Automatic Evaluation of Instructional Videos Based on Video Features and Student Watching Experience

Instructional videos are often a key component of online learning, and their quality significantly influences online learning outcomes and student satisfaction. However, instructional video evaluation is time-consuming. To solve this problem, this study developed an automatic evaluation method for instructional videos. This method first establishes a metric to evaluate instructional videos based on two aspects: video features and watching experience. An automatic scoring method for each indicator was developed based on video and clickstream data. Finally, all the scores were input into the evaluation model to obtain the evaluation result. Our experimental results showed that 85% of the evaluation results using our proposed model are consistent with manual quality evaluation. Therefore, our method can perform automatic evaluation of instructional videos while achieving acceptable accuracy, which is helpful in reducing the workload associated with manual evaluation and improving the quality of online teaching.