Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty

Abstract

Learning analytics has emerged as a promising tool for optimizing the learning experience and results, especially in online educational environments. An important challenge in this area is identifying the most difficult topics for students in a subject, which is of great use to improve the quality of teaching by devoting more effort to those topics of greater difficulty, assigning them more time, resources and materials. We have approached the problem by means of natural language processing techniques. In particular, we propose a solution based on a deep learning model that automatically extracts the main topics that are covered in educational documents. This model is next applied to the problem of identifying the most difficult topics for students in a subject related to the study of algorithms and data structures in a Computer Science degree. Our results show that our topic identification model presents very high accuracy (around 90 percent) and may be efficiently used in learning analytics applications, such as the identification and understanding of what makes the learning of a subject difficult. An exhaustive analysis of the case study has also revealed that there are indeed topics that are consistently more difficult for most students, and also that the perception of difficulty in students and teachers does not always coincide with the actual difficulty indicated by the data, preventing to pay adequate attention to the most challenging topics.

Keywords

• Deep learning
• text mining
• learning analytics
• teaching of algorithms
• challenging topics