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With the aim of capturing learner engagement, we propose and advocate the use of commodity wearable devices and their built-in sensors (e.g., accelerometer, gyroscope, and magnetic sensor) to detect the fine-grained learning activities (e.g., writing notes or raising the hand in class). Next, by leveraging the established theory that links learner activities to learner engagement, the detected learner activities can be used to further infer the learner engagement levels, durations, and other key information. We thus designed a hassle-free and non-intrusive system running on the latest wrist-worn commodity wearable devices, which adopts the latest activity recognition and sensor data fusion techniques. We conducted the system-level evaluation, survey, and interviews involving both students and teachers. The evaluation results show that our system can accomplish the accurate learner activity recognition task, and meanwhile effectively capture the learner engagement. We also provide the engagement-based intervention service during class to illustrate the unique usefulness of the proposed system.

Harnessing Commodity Wearable Devices to Capture Learner Engagement