Rectorate / Rektörlük

Permanent URI for this collectionhttps://hdl.handle.net/11147/6849

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  • 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.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 6
    An Intervention Framework for Developing Interactive Video Lectures Based on Video Clickstream Behavior: a Quasi-Experimental Evaluation
    (Taylor & Francis, 2022) Yürüm, Ozan Raşit; Yıldırım, Soner; Taşkaya Temizel, Tuğba
    The purpose of this study is to develop an intervention framework based on video clickstream interactions for delivering superior user experience for video lectures. Apart from existing studies on data-driven interventions, this study focuses on video clickstream interactions to identify timely interventions for creating interactive video lectures. First, a framework was developed through an exploratory experiment, in which 29 students’ clickstream behaviors were tracked on an online platform and then individual interviews were held with 17 of the students and a subject-matter expert. The framework shows how click types are transformed into interactive elements with five question types (where, why, which, how, what). It includes click types, click reasons, interventions, actions, and interactive elements. Then, a quasi-experimental study was performed with 18 students to investigate the effect of the proposed framework on the students’ satisfaction and engagement. The results showed that students’ satisfaction significantly increased for interactive videos created using the proposed framework when motivation was controlled. In addition, students’ frequency to go back to important points decreased significantly in interactive videos, whilst students’ frequency to skip unimportant points increased significantly in interactive videos. In conclusion, the proposed framework can be used to transform linear videos to interactive videos.