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

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, n...

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Vydané v:International journal of parallel programming Ročník 46; číslo 5; s. 886 - 903
Hlavní autori: Farhan, Muhammad, Jabbar, Sohail, Aslam, Muhammad, Ahmad, Awais, Iqbal, Muhammad Munwar, Khan, Murad, Maria, Martinez-Enriquez Ana
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.10.2018
Springer Nature B.V
Predmet:
Computer Science
Computer vision
Data collection
Data mining
Distance learning
Eye (anatomy)
Infrastructure
Internet of Things
Learning
Online instruction
Processor Architectures
Programming languages
Software Engineering/Programming and Operating Systems
Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm
Students
Teachers
Theory of Computation
Video data
Visual C
Visual programming languages
Internet of Things (IoT)
Parallel machine learning
Parallel implementation
Visual attention
IoT interoperable services
ISSN:0885-7458, 1573-7640
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Shrnutí: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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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-017-0553-7