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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Original scientific paper
Received: July 15, 2025.
Revised: August 29, 2025.
Accepted: September 30, 2025.
UDC:
37.091.39:004.85
10.23947/2334-8496-2025-13-3-719-735
© 2025 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*
Corresponding author:
dejan.viduka@alfa.edu.rs
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 evalu-
ation 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 class-
room 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 per-
formance.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 mod-
erately demanding educational scenarios. The proposed model offers a structured and transparent decision-support frame-
work that can assist researchers and practitioners in selecting optimal AI algorithms for diverse educational applications.
Keywords: Educational technology, Artificial intelligence, Computer vision, Multi-criteria decision making (MCDM)
and Algorithm evaluation.
Nikola Gligorijević
1
, Sonja Djukić Popović
2
, Vojkan Nikolić
3
, Dejan Viduka
1*
, Stefan Popović
4
1
Faculty of Information Technologies, Alfa BK University, Belgrade, Serbia;
e-mail:
nikola.gligorijevic@alfa.edu.rs;dejan.viduka@alfa.edu.rs
2
Faculty of Mathematics, University of Belgrade, Belgrade, Serbia; e-mail: sonjica27@yahoo.com
3
Department of Information Technology, University of criminal investigation and police studies, Belgrade, Serbia;
e-mail:
vojkan.nikolic@kpu.edu.rs
4
Faculty of Mathematics and Computer Sciences, Alfa BK University, Serbia; e-mail:
stefan.popovic@alfa.edu.rs
A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image
Recognition Algorithms in Educational Technology Applications
Introduction
In the past decade, artificial intelligence (AI) technologies have become a key component of the
educational sector, enabling personalized instruction, automated assessment, student behavior detec-
tion, and real-time learning analytics (Rane et al., 2023; Chen et al., 2020; Li et al., 2023). A particularly
significant role in this development is played by computer vision algorithms, which are applied in handwrit-
ing recognition, facial expression analysis, student attendance tracking, and gesture-based interactive
learning (Zabulis, Baltzakis and Argyros, 2009). However, these algorithms are predominantly developed
for industrial applications, and their direct use in education is often hindered by requirements such as
low hardware demand, interpretability, and energy efficiency. Modern educational institutions, especially
those implementing smart classrooms or mobile EdTech platforms, face the challenge of selecting AI al-
gorithms that are both pedagogically appropriate and technically feasible (Dimitriadou and Lanitis, 2023).
In the context of Serbia’s ongoing digital educational reform, these challenges gain further com-
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
plexity. The National Education Development Strategy of the Republic of Serbia until 2030 envisions an
intensive digitalization of the teaching process, with an emphasis on improving digital competencies of
teachers and students, as well as the integration of digital tools into everyday classroom practice. During
the COVID-19 pandemic, the mandatory use of learning management systems (LMS) at the school level,
marked the beginning of a systemic digital transformation in the education sector (
Ibrahim et al., 2020). In
this direction, the e-Dnevnik platform has been widely adopted for electronic documentation of pedagogi-
cal processes, while the Unified Information System of Education (JISP) has become a crucial tool for
monitoring and analyzing educational data at the national level (Krstev et al., 2024).
Additionally, the conference “The World Ahead of Us – Education in the Era of Artificial Intelligence,”
held at the Palace of Serbia, highlighted the latest advancements in AI applications in education, with
particular emphasis on the potential of personalized learning, intelligent tutoring systems, and large-scale
educational data analytics. These developments underscore the growing need for the development and
evaluation of AI algorithms (Kayal, 2024) that are not only technologically efficient but also aligned with ped-
agogical principles and infrastructural limitations of schools. In line with this, Adžić and colleagues (2024)
provide evidence that the acceptance of AI, particularly generative tools, varies across different educational
contexts, further emphasizing the necessity for transparent and adaptable evaluation frameworks.
The selection process is further complicated by the fact that decision-makers are often not technical
experts, which highlights the need for structured, transparent, and replicable evaluation models (Sampson
et al., 2019). In this context, the present study proposes a hybrid multi-criteria decision-making (MCDM)
framework that integrates the SWARA (Step-wise Weight Assessment Ratio Analysis) method with Net
Worth Analysis (NWA) to evaluate and rank nine contemporary AI algorithms for visual recognition. Draw-
ing on domain expertise in educational technology, the proposed model enables the assessment of algo-
rithms based on criteria that reflect the actual needs of the education system: processing speed, accuracy,
robustness to noise, energy efficiency, and suitability for low-end hardware (Tariq et al., 2024).
This approach addresses a gap in the current literature, as most previous studies applying MCDM
methods have focused on the selection of e-learning platforms, curricula, or teaching methodologies,
while the selection of AI algorithms for educational purposes remains underexplored. The proposed model
facilitates a balance between technical performance and educational usability, serving as a practical tool
for evidence-based decision-making in the process of educational digital transformation.
Theoretical Framework
In the field of educational engineering, decision-making is increasingly guided by quantitative and
systematic approaches, particularly in the selection of technologies, teaching methodologies, educational
tools, and infrastructural solutions (Buenaño-Fernandez et al., 2019). Within this context, Multi-Criteria
Decision Making (MCDM) has emerged as a particularly valuable framework, enabling the evaluation
of multiple alternatives based on a variety of often conflicting criteria (Massam, 1988). MCDM methods
have proven useful in non-trivial decision-making scenarios that require alignment between technical,
pedagogical, organizational, and economic considerations, a fact supported by numerous educational
studies (Malik et al., 2021). Recent examples include: Toan et al. (2021), who employed a hybrid MCDM
approach for evaluating e-learning platforms in higher education; Keshavarz-Ghorabaee et al. (2018),
who demonstrated the utility of SWARA and AHP models in selecting digital teaching tools; and Chen and
Luo (2023), who illustrated the effectiveness of fuzzy logic-based MCDM models in assessing teaching
quality. Similarly, Mahmoodi et al. (2025) applied MCDM methods to the selection of teaching strategies
in STEM education, while Troussas et al. (2025) found TOPSIS and VIKOR techniques beneficial in plan-
ning educational infrastructure for primary schools. Marinović et al. (2025) published research optimizing
the selection of operating systems within educational contexts.
The Role of MCDM Methods in Educational Contexts
Educational engineering involves the application of engineering principles, systems theory, and
design methodologies to the educational process (
Dym, 2004).
As institutions face increasingly complex
decisions, such as the selection of e-learning platforms, learning analytics tools, digital content, and AI algo-
rithms, there is a growing need for transparent and replicable evaluation methods (
Colchester et al., 2017)
.
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
MCDM methods allow for a structured analysis of alternatives using sets of both quantitative and qualitative
criteria (Sahoo and Goswami, 2023). Their value in education is particularly evident in the following cases:
• Assessment of digital learning tools and platforms,
• Evaluation and selection of adaptive learning software,
• Design of data-driven curricula and instructional strategies,
• Ranking of teaching methods based on learning outcomes,
• Selection of technologies for smart classrooms or AR/VR systems,
• Evaluation of AI algorithms for behavior analysis, automated grading, or visual recognition.
Over the past decade, the application of MCDM approaches in education has grown exponentially,
both in research and practice, due to their capacity to integrate expert judgment with available data
(
Nguyen, 2024). This trend is further substantiated by recent studies that emphasize the integration of
artificial intelligence in education, such as the work by Milićević et al. (2024), which outlines current chal-
lenges in the implementation of AI in Serbia’s educational system, and Tsankov and Levunlieve (2024),
who examine the structured design of digital content in early childhood education.
Beyond academic insights, concrete shifts in Serbia’s educational landscape further highlight the
relevance of MCDM approaches. The introduction of the Unified Information System of Education (JISP),
mandatory use of the e-Dnevnik platform, and the nationwide transition to accredited LMS systems il-
lustrate the complexity of selecting technologies that must meet pedagogical, technical, security, and
regulatory requirements. By applying MCDM methods, school administrators, ministries, and educational
experts can make informed, rational, and well-documented decisions while balancing trade-offs between
competing factors such as cost, accessibility, scalability, local support, and personalization capacity
(
Popović and Popović, 2021; Popović et al., 2022).
Moreover, educational development strategies, not only in Serbia and the Western Balkans, but
across developed countries, underscore the need for methodological frameworks that support the evalua-
tion and selection of AI solutions in line with strategic goals and the real capacities of educational systems.
In this context, the integration of MCDM methods such as SWARA, AHP, TOPSIS, and NWA is not only
desirable but essential for the systemic digital transformation of education.
Most Commonly Used MCDM Methods in Education
The most frequently used MCDM methods in educational research and practice include: Analyt-
ic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS),
Step-wise Weight Assessment Ratio Analysis (SWARA), Preference Ranking Organization Method for
Enrichment Evaluations (PROMETHEE), and PIvotal Point of the Relative Criteria Impact Assessment
(PIPRECIA). These methods provide structured and systematic evaluations of multiple alternatives based
on a range of criteria-a particularly important feature in educational settings where decisions often involve
selecting technologies, curricula, instructional strategies, or student assessment methods. Their applica-
tion facilitates more transparent, objective, and adaptable decision-making by addressing the inherent
complexity of the educational process and incorporating the diverse perspectives of stakeholders (
Vien-
net and Pont, 2017). In recent years, there has been an increasing number of studies combining MCDM
methods with expertise in educational technology to optimize tools and strategies (Alshamsi et al., 2023).
Analytic Hierarchy Process (AHP)
AHP is one of the most widely used methods, designed for hierarchically structuring problems
and performing pair wise comparisons of criteria and alternatives. Its popularity in education stems from
its ability to break down complex decisions into smaller, logically related components that are easier to
analyze. The method supports expert subjective judgments and converts them into quantitative values,
enabling the analysis of qualitative aspects within education. As such, AHP has become an important tool
for strategic educational decision-making across all levels of governance (
Vaidya and Kumar, 2006).
Common educational applications include:
• Selection of distance learning platforms,
• Prioritization of educational objectives,
• Evaluation of educational program quality.
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Its advantages lie in its systematic structure and analytical rigor, while its limitations include com-
plexity when handling many criteria and the requirement to check consistency.
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
TOPSIS identifies the optimal solution as the one closest to the ideal and farthest from the worst
case scenario. This method is especially useful in education due to its clear and intuitive ranking of alter-
natives against predefined criteria. Its ability to balance between positive and negative ideal values makes
it suitable for decision making situations that require compromise, which is often the case in educational
practice. Because of its flexibility and transparency, TOPSIS has become a favored tool among research-
ers and policy makers in the education sector (
Behzadian et al., 2012).
Applications include:
• Ranking teaching strategies,
• Selection of technologies in higher education,
• Evaluation of educational applications.
While TOPSIS offers intuitive interpretation, it relies on proper data normalization and accurate weighting.
Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)
PROMETHEE uses preference functions and provides flexibility in assessing qualitative criteria.
It is particularly suitable for complex educational problems involving multiple alternatives with differing
characteristics. Its ability to process both quantitative and qualitative data makes it valuable for analyses
involving subjective values, such as pedagogical approaches, student motivation, or the interactivity of
learning materials. PROMETHEE also offers visualization through GAIA planes, facilitating decision mak-
ing in educational institutions (Brans and Mareschal, 2005).
It is commonly applied in:
• Selecting teaching methodologies,
• Choosing courses within study programs,
• Analyzing educational scenarios.
However, it requires expertise in defining preference functions, which can be challenging for non-experts.
Step-wise Weight Assessment Ratio Analysis (SWARA)
SWARA is designed to determine weighting coefficients based on expert opinion. Its strength lies
in its ability to transparently convert qualitative expert assessments into quantitative values suitable for
MCDM analysis. It is particularly applicable in educational contexts where exact data may be lacking, and
expert knowledge is the primary decision making resource. For these reasons, SWARA was selected as
the most appropriate method for achieving the research goals of this study (
Keršulienė et al., 2010).
Advantages include:
• Ease of application,
• Suitability for expert-driven contexts,
• Support for consensus-based decision-making.
In educational engineering, SWARA is used to:
• Prioritize learning quality factors,
• Evaluate technology selection criteria,
• Determine the importance of competencies in curricula.
Its intuitive structure and lower cognitive load make SWARA especially suitable for educational
institutions.
PIvotal Point of the Relative Criteria Impact Assessment (PIPRECIA)
PIPRECIA uses a penalization approach to determine the relative importance of criteria. Unlike
traditional weighting methods, it incorporates the influence of each subsequent evaluation through a
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
penalization mechanism, enabling more precise prioritization. It is thus well suited for complex decisions
involving numerous criteria and stakeholders. In academic environments, PIPRECIA has proven to be a
fast and effective method that delivers consistent results with minimal cognitive burden for experts. Its
strength lies in its efficiency when dealing with large sets of criteria and in providing timely results. It is
increasingly applied in educational studies, particularly for evaluating policies, strategies, and new cur-
ricular designs (Pamučar and Ćirović, 2015).
Integration of MCDM Methods with Educational Technology Systems
In the context of Serbia’s digital transformation of education, there is a pressing need for multi-cri-
teria decision models that combine expert knowledge with quantitative data. The proposed hybrid model
integrates AHP, TOPSIS, PROMETHEE, SWARA, and PIPRECIA, enabling comprehensive and flexible
analysis of educational decisions. AHP structures the problem and determines weights; TOPSIS identifies
optimal solutions based on proximity to ideal values; PROMETHEE addresses subjective aspects through
preference functions; SWARA facilitates the integration of expert opinions; and PIPRECIA allows efficient
evaluation through penalization. This model can be applied to selecting digital platforms, evaluating AI
solutions, budgeting for EdTech, and supporting school level digital decision-making. It is compatible
with tools such as MATLAB, Excel, Python libraries, and interactive web platforms, offering visualization,
interactive analysis, and real-time adaptability. The SWARA–NWA approach further balances subjective
expert insight with objective algorithm performance, enhancing methodological transparency and applica-
bility in educational practice.
Relevance for Selecting AI Algorithms in Education
Most existing research on MCDM in education focuses on the selection of platforms, learning
methods, or instructional content. However, the growing use of artificial intelligence and computer vision
in educational applications, such as automated grading, visual analytics, and behavior detection, neces-
sitates the use of MCDM techniques to support the selection of appropriate AI algorithms.
This study addresses that gap by:
• Defining criteria relevant to the educational context (e.g., computational efficiency, pattern accuracy,
robustness to noise),
• Applying a well-established hybrid MCDM approach (SWARA–NWA),
• And evaluating a set of widely-used computer vision algorithms that are increasingly adopted in edu-
cational systems.
Methodology
This study (see Figure 1) employs a hybrid Multi-Criteria Decision-Making (MCDM) approach, com-
bining the Step-wise Weight Assessment Ratio Analysis (SWARA) method for determining the relative
importance of evaluation criteria and the Net Worth Analysis (NWA) method for calculating overall scores
and ranking AI algorithms in an educational technology context.
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Define evaluation criteria
Criteria relevant to E d Tech context, including hardware adaptability,
processing speed, accuracyrobustness, and energy efficiency
Expert panel formation
Assembling a Delphi-basedexpert groupto provide consistent, informed
input
Apply SWARA method
Experts rank the criteria, performance with respect to each criterion as
SWARA procedurey
Assess algorithm performance
Experts evaluate each algorithm’s performance with respect to each
criterion
Apply NWA method
Weighted scores are computed for each algorithm using SWARA-
defined weightsand expert evaluations
Figure 1. Flowchart of the SWARA-NWA Hybrid Evaluation Framework
Source: Author’s research
Steps of the SWARA Method
The SWARA method (Keršulienė et al., 2010) enables experts in the fields of education and artificial
intelligence to express their judgments regarding the relative importance of predefined evaluation criteria.
1. Criteria Prioritization
Experts rank the criteria in descending order of importance-from the most to the least significant.
(This is a qualitative step that precedes the quantitative analysis.)
2. Relative Importance (s
i
)
Starting from the second criterion, experts estimate how much less important each criterion is in
comparison to the previous one:
S
i
= relative importance of criterion j with respect to criterion j–1
S
1
= 0 (since there is no preceding criterion for the first one)
3. Correction Coefficients
Correction coefficients are calculated using the formula:
k
i
= s
i
+ 1 = s
j
+ 1,
where k
i
= 1 and j = 2, 3, … ,
4. Recalculated Weights (q
j
)
a) Raw weights (w
j
):
w
j
= 1
w
j
=
for j = 2,3,... ,n
b) Normalized weights (q
j
):
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Criteria Definition and Expert Panel
For the purpose of this research, five criteria were defined as particularly relevant for educational settings:
• Computational efficiency (e.g., hardware requirements and startup speed),
• Processing speed (real-time execution performance),
• Pattern recognition accuracy (correct identification of handwriting, symbols, or educational elements),
• Robustness to noise (e.g., variations in lighting and background),
• Energy efficiency (power consumption in mobile or IoT environments).
To obtain reliable expert consensus, the Delphi method was employed, a widely recognized itera-
tive technique for structured elicitation and aggregation of expert opinion under conditions of uncertainty
and subjectivity (
Hsu and Sandford, 2007).
Application of the NWA Method
Following the determination of criteria weights using the SWARA method, the Net Worth Analysis
(NWA) approach (
Zangemeister, 1976) was applied to aggregate performance values and compute the
final score for each algorithm.
The overall score S for each algorithm A is calculated using the following formula:
Where:
• S represents the total score of algorithm i,
• q
j
is the normalized weight of criterion j (from SWARA),
• v
j
is the performance of algorithm i with respect to criterion j,
• n is the total number of criteria.
The performance values v
j
were assigned by domain experts based on a review of prior studies,
technical documentation, and practical use in educational tools.
Method Integration
The structured hybrid model ensures transparency and reproducibility in the evaluation process by
combining subjective expert judgments with quantitative aggregation (
Zellner et al., 2021). The integration
of SWARA and NWA allows expert opinions to be transformed into numerical scores, facilitating objective
comparison between alternatives while preserving flexibility in assessing qualitative dimensions.
This dual-layered approach provides a balanced interplay between analytical formalism and do-
main-specific expertise, which is particularly vital in educational contexts where evaluation criteria are
often multidimensional and interdependent. The resulting output is a ranked list of algorithms according to
their suitability for deployment within educational technology systems, enabling decision-makers to identify
the most effective options aligned with the specific constraints and pedagogical needs of their institutions.
Evaluation Criteria
The effective integration of artificial intelligence into educational systems requires the careful selec-
tion of algorithms that are not only technically advanced but also aligned with pedagogical contexts and
infrastructural constraints (Kaddouri et al., 2025). This study defines five key evaluation criteria that reflect
the practical needs of educational institutions, particularly in the context of smart classrooms, mobile
learning, and digital platforms with limited hardware capabilities. A similar approach is evident in studies
on the integration of sensors and AI in smart classrooms, which emphasize the need to balance pedagogi-
cal, infrastructural, and technical dimensions (Mircea et al., 2023).
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Computational Efficiency
Many primary and secondary schools, as well as educational institutions in rural or underdevel-
oped regions, lack access to advanced hardware infrastructure. Thus, one of the fundamental evaluation
criteria is the algorithm’s ability to operate on low-resource devices, such as older laptops, tablets, or
cost-efficient IoT platforms.
Evaluation dimensions include:
• Memory efficiency (RAM, GPU usage),
• Minimal system requirements for deployment,
• Compatibility with mobile and embedded systems (e.g., Raspberry Pi, Android devices).
Algorithms that rely on complex deep neural networks with high inference costs may be unsuitable
in educational environments where access to high-performance GPUs is limited.
Processing Speed
Many EdTech applications, especially those used for interactive learning, student monitoring, or
automated assessment, require algorithms capable of real-time data processing. Latency in system re-
sponse can negatively affect user experience, student engagement, and instructional effectiveness.
Assessment is based on:
• Average inference time per sample,
• Real-time processing capability of video streams,
• System responsiveness to user input in interactive applications.
Particular emphasis is placed on algorithms that maintain performance when handling live video or
image sequences, which is essential for smart classroom scenarios and augmented reality (AR) applications.
Accuracy
AI algorithms in education must reliably recognize patterns specific to educational activities, includ-
ing handwritten text, mathematical expressions, student gestures, diagrams, and physical objects used
in STEM instruction.
This criterion refers to:
• Ability to accurately classify and segment relevant educational elements,
• Reduction of false positives and false negatives,
• Reliability in evaluating student work.
Accuracy is measured through both technical performance metrics and practical deployments in
real-world educational scenarios. Explainability and transparency remain crucial, especially when AI sys-
tems influence pedagogical or administrative decisions. Though not limited to education,
Spalević et al.
(2024) highlight the importance of explainable AI in sensitive systems, a principle that equally applies to
the classroom environment.
Robustness to Environmental Noise
Educational environments, particularly mobile learning contexts, often lack controlled lighting con-
ditions. A tablet camera used in a hallway, playground, or brightly lit classroom may capture low-quality
images. Therefore, it is essential that algorithms remain stable and accurate under variable conditions.
This criterion includes:
• Resistance to changes in lighting (e.g., overexposure, shadows),
• Ability to extract relevant features from images with complex backgrounds,
• Robustness when handling noise and degraded input quality.
Robust algorithms offer greater flexibility and ensure consistent reliability in everyday teaching
scenarios.
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Energy Efficiency and Sustainability
Modern education increasingly relies on mobile and IoT technologies. In smart classrooms, devices
such as interactive boards, cameras, sensors, and student-owned equipment often depend on battery
power or limited energy sources.
The following aspects are particularly valued:
• Energy consumption during algorithm execution (e.g., on smartphones, tablets, or Raspberry Pi devices),
• Battery life under continuous operation,
• Suitability for “green computing” initiatives in education.
Energy efficiency is regarded as a critical sustainability metric, both from an economic perspective
and in terms of environmental responsibility, including the reduction of CO
2
emissions.
Case Study
To ensure that the evaluation of AI algorithms for educational purposes yields relevant and ap-
plicable results, a case study was conducted involving a panel of experts specializing in educational
technology and the application of artificial intelligence in education. Expert judgment constitutes a fun-
damental component of the SWARA methodology, as both the weights of the evaluation criteria and the
performance assessments of algorithms are grounded in professional expertise and consensus.
Selection and Profile of Experts
For the purposes of this study, a panel of seven experts was assembled, each with substantial
experience across different domains of educational technology.
The selection criteria for expert inclusion were as follows:
• A minimum of five years of professional experience in the development or implementation of EdTech
solutions, or in teaching within the fields of information technology and computer science;
• Active involvement in research or development of AI applications in education;
• Proficiency in computer vision, adaptive learning systems, or smart classroom technologies.
The composition of the expert panel was as follows:
• 2 university professors with expertise in artificial intelligence and practical experience in higher educa-
tion instruction,
• 2 EdTech software engineers involved in the development of adaptive learning and assessment platforms,
• 1 researcher in the field of human-computer interaction and student behavior analysis,
• 1 computer science teacher with practical experience in applying AI tools in classroom settings,
• 1 education policy specialist focused on digital transformation and inclusive education initiatives.
This interdisciplinary composition enabled the evaluation process to integrate diverse perspectives,
ranging from instructional and practical viewpoints to research-oriented and strategic dimensions.
Evaluation Procedure and Consensus Building
The evaluation was conducted in two phases using the Delphi method, allowing for iterative con-
sensus development while preserving the anonymity of individual expert responses.
Phase one involved individual assessment of the following:
• The relative importance of each of the five predefined evaluation criteria (via the SWARA method),
• The performance of each algorithm according to the specified criteria (via the NWA method).
Experts used a five-point scale (where 5 indicated the highest possible value) and were encour-
aged to provide additional qualitative comments and recommendations.
In phase two, aggregated results from the first round were presented to the experts, who were then
given the opportunity to revise their initial evaluations in light of observed trends. This procedure resulted
in a high level of consensus without the influence of dominant individuals. All responses remained anony-
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
mous, and communication was facilitated through an online platform specifically designed for structured
survey-based expert elicitation.
Data Validation and Reliability
To ensure data reliability, several control measures were implemented:
• Consistency checks among expert responses (standard deviation < 0.6 in the majority of cases),
• Cross-referencing with published literature and technical documentation for each algorithm,
• Technical briefings that included concise presentations and practical examples of each algorithm’s
potential use in educational scenarios.
Based on expert assessments and the validated dataset, the SWARA method was applied to cal-
culate the criterion weights, followed by the application of the NWA method to derive the final score and
ranking for each algorithm.
Results and Discussion
This section presents and interprets the findings obtained through the application of the hybrid
SWARA–NWA model for evaluating nine artificial intelligence algorithms within the context of educational
technology. The analysis was conducted in three stages: determining the weights of evaluation criteria,
assessing algorithm performance according to each criterion, and calculating the total scores to establish
a final ranking.
Criterion Weights
Table 1 displays the results derived from the SWARA method, used to determine the relative im-
portance of five predefined evaluation criteria in the context of EdTech applications. Based on the input of
seven experts in educational technology and AI implementation in education, “computational efficiency”
was identified as the most significant criterion, receiving a subjective weight of 0.20 and a final normalized
weight (q
j
) of 0.250. This high value reflects the practical need for algorithms that can operate effectively
on older computers, tablets, and low-performance devices within educational institutions.
Table 1. SWARA-Based Weight Coefficients for Evaluation Criteria (Source: Author’s research)
Criterion (Sj) (Kj = Sj + 1) (Wj) (qj)
Computational efficiency 0.2 1.20 1 0.250
Processing speed 0.15 1.15 0.870 0.217
Pattern accuracy 0.12 1.12 0.776 0.194
Noise robustness 0.1 1.10 0.706 0.176
Energy efficiency 0.08 1.08 0.654 0.163
The second most important criterion, according to the experts, was “processing speed” (q
j
= 0.217),
as rapid system response is critical for user experience and instructional efficacy in interactive classrooms
and adaptive platforms. “Pattern accuracy” was ranked third (q
j
= 0.194), as it reflects the algorithm’s abil-
ity to reliably recognize handwriting, symbols, diagrams, and other educational elements. The remaining
two criteria, “robustness to noise” and “energy efficiency”, received slightly lower final weights (0.176 and
0.163, respectively), though still recognized as relevant, particularly in mobile learning environments and
scenarios with unfavorable lighting conditions.
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Figure 2. SWARA Criterion Evaluation Overview (Source: Author’s research)
These results clearly indicate that experts favored algorithms enabling broad and inclusive application
across diverse educational settings, consistent with the strategic aims of digital transformation in education.
Algorithm Performance Scores
Table 2 presents expert-assigned performance scores for the nine evaluated algorithms across all
five criteria. These scores served as input values for the NWA phase, allowing for a detailed comparison
prior to score aggregation.
Table 2. Expert Performance Scores of AI Algorithms According to Evaluation Criteria (Source: Author’s research)
Algorithm
Computational
Efficiency
Processing
Speed
Pattern Accuracy
Noise
Robustness
Energy Efficiency
Fast R-CNN 0.193 0.245 0.213 0.245 0.245
U-Net 0.193 0.213 0.245 0.213 0.213
DeepLab 0.178 0.213 0.245 0.213 0.213
MobileNet 0.245 0.213 0.193 0.193 0.213
ResNet 0.213 0.193 0.160 0.245 0.245
Binary Segmentation 0.245 0.245 0.213 0.178 0.160
YOLO 0.213 0.245 0.193 0.193 0.193
FCN 0.193 0.193 0.178 0.213 0.178
EfficientDet 0.245 0.160 0.160 0.160 0.160
The analysis shows that Fast R-CNN received the highest scores in nearly all categories, par-
ticularly in processing speed, robustness, and energy efficiency (0.245 each). Although not the lightest
in terms of hardware requirements, its score of 0.193 for computational efficiency suggests acceptable
resource demands.
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Figure 3. Algorithm Evaluation Across Criteria (Source: Author’s research)
U-Net and DeepLab excelled in pattern recognition accuracy (0.245), making them particularly
suitable for educational tasks requiring precise interpretation of visual content, such as diagrams and
handwritten assignments.
MobileNet performed strongly in computational efficiency (0.245) and energy balance (0.213), ren-
dering it well-suited for BYOD (Bring Your Own Device) strategies and mobile classrooms.
Interestingly, industrially successful models such as ResNet and EfficientDet performed relatively
poorly on education-specific criteria. EfficientDet, in particular, received the lowest scores across almost
all dimensions, suggesting that its deployment in education would require significant optimization. Binary
Segmentation, although very fast (0.245), underperformed in terms of robustness (0.178) and energy ef-
ficiency (0.160), limiting its utility in energy-constrained or dynamic environments.
Final Results
Table 3 presents the final NWA scores obtained by applying the weights from Table 1 to the expert
scores from Table 2. The algorithms are ranked from 1 to 9 according to their overall suitability.
Table 3. Final NWA Scores and Ranking of AI Algorithms (Source: Author’s research)
Algorithm Total Score Rank
Fast R-CNN 1.141 1
U-Net 1.077 2
DeepLab 1.062 3
MobileNet 1.057 4
ResNet 1.056 5
Binary Segmentation 1.041 6
YOLO 1.037 7
FCN 0.955 8
EfficientDet 0.885 9
Fast R-CNN achieved the highest total score (1.141), affirming its overall adaptability to the require-
ments of educational technology. It offers a favorable balance between speed, robustness, and energy
efficiency, with reasonable scalability.
U-Net (1.077) ranked second due to its high segmentation accuracy and solid performance across
other criteria. It is especially promising for tasks such as automatic evaluation of handwritten work and
visual analysis of student responses.
DeepLab (1.062) came in third, characterized by high precision and robustness, making it suitable
for AR/VR applications in education.
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
MobileNet (1.057) and ResNet (1.056) had nearly identical total scores. The former is recommend-
ed for mobile applications and low-power environments, while the latter performs reliably in unstructured
settings such as outdoor or field-based learning.
The remaining algorithms, FCN, Binary Segmentation, YOLO, and EfficientDet, showed limitations
across multiple criteria. While not inherently unsuitable, their deployment would require targeted adapta-
tion, hybrid integration, or use in narrowly defined tasks with specific performance requirements.
Unlike previous studies that employed MCDM methods to select e-learning platforms, curricula,
or teaching strategies, this research focuses on evaluating specific computer vision algorithms within
educational contexts. For instance, Toan et al. (2021) applied a hybrid AHP–COPRAS model for selecting
e-learning systems, and Keshavarz-Ghorabaee et al. (2018) used SWARA and AHP for evaluating digital
teaching tools. Marinović et al. (2025) employed the PIPRECIA method to optimize operating system
selection in schools. Compared to these approaches, the proposed SWARA–NWA framework enables
direct evaluation of AI algorithms based on education-specific criteria such as computational efficiency,
robustness to environmental noise, and operability on low-end hardware. This methodological distinction
makes the model particularly suitable for decision support in real-world EdTech scenarios, especially in
resource-constrained educational settings.
Research Limitations
While the hybrid SWARA–NWA model offers a structured and transparent framework for evaluating
AI algorithms in educational environments, several limitations must be acknowledged that may influence
the scope and applicability of the findings:
1. Subjectivity of expert evaluations – The methodology relies on expert judgment to assign weights
and assess algorithm performance, introducing a level of subjectivity.
2. Limited number of experts – The panel consisted of seven experts, which is appropriate for the
Delphi method, yet a larger sample would enhance statistical validation and understanding of as-
sessment variability.
3. Theoretical rather than experimental validation – Algorithm evaluation was based on technical docu-
mentation and expert analysis, without direct benchmarking on real educational datasets.
4. Restricted set of evaluation criteria – Although the five selected criteria were carefully chosen for
EdTech relevance, real-world decision-making may require consideration of additional factors such
as implementation costs, technical support availability, user digital literacy, and alignment with local
educational policies.
5. Focus on computer vision algorithms only – While the selected algorithms are among the most com-
monly used in AI applications, the study does not address other domains such as natural language
processing (NLP), recommendation systems, or intelligent tutoring systems, which are also highly
relevant in modern education.
Conclusion
This study presents a hybrid multi-criteria decision-making model based on the SWARA and Net
Worth Analysis (NWA) methods for the evaluation of artificial intelligence algorithms in the context of
educational technology. The primary objective was to provide a transparent and systematic framework
that supports educational institutions, IT teams, and policy makers in selecting the most appropriate AI
algorithms tailored to the specific needs of EdTech applications.
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732
Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
Figure 4. Graphical Representation of the Final Results of the Hybrid Model (Source: Author’s research)
The evaluation focused on computer vision algorithms and incorporated five key criteria relevant to ed-
ucational scenarios: computational efficiency, processing speed, pattern recognition accuracy, robustness to
lighting and background disturbances, and energy efficiency. The SWARA method was applied to determine
the relative importance of each criterion based on expert assessments, while the NWA method was used to
aggregate the performance scores of each algorithm and generate a final ranking, as illustrated in Figure 2.
The findings indicate that Fast R-CNN emerged as the most suitable algorithm for widespread
deployment in educational systems, owing to its balanced performance across all criteria. U-Net and
DeepLab stood out for their superior accuracy in visual segmentation, making them particularly well-
suited for specialized educational tasks that involve complex visual pattern analysis. MobileNet, due to
its lightweight architecture and energy efficiency, proved to be an ideal solution for mobile and resource-
constrained environments, particularly within the context of inclusive education in underserved regions.
Recommendations for Future Research
Based on the findings and identified limitations of the present study, we propose the following direc-
tions for future research:
• Empirical validation of the model – Future studies should incorporate experimental testing of the algo-
rithms using real-world educational datasets to complement and verify the theoretical assessments.
• Expansion of the algorithm pool – Include algorithms from other areas of AI, such as natural language
processing (NLP), recommender systems, and classification models.
• Incorporation of additional evaluation criteria – Such as cost-efficiency, availability of open-source
implementations, compliance with educational standards, and required teacher training.
• Application of alternative MCDM methods – Including AHP, PIPRECIA, or fuzzy MCDM approaches,
to compare outcomes and test the robustness of the proposed model.
• Involvement of end users (teachers and students) – Through interviews, surveys, and pilot testing to
enrich the results with insights drawn from real-life educational experiences.
The proposed model represents a significant methodological advancement toward the rationaliza-
tion and formalization of decision-making processes related to the adoption of AI-based technologies
in education. In the current era of accelerated digital transformation, where educational institutions are
striving to balance innovation with systemic constraints, it becomes evident that decision-making based
on intuition or fragmented information is no longer sufficient. What is needed is a robust, transparent, and
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Gligorijević, N. et al. (2025). A Hybrid SWARA-NWA Framework for Evaluating AI-Based Image Recognition Algorithms in
Educational Technology Applications, International Journal of Cognitive Research in Science, Engineering and Education
(IJCRSEE), 13(3), 719-735.
multi-criteria framework that facilitates the inclusion of diverse stakeholder groups, from teachers and
learners to IT specialists and educational authorities.
The core value of this model lies in its ability to reconcile the technological potential of artificial intel-
ligence with the practical needs and constraints of the educational sector. In doing so, it enables the for-
mulation of policies and practices that are not only technically sophisticated, but also socially responsible,
pedagogically justified, and systemically sustainable. Ultimately, such tools contribute to the development
of educational strategies that are equitable, scalable, and effective, approaches in which AI is not seen as
an end in itself, but as a means of enhancing the overall quality of education.
Acknowledgements
The research presented in this paper was carried out within the framework of the project: Multi-
criteria analysis modeling for decision optimization in computer sciences, at Alfa BK University, Faculty of
Mathematics and Computer Sciences, No. 1760.
Funding
This research did not receive any specic grant from funding agencies in the public, commercial,
or not-for-prot sectors.
Conflict of interests
The authors declare no conict of interest.
Data availability statement
The data supporting the reported results in this study are contained within the article itself.
Institutional Review Board Statement
Not applicable.
Author Contributions
Nikola Gligorijević: Conceptualization, Investigation, Methodology, Formal analysis, Writing – origi-
nal draft. Nikola Gligorijević is the rst author and led the research design and implementation of the
hybrid SWARA-NWA model.; Sonja Đukić Popović: Validation, Methodology, Writing – review & editing,
Data curation.; Vojkan Nikolić: Software, Data curation, Visualization. Dejan Viduka: Resources, Supervi-
sion.; Stefan Popović: Writing – review & editing, Visualization, Formal analysis. All authors have read and
agreed to the published version of the manuscript.
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