Applying Deep Learning and Wearable Devices for Educational Data Analytics
Zhenxing Zhou, Vincent W. L. Tam, Ki Lui, Edmund Y. M. Lam, Allan H. K. Yuen, Xiao Hu, Nancy Wai Ying Law · 2019
With the popularity of wearable devices, smart watches containing various sensors have been widely adopted for many healthcare applications. Yet there is rarely any research study on the possible uses of smart watches for learning analytics, particularly for analyzing students' learning activities through the physiological and/or movement data collected on their smart watches. This paper considers a pioneering and sophisticated learning analytics platform using fine-tuned deep learning models to predict students' learning activities based on the real-time data, including their heart rates, calories, three-axis accelerometer and gyroscope data, captured on wearable devices and then uploaded onto a cloud server for thorough analyses. To validate on the actual activities conducted by each student, an intelligent mobile application is developed to push instant notifications for students to report their own activities whenever the change of heart rates are deviated significantly from their normal values. Based on students' heart rates and calories, a long-short term memory (LSTM) model is built to classify students' learning states as active or not with an impressive prediction accuracy of 95% whereas another hybrid model combining both the LSTM and convolutional neural networks attains the highest prediction accuracy of 74% to predict students' specific learning activities as based on their physiological and movement data. The prototype implementation clearly demonstrates the feasibility of the proposed framework for learning analytics. More importantly, this work shed lights on various directions including the integration of noise filters to preprocess the collected data for further investigation.