[
Abdelrahman, R.M. (2020). Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students. Heliyon, 6(9), Article number e04192.
]Search in Google Scholar
[
Alshanqiti, A., & Namoun, A. (2020). Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification, IEEE Access, 8, 203827 – 203844.
]Search in Google Scholar
[
Al-Tameemi, R.A.N., Johnson, C., Gitay, R., Abdel-Salam, A-S.G., Al Hazaa, K., BenSaid, A., Romanowski, M.H. (2023). Determinants of poor academic performance among undergraduate students — A systematic literature review. International Journal of Educational Research Open, 4, Article number 100232.
]Search in Google Scholar
[
Anderson, K., Ritter, G., & Zamarro, G. (2019). Understanding a vicious cycle: the relationship between student discipline and student academic outcomes. Educational Researcher, 48(5), 251-262.
]Search in Google Scholar
[
Aria, M., Cuccurullo, C., & Gnasso A. (2021). A comparison among interpretative proposals for Random Forests. Machine Learning with Applications, 6, Article number 100094.
]Search in Google Scholar
[
Astin, A.W. (1999). Student involvement: a developmental theory for higher education. Journal of College Student Development, 40, 518–529.
]Search in Google Scholar
[
Barak, M., Watted, A., & Haick, H. (2016). Motivation to learn in massive open online courses: Examining aspects of language and social engagement. Computers & Education, 94, 49-60.
]Search in Google Scholar
[
Bénard, C., da Veiga, S., & Scornet, E. (2021). MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA. Retrieved from https://arxiv.org/abs/2102.13347.
]Search in Google Scholar
[
Ben-Eliyahu, A., Moore, D., Dorph, R., & Schunn, C.D. (2018). Investigating the multidimensionality of engagement: affective, behavioral, and cognitive engagement across science activities and contexts. Contemporary Educational Psychology, 53, 87-105.
]Search in Google Scholar
[
Black, A.E., & Deci, E.L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: a self-determination theory perspective. Science Education, 84, Article number 740e756.
]Search in Google Scholar
[
Boheim, R., Urdan, T., Knogler, M., & Seidel, T. (2020). Student hand-raising as an indicator of behavioral engagement and its role in classroom learning. Contemporary Educational Psychology, 62, Article number 101894.
]Search in Google Scholar
[
Boulton, C.A, Hughes, E., Kent, C., Smith, J.R., & Williams, H.T.P. (2019). Student engagement and wellbeing over time at a higher education institution. PLoS ONE, 14(11), Article number e0225770.
]Search in Google Scholar
[
Chan-Lau, J.A. (2017). Lasso Regressions and Forecasting Models in Applied Stress Testing, IMF Working Paper, WP/17/108. Retrieved from https://www.elibrary.imf.org/view/journals/001/2017/108/article-A001-en.xml.
]Search in Google Scholar
[
Choi, B.K., & Rhee, B.S. (2014). The influences of student engagement, institutional mission, and cooperative learning climate on the generic competency development of Korean undergraduate. Higher Education, 67(1), 1–18.
]Search in Google Scholar
[
Christenson, S.L., Reschly, A.L., & Wylie, C. (2012). The handbook of research on student engagement. New York, NY: Springer Science.
]Search in Google Scholar
[
Coates, H. (2006). Student engagement in campus-based and online education: University connections. New York, NY: Routledge.
]Search in Google Scholar
[
Collie, R.J., Holliman, A.J., & Martin, A.J. (2017). Adaptability, engagement and academic achievement at university. Educational Psychology, 37(5), 632-647.
]Search in Google Scholar
[
Connell, J.P. (1990). Context, self, and action: A motivational analysis of self-system processes across the life-span, In Cicchetti, D. (Ed.). The self in transition: Infancy to childhood (pp. 61-97). Chicago, IL: University of Chicago.
]Search in Google Scholar
[
Cooper, K.S. (2014). Eliciting engagement in the high school classroom: a mixed-methods examination of teaching practices. American Educational Research Journal, 51(2), 363-402.
]Search in Google Scholar
[
Csikszentmihalyi, M. (2008). Flow: The psychology of optimal experience, HarperCollins e-books. Retrieved from https://blogs.baruch.cuny.edu/authenticityandastonishment2/files/2013/04/Mihaly-Csikszentmihalyi-Flow1.pdf.
]Search in Google Scholar
[
D’Errico, F., Paciello, M., & Cerniglia, L. (2016). When emotions enhance student engagement in e-learning processes. Journal of e-Learning and Knowledge Society, 12(4), 9-23.
]Search in Google Scholar
[
De Graaf, N.D., De Graaf, P.M., & Kraaykamp, G. (2000). Parental cultural capital and educational attainment in the Netherlands: a refinement of the cultural capital perspective. Sociology of Education, 73, 92–111.
]Search in Google Scholar
[
Delfino, A.P. (2019). Student Engagement and Academic Performance of Students of Partido State University. Asian Journal of University Education, 15(1), 42-55.
]Search in Google Scholar
[
Dima, A. M., Busu, M., & Vargas, V. M. (2022). The mediating role of students’ ability to adapt to online activities on the relationship between perceived university culture and academic performance. Oeconomia Copernicana, 13(4), 1253–1281. https://doi.org/10.24136/oc.2022.036.
]Search in Google Scholar
[
Dorn, E., Hancock, B., Sarakatsannis, J., & Viruleg, E. (2020). COVID-19 and student learning in the United States: The hurt could last a lifetime. Retrieved from https://www.mckinsey.com/industries/education/our-insights/covid-19-and-student-learning-in-the-united-states-the-hurt-could-last-a-lifetime.
]Search in Google Scholar
[
Freire, P. (1970). Pedagogy of the oppressed. New York, NY: The Continuum International Publishing Group.
]Search in Google Scholar
[
Finn, J.D. (1993). School Engagement & Students at Risk. National Center for Education Statistics Report NCES-93-470, Washington, DC. Retrieved from https://nces.ed.gov/pubs93/93470a.pdf.
]Search in Google Scholar
[
Fox, K., Vignare, K., Yuan, L., Tesene, M., Beltran, K., Schweizer, H., Brokos, M., & Seaborn, R. (2021). Strategies for Implementing Digital Learning Infrastructure to Support Equitable Outcomes: A Case-based Guidebook for Institutional Leaders. Retrieved from https://www.everylearnereverywhere.org/resources/.
]Search in Google Scholar
[
Fredricks, J.A., Blumenfeld, P.C., & Paris, A.H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109.
]Search in Google Scholar
[
Fredricks, J.A., Parr, A.K., Amemiya, J.L., Ming-Te, W., & Brauer, S. (2019). What matters for urban adolescents’ engagement and disengagement in school: a mixed-methods study. Journal of Adolescent Research, 34(5), 491–527.
]Search in Google Scholar
[
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22.
]Search in Google Scholar
[
Glapaththi, I., Dissanayake, R., Welgama, T., Somachandara, U., Weerarathna, R., & Pathirana, G. (2019). A Study on the relationship between student engagement and their academic achievements. Asian Social Science, 15(11), 1-16.
]Search in Google Scholar
[
Glynn, S.M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science motivation questionnaire II: validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159–1176.
]Search in Google Scholar
[
Gonzalez, R., & Padilla, A.M. (1997). The academic resilience of Mexican American high school students. Hispanic Journal of Behavioral Sciences, 19, 301–317.
]Search in Google Scholar
[
Green, J., Liem, G.A.D. Martin, A.J., Colmar, S., Marsh, H.W., & McInerney, D. (2012). Academic motivation, self-concept, engagement, and performance in high school: Key processes from a longitudinal perspective. Journal of Adolescence, 35, 1111–1122.
]Search in Google Scholar
[
Hanushek, E., & Woessmann, L. (2020). The Economic Impacts of Learning Losses. Retrieved from http://www.oecd.org/education/The-economic-impacts-of-coronavirus-covid-19-learning-losses.pdf.
]Search in Google Scholar
[
Henrie, C.R., Halverson, L.R., & Graham, C.R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36–53.
]Search in Google Scholar
[
Hofferber, N., Basten, M., Großmann, N., & Wilde, M. (2016). The effects of autonomy-supportive and controlling teaching behaviour in biology lessons with primary and secondary experiences on students’ intrinsic motivation and flow-experience. International Journal of Science Education, 38(13), 2114-2132.
]Search in Google Scholar
[
Ituma, A. (2011). An evaluation of students’ perceptions and engagement with e-learning components in a campus based university. Active Learning in Higher Education, 12(1), 57–68.
]Search in Google Scholar
[
Kim, H.J., Hong, A.J., & Hae-Deok, S. (2019). The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments. International Journal of Educational Technology in Higher Education, 16(21), 1-18.
]Search in Google Scholar
[
Klem, A.M., & Connell, J.P. (2004). Relationships matter: Linking teacher support to student engagement and achievement. Journal of School Health, 74, 262–273.
]Search in Google Scholar
[
Korhonen, V., Inkinen, M., Mattsson, M., & Toom, A. (2017). Student engagement and the transition from the first to second year in higher education. In Kyndt, E., Donche, V., Trigwell, K., Lindblom-Ylänne, S. (Eds.). Higher education transitions: Theory and research (pp. 113–134). London: Routledge.
]Search in Google Scholar
[
Krause, K.L., & Coates, H. (2008). Student engagement in first year university. Assessment & Evaluation in Higher Education, 33(5), 493–505.
]Search in Google Scholar
[
Kuh, G.D. (2001). The national survey of student engagement: Conceptual framework and overview of psychometric properties. Bloomington: Indiana University Center for Postsecondary Research and Planning. Retrieved from https://scholarworks.iu.edu/dspace/handle/2022/24268.
]Search in Google Scholar
[
Kuzminykh, I., Ghita, B., & Xiao, H. (2021). The relationship between student engagement and academic performance in online education. ICSET 2021-5th International Conference on E-Society, E-Education and E-Technology, 97–101.
]Search in Google Scholar
[
Lee, J., Shi, Z., & Gao, Z. (2021). On LASSO for predictive regression. Journal of Econometrics, 229(2), 322-349.
]Search in Google Scholar
[
Maamin, M., Maat, S.M., & H. Iksan, Z. (2022). The influence of student engagement on mathematical achievement among secondary school students. Mathematics, 10(1), Article number 41.
]Search in Google Scholar
[
Madigan, D.J., & Kim, L.E. (2021). Does teacher burnout affect students? A systematic review of its association with academic achievement and student-reported outcomes. International Journal of Educational Research, 105, Article number 101714.
]Search in Google Scholar
[
Mappadang, A., Khusaini, K., Sinaga, M., & Elizabeth, E. (2022) Academic interest determines the academic performance of undergraduate accounting students: Multinomial logit evidence, Cogent Business & Management, 9(1), Article number 2101326.
]Search in Google Scholar
[
Marsh, H.W. (2007). Self-concept theory, measurement and research into practice: The role of self-concept in educational psychology. Leicester, UK: British Psychological Society.
]Search in Google Scholar
[
Melkumova, L.E., & Shatskikh, S.Ya. (2017). Comparing Ridge and LASSO estimators for data analysis. Procedia Engineering, 201, 746–755.
]Search in Google Scholar
[
Moorth, A. (2020). How LASSO Regression Works in Machine Learning. Retrieved from https://dataaspirant.com/lasso-regression/#t-1606404715786.
]Search in Google Scholar
[
Morgan-Thomas, A., & Dudau, A. (2019). Of Possums, Hogs and Horses: Capturing Duality of Student Engagement in eLearning. Academy of Management Learning and Education, 18(4), 564-580.
]Search in Google Scholar
[
Nembrini, S., Konig I.R., & Wright M.N. (2018). The revival of the Gini importance? Bioinformatics, 34(21), 3711–3718.
]Search in Google Scholar
[
OECD (2021). Positive, High-achieving Students? What Schools and Teachers Can Do. TALIS, OECD Publishing, Paris. Retrieved from https://doi.org/10.1787/3b9551db-en.
]Search in Google Scholar
[
OECD (2023). Shaping Digital Education: Enabling Factors for Quality, Equity and Efficiency.
]Search in Google Scholar
[
OECD Publishing, Paris. Retrieved from https://doi.org/10.1787/bac4dc9f-en.
]Search in Google Scholar
[
Olelewe, C.J., & Agomuo, E.E. (2016). Effects of B-learning and F2F learning environments on students’ achievement in QBASIC programming. Computers & Education, 103, 76–86.
]Search in Google Scholar
[
Palos, R., Maricutoiu, L.P., & Costea, I. (2019). Relations between academic performance, student engagement and student burnout: A cross-lagged analysis of a two-wave study. Studies in Educational Evaluation, 60, 199–204.
]Search in Google Scholar
[
Pensiero, N., Kelly, A., & Bokhove, C. (2020). Covid-19 lockdown primary-secondary education impact. Retrieved from https://www.southampton.ac.uk/news/2020/07/lockdown-education-impact.page.
]Search in Google Scholar
[
Reeve, J., Cheon, S.H., & Jang, H. (2020). How and why students make academic progress: Reconceptualizing the student engagement construct to increase its explanatory power. Contemporary Educational Psychology, 62, Article number 101899.
]Search in Google Scholar
[
Ribeiro, L., Rosário, P., Núñez, J.C., Gaeta, M., & Fuentes, S. (2019). First-Year Students Background and Academic Achievement: The Mediating Role of Student Engagement. Frontiers in Psychology, 10, Article number 2669.
]Search in Google Scholar
[
Schaufeli, W.B., Bakker, A.B., & Salanova, M. (2006). The Measurement of Work Engagement with a Short Questionnaire: A Cross-National Study. Educational and Psychological Measurement, 66(4), 701-716.
]Search in Google Scholar
[
Skinner, E.A., Kindermann, T.A., Connell, J.P., & Wellborn, J.G. (2009). Engagement as an organizational construct in the dynamics of motivational development. In Wentzel, K., Wigfield, A. (Eds.). Handbook of motivation in school (pp. 223–245). Malwah, NJ: Erlbaum.
]Search in Google Scholar
[
Sneiderman, R. (2020). From Linear Regression to Ridge Regression, the Lasso, and the Elastic Net. Retrieved from https://towardsdatascience.com/from-linear-regression-to-ridge-regression-the-lasso-and-the-elastic-net-4eaecaf5f7e6.
]Search in Google Scholar
[
Snijders, I., Wijnia, L., Rikers, R.M.J.P., & Loyens, S.M.M. (2020). Building bridges in higher education: Student-faculty relationship quality, student engagement, and student loyalty. International Journal of Educational Research, 100, 101538.
]Search in Google Scholar
[
Stefenel, D., & Neagos, I. (2020). Measuring academic engagement among university students in Romania during Covid-19 pandemic. Thesis, 9(2), 3-29.
]Search in Google Scholar
[
Toth, M.D. (2021). Why student engagement is important in a post-COVID world – and 5 strategies to improve it. Retrieved from https://www.learningsciences.com/blog/why-is-student-engagement-important/.
]Search in Google Scholar
[
United Nations (2020). Policy brief: Education during COVID-19 and beyond. Retrieved from https://www.un.org/sites/un2.un.org/files/sg_policy_brief_covid-19_and_education_august_2020.pdf.
]Search in Google Scholar
[
Xie, K., Lu, L., Cheng, S.L., & Izmirli, S. (2017). The interactions between facilitator identity, conflictual presence, and social presence in online collaborative learning. Distance Education, 38(2), 230–244.
]Search in Google Scholar
[
Xie, K., Miller, N.C., & Allison, J.R. (2013). Toward a social conflict evolution model: Examining the adverse power of conflictual social interaction in online learning. Computers & Education, 63, 404–415.
]Search in Google Scholar
[
Xie, K., Vongkulluksn, V.W., Lu, L., & Cheng, S.-L. (2020). A person-centered approach to examining high-school students’ motivation, engagement and academic performance. Contemporary Educational Psychology, 62, 101877.
]Search in Google Scholar