دورية أكاديمية
Recurrent Neural Network-Fitnets: Improving Early Prediction of Student Performanceby Time-Series Knowledge Distillation
| العنوان: | Recurrent Neural Network-Fitnets: Improving Early Prediction of Student Performanceby Time-Series Knowledge Distillation |
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| اللغة: | English |
| المؤلفون: | Murata, Ryusuke, Okubo, Fumiya (ORCID |
| المصدر: | Journal of Educational Computing Research. Jun 2023 61(3):639-670. |
| الإتاحة: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.comTest |
| تمت مراجعته من قبل الزملاء: | Y |
| Page Count: | 32 |
| تاريخ النشر: | 2023 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| الواصفات: | College Students, Academic Achievement, Prediction, Neurology, Models, Knowledge Level, At Risk Students |
| DOI: | 10.1177/07356331221129765 |
| تدمد: | 0735-6331 1541-4140 |
| مستخلص: | This study helps improve the early prediction of student performance by RNN-FitNets, which applies knowledge distillation (KD) to the time series direction of the recurrent neural network (RNN) model. The RNN-FitNets replaces the teacher model in KD with "an RNN model with a long-term time-series in which the features during the entire course are inputted" and the student model in KD with "an RNN model with a short-term time-series in which only the features during the early stages are inputted." As a result, the RNN model in the early stage was trained to output the same results as the more accurate RNN model in the later stages. The experiment compared RNN-FitNets with a normal RNN model on a dataset of 296 university students in total. The results showed that RNN-FitNets can improve early prediction. Moreover, the SHAP value was employed to explain the contribution of the input features to the prediction results by RNN-FitNets. It was shown that RNN-FitNets can consider the future effects of the input features from the early stages of the course. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| رقم الانضمام: | EJ1379480 |
| قاعدة البيانات: | ERIC |
| تدمد: | 0735-6331 1541-4140 |
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| DOI: | 10.1177/07356331221129765 |