Measuring causality between collaborative and individual gaze metrics for collaborative problem‐solving with intelligent tutoring systems. (23rd June 2020)

• Record Type: Journal Article
• Authors: Sharma, Kshitij; Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol
• Is Part Of: Journal of computer assisted learning. Volume 37:Number 1(2021)
• Page Start: 51
• Page End: 68
• Publication Date: 2020-06-23
• Subjects: collaboration -- collaborative learning -- CSCL -- dual eye‐tracking -- Granger causality -- ITS; Computer-assisted instruction -- Periodicals; 371.334
• DOI: 10.1111/jcal.12467
• Languages: English
• ISSNs: 0266-4909

Abstract

Abstract: When students are working collaboratively and communicating verbally in a technology‐enhanced environment, the system cannot track what collaboration is happening outside of the technology, making it difficult to fully assess the collaboration of the students and adapt accordingly. In this article, we propose using gaze measures as a proxy for cognitive processes to achieve collaboration awareness. Specifically, we use Granger causality to analyse the causal relationships between collaborative and individual gaze measures from students working on a fractions intelligent tutoring system and the influence that the students' dialogue, prior knowledge, or success has on these relationships. We found that collaborative gaze patterns drive the individual focus in the pairs with high posttest scores and when they are engaged in problem‐solving dialogues but the opposite with low performing students. Our work adds to the literature by extending the correlational relationships between individual and collaborative gaze measures to causal relationships and suggests indicators that can be used within an adaptive system. Lay Description: What is already known about this topic: Within the field of educational technology, ITSs have shown to have a long track record of successfully supporting individual student learning, particularly within the domain of mathematics. Assessing and supporting computer supported collaboration in an adaptive manner is difficult, since the system cannot track what is happening outside the technology. Dual Eye‐tracking is a highly capable methodology to collect, analyse and understand the socio‐cognitive processes underlying collaboration in diverse learning settings. What this paper adds: Utilizes the dual eye‐tracking techniques in an collaborative ITS setting with dialogues, prior knowledge and performance as covariates to understand the causal relationship between the individual and collaborative gaze patterns. Extends the understanding of Collaborative Learning Mechanisms by proposing a new metric that can be used to assess student collaboration in real‐time even when students are communicating face‐to‐face. Extends the theoretical knowledge of gaze in educational technology by extending our understanding of the relations between different gaze measures and how these relations change as the students' collaborative relationship changes. Implications for practice: Learning Technology researchers shall be able to use eye‐tracking as methodological approach to create new actionable feedback systems, taking advantage of automatic analysis capabilities. Learning design professionals shall have the opportunity to integrate specific eye‐tracking features into the Intelligent Tutoring Systems that can prevent disengagement from collaboration and promote high collaboration quality as well as high collaborative outcome. With the adaptive collaborative learning support the learners shall have the opportunity to deeper engage in learning activities and with the correct intervention shall have higher learning gains.