English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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

Exploring the relation between students' online learning behavior and course performance by including contextual information in data analysis