The state of the art of diagnostic multiparty eye tracking in synchronous computer-mediated collaboration

In recent years, innovative multiparty eye tracking setups have been introduced to synchronously capture eye movements of multiple individuals engaged in computer-mediated collaboration. Despite its great potential for studying cognitive processes within groups, the method was primarily used as an interactive tool to enable and evaluate shared gaze visualizations in remote interaction. We conducted a systematic literature review to provide a comprehensive overview of what to consider when using multiparty eye tracking as a diagnostic method in experiments and how to process the collected data to compute and analyze group-level metrics. By synthesizing our findings in an integrative conceptual framework, we identified fundamental requirements for a meaningful implementation. In addition, we derived several implications for future research, as multiparty eye tracking was mainly used to study the correlation between joint attention and task performance in dyadic interaction. We found multidimensional recurrence quantification analysis, a novel method to quantify group-level dynamics in physiological data, to be a promising procedure for addressing some of the highlighted research gaps. In particular, the computation method enables scholars to investigate more complex cognitive processes within larger groups, as it scales up to multiple data streams

Gaze collaboration patterns of successful and unsuccessful programming pairs using cross-recurrence quantification analysis

A dual eye tracking experiment was performed on pairs of novice programmers as they traced and debugged fragments of code. These programming pairs were categorized into successful and unsuccessful pairs based on their debugging scores. Cross-recurrence quantification analysis (CRQA), an analysis using cross-recurrence plots (CRP), was used to determine whether there are significant differences in the gaze collaboration patterns between these pair categories. Results showed that successful and unsuccessful pairs can be characterized distinctively based on their CRPs and CRQA metrics. This study also attempted to interpret the CRQA metrics in relation to how the pairs collaborated in order to provide a somewhat clear picture of their relevance and meaning. The analysis results could serve as a precursor in helping us understand what makes a programming pair more successful over other pairs and what behaviors exhibited by unsuccessful pairs that should be avoided

The state of the art of diagnostic multiparty eye tracking in synchronous computer-mediated collaboration

In recent years, innovative multiparty eye tracking setups have been introduced to synchronously capture eye movements of multiple individuals engaged in computer-mediated collaboration. Despite its great potential for studying cognitive processes within groups, the method was primarily used as an interactive tool to enable and evaluate shared gaze visualizations in remote interaction. We conducted a systematic literature review to provide a comprehensive overview of what to consider when using multiparty eye tracking as a diagnostic method in experiments and how to process the collected data to compute and analyze group-level metrics. By synthesizing our findings in an integrative conceptual framework, we identified fundamental requirements for a meaningful implementation. In addition, we derived several implications for future research, as multiparty eye tracking was mainly used to study the correlation between joint attention and task performance in dyadic interaction. We found multidimensional recurrence quantification analysis, a novel method to quantify group-level dynamics in physiological data, to be a promising procedure for addressing some of the highlighted research gaps. In particular, the computation method enables scholars to investigate more complex cognitive processes within larger groups, as it scales up to multiple data streams

Artificial intelligence techniques for supporting face-to-face and online collaborative learning

Collaborative learning provides opportunities for data-intensive, educational innovation because learners need to externalize some of their mental processes in the form of dialogue, drawings, and other representations. Digital traces of these externalizations can be captured and analyzed using various Artificial Intelligence and analytic techniques. This can further accelerate researchers’ analysis cycles and help in the development of more effective tools that support collaboration and learning. This chapter describes techniques currently available for supporting face-to-face and online collaborative learning situations. We particularly focus on techniques that provide intelligent support to: (i) form effective groups, (ii) provide direct feedback to students, (iii) collaborate on scripts, (iv) enhance group and teacher awareness, and (v) perform summative assessments in the pre-active, inter-active and post-active phases of collaborative learning. We discuss potential future trends for research and development in this area, emphasizing the need for evaluating the validity, utility and interpretability of emerging techniques for modelling and assessing meaningful aspects of collaborative learning