Next Article in Journal
Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns
Previous Article in Journal
QuickCash: Secure Transfer Payment Systems
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(6), 1382;

A Framework for Learning Analytics Using Commodity Wearable Devices

Advanced Innovation Center for Future Education, Beijing Normal University, Beijing 100875, China
Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore
School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China
School of Electronic and Electrical Engineering, University of Leeds, Leeds LS29JT, UK
Author to whom correspondence should be addressed.
Academic Editor: Stefan Poslad
Received: 27 March 2017 / Revised: 31 May 2017 / Accepted: 6 June 2017 / Published: 14 June 2017
(This article belongs to the Section Sensor Networks)
Full-Text   |   PDF [3738 KB, uploaded 14 June 2017]   |  


We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner’s physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55). View Full-Text
Keywords: wearable sensors; learning analytics; pervasive computing; activity recognition wearable sensors; learning analytics; pervasive computing; activity recognition

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Lu, Y.; Zhang, S.; Zhang, Z.; Xiao, W.; Yu, S. A Framework for Learning Analytics Using Commodity Wearable Devices. Sensors 2017, 17, 1382.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top