses.

∗∗∗ p  < .01.

Students who expressed a preference for self‐study approaches (i.e. reading PDF slides or PowerPoint slides with explanatory notes) did not perform as well, on average, as students who preferred assisted study (i.e. listening to recorded narrated slides or lecture recordings). This result is in line with Means et al. ( 2010 ), where student performance was better for online learning that was collaborative or instructor‐driven than in cases where online learners worked independently. Interestingly, we also observe that the performance of students who often attended in‐person classes before the lockdown decreased. Perhaps these students found the F2F lectures particularly helpful in mastering the course material. From the survey responses, we find that a significant proportion of the students (about 70%) preferred F2F to online lectures. This preference for F2F lectures may also be linked to the factors contributing to the difficulty some students faced in transitioning to online learning.

We find that the performance of low‐income students decreased post‐lockdown, which highlights another potential challenge to transitioning to online learning. The picture and sound quality of the live online lectures also contributed to lower performance. Although this result is not statistically significant, it is worth noting as the implications are linked to the quality of infrastructure currently available for students to access online learning. We find no significant effects of race on changes in students' performance, though males appeared to struggle more with the shift to online teaching than females.

For the robustness check in Table  4 , we consider the average grades of the three assignments taken after the start of the lockdown as a dependent variable (i.e. the post‐lockdown average grades for each student). We then include the pre‐lockdown average grades as an explanatory variable. The findings and overall conclusions in Table  4 are consistent with the previous results.

Robustness check: Predictors for student performance using the average assessment grades for post‐lockdown

As a further robustness check in Table  5 , we create a panel for each student across the six assignment grades so we can control for individual heterogeneity. We create a post‐lockdown binary variable that takes the value of 1 for the lockdown period and 0 otherwise. We interact the post‐lockdown dummy variable with a measure for transition difficulty and internet access. The internet access variable is an indicator variable for mobile internet data, wifi, or zero‐rated access to class materials. The variable wifi is a binary variable taking the value of 1 if the student has access to wifi and 0 otherwise. The zero‐rated variable is a binary variable taking the value of 1 if the student used the university's free portal access and 0 otherwise. We also include assignment and student fixed effects. The results in Table  5 remain consistent with our previous findings that students who had wifi access performed significantly better than their peers.

Interaction model

Notes : Coefficients reported. Robust standard errors in parentheses. The dependent variable is the assessment grades for each student on each assignment. The number of observations include the pre‐post number of assessments multiplied by the number of students.


The Covid‐19 pandemic left many education institutions with no option but to transition to online learning. The University of Pretoria was no exception. We examine the effect of transitioning to online learning on the academic performance of second‐year economic students. We use assessment results from F2F lectures before lockdown, and online lectures post lockdown for the same group of students, together with responses from survey questions. We find that the main contributor to lower academic performance in the online setting was poor internet access, which made transitioning to online learning more difficult. In addition, opting to self‐study (read notes instead of joining online classes and/or watching recordings) did not help the students in their performance.

The implications of the results highlight the need for improved quality of internet infrastructure with affordable internet data pricing. Despite the university's best efforts not to leave any student behind with the zero‐rated website and free monthly internet data, the inequality dynamics in the country are such that invariably some students were negatively affected by this transition, not because the student was struggling academically, but because of inaccessibility of internet (wifi). While the zero‐rated website is a good collaborative initiative between universities and network providers, the infrastructure is not sufficient to accommodate mass students accessing it simultaneously.

This study's findings may highlight some shortcomings in the academic sector that need to be addressed by both the public and private sectors. There is potential for an increase in the digital divide gap resulting from the inequitable distribution of digital infrastructure. This may lead to reinforcement of current inequalities in accessing higher education in the long term. To prepare the country for online learning, some considerations might need to be made to make internet data tariffs more affordable and internet accessible to all. We hope that this study's findings will provide a platform (or will at least start the conversation for taking remedial action) for policy engagements in this regard.

We are aware of some limitations presented by our study. The sample we have at hand makes it difficult to extrapolate our findings to either all students at the University of Pretoria or other higher education students in South Africa. Despite this limitation, our findings highlight the negative effect of the digital divide on students' educational outcomes in the country. The transition to online learning and the high internet data costs in South Africa can also have adverse learning outcomes for low‐income students. With higher education institutions, such as the University of Pretoria, integrating online teaching to overcome the effect of the Covid‐19 pandemic, access to stable internet is vital for students' academic success.

It is also important to note that the data we have at hand does not allow us to isolate wifi's causal effect on students' performance post‐lockdown due to two main reasons. First, wifi access is not randomly assigned; for instance, there is a high chance that students with better‐off family backgrounds might have better access to wifi and other supplementary infrastructure than their poor counterparts. Second, due to the university's data access policy and consent, we could not merge the data at hand with the student's previous year's performance. Therefore, future research might involve examining the importance of these elements to document the causal impact of access to wifi on students' educational outcomes in the country.


The authors acknowledge the helpful comments received from the editor, the anonymous reviewers, and Elizabeth Asiedu.

Chisadza, C. , Clance, M. , Mthembu, T. , Nicholls, N. , & Yitbarek, E. (2021). Online and face‐to‐face learning: Evidence from students’ performance during the Covid‐19 pandemic . Afr Dev Rev , 33 , S114–S125. 10.1111/afdr.12520 [ CrossRef ] [ Google Scholar ]

1 .

2 The 4IR is currently characterized by increased use of new technologies, such as advanced wireless technologies, artificial intelligence, cloud computing, robotics, among others. This era has also facilitated the use of different online learning platforms ( ).

3 Note that we control for income, but it is plausible to assume other unobservable factors such as parental preference and parenting style might also affect access to the internet of students.

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    Essays of fifty-one aeronautical engineering students enrolled in theory of flight were analysed to determine the impact of online classes to their learning due to the pandemic. Collection of...

  22. Online Learning: A Panacea in the Time of COVID-19 Crisis

    Online learning generally has a lot of opportunities available but this time of crisis will allow online learning to boom as most academic institutions have switched to this model. Online Learning, Remote Working, and e-collaborations exploded during the outbreak of Corona Virus crisis (Favale et al., 2020). Now, academic institutions can grab ...

  23. (Pdf) Research on Online Learning

    They include: a critical review of what the research literature can tell us about blended learning relative to each of Sloan-C's five pillars of quality in online learning; two papers on...