ERIC Number: EJ1364113
Record Type: Journal
Publication Date: 2023-Jan
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0007-1013
EISSN: EISSN-1467-8535
Standing on the Shoulders of Giants: Online Formative Assessments as the Foundation for Predictive Learning Analytics Models
Bulut, Okan; Gorgun, Guher; Yildirim-Erbasli, Seyma N.; Wongvorachan, Tarid; Daniels, Lia M.; Gao, Yizhu; Lai, Ka Wing; Shin, Jinnie
British Journal of Educational Technology, v54 n1 p19-39 Jan 2023
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.
Descriptors: Formative Evaluation, Learning Analytics, Models, Learning Management Systems, Grade Prediction, Undergraduate Students, Online Courses, Evaluation Methods
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://bibliotheek.ehb.be:2191/en-us
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Data File: URL: https://osf.io/nsdy5/