Comparison of algorithms for the assessment of student attention in online learning systems

AUTHOR(S)

Anu Joseph, Gigi Joseph, Cini Joseph

DOI: https://doi.org/10.46647/ijetms.2023.v07i04.080

ABSTRACT
Various algorithms are used in online learning systems to compare students' attention in the classroom. Two algorithms are compared in this work. The first algorithm assesses student attentiveness in the classroom by examining their facial expressions. In order to determine if students are paying attention or not during the electronic learning process and to identify instances of academic dishonesty, this algorithm uses an interactive video-capture facial recognition technology. The Classroom Attentiveness Classification Tool (ClassACT), a system created to track student attention during several instructional stages within the learning environment, including lectures, group projects, evaluations, etc., is the second algorithm. ClassACT can tell the difference between attentive and inattentive behaviour by gathering data about the user, the user's surroundings, and the device itself using the many sensors integrated into the tablet. This essay contrasts these two algorithms in terms of methodology and outcomes.

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References:

1.  Duchowski, “Eye tracking methodology: Theory and practice,” NY: Springer, 2007.
2.  T. P. N. a. C. A. Graves, “Classroom Attentiveness Classification Tool: The System Introduction,” in IEEE International Conference on Pervasive Computing and Communications, 2017.
3.  H.-R. CHEN, “Assessment of learners’ attention to e-learning by monitoring facial expressions for computer network courses,” Educational Computing Research, vol. Vol. 47(4), pp. 371-385, 2012.
4. C. A. Graves, “Studying smart spaces using an ’embiquitous’ computing analogy,” IEEE Pervasive Computing, vol. 14.2, pp. 64-68, 2015.
5. A. &. P. R. W. Hyungil, “Affective-cognitive learning and decision making: A motivational reward framework for affective agents,” First International Conference on Affective Computing and Intelligent Interaction, pp. 866-873, 2005.
6. K. Hylton, “Utilizing webcam-based proctoring to deter misconduct in online exams,” Computers and Education, Vols. 92-93, p. 53–63, 2016.
7. N. H. P. Z. B. C. C. C. a. R. N. J. L. Popyack, “Academic dishonesty in a high-tech environment,” ACM SIGCSE Bulletin, vol. 35, no. 1, pp. 357-358, 2003.
8. O. L. M. a. E. W. Wild, “Proximal support vector machine classifiers,” in Proc. KDD-2001: Knowledge Discovery and Data Mining, 2001.
9. S. Haykin, “Neural Networks: A Comprehensive Foundation,” in 2nd ed. Upper Saddle River, NJ: Prentice-Hall 1999




How to Cite This Article:
Anu Joseph, Gigi Joseph, Cini Joseph . Comparison of algorithms for the assessment of student attention in online learning systems . ijetms;7(4):587-592. DOI: 10.46647/ijetms.2023.v07i04.080