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Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning.
Kshitij Sharma1, Serena Lee-Cultura1, Michail Giannakos1
1Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
Frontiers in Artificial Intelligence
|January 31, 2022
Summary
Multimodal data from embodied learning games reveals a Carry Forward Effect, where student actions influence subsequent learning stages. Wristband and eye-tracking data are key for real-time adaptive feedback in educational settings.
Area of Science:
- Educational Technology
- Human-Computer Interaction
- Cognitive Science
Background:
- Multimodal Data (MMD) integration with embodied learning systems (e.g., Motion Based Educational Games, MBEG) offers insights into student interaction and learning.
- Real-time analysis of student-generated MMD from MBEG is complex and under-explored.
- Understanding the synergy between student movement and learning is crucial for designing supportive intelligent agents.
Purpose of the Study:
- To investigate the real-time use of MMD from embodied learning systems.
- To explore the cognitive and physiological dimensions of student progress within the "see-solve-move-respond" (S2MR) cycle.
- To introduce and analyze the Carry Forward Effect (CFE) and its impact on learning.
Main Methods:
- Conducted an in-situ study with 40 children (ages 9-12) playing MBEG for maths and language development.
- Utilized eye-tracking glasses, physiological wristbands, and Kinect for unobtrusive, continuous monitoring of student experiences.
- Analyzed MMD to understand cognitive and physiological states during the S2MR cycle.
Main Results:
- Introduced the Carry Forward Effect (CFE), a phenomenon where MMD-derived effects propagate through S2MR cycles.
- Identified congruence between CFE and learning performance to optimize feedback delivery.
- Demonstrated that wristband and eye-tracking data are crucial indicators for adaptive feedback in MBEG.
Conclusions:
- MMD provides valuable insights into student learning within embodied educational games.
- The CFE highlights the interconnectedness of cognitive and physiological states across learning stages.
- Real-time MMD analysis and adaptive feedback are significant for enhancing student performance in educational settings.
Keywords:
AI agenteducational technologieslearning analyticsmotion-based gamesmulti-modal datasensorsMore Related Videos
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