E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review

Early Bird or Night Owl: Insights into Dutch Students' Study Patterns using the Medical Faculty's E-learning Registrations

Phenomenon: Educational activities for students are typically arranged without consideration of their preferences or peak performance hours. Students might prefer to study at different times based on their chronotype, aiming to optimize their performance. While face-to-face activities during the academic schedule do not offer flexibility and cannot reflect students' natural learning rhythm, asynchronous e-learning facilitates studying at one's preferred time. Given their ubiquitous accessibility, students can use e-learning resources according to their individual needs and preferences. E-learning usage data hence serves as a valuable proxy for certain study behaviors, presenting research opportunities to explore students' study patterns. This retrospective study aims to investigate when and for how long undergraduate students used medical e-learning modules. Approach: We performed a cross-sectional analysis of e-learning usage at one medical faculty in the Netherlands. We used data from 562 undergraduate multimedia e-learning modules for pre-clinical students, covering various medical topics over a span of two academic years (2018/19 and 2019/20). We employed educational data mining approaches to process the data and subsequently identified patterns in access times and durations. Findings: We obtained data from 70,805 e-learning sessions with 116,569 module visits and 1,495,342 page views. On average, students used e-learning for 16.8 min daily and stopped using a module after 10.2 min, but access patterns varied widely. E-learning was used seven days a week with an hourly access pattern during business hours on weekdays. Across all other times, there was a smooth increase or decrease in e-learning usage. During the week, more students started e-learning sessions in the morning (34.5% vs. 19.1%) while fewer students started in the afternoon (42.6% vs. 50.8%) and the evening (19.4% vs. 27.0%). We identified 'early bird' and 'night owl' user groups that show distinct study patterns. Insights: This retrospective educational data mining study reveals new insights into the study patterns of a complete student cohort during and outside lecture hours. These findings underline the value of 24/7 accessible study material. In addition, our findings may serve as a guide for researchers and educationalists seeking to develop more individualized educational programs.

Student course grade prediction using the random forest algorithm: Analysis of predictors' importance

BACKGROUND

Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates.

METHOD

In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students.

RESULTS

Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect.

CONCLUSION

Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.