PERC 2020 Abstract Detail Page
Previous Page | New Search | Browse All
| Abstract Title: | Exploring the relation between students’ online learning behavior and course performance by incorporation of contextual information in data analysis |
|---|---|
| Abstract: | This study examines the relationship between students' online learning behavior and overall performance in an introductory physics course. We constructed four separate linear regression models, incorporating increasing levels of contextual information, and correlating students' pass-fail events in a sequence of online learning modules with their normalized total course score. We found that the best performing model is the one that includes contextual information on instructional condition, internal condition, and learning strategy. The model shows that while most students failed on a challenging learning module, those who continued to study were more likely to obtain higher total course scores, whereas those who resorted to guessing on the following modules tended to receive lower total scores. A possible interpretation of this observation is that both high and low performing students struggle with the same challenging content, but behavior after experiencing a setback is a characteristic difference between the two cohorts. Our results also demonstrate that considering more contextual information related to each online learning event can be an effective method to improve the quality of learning analytics, leading to more accurate and actionable recommendations for instructors. The results and implications of this study are immediately relevant amid the COVID-19 pandemic, as instructors now face the challenge of abruptly switching to online learning while motivating students to continue their learning effort. Results from our analyses have already been submitted as a journal paper and will be expanded as new data from the current semester become available. |
| Abstract Type: | Contributed Poster Presentation |
| Session Time: | Poster Session II |
| Poster Number: | 2.I1 |
Author/Organizer Information | |
| Primary Contact: |
Zhongzhou Chen University of Central Florida Physics Department Orlando, FL 32828 Phone: 2177218411 |
| Co-Author(s) and Co-Presenter(s) |
Matthew Guthrie University of Central Florida |
