Source: Intercultural Education, Vo. 35, Number 3
(Reviewed by the Portal Team)
The current study aims to identify and describe the factors that relate to how students are adjusting to emergency remote teaching (ERT) and the rapid transition to online learning during the COVID-19 pandemic. Identifying these factors will help policymakers in higher education institutes (HEIs) make the necessary adjustments to curricula, teaching-learning methods, and evaluations to meet the new needs of students during periods of ERT.
Methods & Instruments
Participants - The current study included 390 undergraduate students from four academic colleges in Israel, two of them are general colleges with different departments (n1 = 132, n2 = 130), and the other two are teacher training colleges (n3 = 83, n4 = 45).
All colleges serve students from various sections of Israeli society and different religious groups.
The students’ average age was 32 (SD = 10).
Role adjustment to online learning - Adjustment to ERT was examined by the ‘role adjustment to the online learning questionnaire’ (Garrison, Cleveland-Innes, and Fung 2019) that focused on students’ adjustment to the transition from ‘face to face’ learning to online learning.
The questionnaire contains twenty items that ask the participants to express their degree of agreement on a five-level Likert scale (1–5; 1 = Strongly disagree, 5 = Strongly agree).
The items deal with students’ perceptions of their new experiences regarding the transition to online learning compared to former ‘face-to-face’ learning.
Motivated strategies for learning questionnaire (MSLQ) - The questionnaire ‘motivated strategies for the learning questionnaire (MSLQ)’ examined personal factors related to metacognitive strategies.
The questionnaire consists of twenty-seven items based on two categories taken from the whole questionnaire (Pintrich et al. 1993).
The first category regards metacognitive strategies during learning, such as planning, monitoring, and regulating.
The second category regards resource management strategies, such as managing time and study environment, peer learning, and help-seeking.
Participants were required to express their agreement with them on a five-level Likert scale (1–5) (1 = Strongly disagree, 5 = Strongly agree).
Online learning distractions questionnaire - In order to take into consideration factors related to the unique transition to ERT, the current research team developed the online learning distractions questionnaire.
The questionnaire contained two categories: personal and environmental distractions that are associated with the crisis (lack of mood and decreased concentration) (Bashir et al. 2021) and the transition to online learning (lack of suitable learning environment) (Yang et al. 2021).
Participants were required to express their agreement with them on a five-level Likert scale (1–5) (1 = Strongly disagree, 5 = Strongly agree).
Demographic questionnaire - The demographic questionnaire included age, gender, native language, religiousness, and disability information.
It also included information regarding personal changes during the first period of the pandemic, such as a change in residence and work.
Data collection
The questionnaires were anonymous and included an introduction explaining the study and the researchers’ contact details.
The data collection was made during the first two weeks of May 2020 (Coronavirus semester 1).
The questionnaires were sent by mailing lists from academic administrators to all students from all four colleges.
Participation in the research was voluntary.
Results and discussion
Undergraduate students experienced various barriers to online learning during the first COVID-19 semester, which affected their adjustment.
As far as the initial period of the pandemic is concerned, it seems that all types of students had difficulties adjusting to the rapid transformation to online learning, regardless of their cultural or religious group or their different abilities.
The low adjustment was related to high personal and environmental distractions, primarily expressed in difficulty concentrating.
The findings align with Biwer et al.’s (2021) findings that surveyed 1800 students who also reported that the most significant difficulty during the first outbreak of COVID-19 was the ability to concentrate and deal with distractions.
Examining the adjustment to the COVID-19 pandemic by using the PEOP model can shed light on the personal and environmental factors that may increase or decrease adjustment to ERT.
This study identified demographic and personal factors related to learning, including gender, parenthood, academic experience, and metacognitive strategies.
The social groups identified in our study as the most-adjusted groups were relatively-older, female students and parents.
The findings align with Naujoks et al.‘s findings (2021), which reported gender as a significant predictor of external resource management.
In other words, during the outbreak of COVID-19, women structured their learning environment and showed higher abilities in time management than their male counterparts (Naujoks et al. 2021).
Moreover, students who were parents also reported better adjustment to ERT than students who were not parents.
In contrast to previous researches, the main occupation of the participants in the current research was ‘student.’
Parenthood may require more resource management, and manoeuvring between childcare responsibilities and academic responsibilities increases metacognitive practice and improvement.
Moreover, parents who are also students might improve their academic skills through the modelling that they need to provide to their children.
These results are the first indicators that parenthood is also an essential factor in the context of ERT and should be considered by future research on higher-education settings.
Another research finding indicated that more experienced students adjusted better to ERT than entry-level students.
Kalman, Macias Esparza, and Weston (2020) reported similar findings and explained that more experienced students had a base of knowledge to build on that was developed through problem-solving and the need to apply learning strategies in an earlier stage.
Another explanation Kalman, Macias Esparza, and Weston (2020) provided was that more experienced students already gained earlier academic credit from former courses and were more driven to finish the rest of their courses.
The authors’ findings show that students who apply metacognitive strategies, including self-regulated learning and resource management such as peer learning and help-seeking, adjust better to ERT.
Biwer et al. (2021) and Kalman, Macias Esparza, and Weston (2020) also pointed out that self-regulated learning and time management are the leading personal characteristics involved in adjusting to ERT.
In other words, to preserve their academic performance, students need to know when, why, how, and with whom to study.
Regarding the PEOP model’s environmental factors, the study identified the importance of different environmental aspects in adjusting to ERT.
Undergraduate students faced environmental destructions that negatively affected their ability to adjust to ERT, such as a lack of a quiet place to study and proper internet infrastructures.
In a state of transition to ERT, students may face different environmental barriers associated with the unpreparedness of the in-house learning environment, infrastructures, and equipment for the new type of learning (Huang et al. 2020; Husky, Kovess-Masfety, and Swendsen 2020; Yang et al. 2021).
Surprisingly, cultural differences such as language and religion did not indicate the level of adjustment to ERT.
The authors’ findings can have implications for teaching-learning processes in HEIs and provide recommendations that will permit policymakers to enhance undergraduate students’ adjustments to ERT.
Since higher education has changed, new strategies must support students in the online learning environment.
HEIs should focus on supporting entry-level students to help them enhance their metacognitive awareness and resource management while encouraging metacognitive strategies relevant to online learning.
We also recommend that more experienced students mentor entry-level students.
Furthermore, HEIs should improve students’ access to online learning by providing computers, portable internet devices, and other equipment.
Creating local community learning centres is also necessary.
References
Bashir, A., S. Bashir, K. Rana, P. Lambert, and A. Vernallis. 2021. “Post-COVID-19 Adaptations; the Shifts Towards Online Learning, Hybrid Course Delivery and the Implications for Biosciences Courses in the Higher Education Setting.” Frontiers in Education 6 (August): 1–13.
Biwer, F., W. Wiradhany, M. Oude Egbrink, H. Hospers, S. Wasenitz, W. Jansen, and A. de Bruin. 2021. “Changes and Adaptations: How University Students Self-Regulate Their Online Learning During the COVID-19 Pandemic.” Frontiers in Psychology 12: 12.
Garrison, R., M. Cleveland-Innes, and T. Fung. 2019. “Student Role Adjustment in Online Communities of Inquiry: Model and Instrument Validation.” Online Learning 8 (2): 61–74.
Huang, R. H., D. J. Liu, J. Guo, J. F. Yang, J. H. Zhao, X. F. Wei, S. Knyazeva, et al. 2020. Guidance on Flexible Learning During Campus Closures: Ensuring Course Quality of Higher Education in COVID-19 Outbreak. Beijing: Smart Learning Institute of Beijing Normal University (SLIBNU).
Husky, M. M., V. Kovess-Masfety, and J. D. Swendsen. 2020. “Stress and Anxiety Among University Students in France During Covid-19 Mandatory Confinement.” Comprehensive Psychiatry 102 (October): 152191.
Kalman, R., M. Macias Esparza, and C. Weston. 2020. “Student Views of the Online Learning Process During the COVID-19 Pandemic: A Comparison of Upper-Level and Entry-Level Undergraduate Perspectives.” Journal of Chemical Education 97 (9): 3353–3357.
Naujoks, N., S. Bedenlier, M. Gläser-Zikuda, R. Kammerl, B. Kopp, A. Ziegler, and M. Händel. 2021. “Self-Regulated Resource Management in Emergency Remote Higher Education: Status Quo and Predictors.” Frontiers in Psychology 12: 672741.
Pintrich, P. R., D. A. F. Smith, T. Garcia, and W. J. Mckeachie. 1993. “Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (MSLQ).” Educational and Psychological Measurement 53 (3): 801–813.
Yang, X., X. Zhao, X. Tian, and B. Xing. 2021. “Effects of Environment and Posture on the Concentration and Achievement of Students in Mobile Learning.” Interactive Learning Environments 29 (3): 400–413.