Current Trends in the Use of Eye Tracking in Mathematics Education Research: A PME Survey

Eye tracking (ET) is a research method that receives growing interest in mathematics education research (MER). This paper aims to give a literature overview, specifically focusing on the evolution of interest in this technology, ET equipment, and analysis methods used in mathematics education. To capture the current state, we focus on papers published in the proceedings of PME, one of the primary conferences dedicated to MER, of the last ten years. We identify trends in interest, methodology, and methods of analysis that are used in the community, and discuss possible future developments.

💡 Research Summary

This paper provides a systematic literature overview of eye‑tracking (ET) research within mathematics education, focusing specifically on the last ten years of contributions to the Proceedings of the International Conference on Mathematics Education (PME). The authors performed a full‑text keyword search across PME volumes 34–42 using terms such as “Eye Tracking,” “Gaze Tracking,” and “Eye Movement.” After filtering out papers that merely referenced ET without actually collecting eye‑movement data, they identified 33 relevant contributions published between 2013 and 2022.

The study is organized around three research questions. RQ1 asks how the number of ET‑related papers has evolved over time. The analysis shows that no ET papers appeared before 2013, and while the total number of contributions remains modest (33), there is a noticeable concentration of papers in the most recent PME editions, especially research reports, suggesting a shift toward more in‑depth empirical work.

RQ2 investigates which ET hardware is used in the mathematics education community. The majority of studies (21 papers, about 64 %) employ remote, screen‑mounted eye‑trackers, reflecting the traditional reliance on high‑precision, fixed‑position devices. However, a clear recent trend toward portable, glasses‑type trackers is evident: 11 papers (≈33 %) used such devices, with eight of those appearing in the last two PME conferences. Notably, a low‑cost glasses system developed at the University of Helsinki is cited, indicating growing interest in affordable, field‑friendly equipment. Additionally, a nascent movement toward dual‑ or multiple‑tracker setups—both portable and remote—is observed, enabling the study of collaborative problem‑solving and social interaction.

RQ3 examines the analytical and presentation methods applied to ET data. The authors categorize the approaches into three groups. The most common (15 papers, ~58 %) involve quantitative analysis of derived gaze features such as fixation count, fixation duration, dwell time, and saccade direction, typically followed by statistical testing. A second group (9 papers, ~27 %) relies on manual, qualitative coding of gaze videos, sometimes focusing on gaze paths rather than full video overlays; these studies interpret visual attention patterns to infer cognitive strategies. A smaller subset introduces automated pipelines or novel visualisations: one study uses machine‑learning models to predict problem‑solving success from gaze metrics, while others present paragraph‑style plots or gaze‑synchrony graphs to directly link mathematical reasoning with eye‑movement patterns.

The discussion highlights several key observations. First, interest in ET within mathematics education has risen markedly over the past five years, with a growing proportion of papers employing portable devices and dual/multi‑tracker configurations. Second, despite the prevalence of quantitative feature‑based analyses, a substantial portion of the work still depends on labor‑intensive manual coding, underscoring the need for more automated processing tools. Third, web‑camera‑based low‑cost ET remains under‑exploited; its broader adoption could enable large‑scale data collection and real‑time feedback in everyday classroom settings. Finally, the authors argue that as automated analysis pipelines mature, they will not only reduce researcher workload but also facilitate the translation of ET insights into practical educational technologies—such as real‑time attention monitoring and adaptive instructional support.

In sum, the paper demonstrates that eye‑tracking is becoming an established methodological pillar in mathematics education research, driven by advances in affordable hardware, multi‑device synchronization, and emerging automated analysis techniques. These trends are likely to continue, paving the way for more extensive, ecologically valid studies and for the integration of gaze‑based feedback mechanisms into everyday teaching practice.