Building an Online Learning Question Map Through Mining Discussion Content

Abstract

Information and communication technology (ICT) has been widely accepted in education since the COVID-19 outbreak. Today, the convenience that ICT provides in education makes learning independent of time and place. However, compared to face-to-face learning, ICT online learning has the difficulty of finding student questions efficiently. One of the ways to solve this problem is through finding their questions from the online discussion content. With online learning, teachers and students usually send out questions and receive answers on a discussion board without the limitations of time or place. However, because liquid learning is quite convenient, people tend to solve problems in short online texts with a lack of detailed information to express ideas in an online environment. Therefore, the ICT online education environment may result in misunderstandings between teachers and students. For teachers and students to better understand each other’s views, this study aims to classify discussions into a hierarchical structure, named a question map, with several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In brief, by applying short text hierarchical classification, this study constructs a question map that can highlight each student’s learning problems and inform the instructor where the main focus of the future course should be, thus improving the ICT education environment.