Using Data From a Mobile Application to Promote Socially Shared Regulation of Learning

• In Event: Scaffolding Self-Regulation, Co-Regulation, and Socially Shared Regulation of Future Learning: Affordances of Learning Analytics Dashboards

Abstract

Based in the theory of socially shared regulation of learning (SSRL), we created Collabucate; a mobile application to teach students important small group collaboration skills. SSRL includes processes students use to collectively regulate their activity (i.e., plan, monitor, control, and evaluate; Hadwin, Järvelä, & Miller, 2011). We designed the app to adhere to Järvelä et al.’s (2015) three design principles (i.e., awareness, externalization, and promoting regulation), but also provided groups with learning strategies to address student-identified challenges. We included strategies found efficacious in the group meta-cognitive, motivational, and socio-emotional regulation literature (Järvelä & Hadwin, 2013; Järvelä et al., 2015). Collabucate taught students the strategy, its importance, when and how to use it, and demonstrated how a highly effective group would use the strategy compared to a less effective group.
For this study, using multiple methods we conducted analyses of six groups of pharmacy students’ perceptions and implementation of the suggested weekly strategies in a project-based learning course. We analyzed three data sources: log data, focus groups, and video data of group meetings. The log data came from three weekly prompts, including the extent to which the group used the assigned SSRL strategy, the utility of the strategy, and how well the group understood the strategy (see Figure 1). For each focus group, we asked about the app’s specific advantages, disadvantages, and areas in need of improvement. Finally, the video data captured groups’ discussions of the implementation of their strategies.
The word limit of this proposal precludes a full review of all results, but in general our findings covered several main areas. In terms of strategy use, for example, we found when groups received recommendations for the same strategy on multiple occasions, they typically rated those strategies higher over time. We also found that some strategies were more prevalent at different points in the course (see Table 1), and different groups found certain strategies more or less valuable (see Tables 2 and 3). Finally, we found that all areas of the students’ ratings of strategies (i.e., extent of use, utility, and understanding) were statistically significantly correlated with each other (see Table 4), suggesting that students were more likely to use the strategy if they understood it and found it useful. The focus group data revealed important ways to improve the application and the kinds of feedback it provided. Finally, we tested the students’ suggestion to have the app prompt groups to discuss the implementation of their strategy during the first ten minutes of their next group meeting. We found that groups were receptive to this and used the time to discuss how they might implement the strategy and what aspects of the strategy they had previously incorporated in their meetings.
Overall, we found that Collabucate provided students with important feedback on their SSRL, and how to improve it. Through our analysis, we were able to determine students’ perceptions of these strategies and ways to improve them. These data will enable us to refine and enhance the efficacy of Collabucate.

Authors

• Nikki G. Lobczowski, McGill University

• Kayley Lyons, University of North Carolina - Chapel Hill

• Jeff A. Greene, University of North Carolina - Chapel Hill

• Jacqueline E. McLaughlin, University of North Carolina - Chapel Hill