Predicting learner's performance through video sequences viewing behavior analysis using educational data-mining.
Predicting learner's performance through video sequences viewing behavior analysis using educational data-mining.
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DOI：
10.1007/s10639-021-10512-4
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发表时间：
2021
影响因子：
5.5
通讯作者：
Douzi H
中科院分区：
文献类型：
--
作者：
El Aouifi H;El Hajji M;Es-Saady Y;Douzi H
本文分析了学习者如何与教育视频的教学序列进行互动，及其对学习成绩的影响。在这项研究中，建议的视频课程被分割成几个教学序列。事实上，我们关注的不是学习者点击的类型，而是这些点击的教学顺序。我们专注于对观看教育视频的学习者所遵循的路径的解读，以及他们在视频的教学序列中导航的方式，以预测学习者是否能够通过视频课程。对学习者的视频点击进行收集和分类。我们应用教育数据挖掘技术，使用K-近邻和多层感知器算法来预测学习者的表现。分类结果可以接受，其中KNN分类器的分类效果最好，平均准确率为65.07%。实验结果表明，学习者的学习成绩是可以预测的，我们注意到视频序列观看行为与学习成绩之间存在相关性。这种方法可以帮助教师了解学生观看教育视频的方式。它可以用于及早发现学习者的视频观看行为偏差，并允许教师提供及时、有效的指导。
This paper analyzes how learners interact with the pedagogical sequences of educational videos, and its effect on their performance. In this study, the suggested video courses are segmented on several pedagogical sequences. In fact, we’re not focusing on the type of clicks made by learners, but we’re concentrating on the pedagogical sequences in which those clicks were made. We focalize on the interpretation of the path followed by a learner watching an educational video, and the way they navigate the pedagogical sequences of that video, in order to predict whether a learner can pass or fail the video course. Learner’s video clicks are collected and classified. We applied educational data mining technique using K-nearest Neighbours and Multilayer Perceptron algorithms to predict learner’s performance. The classification results are acceptable, the kNN classifier achieves the best results with an average accuracy of 65.07%. The experimental result indicates that learners’ performance could be predicted, we notice a correlation between video sequence viewing behavior and learning performances. This method may help instructors understand the way learners watch educational videos. It can be used for early detection of learners’ video viewing behavior deviation and allow the instructor to provide well-timed, effective guidance.
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影响因子：
2.5
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1080/17445760601125376
发表时间：
2007-01-01
影响因子：
1.1
作者：
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通讯作者：
Mongy, Sylvain