Investigating the Relationship Between the Effectiveness of eE-Learning and Self-Regulated Learning with the Moderating Role of Personality Traits (Case Study: University of Tehran Students)

Document Type : Research Paper

Authors

1 Master's degree, Department of Leadership and Human Capital, Faculty of Management, University of Tehran, Tehran, Iran.

2 Assistant Professor, Department of Leadership and Human Capital, Faculty of Management, University of Tehran, Tehran, Iran.

3 Professor, Department of Leadership and Human Capital, Faculty of Management, University of Tehran, Tehran, Iran.

4 Associate Professor, Department of Policy and Public Affairs Administration, Faculty of Management, University of Tehran, Tehran, Iran.

10.22111/jmr.2025.47806.6143

Abstract

Abstract
This research aims to investigate the relationship between the effectiveness of e-learning and self-regulated learning, taking into account the moderating role of personality traits. The research in the applied research group is a survey and a quantitative research. The main tool for collecting information in this research is 3 standard questionnaires, and the statistical population of this research includes Tehran University students. The sample size in this research is equal to 351 people using the Spss Sample power sampling software and the sampling method is available sampling. The tool for analyzing questionnaire data of this research is SPSS and AMOS statistical software. In order to determine the presence or absence of influence and relationship between variables and to estimate and generalize the results obtained from the sample size to the statistical population, Pearson's correlation coefficient test, structural correlation model, multiple regression model and moderator model were used to evaluate the research hypotheses. According to the findings of this research, there has been a positive and significant relationship between the factors affecting the effectiveness of e-learning and self-regulation learning. 41% of the reason for self-regulation learning is related to the dimensions of factors affecting the effectiveness of e-learning. So, the dimensions of the factors affecting the effectiveness of e-learning have an effect on self-regulation learning, and self-regulation learning can be predicted based on the factors affecting the effectiveness of e-learning in the statistical population. The variable of personality traits has also played a moderating role for the factors affecting the effectiveness of e-learning on self-regulation learning.
Introduction
Educational systems are created with a specific mission and strive to achieve certain goals (Nematollahi et al., 2023). For this purpose, the use of evaluation mechanisms provides the necessary conditions and contexts for the transparency of performance, the accountability of educational systems, and information on the level of achievement of goals and objectives. Learning is a function by which new or existing knowledge, behaviors, abilities, or choices are understood, reinforced, and modified, which may lead to a potential change in the combination of data, depth of knowledge, approach, or behavior relative to the type and range of experiences. (Fisher et al., 2021; Hong et al., 2021; Prasad, 2021). Learning does not happen instantaneously, but grows based on past knowledge. On this basis, learning can be defined along a process (Sarid et al., 2021b; Yukselturk & Bulut, 2007). Learning in humans can be part of the process of education, personal development, and exercises that may be purposeful or motivated (Howlett et al., 2021; Prasad, 2021). In 1998, Forty-four percent of higher education institutions offered distance education courses, an eleven percent increase from 1995 (Cerezo et al., 2020). But due to the rapid development of online education, our understanding of education and learning in this new environment is lagging behind and we have not comprehensively reviewed the literature and many important issues and factors of online education and learning (Bai & Guo, 2021; Maldonado-Mahauad et al., 2018; Sletten, 2017). One of the factors that can affect students' academic performance is self-regulated learning, which is planning based on self-observation that continuously monitors behavior to achieve desired goals. Also, personality traits have been introduced as one of the most important factors in influencing teaching and learning (Maldonado-Mahauad et al., 2018; Panadero et al., 2017; van Houten-Schat et al., 2018). In this regard, in order to improve e-learning systems and self-regulated learning, which is the learning style in e-learning systems, and to explain the role of personality traits, it is necessary to conduct research considering the effect of these variables.
Case study
University of Tehran students
Materials and Methods
This research is based on the objective criterion in the applied research group, based on the data collection time criterion in the survey research group, based on the nature of data criterion and the basis of the research, it is a quantitative research and based on the criterion of the data collection method, it is a field research using a questionnaire tool. The main tool for collecting information in this research is 3 standard questionnaires and the spectrum used in the questionnaire of this research is the Likert scale. In line with the data analysis in this research, a questionnaire data analysis tool was used with SPSS and AMOS structural equation modeling software. The statistical population of this research is the students of Tehran University, the sample size in this research is equal to 351 people using SPSS SAMPLE POWER software and the sampling method in this research is available sampling. In order to determine the presence or absence of influence between variables and to estimate and generalize the results obtained from the sample size to the statistical population, Pearson's correlation coefficient test, structural correlation model, multiple regression model and moderator model have been used to evaluate the research hypotheses.
Discussion and Results
According to the findings of the research, the coefficient of determination in this model indicates that 41% of the cause of self-regulation learning is related to the dimensions of factors affecting the effectiveness of e-learning (error prevention, visibility, flexibility, course management, interaction, feedback and assistance, accessibility, stability and functionality, measurement strategies, memory capabilities, completeness, appearance beauty, reduction of rework). In general, according to the output of the bootstrap test to clarify the significance or non-significance of the determination coefficient of 0.41, because the significance level value in this test is reported as 0.0001, it can be concluded that in general these effects (influence of independent variables on the variable dependent) is significant, so the dimensions of the factors affecting the effectiveness of e-learning have an effect on self-regulation learning (there is a significant relationship between the factors affecting the effectiveness of e-learning and self-regulation learning).Also, the general result of the moderating model is that; It is possible to predict self-regulated learning based on factors affecting the effectiveness of e-learning in the statistical population. Considering that both models have a significant difference in the estimation of the coefficient of influence and the significance level is reported to be smaller than the standard error level of 0.05, it can be concluded that the variable of personality characteristics has an effect on the effectiveness of e-learning on self-regulation learning, It has a moderator.
Conclusion
The results of the Pearson correlation coefficient test and the correlation model used in the evaluation of the hypotheses showed that; The value of the correlation coefficient of these two variables in Amos software is equal to 0.53, which indicates that these two variables have a good correlation. So, in general, it can be concluded that there is a positive and meaningful relationship between the factors affecting the effectiveness of e-learning and self-regulation learning, the slope of the regression risk in the data distribution diagram is also positive, indicating that as the amount of factors affecting the effectiveness of e-learning increases, learning Self-regulation will also increase and vice versa. The value of the coefficient of determination in this model indicates that 41% of the cause of self-regulation learning is related to the dimensions of factors affecting the effectiveness of e-learning. Also, each of the 12 dimensions has a unique effect on the effectiveness of the e-learning system; Also, the moderating role of personality traits has been proven and the role of each of its dimensions is confirmed.

Keywords

References

منابع فارسی
احمدی، حیدر، نعمت الهی، حمیدرضا و اسدالهی دهکردی، الهه. (۱۴۰۱). مراحل اجرا و تحقق حکمرانی مشارکتی در آموزش عالی. مطالعات مدیریت بهبود و تحول، ۳۱(۱۰۵)، ۱۰۳-۱۳۵.
زارعی ساروکلائی، مائده، شمس، غلامرضا، رضایی‌زاده، مرتضی و قهرمانی، محمد. (۱۳۹۹). تعیین‌کنندگان اثربخشی یادگیری الکترونیکی: مطالعه‌ای کیفی بر مدرس. تدریس پژوهی، ۸(۲)، ۷۹-۵۵.
عبدالحمید مهدی، نعمت اللهی حمیدرضا، عبدالحسین‌زاده محمد. دستاورد‌ها و کارکرد‌های رویکرد مدیریت مشارکتی در نظام آموزش عالی. آموزش عالی ایران. ۱۴۰۱؛ ۱۴ (۱): ۱-۲۳.
عبدالهی، محمد حسن، غلامی ترکسلویه، سجاد و عباسیان، مهدی. (۱۴۰۰). تدوین مدل عوامل مؤثر در اثربخشی آموزش مجازی درس تربیت بدنی عمومی در شرایط همه‌گیری کرونا. پژوهش در ورزش تربیتی، ۹(۲۵)، ۸۹-۱۱۰.
محمدی چمردانی، هاشم و رحمانی، محمدنور. (۱۳۹۸). شناسایی عوامل مؤثر در موفقیت دوره‌های آموزشی الکترونیکی (پژوهشی آمیخته). علوم تربیتی، ۲۶(۱)، ۱۳۷-۱۵۴.
نعمت الهی، حمیدرضا، احمدی، حیدر و اسدالهی دهکردی، الهه. (۱۴۰۱). پدیدارشناسی موانع اجرای حکمرانی مشارکتی در نهاد‌های آموزشی و پژوهشی. مطالعات راهبردی سیاستگذاری عمومی، ۱۲(۴۴)، ۱۸۹-۱۶۴.
نعمت الهی، حمیدرضا، ارمان، ‌مانی و تنگستانی، الهام. (۱۴۰۲). واکاوی راهبرد‌های مؤثر بر توسعه تاب‌آوری دانشجویان در محیط آموزش الکترونیکی طی همه‌گیری COVID-۱۹: یک مطالعه کیفی. نامه آموزش عالی، ۱۶(۶۳)، ۱۵۳-۱۸۴.
نعمت الهی، حمیدرضا، محمداسمعیلی، ندا، قلی پور، آرین، پاکدل، سعید. (۱۴۰۲). شناسایی و رتبه‌بندی عوامل اثربخشی سیستم اموزش الکترونیکی: فصلنامه آموزش و توسعه منابع انسانی، ۳۷ (۱۰)، ۱۴۳-۱۷۶.
References
Abdolhamid, M., Nematollahi, H., & Abdolhosseinzadeh, M. (2022). Achievements and functions of participatory governance approach in higher education system. Iranian Higher Education, 14(1), 1-23. (In Persian).
Ahmadi, H., Nematollahi, H., & Dehkordi, E. A. (2022). Steps of Implementation and Realization of Participatory Governance in Higher Education Institutions. Management Studies in Development & Evolution.31(105), 103-135. (In Persian).
Abdollahi, M. H., gholami torksaluye, S., & abbasian, M. (2022). Developing a model of effective factors in the effectiveness of virtual education in general physical education lessons in corona pandemic conditions. Research on Educational Sport, 9(25), 89-110. doi:10.22089/res.2021.10469.2092. (In Persian).
Abouzeid, E., Fouad, S., Wasfy, N. F., Alkhadragy, R., Hefny, M., & Kamal, D. (2021). Influence of personality traits and learning styles on undergraduate medical students’ academic achievement. Advances in Medical Education and Practice, 12. https://doi.org/10.2147/AMEP.S314644
Ambarwati, S. (2021). Implementasi Supervisi Akademik Dan Kesehatan Sekolah Pada0 Masa Pandemi Covid19 Di Sdn Bener 01 Tahun Pelajaran 2020/2021. Florea : Jurnal Biologi Dan Pembelajarannya, 8(1). https://doi.org/10.25273/florea.v8i1.9270
Araka, E., Maina, E., Gitonga, R., & Oboko, R. (2020). Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018). In Research and Practice in Technology Enhanced Learning (Vol. 15, Issue 1). https://doi.org/10.1186/s41039-020-00129-5
Balakrishnan, V., Khan, S., & Arabnia, H. R. (2020). Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Computers and Security, 90. https://doi.org/10.1016/j.cose.2019.101710
Bandura, A. (1982a). Self-efficacy mechanism in human agency. American Psychologist, 37(2). https://doi.org/10.1037//0003-066x.37.2.122
Bandura, A. (1982b). Self-efficacy mechanism in human agency. American Psychologist, 37(2). https://doi.org/10.1037/0003-066X.37.2.122
Barros, A., Simão, A. M. V., & Frisson, L. (2021). Self-regulation of learning and conscientiousness in Portuguese and Brazilian samples. Current Psychology. https://doi.org/10.1007/s12144-020-01232-y
Bashier, L., & Elmossad, Y. M. (2020). Students’ perception on E. Learning and Remote Exams during COVID 19 Outbreak 2020. International Journal of Pharmaceutical and Phytopharmacological Research, 10(5).
Bruso, J. L. (2019). An Examination of Personality Traits as A Predictor of the Use of Self-Regulated Learning Strategies. ProQuest Dissertations and Theses.
Bruso, J., Stefaniak, J., & Bol, L. (2020). An examination of personality traits as a predictor of the use of self-regulated learning strategies and considerations for online instruction. Educational Technology Research and Development, 68(5). https://doi.org/10.1007/s11423-020-09797-y
Cataudella, S., Carta, S., Mascia, M. L., Masala, C., Petretto, D. R., & Penna, M. P. (2021). Psychological Aspects of Students With Learning Disabilities in E-Environments: A Mini Review and Future Research Directions. In Frontiers in Psychology (Vol. 11). https://doi.org/10.3389/fpsyg.2020.611818
Chen, C. M., Chen, L. C., & Horng, W. J. (2021). A collaborative reading annotation system with formative assessment and feedback mechanisms to promote digital reading performance. Interactive Learning Environments, 29(5). https://doi.org/10.1080/10494820.2019.1636091
Costa, P. T., & McCrae, R. R. (1995). Domains and Facets: Hierarchical Personality Assessment Using the Revised NEO Personality Inventory. Journal of Personality Assessment, 64(1). https://doi.org/10.1207/s15327752jpa6401_2
DePietro, D. M., Santucci, S. E., Harrison, N. E., Kiefer, R. M., Trerotola, S. O., Sudheendra, D., & Shamimi-Noori, S. (2021). Medical Student Education During the COVID-19 Pandemic: Initial Experiences Implementing a Virtual Interventional Radiology Elective Course. Academic Radiology, 28(1). https://doi.org/10.1016/j.acra.2020.10.005
Dignath, C., & Veenman, M. V. J. (2021). The Role of Direct Strategy Instruction and Indirect Activation of Self-Regulated Learning—Evidence from Classroom Observation Studies. In Educational Psychology Review (Vol. 33, Issue 2). https://doi.org/10.1007/s10648-020-09534-0
Fida, H. (2020). Peran Self Regulated Learning Di Era Pandemi Covid-19. Jurnal Ilmiah Fakultas Keguruan Dan Ilmu Pendidikan, 6(2).
Gaeta, M. L., Gaeta, L., & Rodriguez, M. del S. (2021). The Impact of COVID-19 Home Confinement on Mexican University Students: Emotions, Coping Strategies, and Self-Regulated Learning. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.642823
Granberg, C., Palm, T., & Palmberg, B. (2021). A case study of a formative assessment practice and the effects on students’ self-regulated learning. Studies in Educational Evaluation, 68. https://doi.org/10.1016/j.stueduc.2020.100955
Greene, J. A., Plumley, R. D., Urban, C. J., Bernacki, M. L., Gates, K. M., Hogan, K. A., Demetriou, C., & Panter, A. T. (2021). Modeling temporal self-regulatory processing in a higher education biology course. Learning and Instruction, 72. https://doi.org/10.1016/j.learninstruc.2019.04.002
Gupta, S., Dabas, A., Swarnim, S., & Mishra, D. (2021). Medical education during COVID-19 associated lockdown: Faculty and students’ perspective. Medical Journal Armed Forces India, 77. https://doi.org/10.1016/j.mjafi.2020.12.008
Hernández-Ramos, J. P., Martínez-Abad, F., & Sánchez-Prieto, J. C. (2021). empleo de videotutoriales en la era post COVID19: valoración e influencia en la identidad docente del futuro profesional. Revista de Educación a Distancia (RED), 21(65). https://doi.org/10.6018/red.449321
Hertel, S., & Karlen, Y. (2021). Implicit theories of self-regulated learning: Interplay with students’ achievement goals, learning strategies, and metacognition. British Journal of Educational Psychology, 91(3). https://doi.org/10.1111/bjep.12402
Hong, D., Gao, L., Yokoya, N., Yao, J., Chanussot, J., Du, Q., & Zhang, B. (2021). More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing, 59(5). https://doi.org/10.1109/TGRS.2020.3016820
Hudaifah, F. (2020). Peran self regulated learning di era pandemi covid-19. Jurnal Ilmiah Fakultas Keguruan Dan Ilmu Pendidikan, 6(2).
Jonsdottir, G. A., Einarsson, G., Thorleifsson, G., Magnusson, S. H., Gunnarsson, A. F., Frigge, M. L., Gisladottir, R. S., Unnsteinsdottir, U., Gunnarsson, B., Walters, G. B., Steinthorsdottir, V., Helgadottir, A., Jonsdottir, I., Gislason, T., Thorsteinsson, H. S., Sigurdsson, E., Haraldsson, M., Sigurdsson, E. L., Bjarnason, R., … Stefansson, K. (2021). Genetic propensities for verbal and spatial ability have opposite effects on body mass index and risk of schizophrenia. Intelligence, 88. https://doi.org/10.1016/j.intell.2021.101565
Khan, Z. R., Sivasubramaniam, S., Anand, P., & Hysaj, A. (2021). ‘e’-thinking teaching and assessment to uphold academic integrity: lessons learned from emergency distance learning. International Journal for Educational Integrity, 17(1). https://doi.org/10.1007/s40979-021-00079-5
Korostyshevskiy, V. (2021). Self-Regulated Learning and Student Life. About Campus: Enriching the Student Learning Experience, 26(2). https://doi.org/10.1177/1086482220971270
Kuleshov, M. V., Stein, D. J., Clarke, D. J. B., Kropiwnicki, E., Jagodnik, K. M., Bartal, A., Evangelista, J. E., Hom, J., Cheng, M., Bailey, A., Zhou, A., Ferguson, L. B., Lachmann, A., & Ma’ayan, A. (2020). The COVID-19 Drug and Gene Set Library. Patterns, 1(6). https://doi.org/10.1016/j.patter.2020.100090
Lai, S., Sun, B., Wu, F., & Xiao, R. (2020). Automatic Personality Identification Using Students’ Online Learning Behavior. IEEE Transactions on Learning Technologies, 13(1). https://doi.org/10.1109/TLT.2019.2924223
Lestari, F. P., Ahmadi, F., & Rochmad, R. (2021). The implementation of mathematics comic through contextual teaching and learning to improve critical thinking ability and character. European Journal of Educational Research, 10(1). https://doi.org/10.12973/EU-JER.10.1.497
Leung, J., Robert Cloninger, C., Hong, B. A., Cloninger, K. M., & Eley, D. S. (2019). Temperament and character profiles of medical students associated with tolerance of ambiguity and perfectionism. PeerJ, 2019(6). https://doi.org/10.7717/peerj.7109
Li, H., Wu, Y., & Chen, M. (2021). Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm. IEEE Transactions on Cybernetics, 51(3). https://doi.org/10.1109/TCYB.2020.2982168
Li, J. P. O., Liu, H., Ting, D. S. J., Jeon, S., Chan, R. V. P., Kim, J. E., Sim, D. A., Thomas, P. B. M., Lin, H., Chen, Y., Sakomoto, T., Loewenstein, A., Lam, D. S. C., Pasquale, L. R., Wong, T. Y., Lam, L. A., & Ting, D. S. W. (2021). Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. In Progress in Retinal and Eye Research (Vol. 82). https://doi.org/10.1016/j.preteyeres.2020.100900
Lim, L. A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2021). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 72. https://doi.org/10.1016/j.learninstruc.2019.04.003
Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., & Munoz-Gama, J. (2018). Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses. Computers in Human Behavior, 80. https://doi.org/10.1016/j.chb.2017.11.011
Martin, J. (2004). Self-regulated learning, social cognitive theory, and agency. In Educational Psychologist (Vol. 39, Issue 2). https://doi.org/10.1207/s15326985ep3902_4
Martin, J. S., Koski, S. E., Bugnyar, T., Jaeggi, A. V., & Massen, J. J. M. (2021). Prosociality, social tolerance and partner choice facilitate mutually beneficial cooperation in common marmosets, Callithrix jacchus. Animal Behaviour, 173. https://doi.org/10.1016/j.anbehav.2020.12.016
Mitra, N. K., Aung, H. H., Kumari, M., Perera, J., Sivakumar, A., Singh, A., & Nadarajah, V. D. (2021). Improving the learning process in anatomy practical sessions of chiropractic program using e-learning tool. Translational Research in Anatomy, 23. https://doi.org/10.1016/j.tria.2020.100100
Molenaar, I., Horvers, A., & Baker, R. S. (2021). What can moment-by-moment learning curves tell about students’ self-regulated learning? Learning and Instruction, 72. https://doi.org/10.1016/j.learninstruc.2019.05.003
Mora-Beltrán, C. E., Rojas, A. E., & Mejía-Moncayo, C. (2020). An immersive experience in the virtual 3D VirBELA environment for leadership development in undergraduate students during the COVID-19 quarantine. CEUR Workshop Proceedings, 2714.
Moreno-Armendariz, M. A., Duchanoy Martinez, C. A., Calvo, H., & Moreno-Sotelo, M. (2020). Estimation of Personality Traits from Portrait Pictures Using the Five-Factor Model. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.3034639
Mornell, A., Osborne, M. S., & McPherson, G. E. (2020). Evaluating practice strategies, behavior and learning progress in elite performers: An exploratory study. Musicae Scientiae, 24(1). https://doi.org/10.1177/1029864918771731
Mohamadichemardani, H., & Rahmani, M.N. (2019). Identifying Effective Factors in Success of E-Learning Courses, Journal of Educational Sciences, 26 (1), 137-154. DOI: 10.22055/edus.2019.27633.2677. (In Persian).
Nikou, S., & Maslov, I. (2021). An analysis of students’ perspectives on e-learning participation – the case of COVID-19 pandemic. International Journal of Information and Learning Technology, 38(3). https://doi.org/10.1108/IJILT-12-2020-0220
Nematollahi, H., Ahmadi, H., & Asadolahidehkordi, E. (2022). Phenomenology of barriers to participatory governance in educational and research institutions. Strategic Studies of public policy, 12(44), 189-164. (In Persian).
Nematollahi, H., Mohammad Esmaeili, N., Gholipour, A., & Pakdel, S.,  (2023). Identifying and Ranking Effectiveness Factors of E-Learning System. Quarterly Journal of Training and Development of Human Resources, 37(37), 143. (In Persian).
Nematollahi, H., Arman, M., & Tangestani, E. (2023). Analysis of Strategies Affecting the Development of Student Resilience in the E-Learning Environment During the COVID-19 Epidemic: A Qualitative Study. Higher Education Letter, 16(63), 124-148. (In Persian).
Opelt, F., & Schwinger, M. (2020). Relationships Between Narrow Personality Traits and Self-Regulated Learning Strategies: Exploring the Role of Mindfulness, Contingent Self-Esteem, and Self-Control. AERA Open, 6(3). https://doi.org/10.1177/2332858420949499
Öztürk, M., & Çakıroğlu, Ü. (2021). Flipped learning design in EFL classrooms: implementing self-regulated learning strategies to develop language skills. Smart Learning Environments, 8(1). https://doi.org/10.1186/s40561-021-00146-x
Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. In Educational Research Review (Vol. 22). https://doi.org/10.1016/j.edurev.2017.08.004
Pelikan, E. R., Lüftenegger, M., Holzer, J., Korlat, S., Spiel, C., & Schober, B. (2021). Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination for perceived competence. Zeitschrift Fur Erziehungswissenschaft, 24(2). https://doi.org/10.1007/s11618-021-01002-x
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and Self-Regulated Learning Components of Classroom Academic Performance. Journal of Educational Psychology, 82(1). https://doi.org/10.1037/0022-0663.82.1.33
Pratama, R. A., & Pratiwi, I. M. (2019). Hasil Belajar Sejarah Indonesia Melalui Pembelajaran Aktif Tipe Everyone Is a Teacher Here Berdasarkan Kemandirian Belajar. SOSIAL HORIZON: Jurnal Pendidikan Sosial, 6(1).
Punnoose, A. C. (2012). Determinants of intention to use eLearning based on the technology acceptance model. Journal of Information Technology Education:Research, 11(1). https://doi.org/10.28945/1744
Rajkumar, R., & Ganapathy, V. (2020). Bio-Inspiring Learning Style Chatbot Inventory Using Brain Computing Interface to Increase the Efficiency of E-Learning. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2984591
Rania Al Omari. (2020). The effect of covid19 on the academic achievement of accounting department students at the university of jordan, aqaba branch an experimental study. Systematic Reviews in Pharmacy, 11(11). https://doi.org/10.31838/srp.2020.11.109
Ray, A., Bala, P. K., & Dwivedi, Y. K. (2021). Exploring values affecting e-Learning adoption from the user-generated-content: A consumption-value-theory perspective. Journal of Strategic Marketing, 29(5). https://doi.org/10.1080/0965254X.2020.1749875
Rosito, A. C. (2018). Kepribadian dan Self-Regulated Learning. Jurnal Psikologi, 45(3). https://doi.org/10.22146/jpsi.28530
Shi, J., Miskin, N., Dabiri, B. E., DeSimone, A. K., Schaefer, P. M., Matalon, S. A., Uyeda, J. W., Guenette, J. P., & Gaviola, G. C. (2021). Beyond business as usual: Radiology residency educational response to the COVID-2019 pandemic. In Clinical Imaging (Vol. 69). https://doi.org/10.1016/j.clinimag.2020.10.010
Shrivastava, S., & Shrivastava, P. (2020). Assessing the impact of e-learning in medical education. International Journal of Academic Medicine, 6(1). https://doi.org/10.4103/IJAM.IJAM_5_19
Suppan, M., Stuby, L., Carrera, E., Cottet, P., Koka, A., Assal, F., Savoldelli, G. L., & Suppan, L. (2021). Asynchronous Distance Learning of the National Institutes of Health Stroke Scale during the COVID-19 Pandemic (E-Learning vs Video): Randomized Controlled Trial. Journal of Medical Internet Research, 23(1). https://doi.org/10.2196/23594
Taub, M., Azevedo, R., Rajendran, R., Cloude, E. B., Biswas, G., & Price, M. J. (2021). How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system? Learning and Instruction, 72. https://doi.org/10.1016/j.learninstruc.2019.04.001
Tsekea, S., & Chigwada, J. P. (2021). COVID-19: strategies for positioning the university library in support of e-learning. Digital Library Perspectives, 37(1). https://doi.org/10.1108/DLP-06-2020-0058
Valverde-Berrocoso, J., del Carmen Garrido-Arroyo, M., Burgos-Videla, C., & Morales-Cevallos, M. B. (2020). Trends in educational research about e-Learning: A systematic literature review (2009-2018). In Sustainability (Switzerland) (Vol. 12, Issue 12). https://doi.org/10.3390/su12125153
van Houten-Schat, M. A., Berkhout, J. J., van Dijk, N., Endedijk, M. D., Jaarsma, A. D. C., & Diemers, A. D. (2018). Self-regulated learning in the clinical context: a systematic review. In Medical Education (Vol. 52, Issue 10). https://doi.org/10.1111/medu.13615
Van Overwalle, F., Manto, M., Cattaneo, Z., Clausi, S., Ferrari, C., Gabrieli, J. D. E., Guell, X., Heleven, E., Lupo, M., Ma, Q., Michelutti, M., Olivito, G., Pu, M., Rice, L. C., Schmahmann, J. D., Siciliano, L., Sokolov, A. A., Stoodley, C. J., van Dun, K., … Leggio, M. (2020). Consensus Paper: Cerebellum and Social Cognition. Cerebellum, 19(6). https://doi.org/10.1007/s12311-020-01155-1
Vilkova, K., & Shcheglova, I. (2021). Deconstructing self-regulated learning in MOOCs: In search of help-seeking mechanisms. Education and Information Technologies, 26(1). https://doi.org/10.1007/s10639-020-10244-x
Vlachogianni, P., & Tselios, N. (2021). Investigating the impact of personality traits on perceived usability evaluation of e-learning platforms. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-02-2021-0024
Vlachogianni, P., Tselios, N., & Xenos, M. (2020). Personality traits and perceived usability evaluation of e-learning platforms: A case study. 17th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2020. https://doi.org/10.33965/celda2020_202014c056
Vladova, G., Ullrich, A., Bender, B., & Gronau, N. (2021). Students’ Acceptance of Technology-Mediated Teaching – How It Was Influenced During the COVID-19 Pandemic in 2020: A Study From Germany. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.636086
Vo, N. N. Y., Liu, S., Li, X., & Xu, G. (2021). Leveraging unstructured call log data for customer churn prediction. Knowledge-Based Systems, 212. https://doi.org/10.1016/j.knosys.2020.106586
Wartiningsih, & Surjono, H. D. (2020). Adaptive E-Learning Model in Learning Personality Characters. https://doi.org/10.2991/assehr.k.200521.004
Wat, K. K. Y., Banks, P. B., & McArthur, C. (2020). Linking animal personality to problem-solving performance in urban common brushtail possums. Animal Behaviour, 162. https://doi.org/10.1016/j.anbehav.2020.01.013
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1). https://doi.org/10.3102/0002831207312909
Zimmerman, B. J., & Bandura, A. (1994). Impact of Self-Regulatory Influences on Writing Course Attainment. American Educational Research Journal, 31(4). https://doi.org/10.2307/1163397
 

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