Using Deep Learning Technique for Vietnam Student Performance Prediction with Categorical Variables

Colloque du CRIFPE
Brève communication orale
Thème(s)
Le numérique éducatif
Résumé
There is an increase in interest and application of Deep Learning Technique to predict student performance and create a computer-aided recommender system to improve student performance in university. However, data on student performance often include categorical variables. How to encode these variables in a deep learning process can be important in the predictive system as Deep Learning only works well with numerical data. The study used a data set of nearly 3000 students in business schools in Vietnam, with academic records and additional surveyed information. Different categorical variables encoding methods were experimented with several deep learning models, in which categorical variables encoding was either embedded or non-embedded in the learning process to find the most effective encoding method. The study experiment shows the learned embedding combined with the student performance prediction deep learning models give good results. The embedded encoding method improves the performance of both Deep Dense and Long Short Term Memory models, with the average accuracy of 86.26%. The study shows the importance of treating categorical variables in a deep learning system for student performance prediction. The embedded categorical variables encoding is recommended to improve the performance and effectiveness of the prediction models.
Auteur.e.s
Son Dao
Thuongmai University - Viet nam

Dao The Son graduated from Foreign Trade University with a bachelor degree in international trade in 1999 and the Australian National University with a master degree in international and development economics in 2006. Currently he is a lecturer of the department of economics TMU. He has more than 10 years of experience working as consultant and policy researcher for the government (Ministry of Finance, Ministry of Health), as well as international development organisations. His research interest include applying a multi-discipline approach in practical situations, from applying economics and political analysis in commerical determianance of health policies to applying machine learning, big data in education analysis. He is the lead of a research lab in Thuongmai University with combined experts from economics, business, education and computer sciences.

Nguyen Giap Cu
Thuongmai University - Viet nam

Huu Duc Bui
Thuongmai University - Viet nam

Séance
C-J503
Heure
2022-05-05 15 h 55
Durée
15 minutes
Salle
513b