Studying student behavior and chemistry skill using browser-based tools and eye-tracking hardware

Abstract

Browser-based tools were created to collect quantitative data about university student problem-solving skills. Three of these tools have been described: a word question tool that creates ideal gas law and stoichiometry questions using a set of complexity factors; a Lewis structure drawing tool that enables the user to draw a solution to an ion or molecule assigned to them; and a "spheres" tool that uses spheres to represent atoms and molecules to denote the particulate nature of matter. Results from these studies show that relatively simple questions can be made very complex by the addition of many complexity factors that challenge the cognitive skills. The drawing tools can be used for instruction or to collect data about student understanding; the outcomes suggest that students with more instruction in chemistry are more successful but even the performance of students after four semesters is somewhat disappointing. Eye-tracking hardware enabled the study of the student use of the visual interface of the other tools and to study molecular representation or interpret spectral data.

Eye-Tracking as a Promising Tool in Pre-Service Teacher Education─A New Approach to Promote Skills for Digital Multimedia Design

“…So far, eye-tracking technology has been used predominantly as a diagnostic tool for research purposes in science and chemistry education. In this context, the focus was often on evaluating problem solving processes, − usage of representations, ,,− usability of learning materials, − ,− expertise comparisons, − ,,− and working memory. , Hansen et al however also demonstrated that eye-tracking can be used as a tool for visual feedback. In their study, they used inverse opacity images (blacked-out heat maps) to stimulate participants to reflect on their visual behavior.…”

Recognizing Multidimensional Engagement of E-Learners Based on Multi-Channel Data in E-Learning Environment

“…On the other hand, the rapid development in sensory technology has enabled researchers and practitioners to push the boundaries of learning engagement detection and its analysis by investigating various machine-readable signals or behaviors, such as electroencephalogram (EEG) signal, physiological signal, electrodermal activity, facial expression, gaze, keystroke and mouse movement [17]. In order to obtain high quality data generated from the learning activities, intrusive or wearable devices such as electrode headset [18], wristband [19], or costly sophisticated eye tracker [20] have to be adopted. The adoption of these devices may cause inconvenience or discomfort to learners during data acquisition process.…”