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Students Performance Analysis Using Moocs Platform

Issue Abstract

Abstract
Our goal in this work was to provide interested academics with up-to-date information on the latest findings and investigations concerning student performance analysis and learning analytics in massively open online courses (MOOCs). Examining the application of performance prediction and learning analytics in MOOCs is the goal of this. To introduce readers to our work, we first provide literature-based explanations of key concepts. To help understand the relative relevance of the qualities and their relationships, details on a number of studies were then provided. Next is a summary of the outcomes of learning analytics and student performance prediction applied to MOOCs.


Author Information
M. Kannan
Issue No
3
Volume No
6
Issue Publish Date
05 Mar 2024
Issue Pages
109-113

Issue References

References 
[1] Pappano, Laura. "The Year of the MOOC." The New York Times 2.12 (2012): 2012.
[2] Kerr, J., et al. "Building and executing MOOCs: A practical review of GlasgowâA˘ Zs first two MOOCs (massive open online courses). ´ University of Glasgow." (2015).
[3] Ripley, Brian D. "The R project in statistical computing." MSOR Connections. The newsletter of the LTSN Maths, Stats and OR Network 1.1 (2001): 23-25.
[4] Paradis, Emmanuel, Julien Claude, and Korbinian Strimmer. "APE: analyses of phylogenetics and evolution in R language." Bioinformatics 20.2 (2004): 289-290.
[5] KlÃijsener, Marcus, and Albrecht Fortenbacher. "Predicting students’ success based on forum activities in MOOCs." Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015 IEEE 8th International Conference on. Vol. 2. IEEE, 2015.
[6] Ashenafi, Michael Mogessie, Giuseppe Riccardi, and Marco Ronchetti. "Predicting students’ final exam scores from their course activities." Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE. IEEE, 2015.
[7] Wolff, Annika, et al. "Predicting student performance from combined data sources." Educational Data Mining. Springer International Publishing, 2014. 175-202.
[8] DeBoer, Jennifer, and Lori Breslow. "Tracking progress: predictors of students’ weekly achievement during a circuits and electronics MOOC." Proceedings of the first ACM conference on Learning@ scale conference. ACM, 2014.
[9] Ramesh, Arti, et al. "Uncovering hidden engagement patterns for predicting learner performance in MOOCs." Proceedings of the first ACM conference on Learning@ scale conference. ACM, 2014.
[10] Feng, Yunping, et al. "The Impact of Students And TAs’ Participation on Students’ Academic Performance in MOOC." Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. ACM, 2015.
[11] Hughes, Glyn, and Chelsea Dobbins. "The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs)." Research and Practice in Technology Enhanced Learning 10.1 (2015).
[12] Ye, Cheng, and Gautam Biswas. "Early Prediction of Student Dropout and Performance in MOOCs using Higher Granularity Temporal Information." test 1.3 (2014): 169-172.
[13] Xu, Bin, and Dan Yang. "Motivation classification and grade prediction for MOOCs learners." Computational intelligence and neuroscience 2016 (2016): 4.
[14] Coffrin, Carleton, et al. "Visualizing patterns of student engagement and performance in MOOCs." Proceedings of the fourth international conference on learning analytics and knowledge. ACM, 2014.
[15] MÅCynarska, Ewa, Derek Greene, and PÃ ˛adraig Cunningham. "Indi- ´ cators of Good Student Performance in Moodle Activity Data." arXiv preprint arXiv:1601.02975 (2016).