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Online education performance prediction via time-related features | IEEE Conference Publication | IEEE Xplore

Online education performance prediction via time-related features


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 More

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.
Date of Conference: 24-26 May 2017
Date Added to IEEE Xplore: 29 June 2017
ISBN Information:
Conference Location: Wuhan, China

References

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