In search for the most informative data for feedback generation: Learning analytics in a data-rich context

447 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Predicting academic performance of students from VLE big data using deep learning models (2020, background): “In Journal Pre-proof contrast to the clickstream behavior that only captures 10% variance of student learning dynamics, assessment related activities enable to tap a much broader variation of student behavior (Tempelaar et al., 2015).”
The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules (2016, methodology): “…data analysis of over 120 variables from three different VLE systems and a range of motivational, emotions and learning styles indicators, Tempelaar et al. (2015) found that most of the 40 proxies of “simple” VLE LAmetrics provided limited insights into the complexity of learning…”
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, background): “Predictive models are currently used in many educational institutions [21].”
Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS (2017, methodology): “2 Total time online (min) [13], [28], [31], [32], [37], [39] 4,989 815 678 Number of course page views [32], [33] 4,989 208 144 Irregularity of study time [31], [42], [43] 4,989 1,926 993 Irregularity of study interval 4,989 309,000 252,000 Largest period of inactivity (min) 4,989 20,500 13,100 Time”