A Novel Group Engagement Score for Virtual Learning Environments

Abstract

STEM (Science, Technology, Engineering and Math) education is currently receiving much attention from governments and educational institutions. Our work is based on active learning and video-based learning approaches to support STEM education. Here we aimed to increase student's engagement through reflective processes that embrace video filmmaking, and subsequent on-line discussion and evaluation of those videos. We propose a group engagement score that takes into account both individual activity and similarity of participation, thus allowing corrective actions to be taken when unengaged students or groups are identified. We tested these ideas using our own social learning platform that combines the principal features of Social Networks with tools that facilitate collaborative learning design. This platform stimulates students' learning by means of two main reflective processes: participatory production and peer-review. We evaluated this platform and the learning approach it supports in an Object Oriented Programming course and identified interesting differences between group engagement and video ratings. Our principal conclusion is that greater teacher and student awareness of the ongoing activities and group engagement are needed.

Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning

“…Moreover, from Appendix D one can observe that only few analytics indicators have been explicitly devised to collect and present educator's data (i.e., data gathered from teachers, that allows teachers to reflect upon their teaching practices and course design) [47], [67], [63], [75], [69], [90], [42]. This shows that there is some educator's data stored in databases, mostly data from teacher's interactions with content or students, or log data from teacher's use of dashboards.…”

“…• utilization of learning analytics tools from which analytics were extracted and used to further develop the tools, as to offer better practical support and informed decision making [71], [77], [76], [94], [74], [93], [73], [80]; • development of frameworks that add a theoretical clarity to the learning process, identify analytics metrics, and create guidelines and recommendations that can inform the design of learning activities [43], [87], [75], [48], [69], [49], [81], [64].…”

“…As a result, increased awareness lead to improved and informed decision-making, and potential growth in users' skills and competencies [42], [47]. Next, although the usage of learning analytics could have impact on user's behavior, there are chances that users can utilize learning analytics to monitor their progress but not necessarily change their behavior [69]. However, learning analytics could also do the opposite, enact unintentional change [7].…”

“…The rest of the studies used WBE (n = 13 studies), CBE (n = 1 studies), multimodality (n = 9 studies), and mobile (n = 1 studies). Moreover, the studies that were conducted within LMSs and who reported the type of the learning platform, have shown that Moodle was the most used type of a learning platform (n = 6 studies), followed by Blackboard (n = 2 studies), and other social learning platforms (n = 8 studies) such as EdX [64], Khan Academy [65], Coursera [66], Elgg [67], THEOL [63], ed-Venture [68], video-based social platform [69] and virtual world platform [70]. Furthermore, with regard to the setting of the learning environment, some studies were conducted in purely digital learning environments (n = 15 studies), some in blended learning environments (n = 11 studies), and some in face-toface learning environments (n = 10 studies), where students collaborated face-to-face using digital tools [71], multi-surface tabletops [42], or used mobile devices as a learning mediator [72].…”

“…From the results it can be observed that learning analytics usage generally increased user awareness and user informedness in the learning environment [47], [67], [73], [74]. Next, usage of learning analytics assisted teachers to manage time better [47], [75], [42]; to identify problems in the course design [73], [72]; to follow student behavior and engagement with content overtime and apply informed changes to keep the level of instructional quality [76], [77]; to arrange the monitoring process according to their needs and think a priori about possible solutions [47], [69]; and to utilize realtime tracking and provide instantly corrective feedback [42], [68], [7], [71]. On the other hand, usage of learning analytics helped students to apply diagnostic assessment following their own performance [74], [72], [78], [49]; to initiate and steer conversation with their peers or the teacher [42]; to better navigate and utilize the course content [77]; to reflect, selfdirect their progress, and make informed decisions how to continue reaching learning goals [7], [78], [71].…”