Text Mining Data from Students to Reveal Meaningful Information for Educators

Abstract

Academic institutions adopt different advising tools for various objectives. Past research used both numeric and text data to predict students’ performance. Moreover, numerous research projects have been conducted to find different learning strategies and profiles of students. Those strategies of learning together with academic profiles assisted in the advising process. This research proposes an approach to supplement these activities by text mining students’ essays to better understand different students’ profiles across different courses (subjects). Text analysis was performed on 99 essays written by undergraduate students in three different courses. The essays and terms were projected in a 20-dimensional vector space. The 20 dimensions were used as independent variables in a regression analysis to predict a student’s final grade in a course. Further analyses were performed on the dimensions found statistically significant. This study is a preliminary analysis to demonstrate a novel approach of extracting meaningful information by text mining essays written by students to develop an advising tool that can be used by educators.