The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach

• Published in: Education and information technologies Vol. 28; no. 5; pp. 5209 - 5240
• Main Authors: Yürüm, Ozan Raşit, Taşkaya-Temizel, Tuğba, Yıldırım, Soner
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2023; Springer; Springer Nature B.V
• Subjects: Academic achievement; At Risk Students; Audience Response Systems; College Students; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computers and Education; Correlation; Data Collection; Data mining; Data Use; Distance learning; Education; Educational Technology; Identification; Information Systems Applications (incl.Internet); Learning Analytics; Learning Management Systems; Online Courses; Prediction; Student behavior; Testing; University students; User Interfaces and Human Computer Interaction; Video Technology; Performance prediction; Educational data mining; Learning analytics; University students; Video clickstream interactions
• ISSN: 1360-2357; 1573-7608
• DOI: 10.1007/s10639-022-11403-y

Video clickstream behaviors such as pause, forward, and backward offer great potential for educational data mining and learning analytics since students exhibit a significant amount of these behaviors in online courses. The purpose of this study is to investigate the predictive relationship between video clickstream behaviors and students’ test performance with two consecutive experiments. The first experiment was performed as an exploratory study with 22 university students using a single test performance measure and basic statistical techniques. The second experiment was performed as a conclusive study with 16 students using repeated measures and comprehensive data mining techniques. The findings show that a positive correlation exists between the total number of clicks and students’ test performance. Those students who performed a high number of clicks, slow backward speed or doing backwards or pauses achieved better test performance than those who performed a lower number of clicks, or who used fast-backward or fast-forward. In addition, students’ test performance could be predicted using video clickstream data with a good level of accuracy (Root Mean Squared Error Percentage (%RMSE) ranged between 15 and 20). Furthermore, the mean of backward speed, number of pauses, and number/percentage of backwards were found to be the most important indicators in predicting students’ test performance. These findings may help educators or researchers identify students who are at risk of failure. Finally, the study provides design suggestions based on the findings for the preparation of video-based lectures.