El Aouifi Houssam, El Hajji Mohamed, Es-Saady Youssef, Douzi Hassan
IRF-SIC Laboratory, Ibn Zohr University, Agadir, Morocco.
CRMEF-SM, Agadir, Morocco.
Educ Inf Technol (Dordr). 2021;26(5):5799-5814. doi: 10.1007/s10639-021-10512-4. Epub 2021 May 3.
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.
本文分析了学习者如何与教育视频的教学序列进行交互，以及这种交互对他们学习表现的影响。在本研究中，所建议的视频课程被分割成几个教学序列。实际上，我们关注的不是学习者所做点击的类型，而是关注做出这些点击时所处的教学序列。我们聚焦于对观看教育视频的学习者所遵循路径的解读，以及他们在该视频教学序列中的导航方式，以便预测学习者能否通过视频课程。收集并分类学习者的视频点击。我们应用教育数据挖掘技术，使用K近邻算法和多层感知器算法来预测学习者的表现。分类结果是可接受的，kNN分类器取得了最佳结果，平均准确率为65.07%。实验结果表明，可以预测学习者的表现，我们注意到视频序列观看行为与学习表现之间存在相关性。这种方法可能有助于教师了解学习者观看教育视频的方式。它可用于早期检测学习者视频观看行为的偏差，并使教师能够提供及时、有效的指导。
