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Eye-tracking research in mathematics and statistics education: recent developments and future trends. A systematic literature review
University of Cologne, Cologne, Germany.
University of Cologne, Cologne, Germany.
TU Dortmund University, Dortmund, Germany.
Örebro University, School of Science and Technology. Technical University of Munich (TUM), Munich, Germany.ORCID iD: 0000-0003-0217-9326
2025 (English)In: ZDM - the International Journal on Mathematics Education, ISSN 1863-9690, E-ISSN 1863-9704, Vol. 57, p. 727-743Article, review/survey (Refereed) Published
Abstract [en]

Eye tracking is gaining significance in mathematics education research at a tremendous speed. For the discipline to grow, it is essential to monitor, structure, and synthesize the research in this rapidly evolving field, which calls for a systematic literature review. However, a comprehensive and systematic review does not exist for the research for the past five years. This is a profound gap considering the dynamics of the field, which is fueled by technological advancements in hard- and software and the increasing usability and availability of eye-tracking systems. The aim of this paper is to provide a comprehensive and systematic literature review on eye-tracking research in mathematics and statistics education published in the past five years. Using a systematic database search, we identified and reviewed 116 eye-tracking studies published between 2019 and the first quarter of 2024. We found that the studies addressed a wide range of topics in all relevant curriculum content areas as well as a multitude of phenomena, including teacher-student interaction and digital learning. Interestingly, the studies increasingly involved school students, partially in authentic classroom settings. We also found that the majority of the papers referred to a theoretical framework or made assumptions about the (domain-specific) interpretation of eye movements explicit. As a further important trend, probably still in its infancy, we observed the use of AI techniques for data analysis purposes, which allows for qualitative insights despite bigger numbers of participants. Our paper provides an overview and detailed insights into trends, of which many have not been visible in earlier review studies.

Place, publisher, year, edition, pages
Springer, 2025. Vol. 57, p. 727-743
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-121389DOI: 10.1007/s11858-025-01699-8ISI: 001494915600001Scopus ID: 2-s2.0-105006438599OAI: oai:DiVA.org:oru-121389DiVA, id: diva2:1965730
Note

Open Access funding enabled and organized by Projekt DEAL.

Available from: 2025-06-09 Created: 2025-06-09 Last updated: 2026-01-07Bibliographically approved

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Lilienthal, Achim J.

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