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

2020 ◽  
Vol 17 (2) ◽  
pp. 229-247
Author(s):  
Zhanni Luo ◽  
Billy O'Steen ◽  
Cheryl Brown
Keyword(s):  
Eye Tracking ◽  
Eye Movement ◽  
Adaptive Learning ◽  
Learning Systems ◽  
Learning Context ◽  
Contributing Factors ◽  
Content Type ◽  
Accuracy Measure ◽  
Adaptive Learning Systems ◽  
Tracking Technology

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.

scholarly journals Identifying learning style through eye tracking technology in adaptive learning systems

2019 ◽  
Vol 9 (5) ◽  
pp. 4408
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Mohammed Al Achhab ◽  
Ismail Jellouli ◽  
Badr Eddine EL Mohajir
Keyword(s):  
Eye Movements ◽  
Learning Styles ◽  
Eye Tracking ◽  
Learning Style ◽  
Adaptive Learning ◽  
Good Condition ◽  
Learning Systems ◽  
Gaze Behavior ◽  
Adaptive Learning Systems ◽  
Tracking Technology

Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning.

Seeking attention: an eye tracking study of in-store merchandise displays

2015 ◽  
Vol 43 (6) ◽  
pp. 561-574 ◽  
Author(s):  
Patricia Huddleston ◽  
Bridget K. Behe ◽  
Stella Minahan ◽  
R. Thomas Fernandez
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Purchase Intention ◽  
Product Information ◽  
Product Category ◽  
Practical Significance ◽  
Content Type ◽  
First Time ◽  
Tracking Technology

Purpose – The purpose of this paper is to elucidate the role that visual measures of attention to product and information and price display signage have on purchase intention. The authors assessed the effect of visual attention to the product, information or price sign on purchase intention, as measured by likelihood to buy. Design/methodology/approach – The authors used eye-tracking technology to collect data from Australian and US garden centre customers, who viewed eight plant displays in which the signs had been altered to show either price or supplemental information (16 images total). The authors compared the role of visual attention to price and information sign, and the role of visual attention to the product when either sign was present on likelihood to buy. Findings – Overall, providing product information on a sign without price elicited higher likelihood to buy than providing a sign with price. The authors found a positive relationship between visual attention to price on the display sign and likelihood to buy, but an inverse relationship between visual attention to information and likelihood to buy. Research limitations/implications – An understanding of the attention-capturing power of merchandise display elements, especially signs, has practical significance. The findings will assist retailers in creating more effective and efficient display signage content, for example, featuring the product information more prominently than the price. The study was conducted on a minimally packaged product, live plants, which may reduce the ability to generalize findings to other product types. Practical implications – The findings will assist retailers in creating more effective and efficient display signage content. The study used only one product category (plants) which may reduce the ability to generalize findings to other product types. Originality/value – The study is one of the first to use eye-tracking in a macro-level, holistic investigation of the attention-capturing value of display signage information and its relationship to likelihood to buy. Researchers, for the first time, now have the ability to empirically test the degree to which attention and decision-making are linked.

Emotion-Based Adaptive Learning Systems

2020 ◽  
pp. 273-286
Author(s):  
Sai Prithvisingh Taurah ◽  
Jeshta Bhoyedhur ◽  
Roopesh Kevin Sungkur
Keyword(s):  
Adaptive Learning ◽  
Learning Systems ◽  
Adaptive Learning Systems

Definition of a Feature Vector to Characterise Learners in Adaptive Learning Systems

2019 ◽  
pp. 75-89 ◽  
Author(s):  
Alberto Real-Fernández ◽  
Rafael Molina-Carmona ◽  
María L. Pertegal-Felices ◽  
Faraón Llorens-Largo
Keyword(s):  
Adaptive Learning ◽  
Feature Vector ◽  
Learning Systems ◽  
Definition Of ◽  
Adaptive Learning Systems

scholarly journals INCA: A cognitive multi-agents architecture for designing intelligent & adaptive learning systems

2005 ◽  
Vol 2 (2) ◽  
pp. 99-114 ◽  
Author(s):  
Thierry Nabeth ◽  
Liana Razmerita ◽  
Albert Angehrn ◽  
Claudia Roda
Keyword(s):  
Knowledge Sharing ◽  
Adaptive Learning ◽  
Management Practices ◽  
Learning Systems ◽  
Learning System ◽  
Cognitive Agents ◽  
E Learning ◽  
Personal Agent ◽  
Adaptive Learning Systems ◽  
Multi Agents

This paper presents a cognitive multi-agents architecture called Intelligent Cognitive Agents (InCA) that was elaborated for the design of Intelligent Adaptive Learning Systems. The InCA architecture relies on a personal agent that is aware of the user's characteristics, and that coordinates the intervention of a set of expert cognitive agents (such as story telling agents, assessment agents, stimulation agents or help agents). This InCA architecture has been applied for the design of K"InCA, an e-learning system aimed at helping people to learn and adopt knowledge-sharing management practices.

scholarly journals Adaptive Learning Systems and Interference in Causal Inference

2021 ◽  
Author(s):  
Alexander Olof Savi ◽  
Nick ten Broeke ◽  
Abe Dirk Hofman
Keyword(s):  
Causal Inference ◽  
Adaptive Learning ◽  
External Validity ◽  
Learning Systems ◽  
Learning System ◽  
Adaptive Learning System ◽  
Adaptive Learning Systems

Adaptive learning systems can be susceptible to between-subject cross-condition interference by design. This interference has important implications for the implementation and evaluation of A/B tests in such systems, as it obstructs causal inference and hurts external validity. We illustrate the problem in an Elo based adaptive learning system, discuss sources and degrees of interference, and provide solutions, using an example in the study of dropout.

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