Personality Analysis of Students’ Writing in Social Media-Based Learning Environments

Abstract

Comprehending the personality of students is a crucial issue of education. Students interact in learning environments using a variety of social media technologies. Therefore, in the availability of several artificial intelligence-based solutions that discern personality from written language, we aim to use students’ text data to determine their personalities. Due to these considerations, the primary purpose of this research is to determine whether the text produced by students in social media-based learning environments can provide insight into their personalities. This work also aims to find out whether students exhibit their real personalities while interacting in the learning environment, mainly with their classmates. This paper describes an experimental study reported on a sample of students at the Private University of Fez, in which their data was gathered and analyzed by three Artificial Intelligence-based personality detection tools. Personality traits discovered by these instruments were evaluated and compared to the findings of big five questionnaire assessed to the same students. The findings demonstrate that the majority of the students’ personality traits were accurately identified in the written text of the students based on tested Natural Language Processing tools, and that strong data quality is a crucial factor in achieving good accuracy.

Keywords

• Personality prediction
• social media-based learning
• personality in learning environments
• big five
• natural language processing