Visualisation and interpretation of student strategies in solving natural science-based tasks using the eye-tracker

This paper presents a research on students using the Gazepoint device to visualise the practices and strategies that students used in order to solve assignments in the disciplines of natural science. The analysis of visual perception of students is complemented by a questionnaire survey for a group of respondents aged 15-16. The essence of the study was to find out how the students proceeded in monitoring assignments displayed on the screen, how they continued working with the assignments, and whether the layout of the schematics, tables and applied images affected students ‘correctness for the solution. The main aim of the research was to find some similar segments in the experimental data and obtained clusters that would suggest a similar approach of problem solving by students – respondents, and to find out if, and possibly how, some strategies in the assignments differ for the talented students from the standard pupil population and compare the outcomes with students’ characteristics. The other aim of study was to confirm the presence of gifted students in natural sciences in a given sample of respondents on the basis of eye-tracking technology. Also on the basis of similarities in assigned task solving the aim was to find other students who can be seen similarly to the gifted ones from a view of e.g. physiological dynamics of eyes of the students in the context of the given selected seven tasks in the area of the chemical elements identification. For both groups of students, some basic measures are proposed to increase the efficiency of students‘ work with an assignment displayed on a computer screen. Our results show that in the task solving, one gifted student was identified next to a cluster of four similarly performing students on the basis of eye-movements parameters

Predicting student performance in an augmented reality learning environment using eye-tracking data

This paper investigates the use of eye-tracking data as a predictor of student performance in an augmented reality (AR) learning environment. 33 undergraduate students enrolled in an ergonomics course at the University of Missouri-Columbia participated in an AR biomechanics lecture consisting of 14 modules. Following each module students answered learning comprehension questions to test their understanding of the lecture material. An additional dataset was recorded for each module in which the participant perfectly follows the virtual instructor throughout the learning space. This dataset, referred to as the baseline, can be used as a comparison tool to gauge how well students follows the lecture material. Two methods are proposed to quantify the student's attention level for each module. The average difference method calculates the average distance between the student and baseline coordinates for each module. The distraction rate method expands upon the average difference method and aims to reduce the amount noise detected. This is done by incorporating a minimum distance threshold, a binary detection signal, and a moving average window. Both metrics are tested as factors in a set of logistic regression models to determine whether they can accurately predict student answer correctness. Average difference showed a correlation with student answer correctness, but with an underwhelming level of significance. Distraction rate outperformed average difference and proved to be a strong and statistically significant predictor of student answer correctness. Finally, two feedback systems are proposed which use distraction rate to detect when students have become distracted so that their attention can be regained through the use of module-based feedback or a real-time attention guidance system.Includes bibliographical references