Identifying significant indicators using LMS data to predict course achievement in online learning

By Ji Won You. Internet and Higher Education, 2016

362 citations recorded by the source corpus.

Subfields: Computer Science Applications, Developmental and Educational Psychology, Education

View Paper: https://www.semanticscholar.org/paper/192153d8fe15ffaf2a423b673fc67214c40bff56

Citations in the corpus

• Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS (2017, neutral): “Total number of clicks [13], [17], [34], [37], [42] 4,989 605 630 Number of online sessions [10], [18], [29], [31], [32], [33], [35], [36], [39], [43], [44] 4,989 30.”
• Educational data mining and learning analytics for 21st century higher education: A review and synthesis (2019, neutral): “…have been widely associated with the prediction of 1) student success (Simpson, 2006), 2) self-regulated learning and online course achievement (You, 2016), 3) student motivation (Lonn et al., 2015), 4) students’ retention in college (DeBerard et al., 2004), and 5) students’ graduation rates.”