IoT-based students interaction framework using attention-scoring assessment in eLearning

• Published in: Future generation computer systems 2018-02, Vol.79, p.909-919
• Main Authors: Farhan, Muhammad, Jabbar, Sohail, Aslam, Muhammad, Hammoudeh, Mohammad, Ahmad, Mudassar, Khalid, Shehzad, Khan, Murad, Han, Kijun
• Format: Article
• Language: English
• Subjects: Interaction in eLearning; Internet of Things (IoT); IoT interoperable services; Learning behavior; Learning management system (LMS); Visual attention
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2017.09.037

Students’ interaction and collaboration using Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of educational assessment. As new learning styles develop, new tools and assessment methods are also needed. The focus of this paper is to develop IoT-based interaction framework and analysis of the student experience of electronic learning (eLearning). The learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms. An attention-scoring algorithm, its workflow, and the mathematical formulation for the smart assessment of the student learning experience are established. This setup has a data collection module, which can be reproduced by implementing the algorithm in any modern programming language. Some faces, eyes, and status of eyes are extracted from video stream taken from a webcam using this module. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to take student learning behaviors into account when assessing electronic learning strategies. •The focus is to develop IoT based interaction framework.•Next is the analysis of the student experience of electronic learning.•The learning behaviors of students attending remote video lectures are assessed.•Their behaviors are logged and the resulting multimedia data is analyzed.•Attention-scoring algorithm is setup for assessment of student learning experience.