We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors’ degrees in 6 yearsÂ . We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86.
|Title of host publication||Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings|
|Editors||Seiji Isotani, Eva Millán, Amy Ogan, Bruce McLaren, Peter Hastings, Rose Luckin|
|Number of pages||13|
|Publication status||Published - 2019|
|Event||20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago, United States|
Duration: 2019 Jun 25 → 2019 Jun 29
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||20th International Conference on Artificial Intelligence in Education, AIED 2019|
|Period||19/6/25 → 19/6/29|
Bibliographical noteFunding Information:
Acknowledgements. This work is supported by the National Science Foundation under Grant No. 1820862. Noseong Park and Mohsen Dorodchi are the co-corresponding authors.
© Springer Nature Switzerland AG 2019.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)