1 research outputs found
Exploring the relation between students' online learning behavior and course performance by including contextual information in data analysis
This study examines whether including more contextual information in data
analysis could improve our ability to identify the relation between students'
online learning behavior and overall performance in an introductory physics
course. We created four linear regression models correlating students'
pass-fail events in a sequence of online learning modules with their normalized
total course score. Each model takes into account an additional level of
contextual information than the previous one, such as student learning strategy
and duration of assessment attempts. Each of the latter three models is also
accompanied by a visual representation of students' interaction states on each
learning module. We found that the best performing model is the one that
includes the most contextual information, including instruction condition,
internal condition, and learning strategy. The model shows that while most
students failed on the most challenging learning module, those with normal
learning behavior are more likely to obtain higher total course scores, whereas
students who resorted to guessing on the assessments of subsequent modules
tended to receive lower total scores. Our results suggest that considering more
contextual information related to each event can be an effective method to
improve the quality of learning analytics, leading to more accurate and
actionable recommendations for instructors