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互动，及其对他们表现的影响。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，我们并没有关注学习者点击的类型，而是专注于点击的教学顺序。我们专注于解释学习者观看教育视频所遵循的路径，以及他们浏览该视频的教学序列的方式，以预测学习者是否可以通过或失败视频课程。学习者的视频点击被收集和分类。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，kNN 分类器以 65.07% 的平均准确率达到最佳结果。实验结果表明学习者的表现是可以预测的，我们注意到视频序列观看行为和学习表现之间存在相关性。这种方法可以帮助教师了解学习者观看教育视频的方式。可用于早期发现学习者的视频观看行为偏差，让指导者及时、有效地进行指导。