مؤتمر
Use of Professor Comments in Predicting Student Success
| العنوان: | Use of Professor Comments in Predicting Student Success |
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| المؤلفون: | Bell, Timothy, Dartigues-Pallez, Christel, Jaillet, Florent, Genolini, Christophe |
| المساهمون: | Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA), Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA), Zebrys, Asia-Pacific Society for Computers in Education (APSCE), Sridhar IYER, Ju-Ling SHIH, Weiqin CHEN, Mas Nida MD KHAMBARI |
| المصدر: | Proceedings of the 30th International Conference on Computers in Education ; 30th International Conference on Computers in Education (ICCE 2022) ; https://hal.science/hal-04132005Test ; 30th International Conference on Computers in Education (ICCE 2022), Asia-Pacific Society for Computers in Education (APSCE), Nov 2022, Kuala Lumpur, Malaysia. pp.38-43 ; https://icce2022.apsce.netTest/ |
| بيانات النشر: | HAL CCSD Asia-Pacific Society for Computers in Education (APSCE) |
| سنة النشر: | 2022 |
| المجموعة: | HAL Université Côte d'Azur |
| مصطلحات موضوعية: | Student Success, Random Forest, Allocation, Text Prediction, Time Series, [INFO]Computer Science [cs] |
| جغرافية الموضوع: | Kuala Lumpur, Malaysia |
| الوصف: | International audience ; During their studies students receive written notes and comments from their professors assessing their grades, attitudes, qualities, and lacuna. These characteristics reflect a more subjective approach as opposed to the typical grading system. This paper, through topic modelling and word vectorization approaches, uses textual data to predict at-risk students in their first year of university studies with a Random Forest model. First, we introduce the used methods and analyze the corpus at hand. Then we vectorize the data (by Latent Dirichlet Allocation and other vectorizing methods) to categorize it and use it in the classifier. We then propose adding a dynamic element to the prediction through linear regression when using our data as a time series. Finally, we will review the prediction accuracy and feature importance to assert if these professor comments do indeed reflect the student's scholar capacities. After comparing with the raw numerical grade data, we have better or as-good-as results by using our augmented textual data. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| ردمك: | 978-986-97214-9-3 986-97214-9-4 |
| العلاقة: | hal-04132005; https://hal.science/hal-04132005Test |
| الإتاحة: | https://hal.science/hal-04132005Test |
| رقم الانضمام: | edsbas.99FC61F |
| قاعدة البيانات: | BASE |
| ردمك: | 9789869721493 9869721494 |
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