Visualizing a Task Performer’s Gaze to Foster Observers’ Performance and Learning : a Systematic Literature Review on Eye Movement Modeling Examples

Eye movement modeling examples (EMMEs) are instructional videos (e.g., tutorials) that visualize another person’s gaze location while they demonstrate how to perform a task. This systematic literature review provides a detailed overview of studies on the effects of EMME to foster observers’ performance and learning and highlights their differences in EMME designs. Through a broad, systematic search on four relevant databases, we identified 72 EMME studies (78 experiments). First, we created an overview of the different study backgrounds. Studies most often taught tasks from the domains of sports/physical education, medicine, aviation, and STEM areas and had different rationales for displaying EMME. Next, we outlined how studies differed in terms of participant characteristics, task types, and the design of the EMME materials, which makes it hard to infer how these differences affect performance and learning. Third, we concluded that the vast majority of the experiments showed at least some positive effects of EMME during learning, on tests directly after learning, and tests after a delay. Finally, our results provide a first indication of which EMME characteristics may positively influence learning. Future research should start to more systematically examine the effects of specific EMME design choices for specific participant populations and task types

Designing Gaze-Aware Attention Feedback for Learning in Mixed Reality

Mixed Reality (MR) has demonstrated its potential in the application field of education. In particular, in contrast to traditional learning, students using MR get the possibility of learning and exploring the content in a self-directed way. Meanwhile, research in learning technology has revealed the significance of supporting learning activities with feedback. Since such feedback is often missing in MR-based learning environments, we propose a solution of using eye-tracking in MR to provide gaze-aware attention feedback to students and evaluate it with potential users in a preliminary user study

Tutor In-sight: Guiding and Visualizing Students Attention with Mixed Reality Avatar Presentation Tools

Remote conferencing systems are increasingly used to supplement or even replace in-person teaching. However, prevailing conferencing systems restrict the teacher’s representation to a webcam live-stream, hamper the teacher’s use of body-language, and result in students’ decreased sense of co-presence and participation. While Virtual Reality (VR) systems may increase student engagement, the teacher may not have the time or expertise to conduct the lecture in VR. To address this issue and bridge the requirements between students and teachers, we have developed Tutor In-sight, a Mixed Reality (MR) avatar augmented into the student’s workspace based on four design requirements derived from the existing literature, namely: integrated virtual with physical space, improved teacher’s co-presence through avatar, direct attention with auto-generated body language, and usable workfow for teachers. Two user studies were conducted from the perspectives of students and teachers to determine the advantages of Tutor In-sight in comparison to two existing conferencing systems, Zoom (video-based) and Mozilla Hubs (VR-based). The participants of both studies favoured Tutor In-sight. Among others, this main fnding indicates that Tutor Insight satisfed the needs of both teachers and students. In addition, the participants’ feedback was used to empirically determine the four main teacher requirements and the four main student requirements in order to improve the future design of MR educational tools

Extracting and Visualizing Data from Mobile and Static Eye Trackers in R and Matlab

Eye tracking is the process of measuring where people are looking at with an eye tracker device. Eye tracking has been used in many scientific fields, such as education, usability research, sports, psychology, and marketing. Eye tracking data are often obtained from a static eye tracker or are manually extracted from a mobile eye tracker. Visualization usually plays an important role in the analysis of eye tracking data. So far, there existed no software package that contains a whole collection of eye tracking data processing and visualization tools. In this dissertation, we review the eye tracking technology, the eye tracking techniques, the existing software related to eye tracking, and the research on eye tracking for posters and related media. We then discuss the three main goals we have achieved in this dissertation: (i) development of a Matlab toolbox for automatically extracting mobile eye tracking data; (ii) development of the linked microposter plots family as new means for the visualization of eye tracking data; (iii) development of an R package for automatically extracting and visualizing data from mobile and static eye trackers

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

© 2019, Copyright © 2017 Taylor & Francis Group, LLC. Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations

Instruments for visualization of self, co, and socially shared regulation of learning using multimodal analytics:a systematic review

Abstract. This thesis presents a systematic literature review in the intersection of multimodal learning analytics, regulation theories of learning, and visual analytics literature of the last decade (2011- 2021). This review is to collect existing research-based instruments designed to visualize Self-Regulation of Learning (SRL), Co-Regulation of learning (CoRL), and Socially Shared Regulation of learning (SSRL) using dashboards and multimodal data. The inclusion and exclusion criteria used in this review addressed two main aims. First, to distil settings, instruments, constructs, and audiences. Second, to identify visualization used for targets (i.e., cognition, motivation, and emotion), phases (i.e., forethought, performance, and reflection), and types of regulation (i.e., SRL, CoRL, and SSRL). By following the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) guidelines, this thesis included 23 peer-reviewed articles out of 383 articles retrieved from 5 different databases searched in April 2021. The main findings from this literature review are (a) the included articles used theoretical grounding of SRL in all articles while CoRL is used only in 3 articles and SSRL only in 2 articles; (b) most articles used both teachers and students as the audience for visual feedback and operated in online learning settings; (c) selected articles focused mainly on visualizing cognition and motivation (17 articles each) as targets of regulation, while emotion as the target was applied only in 6 articles; (d) The performance phase was common to most of the articles and used various visualizations followed by reflection and forethought phases respectively. Simple visualizations, i.e., progress bar chart, line chart, color coding, are used more frequently than bubble chart, stacked column chart, funnel chart, heat maps, and Sankey diagram. Most of the dashboard instruments identified in the review are still improving their designs. Therefore, the results of this review should be put into the context of future studies to be utilized by researchers and teachers in recognizing the missing targets and phases of SRL, CoRL, and SSRL in visualized feedback. Addressing these could also assist them in giving timely feedback on students’ learning strategies to improve their regulatory skills