Personality Recognition in Learning Environments: A Multimodality Machine Learning Framework

Yalan Dai; Amena Mahmoud; Eiko Takaoka

doi:10.48084/etasr.17778

Authors

Yalan Dai Information Science Division, Graduate School of Science and Technology, Sophia University, Tokyo, Japan
Amena Mahmoud Department of Computer Science, Faculty of Computers and Information, Kafrelsheikh University, Governorate, Egypt
Eiko Takaoka Department of Information and Communication Sciences, Faculty of Science and Technology, Sophia University, Tokyo, Japan

Volume: 16 | Issue: 3 | Pages: 36369-36383 | June 2026 | https://doi.org/10.48084/etasr.17778

Received: 29 January 2026 | Revised: 2 March 2026 and 14 March 2026 | Accepted: 18 March 2026 | Online: 6 June 2026

Corresponding author: Yalan Dai

Abstract

Learning Analytics (LA) and Educational Data Mining (EDM) have emerged as powerful tools for understanding student behavior and improving educational outcomes. This research presents an approach to personality recognition in educational settings by leveraging multimodal data mining techniques across diverse educational datasets. It analyzes student interactions, learning patterns, and behavioral indicators collected from university educational platforms, including learning management systems. Our methodology combines traditional Machine Learning (ML) algorithms with Deep Learning (DL) approaches to process and analyze multiple data streams simultaneously. We employed a comprehensive dataset of 76 students across two different courses to incorporate students' features. Results demonstrate significant improvements in personality recognition accuracy compared to single-model approaches. The proposed system achieved an overall accuracy of 96% in identifying personality traits, with particularly strong performance in recognizing Extraversion and Conscientiousness. Furthermore, this study revealed notable correlations between certain personality traits and specific learning behaviors, providing valuable insights for personalized learning interventions. This research contributes to the field by introducing a scalable framework for personality recognition in educational contexts, offering practical applications for adaptive learning systems and personalized educational support. The findings have important implications for developing targeted interventions and customized learning experiences based on individual personality profiles.

Keywords:

personalized learning, Machine Learning (ML), Deep Learning (DL), personality recognition

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