WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Language: search.filters.language.enSubject: search.filters.subject.Educational data mining

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  • Review
    Citation - WoS: 3
    Citation - Scopus: 4
    Predictive Video Analytics in Online Courses: a Systematic Literature Review
    (SPRINGER, 2023) Yurum, Ozan Rasit; Taskaya-Temizel, Tuğba; Yildirim, Soner
    The purpose of this study was to investigate the use of predictive video analytics in online courses in the literature. A systematic literature review was performed based on a hybrid search strategy that included both database searching and backward snowballing. In total, 77 related publications published between 2011 and April 2023 were identified. The findings revealed an increase in the number of publications on predictive video analytics since 2016. In the majority of studies, edX and Coursera platforms were used to collect learners' video interaction data. In addition, computer science was shown to be the top course domain, whilst data collection from a single course was found to be the most common. The results related to input measures showed that pause, play, backward, and forward were the top in-video interactions, whilst video transcript and subtitle were the least used. Learner performance and dropout were the primary output measures, whereas learning variables such as engagement, satisfaction, and motivation were investigated in only a few studies. Furthermore, most of the studies utilized data related to forums, navigation, and exams in addition to video data. The top algorithms used were Support Vector Machine, Random Forest, Logistic Regression, and Recurrent Neural Networks, with Random Forest and Recurrent Neural Networks being two rising algorithms in recent years. The top three evaluation metrics used were Accuracy, Area Under the Curve, and F1 Score. The findings of this study may be used to aid effective learning design and guide future research.
  • Article
    Citation - WoS: 14
    Citation - Scopus: 18
    The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach
    (Springer, 2022) Yürüm, Ozan Raşit; Taşkaya Temizel, Tuğba; Yıldırım, Soner
    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.