Predicting students' grades based on free style comments data by artificial neural network - Sorour, Shaymaa E.; Mine, Tsunenori; Goda, Kazumasa; Hirokawa, Sachio (2014 IEEE Frontiers in Education Conference (FIE) Proceedings, 2014)
Predicting students' academic achievement with high accuracy has an important vital role in many academic disciplines. Most recent studies indicate the important role of the data type selection. They also attempt to understand individual students more deeply by analyzing questionnaire for a particular purpose. The present study uses free-style comments written by students after each lesson, to predict their performance. These comments reflect their learning attitudes to the lesson, understanding of subjects, difficulties to learn, and learning activities in the classroom. To reveal the high accuracy of predicting student's grade, we employ (LSA) latent semantic analysis technique to extract semantic information from students' comments by using statistically derived conceptual indices instead of individual words, then apply (ANN) artificial neural network model to the analyzed comments for predicting students' performance. We chose five grades instead of the mark itself to predict student's final result. Our proposed method averagely achieves 82.6% and 76.1% prediction accuracy and F-measure of students' grades, respectively.