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A Real-Time Data Mining Approach for Interaction Analytics Assessment: IoT Based Student Interaction Framework

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Abstract

Students’ interaction and collaboration with the fellows and teachers using the Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of the educational assessment for students’ interaction. As new learning styles develop, new tools and assessment methods are also needed. The focus in this paper is to develop IoT based interaction framework and analysis of the student experience in electronic learning (eLearning) so that the students can take full advantage of the modern interaction technology and their learning can increase to a high level. This setup has a data collection module, which is implemented using Visual C# programming language and computer vision library. The number of faces, number of eyes, and status of eyes are extracted from the video stream, which is taken from a video camera. 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 consider student-learning behaviors when assessing electronic learning strategies. The tools are also developed for the data collection on both student and teacher ends. Correlation of data and hidden meaning are extracted to make the learning experience and teaching performance better and adaptable. IoT based infrastructure provides the facilities to fellow students about location awareness, fellows’ accessibility, social behavior and helping hand.

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Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal, Pakistan

    Muhammad Farhan

  2. Department of Computer Science, National Textile University, Faisalabad, Pakistan

    Sohail Jabbar

  3. Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan

    Muhammad Aslam

  4. Department of Information and Communication Engineering, Yeungnam University, Gyeongbuk, 280 Daehak-Ro, Gyeongsan, 38541, Republic of Korea

    Awais Ahmad

  5. Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

    Muhammad Munwar Iqbal

  6. Sarhad University of Information Technology, Peshawar, Pakistan

    Murad Khan

  7. Department of CS, CINVESTAV-IPN, Mexico City, Mexico

    Martinez-Enriquez Ana Maria

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  1. Muhammad Farhan
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  2. Sohail Jabbar
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  3. Muhammad Aslam
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  5. Muhammad Munwar Iqbal
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  7. Martinez-Enriquez Ana Maria
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Correspondence to Sohail Jabbar.

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Farhan, M., Jabbar, S., Aslam, M. et al. A Real-Time Data Mining Approach for Interaction Analytics Assessment: IoT Based Student Interaction Framework. Int J Parallel Prog 46, 886–903 (2018). https://doi.org/10.1007/s10766-017-0553-7

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