التفاصيل البيبلوغرافية
| العنوان: |
Standing on the shoulders of giants: Online formative assessments as the foundation for predictive learning analytics models |
| المؤلفون: |
Bulut, Okan, Gorgun, Guher, Yildirim‐Erbasli, Seyma N., Wongvorachan, Tarid, Daniels, Lia M., Gao, Yizhu, Lai, Ka Wing, Shin, Jinnie |
| المصدر: |
British Journal of Educational Technology ; volume 54, issue 1, page 19-39 ; ISSN 0007-1013 1467-8535 |
| بيانات النشر: |
Wiley |
| سنة النشر: |
2022 |
| المجموعة: |
Wiley Online Library (Open Access Articles via Crossref) |
| الوصف: |
As universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data‐driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their attention to learning analytics (LA) applications using educational data mining techniques. Numerous LA models have been proposed to predict student achievement in university courses. To design predictive LA models, researchers often follow a data‐driven approach that prioritizes prediction accuracy while sacrificing theoretical links to learning theory and its pedagogical implications. In this study, we argue that instead of complex variables (e.g., event logs, clickstream data, timestamps of learning activities), data extracted from online formative assessments should be the starting point for building predictive LA models. Using the LMS data from multiple offerings of an asynchronous undergraduate course, we analysed the utility of online formative assessments in predicting students' final course performance. Our findings showed that the features extracted from online formative assessments (e.g., completion, timestamps and scores) served as strong and significant predictors of students' final course performance. Scores from online formative assessments were consistently the strongest predictor of student performance across the three sections of the course. The number of clicks in the LMS and the time difference between first access and due dates of formative assessments were also significant predictors. Overall, our findings emphasize the need for online formative assessments to build predictive LA models informed by theory and learning design. Practitioner notes What is already known about this topic Higher education institutions often use learning analytics for the early identification of low‐performing students or students at risk of dropping out. Most ... |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| DOI: |
10.1111/bjet.13276 |
| الإتاحة: |
https://doi.org/10.1111/bjet.13276Test |
| حقوق: |
http://onlinelibrary.wiley.com/termsAndConditions#vorTest |
| رقم الانضمام: |
edsbas.DFEC1670 |
| قاعدة البيانات: |
BASE |
