Despite the potential of learning analytics dashboards (LADs) to support learners’ needs for autonomy, little research has been conducted on designing LADs to support student autonomy. In this paper, we reported the process of designing a student-facing LAD that offers students’ autonomy support by providing necessary information for students to set their own goals and choose learning activities that are aligned with their goals. A leaderboard was also integrated into the LAD to promote student motivation. Reeves’s (2006) design-based research model was adopted to develop the LAD. The final version of the LAD was presented, and the significance of the work was discussed.
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Developing Effective Visualizations to Understand and Scaffold Collaborative Textual Practices
This poster will describe and compare different visualizations for collaborative writing using
cloud-based platforms (e.g., Google Docs). The aim is to develop effective visualizations for a Learning Analytics Dashboard (LAD) to understand the processes undergirding cloud-based group writing and to support team textual practices. Theoretically situated in the literatures on Collaborative Learning Analytics, collaborative sense making, and social annotations, the visualizations will provide valuable and varied insights to researchers, educators, and students. We will be testing the visualizations and the final LAD with undergraduate students, instructors, and researchers from the fields of learning sciences and educational psychology.
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- Award ID(s):
- 1915563
- PAR ID:
- 10258075
- Date Published:
- Journal Name:
- Learning Analytics and Knowledge 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This study reported the process of developing and evaluating a student-facing learning analytics dashboard (LAD) for an online STEM skill practice system from a user experience approach. A usability survey was administered to 19 LAD users to gather information on what the learners believed were the most important features and what needed to be done to further improve the design of the LAD. Our findings showed that the most important LAD feature to students was showing the accuracy level of their practice and providing the option to redo the practice. These findings informed the revisions of the preliminary design of the LAD and provided insights into future development of student-facing LADs in online learning environments.more » « less
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An instrumental case study was conducted at a large, public research university in the mid-Atlantic region of the United States to understand undergraduate use of learning analytics dashboard (LAD) interventions. Eighty-one undergraduate students participated in focus groups. Scenario-based questions, modeled on current and future LAD interventions, were asked to understand student uses, perceptions, and reactions to these interfaces. Results indicate that student sensemaking is tied to data relevance, accuracy, and context. Further, trust in both data, especially predictive data, and in relationships, especially with faculty, are foundational to the sensemaking of LAD data and interventions. A model of student sensemaking of LAD interventions is provided.
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Abstract This exploratory paper highlights how problem‐based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from C
rystal Island: EcoJourneys , a collaborative game‐based learning environment centred on supporting science inquiry. In Crystal Island: EcoJourneys , students work in teams of four, investigate the problem individually and then utilize a brainstorming board, an in‐game PBL whiteboard that structured the collaborative inquiry process. The paper addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game‐based learning environment? Drawing on a mixed method approach, we first analyzed students' pre‐ and post‐test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre‐ and post‐test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game‐based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game‐based learning environment: students engaged in low to moderate self‐directed actions with (1) high and (2) moderate collaborative sense‐making actions, (3) low self‐directed with low collaborative sense‐making actions and (4) high self‐directed actions with low collaborative sense‐making actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self‐directed and high collaborative sense‐making cluster actively initiated discussions and integrated information they learned to the problem, whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students' interactions with in‐game activities.Practitioner notes What is already known about this topic
Learning analytic methods have been effective for understanding student learning interactions for the purposes of assessment, profiling student behaviour and the effectiveness of interventions.
However, the interpretation of analytics from these diverse data sets are not always grounded in theory and challenges of interpreting student data are further compounded in collaborative inquiry settings, where students work in groups to solve a problem.
What this paper adds
Problem‐based learning as a pedagogical framework allowed for the design to focus on individual and collaborative actions in a game‐based learning environment and, in turn, informed the interpretation of game‐based analytics as it relates to student's self‐directed learning in their individual investigations and collaborative inquiry discussions.
The combination of principal component analysis and qualitative interaction analysis was critical in understanding the nuances of student collaborative inquiry.
Implications for practice and/or policy
Self‐directed actions in individual investigations are critical steps to collaborative inquiry. However, students may need to be encouraged to engage in these actions.
Clustering student data can inform which scaffolds can be delivered to support both self‐directed learning and collaborative inquiry interactions.
All students can engage in knowledge‐integration discourse, but some students may need more direct support from teachers to achieve this.
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