Meta-LAD: a dashboard supporting metacognition

The project aims to support online learners’ self-regulated learning, performance, and retention by designing a learning analytics dashboard (LAD).

The research questions:

  • What are indicators predicting learners’ successful completion of online courses?
  • How does the use of a LAD with metacognitive indicators and actionable feedback throughout an online course improve the learning experiences of learners?

LAD design process:

To address the theoretical gap and context-specific needs, we focused on four design dimensions as below:

  • Theoretical grounding  
  • Contextualization  
  • Avoiding stressful social comparison
  • Providing actionable feedback

Meta-LAD:

Key Findings:

We currently completed the LAD design and usability testing. From usability testing, we found that participants generally agreed on the potential positive effects of helping planning, monitoring, and reflection, which are three phases of self-regulated learning. They also agreed with the need to show different goals for learners aiming for different targets, which we tried to address through the LAD design. Lastly, they liked the flow of feedback on the messages starting with encouragement and then moving on to the actionable items.

Research Team

Dr. Chris Caplice

Dr. Chris Caplice

Senior Research Scientist

Dr.Heeryung Choi

Dr.Heeryung Choi

Postdoctoral Associate

Dr. Eva Ponce

Dr. Eva Ponce

Director Research Scientist

Connor Makowski

Connor Makowski

Digital Learning Lead

Dr. Inma Borrella

Dr. Inma Borrella

Research Scientist