Intelligent Learning Analytics: Using Multimodal Data to Understand Student Engagement and Academic Progress

Cindy Nakatsu

Intelligent Learning Analytics: Using Multimodal Data to Understand Student Engagement and Academic Progress

Author(s)	Cindy Nakatsu
Country	United States
Abstract	The increasing adoption of digital learning environments has generated large volumes of educational data that can be used to understand student behaviour, engagement and academic progress. Traditional learning analytics primarily relies on structured indicators such as grades, attendance and assessment scores, whereas intelligent learning analytics increasingly incorporates multimodal data derived from learning management systems, classroom interactions, assessment responses, language, video, audio, eye-tracking, clickstream activity and other digital traces. This paper examines how multimodal data and artificial intelligence can support a more comprehensive understanding of student engagement and academic progress. A conceptual framework is proposed that integrates data acquisition, preprocessing, multimodal feature extraction, artificial intelligence-based analytics, learner profiling, early-warning mechanisms and personalised interventions. The paper discusses how behavioural, cognitive, emotional and social dimensions of engagement can be analysed through complementary data sources. It further examines challenges associated with data quality, privacy, algorithmic bias, interpretability, consent and the risk of excessive student surveillance. Particular attention is given to the distinction between observable digital behaviour and genuine learning, emphasising that increased platform activity does not necessarily indicate deeper engagement or improved academic achievement. The paper proposes a human-centred intelligent learning analytics framework in which AI-generated insights support rather than replace teacher judgement. The study concludes that multimodal learning analytics can strengthen early identification of learning difficulties, personalised feedback and evidence-based instructional design when implemented with appropriate pedagogical validation, ethical safeguards and transparent governance.
Keywords	Learning Analytics, Multimodal Data, Student Engagement, Academic Progress, Artificial Intelligence, Educational Data Mining, Learning Management Systems, Personalised Learning, Predictive Analytics, Student Success.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-19

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Intelligent Learning Analytics: Using Multimodal Data to Understand Student Engagement and Academic Progress

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