This work is licensed under the Creative Commons Attribution 4.0 International License.
Haataja, E., Dindar, M., Malmberg, J., et al. (2022). Individuals in a group: Metacognitive and regulatory predictors of learning achievement in collaborative learning. Learning and Individual Differences, 96, 102146.Search in Google Scholar
Persico, D., Pozzi, F. (2015). Informing learning design with learning analytics to improve teacher inquiry. British Journal of Educational Technology, 46(2).Search in Google Scholar
Van, Wart, M., Ni, A., Rose, L., et al. (2019). A literature review and model of online teaching effectiveness integrating concerns for learning achievement, student satisfaction, faculty satisfaction, and institutional results. Pan-Pacific Journal of Business Research, 10(1), 1-22.Search in Google Scholar
Sukelasmini, I. G. A. M. (2019). The Implementation of Think Pair Share (TPS) Type of Cooperative Learning Model To Improve Student’s Motivation And Nutrition Science Learning Achievement. Journal of Education Action Research, 3(1), 9-15.Search in Google Scholar
Dunn, T. J., Kennedy, M. (2019). Technology Enhanced Learning in higher education; motivations, engagement and academic achievement. Computers & Education, 137, 104-113.Search in Google Scholar
Adnan, M., Habib, A., Ashraf, J., et al. (2021). Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models.IEEE Access, 9, 7519-7539.Search in Google Scholar
Lopez, M., Arriaga, J, Lvarez, J, et al. (2021). Virtual reality vs traditional education: Is there any advantage in human neuroanatomy teaching?. Computers & Electrical Engineering, 93(3), 107282.Search in Google Scholar
Chen, Y. H., Cheng, C. H., Liu, J. W.. (2010). Intelligent preference selection model based on NRE for evaluating student learning achievement. Computers & education, (4), 54.Search in Google Scholar
Ulfa, S., Fatawi, I. (2021). Predicting Factors that Influence Students’ Learning Outcomes Using Learning Analytics in Online Learning Environment. International Journal of Emerging Technologies in Learning (iJET), (01).Search in Google Scholar
Yoon, M., Lee, J, Jo, I. H. (2021). Video learning analytics: Investigating behavioral patterns and learner clusters in video-based online learning. The Internet and Higher Education, 50(3), 100806.Search in Google Scholar
Waheed, H., Hassan, S. U., Aljohani, N. R., et al. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human behavior, 104, 106189.Search in Google Scholar
Lee, A. (2021). Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning. Educational Technology & Society, 24(1), 236-249.Search in Google Scholar
Conijn, R., Snijders, C., Kleingeld, A., et al. (2017). Predicting Student Performance from LMSData: A Comparison of 17 Blended Courses Using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29.Search in Google Scholar
Hussain, M., Zhu, W., Zhang, W., et al. (2019). Using machine learning to predict student467difficulties from learning session data. Artificial Intelligence Review, 52(1), 381-407Search in Google Scholar
Kim, D., Jung, E., Yoon, M., et al. (2021). Exploring the structural relationships between course design factors, learner commitment, self-directed learning, and intentions for further learning in a self-paced MOOC. Computers & Education,166, 104171.Search in Google Scholar
Adejo, O. W., Connolly, T. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61-75.Search in Google Scholar
Tomasevic, N., Gvozdenovic, N., Vranes, S. (2020). An overview and comparison of superviseddata mining techniques for student exam performance prediction. Computers &Education, 143, 103676.Search in Google Scholar