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

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...

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Autores principales: Yürüm, Ozan Raşit, Taşkaya-Temizel, Tuğba, Yıldırım, Soner
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617048/
https://www.ncbi.nlm.nih.gov/pubmed/36338598
http://dx.doi.org/10.1007/s10639-022-11403-y
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author Yürüm, Ozan Raşit
Taşkaya-Temizel, Tuğba
Yıldırım, Soner
author_facet Yürüm, Ozan Raşit
Taşkaya-Temizel, Tuğba
Yıldırım, Soner
author_sort Yürüm, Ozan Raşit
collection PubMed
description 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.
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spelling pubmed-96170482022-10-31 The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach Yürüm, Ozan Raşit Taşkaya-Temizel, Tuğba Yıldırım, Soner Educ Inf Technol (Dordr) Article 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. Springer US 2022-10-29 2023 /pmc/articles/PMC9617048/ /pubmed/36338598 http://dx.doi.org/10.1007/s10639-022-11403-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Yürüm, Ozan Raşit
Taşkaya-Temizel, Tuğba
Yıldırım, Soner
The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach
title The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach
title_full The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach
title_fullStr The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach
title_full_unstemmed The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach
title_short The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach
title_sort use of video clickstream data to predict university students’ test performance: a comprehensive educational data mining approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9617048/
https://www.ncbi.nlm.nih.gov/pubmed/36338598
http://dx.doi.org/10.1007/s10639-022-11403-y
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