Impacts of engagement on academic outcomes in technology-enhanced learning

References

• Abdigapbarova, U., & Zhiyenbayeva, N. (2023). Organization of student-centered learning within the professional training of a future teacher in a digital environment. Education and Information Technologies, 28(1), 647–661. https://doi.org/10.1007/s10639-022-11159-5
   PubMed Web of Science ®Google Scholar
• Alhazbi, S., Al-ali, A., Tabassum, A., Al-Ali, A., Al-Emadi, A., Khattab, T., & Hasan, M. A. (2024). Using learning analytics to measure self-regulated learning: A systematic review of empirical studies in higher education. Journal of Computer Assisted Learning. Advance online publication. https://doi.org/10.1111/jcal.12982
   Web of Science ®Google Scholar
• Becker, K. Vanzin, M. Marquardt, C & Ruiz, D (2006). Applying web usage mining for the analysis of behavior in web-based learning environments. In Romero, C & Ventura, S. (Eds.), Data mining in elearning (pp. 117–137). WitPress.
   Google Scholar
• Blockeel, H., & Struyf, J. (2002). Efficient algorithms for decision tree cross-validation. Journal of Machine Learning Research, 3(12), 621–650.
   Google Scholar
• Boekaerts, M. (1999). Self-regulated learning: Where we are today. International Journal of Educational Research, 31(6), 445–457. https://doi.org/10.1016/S0883-0355(99)00014-2
   Google Scholar
• Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007
   Web of Science ®Google Scholar
• Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281. https://doi.org/10.3102/00346543065003245
   Web of Science ®Google Scholar
• Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52, 22–28. https://doi.org/10.1016/j.chb.2015.05.049
   Web of Science ®Google Scholar
• Cantabella, M., Martínez-España, R., Ayuso, B., Yáñez, J. A., & Muñoz, A. (2019). Analysis of student behavior in learning management systems through a Big Data framework. Future Generation Computer Systems, 90, 262–272. https://doi.org/10.1016/j.future.2018.08.003
   Web of Science ®Google Scholar
• Conijn, R., Van den Beemt, A., & Cuijpers, P. (2018). Predicting student performance in a blended MOOC. Journal of Computer Assisted Learning, 34(5), 615–628. https://doi.org/10.1111/jcal.12270
   Web of Science ®Google Scholar
• Crossley, S., McNamara, D. S., Baker, R., Wang, Y., Paquette, L., Barnes, T., & Bergner, Y. (2015). Language to completion. Success in an educational data mining massive open online class. International Educational Data Mining Society.
   Google Scholar
• Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3–8. https://doi.org/10.1016/j.iheduc.2011.06.002
   Web of Science ®Google Scholar
• Dumford, A. D., & Miller, A. L. (2018). Online learning in higher education: Exploring advantages and disadvantages for engagement. Journal of Computing in Higher Education, 30(3), 452–465. https://doi.org/10.1007/s12528-018-9179-z
   Web of Science ®Google Scholar
• Ellis, R. K. (2009). Field guide to learning management systems (pp. 1–8). ASTD Learning Circuits.
   Google Scholar
• Esnaashari, S., Gardner, L. A., Arthanari, T. S., & Rehm, M. (2023). Unfolding self-regulated learning profiles of students: A longitudinal study. Journal of Computer Assisted Learning, 39(4), 1116–1131. https://doi.org/10.1111/jcal.12830
   Google Scholar
• Giannakos, M., & Cukurova, M. (2023). The role of learning theory in multimodal learning analytics. British Journal of Educational Technology, 54(5), 1246–1267. https://doi.org/10.1111/bjet.13320
   Google Scholar
• Greene, J. A., & Azevedo, R. (2010). The measurement of learners’ self-regulated cognitive and metacognitive processes while using computer-based learning environments. Educational Psychologist, 45(4), 203–209. https://doi.org/10.1080/00461520.2010.515935
   Web of Science ®Google Scholar
• Guo, J. P., Lv, S., Wang, S. C., Wei, S. M., Guo, Y. R., & Yang, L. Y. (2023). Reciprocal modeling of university students’ perceptions of the learning environment, engagement, and learning outcome: A longitudinal study. Learning and Instruction, 83, 101692. https://doi.org/10.1016/j.learninstruc.2022.101692
   Web of Science ®Google Scholar
• Han, F., & Ellis, R. A. (2020). Combining self-reported and observational measures to assess university student academic performance in blended course designs. Australasian Journal of Educational Technology, 36(6), 1–14. https://doi.org/10.14742/ajet.6369
   Web of Science ®Google Scholar
• Higgins, N. L., Rathner, J. A., & Frankland, S. (2023). Development of self-regulated learning: a longitudinal study on academic performance in undergraduate science. Research in Science & Technological Education, 41(4), 1242–1266. https://doi.org/10.1080/02635143.2021.1997978
   Web of Science ®Google Scholar
• Hung, J. L., & Crooks, S. M. (2009). Examining online learning patterns with data mining techniques in peer-moderated and teacher-moderated courses. Journal of Educational Computing Research, 40(2), 183–210. https://doi.org/10.2190/EC.40.2.c
   Web of Science ®Google Scholar
• Jiang, S., Williams, A., Schenke, K., Warschauer, M., & O'dowd, D. (2014). Predicting MOOC performance with week 1 behavior [Paper presentation]. Educational Data Mining 2014.
   Google Scholar
• Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: what is ‘enhanced’and how do we know? A critical literature review. Learning, Media and Technology, 39(1), 6–36. https://doi.org/10.1080/17439884.2013.770404
   Web of Science ®Google Scholar
• Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001
   Web of Science ®Google Scholar
• Kloft, M., Stiehler, F., Zheng, Z., & Pinkwart, N. (2014). Predicting MOOC dropout over weeks using machine learning methods [paper presentation]. Proceedings of the EMNLP 2014 workshop on analysis of large-scale social interaction in MOOCs (pp. 60–65.).
   Google Scholar
• Lan, M., & Hew, K. F. (2020). Examining learning engagement in MOOCs: A self-determination theoretical perspective using mixed method. International Journal of Educational Technology in Higher Education, 17(1), 7.
   Google Scholar
• Liu, Z., Xiong, F., Zou, K., & Wang, H. (2018). Predicting learning status in MOOCs using LSTM. arXiv Preprint arXiv:1808.01616,
   Google Scholar
• Lu, C., & Cutumisu, M. (2022). Online engagement and performance on formative assessments mediate the relationship between attendance and course performance. International Journal of Educational Technology in Higher Education, 19(1), 2. https://doi.org/10.1186/s41239-021-00307-5
   PubMed Web of Science ®Google Scholar
• Luo, Y., Zhou, G., Li, J., & Xiao, X. (2018). Study on MOOC scoring algorithm based on Chinese University MOOC learning behavior data. Heliyon, 4(11), e00960. https://doi.org/10.1016/j.heliyon.2018.e00960
   PubMedGoogle Scholar
• Milikić, N., Gašević, D., & Jovanović, J. (2018). Measuring effects of technology-enabled mirroring scaffolds on self-regulated learning. IEEE Transactions on Learning Technologies, 13(1), 150–163.
   Web of Science ®Google Scholar
• Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics, 18(6), 275–285. https://doi.org/10.1002/cem.873
   Web of Science ®Google Scholar
• Onah, D. F., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses: behavioural patterns. EDULEARN14 Proceedings, 5825–5834.
   Google Scholar
• Pakhira, M. K., Bandyopadhyay, S., & Maulik, U. (2004). Validity index for crisp and fuzzy clusters. Pattern Recognition, 37(3), 487–501. https://doi.org/10.1016/j.patcog.2003.06.005
   Web of Science ®Google Scholar
• Petrovic, S. (2006). A comparison between the silhouette index and the davies-bouldin index in labelling ids clusters [Paper presentation]. Proceedings of the 11th Nordic Workshop of Secure IT Systems (Vol. 2006, pp. 53–64). SN
   Google Scholar
• Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In Handbook of self-regulation (pp. 451–502). Academic Press.
   Google Scholar
• Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications. Prentice Hall.
   Google Scholar
• R Core Team. (2019). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing.
   Google Scholar
• Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353.
   Google Scholar
• Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660–674. https://doi.org/10.1109/21.97458
   Web of Science ®Google Scholar
• Saqr, M., López-Pernas, S., Helske, S., & Hrastinski, S. (2023). The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: A full program study. Computers & Education, 199, 104787. https://doi.org/10.1016/j.compedu.2023.104787
   Web of Science ®Google Scholar
• Schumacher, C., & Ifenthaler, D. (2021). Investigating prompts for supporting students’ self-regulation – A remaining challenge for learning analytics approaches? The Internet and Higher Education, 49, 100791. https://doi.org/10.1016/j.iheduc.2020.100791
   Web of Science ®Google Scholar
• Sharma, B., Nand, R., Naseem, M., & Reddy, E. V. (2020). Effectiveness of online presence in a blended higher learning environment in the e Pacific. Studies in Higher Education, 45(8), 1547–1565. https://doi.org/10.1080/03075079.2019.1602756
   Web of Science ®Google Scholar
• Scheffel, M., Drachsler, H., de Kraker, J., Kreijns, K., Slootmaker, A., & Specht, M. (2017). Widget, widget on the wall, Am I performing well at all? IEEE Transactions on Learning Technologies, 10(1), 42–52. https://doi.org/10.1109/TLT.2016.2622268
   Web of Science ®Google Scholar
• Shi, C., Fu, S., Chen, Q., & Qu, H. (2015). VisMOOC: Visualizing video clickstream data from massive open online courses [Paper presentation]. Visualization Symposium (PacificVis), 2015 IEEE Pacific (pp. 159–166). IEEE.
   Google Scholar
• Tempelaar, D., Rienties, B., & Nguyen, Q. (2020). Subjective data, objective data and the role of bias in predictive modelling: Lessons from a dispositional learning analytics application. PLoS One, 15(6), e0233977. https://doi.org/10.1371/journal.pone.0233977
   PubMed Web of Science ®Google Scholar
• Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267–276. https://doi.org/10.1007/BF02289263
   Google Scholar
• Wang, Q., & Mousavi, A. (2023). Which log variables significantly predict academic achievement? A systematic review and meta-analysis. British Journal of Educational Technology, 54(1), 142–191. https://doi.org/10.1111/bjet.13282
   Web of Science ®Google Scholar
• Wang, Q., Mousavi, A., & Lu, C. (2022). A scoping review of empirical studies on theory-driven learning analytics. Distance Education, 43(1), 6–29. https://doi.org/10.1080/01587919.2021.2020621
   Web of Science ®Google Scholar
• Winne, P. H., & Baker, R. S. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. JEDM-Journal of Educational Data Mining, 5(1), 1–8.
   Google Scholar
• Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. Metacognition in Educational Theory and Practice, 93, 27–30.
   Google Scholar
• Wise, A. F., Cui, Y., Jin, W., & Vytasek, J. (2017). Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling. The Internet and Higher Education, 32, 11–28. https://doi.org/10.1016/j.iheduc.2016.08.001
   Web of Science ®Google Scholar
• You, J. W. (2015). Examining the effect of academic procrastination on achievement using LMS data in e-learning. Educational Technology & Society, 18(3), 124–134.
   Web of Science ®Google Scholar
• You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23–30. https://doi.org/10.1016/j.iheduc.2015.11.003
   Web of Science ®Google Scholar
• You, J. W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Computers & Education, 77, 125–133. https://doi.org/10.1016/j.compedu.2014.04.018
   Web of Science ®Google Scholar
• Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Journal of Educational Technology & Society, 10(2), 71–83.
   Google Scholar
• Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44–53. https://doi.org/10.1016/j.iheduc.2015.05.002
   Web of Science ®Google Scholar
• Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology, 11(4), 307–313. https://doi.org/10.1016/0361-476X(86)90027-5
   Web of Science ®Google Scholar
• Zimmerman, B. J., & Pons, M. M. (1986). Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal, 23(4), 614–628. https://doi.org/10.3102/00028312023004614
   Web of Science ®Google Scholar
• Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13–39).
   Google Scholar
• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
   Web of Science ®Google Scholar