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From Deep Learning to Digital Insights: Twenty-Five Years of Understanding How Students Learn in Higher Education

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Title: From Deep Learning to Digital Insights: Twenty-Five Years of Understanding How Students Learn in Higher Education
Language: English
Authors: David Gijbels (ORCID 0000-0001-8369-9213)
Source: Active Learning in Higher Education. 2026 27(2):221-229.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 9
Publication Date: 2026
Document Type: Journal Articles
Reports - Evaluative
Education Level: Higher Education
Postsecondary Education
Descriptors: Review (Reexamination), Literature Reviews, Educational Research, Learning Strategies, Higher Education, Active Learning, Educational Environment, Eye Movements, Artificial Intelligence, Technological Advancement, Research Methodology, Longitudinal Studies
DOI: 10.1177/14697874261424517
ISSN: 1469-7874
1741-2625
Abstract: This article marks the 25th anniversary of "Active Learning in Higher Education" and offers a reflective overview of how research on student learning has evolved since the journal's inception. Drawing from my own academic journey, I first revisit the origins of deep and surface approaches to learning and the subsequent development of influential questionnaires. I then discuss how early research primarily relied on cross-sectional, correlational designs that linked students' perceptions of the learning environment to their approaches to learning, consistently showing that positive perceptions were associated with deeper engagement. Over time, however, researchers recognized the limitations of these designs and shifted toward longitudinal studies. Although it is often assumed that higher education naturally fosters deeper approaches to learning, systematic reviews reveal that changes in learning approaches are neither linear nor universal; instead, they are influenced by individual differences, learning contexts, and disciplinary practices. In the past decade, the field has increasingly embraced multimodal and behavioral data, integrating tools such as eye tracking to gain deeper insight into students' learning processes. This shift has opened new avenues for understanding how learners engage with texts, videos, and other instructional materials. The article concludes by outlining emerging opportunities at the intersection of artificial intelligence and multimodal learning analytics, illustrated through the EYE-TEACH project, which seeks to provide higher education teachers with actionable, ethically grounded insights to better support students' active learning in real time.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1502926
Database: ERIC
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: This article marks the 25th anniversary of "Active Learning in Higher Education" and offers a reflective overview of how research on student learning has evolved since the journal's inception. Drawing from my own academic journey, I first revisit the origins of deep and surface approaches to learning and the subsequent development of influential questionnaires. I then discuss how early research primarily relied on cross-sectional, correlational designs that linked students' perceptions of the learning environment to their approaches to learning, consistently showing that positive perceptions were associated with deeper engagement. Over time, however, researchers recognized the limitations of these designs and shifted toward longitudinal studies. Although it is often assumed that higher education naturally fosters deeper approaches to learning, systematic reviews reveal that changes in learning approaches are neither linear nor universal; instead, they are influenced by individual differences, learning contexts, and disciplinary practices. In the past decade, the field has increasingly embraced multimodal and behavioral data, integrating tools such as eye tracking to gain deeper insight into students' learning processes. This shift has opened new avenues for understanding how learners engage with texts, videos, and other instructional materials. The article concludes by outlining emerging opportunities at the intersection of artificial intelligence and multimodal learning analytics, illustrated through the EYE-TEACH project, which seeks to provide higher education teachers with actionable, ethically grounded insights to better support students' active learning in real time.
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