A Data-Driven Approach to Quantify and Measure Students’ Engagement in Synchronous Virtual Learning Environments

Abstract

In face-to-face learning environments, instructors (sub)consciously measure student engagement to obtain immediate feedback regarding the training they are leading. This constant monitoring process enables instructors to dynamically adapt the training activities according to the perceived student reactions, which aims to keep them engaged in the learning process. However, when shifting from face-to-face to synchronous virtual learning environments (VLEs), assessing to what extent students are engaged to the training process during the lecture has become a challenging and arduous task. Typical indicators such as students’ faces, gestural poses, or even hearing their voice can be easily masked by the intrinsic nature of the virtual domain (e.g., cameras and microphones can be turned off). The purpose of this paper is to propose a methodology and its associated model to measure student engagement in VLEs that can be obtained from the systematic analysis of more than 30 types of digital interactions and events during a synchronous lesson. To validate the feasibility of this approach, a software prototype has been implemented to measure student engagement in two different learning activities in a synchronous learning session: a masterclass and a hands-on session. The obtained results aim to help those instructors who feel that the connection with their students has weakened due to the virtuality of the learning environment.

Challenges That Need to Be Addressed before Starting New Emergency Remote Teaching at HEIs and Proposed Solutions

“…The most important conclusion is that the technical challenges during the development of the pandemic were resolved reasonably quickly, while pedagogical and social challenges were and are still latent both during ERT and non-crisis times. This finding might support and stimulate the development of tools (e.g., [53]) aimed to help eliminate persistent problems faster.…”

Affective e-learning approaches, technology and implementation model: a systematic review

“…The study in [31], advocates that measuring how engaged students are during lectures has gotten difficult and difficult due to the change from physicalto synchronous virtual environments. Since the virtual world has certain intrinsic features, typical signs like students' faces, gestural expressions, or even hearing their voices can be easily concealed (e.g., cameras and microphones can be turned off).…”

“…As seen in [51], a chart of the most recent e-learning model is constructed in Figure 4 and the summary is presented in Table 8. [17], [19]- [24], [26], [28], [29], [30], [32], [35], [41] Machine learning [16], [18], [25], [27], [33], [34], [37], [38], [44], [45] Algorithmic method [31], [36], [39], [40], [42], [43] RQ3: What is the best technology used in capturing emotion in student engagement?…”

From Sensor Data to Educational Insights

“…Finally, we have the last group of three additional papers that have diverse objectives in their work. For example, Solé-Beteta et al [ 30 ] proposed a methodology and associated model to measure student engagement in VLEs using more than 30 digital interactions and events during a synchronous lesson. Of course, many of these digital interactions are captured via sensors, such as students’ faces, gestural poses, or even audio from their voices.…”