The use of eye-tracking technology to identify visualisers and verbalisers: accuracy and contributing factors

Abstract

Purpose

To build adaptive learning systems for a better learning experience, designers need to identify users’ behaviour patterns and provide adaptive learning materials accordingly. This study involved a quasi-experiment and also this paper aims to investigate the accuracy of eye-tracking technology in identifying visualisers and verbalisers and the contributing factors to diverse levels of accuracy, which lays the foundation for the establishment of adaptive learning systems.

Design/methodology/approach

The authors prepared eight documents with different image-text combinations with the intention of triggering participants’ natural reading habits. By analysing the eye-movement data, this author categorised the 22 participants as visualisers or verbalisers. The results were compared for accuracy measure with participants’ self-reports in response to the index of learning style questionnaire.

Findings

The results showed that visualisers and verbalisers presented significantly different eye-movement patterns, which was confirmed by the fixation data from the Tobii eye-tracker with the detection accuracy ranged from 38% to 77%. Various factors contributed to a range of levels of accuracy, including highlighted elements, learning context, complex background, low relevance of images and texts, learner differences, awareness of experimental settings, self-conception and prior knowledge.

Originality/value

To the authors’ knowledge, this is the first paper investigating the feasibility of eye-tracking technology to identify visualisers and verbalisers for the development of adaptive learning systems.

Citation

Luo, Z., O'Steen, B. and Brown, C. (2020), "The use of eye-tracking technology to identify visualisers and verbalisers: accuracy and contributing factors", Interactive Technology and Smart Education, Vol. 17 No. 2, pp. 229-247. https://doi.org/10.1108/ITSE-12-2019-0087