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<p>Learning Analytics Dashboards (LADs) have shown limited impact on student outcomes, often functioning as static visualizations. In this paper, we present a case study reimagining LADs as interactive tools that promote student engagement. Namely, we extend a conventional LAD with a Large Language Model (LLM)-powered pedagogical agent and an interactive Judgement of Learning (JoL) feature to support students’ awareness of their study progress. The pedagogical agent engaged students in conversations about their learning data, while the JoL feature required self-assessment before viewing system metrics, encouraging learners’ calibration of their judgement. This interactive LAD (ILAD) was implemented in a university programming course with three randomly assigned conditions: no agent, a “telling” agent providing information about learner data, and an “eliciting” agent asking questions about learner data. The case study reports on the data collected over five weeks from thirty students paid to regularly use the ILAD within their computer science course. Data analysis from this small sample showed that the students in the “elicit” condition engaged in more reflection and more accurately judged their own mastery. This study highlights ILADs’ potential to foster student engagement with learning data and improve metacognitive outcomes, offering new directions for learning dashboard design.</p>

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Interactive learning dashboards: rethinking learning visualisations as engagement tools

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Laura Graf,
  • Patrick Bassner,
  • Maximilian Anzinger,
  • Felix Dietrich,
  • Stephan Krusche,
  • Oleksandra Poquet

摘要

Learning Analytics Dashboards (LADs) have shown limited impact on student outcomes, often functioning as static visualizations. In this paper, we present a case study reimagining LADs as interactive tools that promote student engagement. Namely, we extend a conventional LAD with a Large Language Model (LLM)-powered pedagogical agent and an interactive Judgement of Learning (JoL) feature to support students’ awareness of their study progress. The pedagogical agent engaged students in conversations about their learning data, while the JoL feature required self-assessment before viewing system metrics, encouraging learners’ calibration of their judgement. This interactive LAD (ILAD) was implemented in a university programming course with three randomly assigned conditions: no agent, a “telling” agent providing information about learner data, and an “eliciting” agent asking questions about learner data. The case study reports on the data collected over five weeks from thirty students paid to regularly use the ILAD within their computer science course. Data analysis from this small sample showed that the students in the “elicit” condition engaged in more reflection and more accurately judged their own mastery. This study highlights ILADs’ potential to foster student engagement with learning data and improve metacognitive outcomes, offering new directions for learning dashboard design.