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Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

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A Framework for Learning Analytics Using Commodity Wearable Devices.

Yu Lu^1,2, Sen Zhang³, Zhiqiang Zhang⁴

¹Advanced Innovation Center for Future Education, Beijing Normal University, Beijing 100875, China. luyu@bnu.edu.cn.

Sensors (Basel, Switzerland)

|June 15, 2017

Summary

This summary is machine-generated.

We introduce LEARNSense, a framework using wearable devices for learning analytics. It captures physical actions to infer student context and engagement, achieving high accuracy and learner satisfaction.

Keywords:

activity recognition learning analytics pervasive computing wearable sensors

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Area of Science:

Educational Technology
Human-Computer Interaction
Machine Learning

Background:

Understanding individual learner actions is key to effective learning analytics.
Wearable devices offer a non-intrusive method for collecting fine-grained learner data.
Existing learning analytics often lack real-time, context-aware insights into learner behavior.

Purpose of the Study:

To introduce LEARNSense, a framework for learning analytics leveraging commodity wearable devices.
To develop a system for capturing physical actions and inferring learner context, including engagement status.
To demonstrate the framework's effectiveness through a real-world classroom use case.

Main Methods:

Designed and implemented a sensor-based learner context collector for wearable devices.
Utilized data mining and sensor data processing techniques for action and context detection.
Developed novel intervention and feedback mechanisms based on collected learner data.

Main Results:

Achieved an F1 score of 0.9 for student action classification.
Successfully differentiated three distinct learner states (e.g., engagement levels).
Demonstrated system effectiveness and impact through real-world experiments, surveys, and interviews.

Conclusions:

The LEARNSense framework provides an effective and non-intrusive approach to learning analytics.
Commodity wearable devices can be successfully utilized to infer learner context and engagement.
Learners reported satisfaction with the system, indicating its potential for educational applications.