International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)

IJCRSEE Editorial Team; Nikola Gligorijević; Sonja Popović; Vojkan Nikolić; Dejan Viduka; Stefan Popović

doi:10.23947/2334-8496-2025-13-3-719-735

Authors

Nikola Gligorijević Faculty of Information Technologies, Alfa BK University, Belgrade, Serbia https://orcid.org/0009-0001-1307-0839
Sonja Popović Faculty of Mathematics, University of Belgrade, Belgrade, Serbia https://orcid.org/0000-0001-8169-8866
Vojkan Nikolić Department of Information Technology, University of criminal investigation and police studies, Belgrade, Serbia https://orcid.org/0000-0002-9230-7549
Dejan Viduka Faculty of Mathematics and Computer Sciences, University Alfa BK https://orcid.org/0000-0001-9147-8103
Stefan Popović Faculty of Mathematics and Computer Sciences, Alfa BK University, Belgrade, Serbia https://orcid.org/0000-0002-5288-4560

DOI:

https://doi.org/10.23947/2334-8496-2025-13-3-719-735

Keywords:

Educational technology, Artificial intelligence, Computer vision, Multi-criteria decision making (MCDM) and Algorithm evaluation

Abstract

Artificial Intelligence (AI) and computer vision technologies are increasingly integrated into educational environments through intelligent tutoring systems, gesture-based learning, facial expression analysis, and automated evaluation tools. However, selecting the most appropriate image recognition algorithms for educational applications remains a challenge due to varying requirements regarding speed, accuracy, hardware compatibility, and usability in dynamic classroom conditions.This paper proposes a hybrid multi-criteria decision-making (MCDM) model based on the Step-wise Weight Assessment Ratio Analysis (SWARA) and Net Worth Analysis (NWA) methods to evaluate and rank nine widely used AI-based visual recognition algorithms. The evaluation is conducted using five education-relevant criteria: processing speed, recognition accuracy, robustness to classroom noise, compatibility with low-end devices, and energy efficiency. Expert assessments from the field of educational technology were used to derive weight coefficients and evaluate algorithm performance.The results show that Fast R-CNN achieved the highest overall score (1.141), followed by U-Net (1.077) and DeepLab (1.062), indicating their suitability for real-time and resource-constrained EdTech environments. Algorithms such as MobileNet (1.057) and YOLO (1.037) also demonstrated balanced performance, making them viable for mobile or moderately demanding educational scenarios. The proposed model offers a structured and transparent decision-support framework that can assist researchers and practitioners in selecting optimal AI algorithms for diverse educational applications.

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