Farman, Tatag Yuli Eko Siswono, Agung Lukito, Ratna Sari Dewi, Oscar Ndayizeye, Fitriyani Hali
Recent empirical research on mathematical creativity using eye-tracking (ET) technology has faced challenges in developing comprehensive overviews due to the diversity of tools, task types, ET data, and identification methods. Thus, this systematic review attempts to examine studies that focus on mathematical creativity and incorporate ET technology. Guided by Newman and Gough’s seven-step approach, a Scopus database search covering publications up to 2024 identified five eligible empirical studies collected for this study. Of the 29 papers, 5 were selected, and their methodological validity was assessed using the Mixed Method Appraisal Tool (MMAT). The review reveals that researchers employed two primary types of eye trackers: mobile eye trackers, suitable for engaging in paper-and-pencil tasks while moving naturally, and screen-based eye trackers, preferable for computer-based creative tasks because they provide more precise gaze recordings without requiring wearable equipment. To stimulate creative thinking, these studies used tasks that encourage divergent exploration, such as multiple solution tasks (MSTs), multiple representation tasks (MRs), and creative problem-solving. The majority of studies utilized the geometry domain, which is considered particularly well-suited for ET-based research due to its visual representations. ET data included both quantitative data (e.g., fixations, saccades) and qualitative data (gaze-overlaid videos), which complemented each other. Two primary methods for investigating mathematical creativity were identified. Four studies combined eye-tracking (ET) with stimulated recall interviews (SRI) to directly capture the processes of mathematical creativity. In contrast, one study integrated ET with multimodal sensors such as skin conductance (SC) and electroencephalography (EEG), where creativity was first assessed from students’ problem-solving products (fluency, flexibility, originality) and subsequently modeled by linking visual attention patterns and physiological responses to distinguish between high-and low-creativity groups. These findings emphasize the importance of cognitively challenging task design and data triangulation approaches in deepening our understanding of the dynamics of creativity in mathematical problem-solving. © 2025, PT Mattawang Mediatama Solution. All rights reserved.
Universitas Negeri Surabaya, Surabaya, Indonesia; Universitas Sembilanbelas November Kolaka, Kolaka, Indonesia; Institut Teknologi Sepuluh Nopember Surabaya, Surabaya, Indonesia; Ecole Normale Superieure (ENS) du Burundi, Bujumbura, Burundi; Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
