Abstract:
In this work, we studied time management behavior, performance of students, and their association in online learning. We propose three novel time-related features, i.e., ...Show MoreMetadata
Abstract:
In this work, we studied time management behavior, performance of students, and their association in online learning. We propose three novel time-related features, i.e., video-watching frequency, video-watching interval and learning efficiency. Leveraging these features, we train classifiers for identifying study habits of students and assessing the impact of behavior on performance. Experimental results on dataset from our online learning platform demonstrate that the proposed features are effective for learning behavior description. Comprehensive experiments show that our method can get promising performance.
Published in: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)
Date of Conference: 24-26 May 2017
Date Added to IEEE Xplore: 29 June 2017
ISBN Information: