International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print)

ISSN: 2075-017X (Online)

DOI: https://doi.org/10.5815/ijmecs

Website: https://www.mecs-press.org/ijmecs

Published By: MECS Press

Frequency: 6 issues per year

Number(s) Available: 138

(IJMECS) in Google Scholar Citations / h5-index

IJMECS is committed to bridge the theory and practice of modern education and computer science. From innovative ideas to specific algorithms and full system implementations, IJMECS publishes original, peer-reviewed, and high quality articles in the areas of modern education and computer science. IJMECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of computer science, modern education and applications.

 

IJMECS has been abstracted or indexed by several world class databases: Scopus, SCImago, Google Scholar, Microsoft Academic Search, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, JournalSeek, ULRICH's Periodicals Directory, WorldCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, ProQuest, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc..

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IJMECS Vol. 17, No. 5, Oct. 2025

REGULAR PAPERS

Exploring AI Tools and Large Language Models for Students' Performance Enhancement in Riddle Based Logical Reasoning

By Azeddine Benelrhali Khalid Berrada

DOI: https://doi.org/10.5815/ijmecs.2025.05.01, Pub. Date: 8 Oct. 2025

In the era of Artificial Intelligence (AI), where technology is transforming industries, education stands at a pivotal juncture. With an increasing emphasis on critical thinking and problem-solving, there is a growing need for innovative tools that can foster these essential skills among students. Traditional education methods need help making personalized scalable and interesting experiences for students at this task type which this research aims to solve. The research uses AI and deep learning tools to build an effective framework that enables better riddle solving for students by proposing state of the art deep features including sentence embeddings and ULMfit to be applied as input to deep learning models. In contrast, this study examines different traditional machine learning and deep learning models including ensemble learning models, used as baseline models for comparing the performance of the proposed transformer architectures based on RoBERTa-Large to determine which approach works best, achieving highest accuracy of 96% to effectively handle riddle complexity. The research studies used text data patterns using TF-IDF, Count Vectorization, and word embedding techniques which apply in the form of Roberta. Our research findings help educators, technology experts and scientific teams design educational tools with an easy-to-deploy AI solution. 

[...] Read more.
Artificial Intelligence Attitude Scale for Primary School Students

By Taha Oruc Ozgen KORKMAZ Murat Kurt

DOI: https://doi.org/10.5815/ijmecs.2025.05.02, Pub. Date: 8 Oct. 2025

Based on this gap in the literature, the problem situation identified was deemed worth investigating in terms of contributing to the accumulation of knowledge on the subject area. In addition, it is thought that this study will contribute to future studies on artificial intelligence in primary school education. The aim of this study is to create a Likert-type attitude scale that can be used to determine primary school students’ attitudes towards artificial intelligence. In this study, exploratory sequential design, one of the mixed research method types, was used. A 32-item draft scale form was prepared in the light of the literature review, student opinions collected through a structured interview form and data obtained from field experts. In order to examine the validity of the scale, exploratory and confirmatory factor analyses, item-factor total correlations and item discriminations were evaluated. The goodness of fit values obtained in confirmatory factor analysis were [CMIN=245,020, df=159 (CMIN/df= 1.541), RMSEA= 0.45, RMR= 0.035, GFI= 0.916, AGFI= 0.889, CFI= 0.903, NFI= 0.773, IFI= 0.906]. To evaluate the reliability of the scale, internal consistency coefficient was calculated, and test-retest analysis was performed. Cronbach’s Alpha reliability coefficient for the overall scale was 0.807 and McDonald’s Omega coefficient was 0.816. As a result, it was determined that the Artificial Intelligence Attitude Scale, which consists of 4 factors and 20 items, is an appropriate, valid and reliable tool for evaluating primary school students’ attitudes towards artificial intelligence.

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Climate Literacy: Creating a Multilevel Interactive Platform for Climate Education

By Ruth George Phiri Lameck Nsama Ngula Walubita Swati Samantaray Sudhansu Shekhar Patra Manoj Ranjan Mishra Mahendra Kumar Gourisaria

DOI: https://doi.org/10.5815/ijmecs.2025.05.03, Pub. Date: 8 Oct. 2025

Climate literacy is crucial to increasing public understanding and engagement with the global climate catastrophe. However, current climate education approaches often fail to effectively raise concern and action, particularly across diverse age groups. This study makes a modest attempt to detail the design and development of a novel multilevel interactive digital climate education platform for early learners, adolescents, and adults using adaptive learning pathways, personalized content delivery, multimedia interactivity, and gamification features to promote learner engagement as well as learning outcomes across different age levels. A mixed-methods research design was used involving pre and post-survey quantitative measures as well as qualitative user experience testing. Post-survey results indicated significant improvement in climate literacy knowledge, attitudes towards the environment, and self-efficacy beliefs regarding individual efforts to mitigate future climate impacts (response efficacy), regardless of learner age group. The comparative analysis thus revealed certain content preferences by age as well as interaction patterns among functionalities and learning gains between groups based on user perspectives that point towards differentiated preference areas linked with diverse ages. The resulting platform exemplifies interactive digital technologies’ potential for achieving sustainable behavior change through optimised synergies with large-scale educational interventions for inducing positive spillover effects in terms of broader widespread climate change engagement impact over generational transition pragma.

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Assessing the Impact of Artificial Intelligence on the Role of the Teacher, Student Learning and the Transmission of Knowledge in Education

By Hassan RAZOUKI Abdellatif Hair Bouchaib Cherradi Abdelhadi Razouki

DOI: https://doi.org/10.5815/ijmecs.2025.05.04, Pub. Date: 8 Oct. 2025

The integration of artificial intelligence (AI) in education is a promising transformation. Drawing on advanced technologies, AI enriches the learning experience through intelligent systems capable of analyzing, adapting and personalizing teaching. Despite a growing volume of scientific publications, there remains a lack of critical synthesis on the real impact of AI on the role of teachers, student learning and the transmission of knowledge. To fill this gap, this article proposes a systematic literature review, conducted using the PRISMA method, to identify the opportunities and limitations of AI in educational environments. From 1,248 publications extracted from the Scopus database between 2018 and 2024, 20 relevant studies were selected and analyzed after applying inclusion and exclusion criteria. The results show significant growth in research in this field, and demonstrate that AI enables teachers to automate certain tasks, personalize teaching and better meet learners' individual needs. However, significant obstacles remain, including lack of digital skills, resistance to change, and ethical concerns. The study also points out that AI enhances learners' skills, promoting the personalization of pathways, the identification of struggling students, the adaptation of materials, as well as real-time engagement and monitoring. It also makes it possible to model and transmit knowledge through the creation and adaptation of digital educational resources. However, AI also presents certain limitations in the educational context, such as excessive dependence on technology, inequalities of access, automatic generation of answers without real learning, as well as issues relating to the confidentiality of personal data. AI is a powerful but complex lever in the field of education. Its effective integration requires targeted training for teachers, critical reflection on its uses, and a rigorous ethical framework. This review thus provides a solid basis for guiding future research towards complementary empirical studies, while accompanying practitioners in a reasoned and beneficial adoption of AI in educational contexts.

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Enhancing Student Performance Insights Through Multi Parametric STBO Based Analysis in Engineering Education

By Santhosh Kumar Medishetti Ravindra Eklarker Kommuri Venkatrao Maheswari Bandi Rameshwaraiah Kurupati

DOI: https://doi.org/10.5815/ijmecs.2025.05.05, Pub. Date: 8 Oct. 2025

This research presents a novel approach to evaluating student academic performance at Nalla Narasimha Reddy Group of Institutions (NNRG) by implementing a Student Training Based Optimization (STBO) algorithm. The proposed method draws inspiration from the structured training and adaptive learning behavior of students, simulating their progression through knowledge acquisition, skill refinement, and performance enhancement phases. The STBO algorithm is applied to optimize academic performance assessment by identifying key parameters such as attendance, internal assessments, learning pace, participation, and project outcomes. By modelling student development as a dynamic optimization process, the algorithm effectively predicts academic outcomes and recommends personalized strategies for improvement. Experimental evaluation on real academic datasets from NNRG CSE, CSE (Data Science), and CSE (AIML) Students demonstrates that the STBO algorithm achieves higher prediction accuracy and adaptive feedback generation when compared to traditional statistical and machine learning techniques. This approach also facilitates early identification of at-risk students and promotes data-driven decision-making for faculty and administration. Overall, the STBO-based framework not only enhances performance assessment but also contributes to academic excellence by aligning learning strategies with individual student needs.

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The Influence of an Adaptive Non-Formal Education Model on Student Learning Activity and Engagement

By Tulebike Kulgildinova Arayly Sarsembayeva Darya Surova Indira Kalelova Meruyert Zhaizhatyrova Saniya Nurgaliyeva

DOI: https://doi.org/10.5815/ijmecs.2025.05.06, Pub. Date: 8 Oct. 2025

In the context of ongoing digitalization and the growing importance of non-formal education in Kazakhstan’s higher education system, there is an increasing demand for adaptive educational models that address students' individual learning needs and broaden the scope of academic engagement. This study examines the effects of an adaptive non-formal education model on students' learning activity and engagement, and identifies the model components with the most significant impact. A quantitative quasi-experimental design was employed, involving pre- and post-intervention assessments using validated questionnaires. Key indicators included participation in supplementary educational activities, online learning platforms, external courses, and project-based or volunteer initiatives. The results indicate a statistically significant improvement in students’ educational involvement in the experimental group, as demonstrated by increased participation in external learning events, greater self-directed learning, and the development of personalized educational trajectories. The study highlights the potential of adaptive non-formal education as a strategic tool to enhance institutional flexibility and student motivation. Its novelty lies in testing a context-sensitive adaptive non-formal education model tailored to Kazakhstan’s institutional realities. The findings contribute to the global discourse on flexible education strategies and suggest directions for scaling and integrating the model into digital academic ecosystems.

[...] Read more.
Local Agentic RAG-Based Information System Development for Intelligent Analysis of GitHub Code Repositories in Computer Science Education

By Zhengbing Hu Markiian-Mykhailo Paprotskyi Victoria Vysotska Lyubomyr Chyrun Yuriy Ushenko Dmytro Uhryn

DOI: https://doi.org/10.5815/ijmecs.2025.05.07, Pub. Date: 8 Oct. 2025

This study presents the development and evaluation of a local agent-based Retrieval-Augmented Generation (Agentic RAG) system designed for the intelligent analysis of GitHub repositories in computer science education and IT practice. The novelty of this work lies not in inventing a new RAG algorithm, but in orchestrating multiple existing components (LangChain, Redis, SentenceTransformer, and LLMs) into a multi-stage agent pipeline with integrated relevance evaluation, specifically adapted to offline repository mining. The proposed pipeline consists of four sequential stages: (1) query reformulation by a dedicated LLM agent, (2) semantic retrieval using SentenceTransformer embeddings stored in Redis, (3) response generation by a second LLM, and (4) relevance scoring through a verification agent with retry logic. Relevance is assessed via cosine similarity and LLM-based scoring, allowing iterative refinement of answers. Experimental testing compared the system against two baselines: keyword search and a non-agentic single-stage RAG pipeline. Results showed an average MRR@10 of 0.72, compared to 0.48 for keyword search and 0.61 for non-agentic RAG, representing a 33% relative improvement in retrieval quality. Human evaluators (n=15, computer science students) rated generated explanations on a 5-point Likert scale; the proposed system achieved an average 4.3/5 for clarity and correctness, compared to 3.6/5 for the baseline. Precision@5 for code retrieval improved from 0.54 (keyword) and 0.67 (non-agentic RAG) to 0.76 in the proposed system. Average query latency in the local environment was 3.8 seconds, indicating acceptable performance for educational and small-team IT use cases. The system demonstrates high autonomy by operating fully on-premises with only optional API access to LLMs, ensuring privacy and independence from cloud providers. Ease of use was measured through a System Usability Scale (SUS) questionnaire, yielding a score of 78/100, reflecting positive user perception of the Streamlit interface and minimal setup requirements. Nevertheless, several limitations were observed: the high computational cost of running embeddings and LLMs locally, potential hallucinations in generated explanations (particularly for complex or unfamiliar code), and the inability of vector search to fully capture code syntax and control flow structures. Furthermore, while the Analytic Hierarchy Process (AHP) was applied to select the system architecture, future work should complement this with benchmark-driven evaluations for greater objectivity. The contribution of this study is threefold: (1) introducing a multi-agent orchestration logic tailored to educational code repositories; (2) empirically demonstrating measurable gains in retrieval quality and explanation usefulness over baselines; and (3) highlighting both opportunities and limitations of deploying autonomous RAG systems locally. The proposed technology can benefit IT companies seeking secure in-house tools for repository analysis, universities aiming to integrate intelligent assistants into programming courses, and research institutions requiring reproducible, privacy-preserving environments for code exploration.

[...] Read more.
Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches

By Mukesh Kumar Nidhi Walia Sushil Bansal Girish Kumar Korhan Cengiz

DOI: https://doi.org/10.5815/ijmecs.2023.06.01, Pub. Date: 8 Dec. 2023

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.

[...] Read more.
Teachers’ Use of Technology and Constructivism

By Abbas Pourhosein Gilakjani Lai-Mei Leong Hairul Nizam Ismail

DOI: https://doi.org/10.5815/ijmecs.2013.04.07, Pub. Date: 8 Apr. 2013

Technology has changed the way we teach and the way we learn. Many learning theories can be used to apply and integrate this technology more effectively. There is a close relationship between technology and constructivism, the implementation of each one benefiting the other. Constructivism states that learning takes place in contexts, while technology refers to the designs and environments that engage learners. Recent efforts to integrate technology in the classroom have been within the context of a constructivist framework. The purpose of this paper is to examine the definition of constructivism, incorporating technology into the classroom, successful technology integration into the classroom, factors contributing to teachers’ use of technology, role of technology in a constructivist classroom, teacher’s use of learning theories to enable more effective use of technology, learning with technology: constructivist perspective, and constructivism as a framework for educational technology. This paper explains whether technology by itself can make the education process more effective or if technology needs an appropriate instructional theory to indicate its positive effect on the learner.

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House Price Prediction using a Machine Learning Model: A Survey of Literature

By Nor Hamizah Zulkifley Shuzlina Abdul Rahman Nor Hasbiah Ubaidullah Ismail Ibrahim

DOI: https://doi.org/10.5815/ijmecs.2020.06.04, Pub. Date: 8 Dec. 2020

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

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LLMs Performance on Vietnamese High School Biology Examination

By Xuan-Quy Dao Ngoc-Bich Le

DOI: https://doi.org/10.5815/ijmecs.2023.06.02, Pub. Date: 8 Dec. 2023

Large Language Models (LLMs) have received significant attention due to their potential to transform the field of education and assessment through the provision of automated responses to a diverse range of inquiries. The objective of this research is to examine the efficacy of three LLMs - ChatGPT, BingChat, and Bard - in relation to their performance on the Vietnamese High School Biology Examination dataset. This dataset consists of a wide range of biology questions that vary in difficulty and context. By conducting a thorough analysis, we are able to reveal the merits and drawbacks of each LLM, thereby providing valuable insights for their successful incorporation into educational platforms. This study examines the proficiency of LLMs in various levels of questioning, namely Knowledge, Comprehension, Application, and High Application. The findings of the study reveal complex and subtle patterns in performance. The versatility of ChatGPT is evident as it showcases potential across multiple levels. Nevertheless, it encounters difficulties in maintaining consistency and effectively addressing complex application queries. BingChat and Bard demonstrate strong performance in tasks related to factual recall, comprehension, and interpretation, indicating their effectiveness in facilitating fundamental learning. Additional investigation encompasses educational environments. The analysis indicates that the utilization of BingChat and Bard has the potential to augment factual and comprehension learning experiences. However, it is crucial to acknowledge the indispensable significance of human expertise in tackling complex application inquiries. The research conducted emphasizes the importance of adopting a well-rounded approach to the integration of LLMs, taking into account their capabilities while also recognizing their limitations. The refinement of LLM capabilities and the resolution of challenges in addressing advanced application scenarios can be achieved through collaboration among educators, developers, and AI researchers.

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Project-based Learning in Vocational Education: A Bibliometric Approach

By Selamat Triono Ahmad Ronal Watrianthos Agariadne Dwinggo Samala Mukhlidi Muskhir Gimba Dogara

DOI: https://doi.org/10.5815/ijmecs.2023.04.04, Pub. Date: 8 Aug. 2023

The project-based learning (PjBL) paradigm is often considered the most advanced in vocational education. The increasing use of the PjBL paradigm in vocational education is an intriguing topic of study. In line with the rapid growth of information technology, it enables PjBL in vocational education to help students develop problem-solving, critical thinking, and teamwork skills. In this study, a bibliometric method is used to provide insight into the structure of the subject, social networks, research trends, and issues reflecting project-based learning in vocational education. On November 27, 2022, the Scopus database was searched using project-based learning terms in the title. The second search field appears in the title, abstract, and keywords vocational education or TVET, restricted to journal articles or proceedings and in English to keep them current. This analysis revealed 60 articles in Scopus-indexed journals and proceedings between 2010 and 2022. Dwi Agus Sudjimat from Malang State University, Indonesia, was the most prolific author, having authored four articles on the subject. Indonesia is the nation investing the most in developing PjBL models. According to the thematic data, project-based learning is located in the first quadrant, has high centrality and density, and has well-developed questions related to the study topic. The results of this study show that the project-based learning model that is evolving in vocational education is likely to continue to be an important teaching approach in this field.

[...] Read more.
Project-Based Learning with Gallery Walk: The Association with the Learning Motivation and Achievement

By Zamree Che-aron Wannisa Matcha

DOI: https://doi.org/10.5815/ijmecs.2023.05.01, Pub. Date: 8 Oct. 2023

With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.

[...] Read more.
Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression

By Dao Thi Thanh Loan Nguyen Duy Tho Nguyen Huu Nghia Vu Dinh Chien Tran Anh Tuan

DOI: https://doi.org/10.5815/ijmecs.2024.01.01, Pub. Date: 8 Feb. 2024

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

[...] Read more.
A Study on the Role of Motivation in Foreign Language Learning and Teaching

By Abbas Pourhosein Gilakjani Lai-Mei Leong Narjes Banou Sabouri

DOI: https://doi.org/10.5815/ijmecs.2012.07.02, Pub. Date: 8 Jul. 2012

Motivation has been called the “neglected heart” of language teaching. As teachers, we often forget that all of our learning activities are filtered through our students’ motivation. In this sense, students control the flow of the classroom. Without student motivation, there is no pulse, there is no life in the class. When we learn to incorporate direct approaches to generating student motivation in our teaching, we will become happier and more successful teachers. This paper is an attempt to look at EFL learners’ motivation in learning a foreign language from a theoretical approach. It includes a definition of the concept, the importance of motivation, specific approaches for generating motivation, difference between integrative and instrumental motivation, difference between intrinsic and extrinsic motivation, factors influencing motivation, and adopting motivational teaching practice.

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A Match or Mismatch Between Learning Styles of the Learners and Teaching Styles of the Teachers

By Abbas Pourhosein Gilakjani

DOI: https://doi.org/10.5815/ijmecs.2012.11.05, Pub. Date: 8 Nov. 2012

It is important to study learning styles because recent studies have shown that a match between teaching and learning styles helps to motivate students´ process of learning. That is why teachers should identify their own teaching styles as well as their learning styles to obtain better results in the classroom. The aim is to have a balanced teaching style and to adapt activities to meet students´ style and to involve teachers in this type of research to assure the results found in this research study. Over 100 students complete a questionnaire to determine if their learning styles are auditory, visual, or kinesthetic. Discovering these learning styles will allow the students to determine their own personal strengths and weaknesses and learn from them. Teachers can incorporate learning styles into their classroom by identifying the learning styles of each of their students, matching teaching styles to learning styles for difficult tasks, strengthening weaker learning styles. The purpose of this study is to explain learning styles, teaching styles match or mismatch between learning and teaching styles, visual, auditory, and kinesthetic learning styles among Iranian learners, and pedagogical implications for the EFL/ESL classroom. A review of the literature along with analysis of the data will determine how learning styles match the teaching styles.

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Factors Affecting Entrepreneurial Motivation and Intention of University Students in Hanoi, Vietnam

By Do Thi Minh Hue Tran Phuong Thao Pham Canh Toan Hoang Dinh Luong Phan Thi Hao Do Thi Huyen Nguyen Thi Hoa

DOI: https://doi.org/10.5815/ijmecs.2022.02.01, Pub. Date: 8 Apr. 2022

Entrepreneurship is the key driver of economic progress in many countries; thus, many countries have introduced policies to promote a more entrepreneurial environment. This study assesses the impact of factors affecting entrepreneurial intention of university students. The data was collected through a survey of 341 students at 09 leading universities in Hanoi, Vietnam and analyzed using structural equation modeling (SEM) with SPSS and Amos software. The research results show that entrepreneurial skills, entrepreneurial environment and subjective norms either directly or indirectly affect business motivation and entrepreneurial intention of university students. Thus, it is suggested that university and other educational institutions should provide more activities and taught courses that help students acquire the knowledge and skills necessary for entrepreneurship.

[...] Read more.
Predicting College Students’ Placements Based on Academic Performance Using Machine Learning Approaches

By Mukesh Kumar Nidhi Walia Sushil Bansal Girish Kumar Korhan Cengiz

DOI: https://doi.org/10.5815/ijmecs.2023.06.01, Pub. Date: 8 Dec. 2023

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.

[...] Read more.
Development of Collaborative Learning and Programming (CLP): A Learning Model on Object Oriented Programming Course

By Efan Efan Krismadinata Krismadinata Cherifa Boudia Muhammad Giatman Mukhlidi Muskhir Hasan Maksum

DOI: https://doi.org/10.5815/ijmecs.2024.03.01, Pub. Date: 8 Jun. 2024

There appears to be a tendency for the strategies and methods that have been offered in OOP course learning to affect the improvement of individual skills only. There is a significant need for learning strategies which are relevant and able of improving collaborative working skills. The purpose of this study is to develop a Collaborative Learning and Programming model suitable for Object-Oriented Programming courses and assess its validity, practicality, and effectiveness. The implementation of the CLP model was conducted using the ADDIE development procedure by involving 7 experts, 35 experimental class students, 23 control class students and 4 lecturers of the Object-Oriented Programming course. The survey results showed that the CLP model was valid, practical, and effective in achieving these goals. The validity test results were verified based on experts' assessment, indicating that the aspects contained in the CLP model were valid with an Aiken's value V =0.89. The practicality test results indicated that the model was highly practical with the practicality value of 89.95% from students and 89.67% from lecturers. Finally, using the CLP model demonstrated its effectiveness in reducing the abstraction and complexity of OOP courses and improving student collaboration, particularly in programming tasks. The significance of conducting this survey is that it provides evidence for the effectiveness of the CLP model in achieving its intended goals and can inform the development of future OOP courses and programming tasks. The survey was conducted well, as it used both expert assessment and student and lecturer feedback to assess the validity, practicality, and effectiveness of the CLP model.

[...] Read more.
Project-Based Learning with Gallery Walk: The Association with the Learning Motivation and Achievement

By Zamree Che-aron Wannisa Matcha

DOI: https://doi.org/10.5815/ijmecs.2023.05.01, Pub. Date: 8 Oct. 2023

With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.

[...] Read more.
Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression

By Dao Thi Thanh Loan Nguyen Duy Tho Nguyen Huu Nghia Vu Dinh Chien Tran Anh Tuan

DOI: https://doi.org/10.5815/ijmecs.2024.01.01, Pub. Date: 8 Feb. 2024

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

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Project-based Learning in Vocational Education: A Bibliometric Approach

By Selamat Triono Ahmad Ronal Watrianthos Agariadne Dwinggo Samala Mukhlidi Muskhir Gimba Dogara

DOI: https://doi.org/10.5815/ijmecs.2023.04.04, Pub. Date: 8 Aug. 2023

The project-based learning (PjBL) paradigm is often considered the most advanced in vocational education. The increasing use of the PjBL paradigm in vocational education is an intriguing topic of study. In line with the rapid growth of information technology, it enables PjBL in vocational education to help students develop problem-solving, critical thinking, and teamwork skills. In this study, a bibliometric method is used to provide insight into the structure of the subject, social networks, research trends, and issues reflecting project-based learning in vocational education. On November 27, 2022, the Scopus database was searched using project-based learning terms in the title. The second search field appears in the title, abstract, and keywords vocational education or TVET, restricted to journal articles or proceedings and in English to keep them current. This analysis revealed 60 articles in Scopus-indexed journals and proceedings between 2010 and 2022. Dwi Agus Sudjimat from Malang State University, Indonesia, was the most prolific author, having authored four articles on the subject. Indonesia is the nation investing the most in developing PjBL models. According to the thematic data, project-based learning is located in the first quadrant, has high centrality and density, and has well-developed questions related to the study topic. The results of this study show that the project-based learning model that is evolving in vocational education is likely to continue to be an important teaching approach in this field.

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House Price Prediction using a Machine Learning Model: A Survey of Literature

By Nor Hamizah Zulkifley Shuzlina Abdul Rahman Nor Hasbiah Ubaidullah Ismail Ibrahim

DOI: https://doi.org/10.5815/ijmecs.2020.06.04, Pub. Date: 8 Dec. 2020

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

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Factors Affecting Entrepreneurial Motivation and Intention of University Students in Hanoi, Vietnam

By Do Thi Minh Hue Tran Phuong Thao Pham Canh Toan Hoang Dinh Luong Phan Thi Hao Do Thi Huyen Nguyen Thi Hoa

DOI: https://doi.org/10.5815/ijmecs.2022.02.01, Pub. Date: 8 Apr. 2022

Entrepreneurship is the key driver of economic progress in many countries; thus, many countries have introduced policies to promote a more entrepreneurial environment. This study assesses the impact of factors affecting entrepreneurial intention of university students. The data was collected through a survey of 341 students at 09 leading universities in Hanoi, Vietnam and analyzed using structural equation modeling (SEM) with SPSS and Amos software. The research results show that entrepreneurial skills, entrepreneurial environment and subjective norms either directly or indirectly affect business motivation and entrepreneurial intention of university students. Thus, it is suggested that university and other educational institutions should provide more activities and taught courses that help students acquire the knowledge and skills necessary for entrepreneurship.

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Analysis of Student’s Academic Performance based on their Time Spent on Extra-Curricular Activities using Machine Learning Techniques

By Neeta Sharma Shanmuganathan Appukutti Umang Garg Jayati Mukherjee Sneha Mishra

DOI: https://doi.org/10.5815/ijmecs.2023.01.04, Pub. Date: 8 Feb. 2023

The foundational tenet of any nation's prosperity, character, and progress is education. Thus, a lot of emphasis is laid on quality of education and education delivery system in India with current financial year (2022-23) education budget outlay of Rs. 1,04,277.72 crores. This research contributes in analyzing how students perform in academics depending upon the time spent on their extracurricular activities with the help of three Machine Learning prediction algorithms namely Decision Tree, Random Forest and KNN. Additionally, in order to comprehend the underlying causes of the shortcomings in each machine learning technique, comparisons of the prediction outcomes obtained by these various techniques are made. On our dataset, the Decision Tree outscored all other algorithms, achieving F1 84 and an accuracy of 85%. The research, which is at an introductory level, is meant to open the door for more complexes, specialised, and in-depth studies in the area of predicting the performance in academics.

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Comparison of Simple Additive Weighting Method and Weighted Performance Indicator Method for Lecturer Performance Assessment

By Terttiaavini Yusuf Hartono Ermatita Dian Palupi Rini

DOI: https://doi.org/10.5815/ijmecs.2023.02.01, Pub. Date: 8 Apr. 2023

The development of methods for assessing lecturers' performance is needed to motivate lecturers to achieve institutional targets. Currently, lecturers are required to be able to adapt to the rapid development of technology. Lecturer performance assessment must be done periodically. Competence is measured as a basis for planning resource development activities. The method that is often used for assessing lecturer performance is the Simple Additive Weighting (SAW) method. However, the SAW method has drawbacks, namely 1) the process of determining criteria is only carried out by the leadership (subjective); 2) The SAW method can only be applied to multi-criteria data ; 3) Data ranking problems. Based on this deficiency, a new method was built, namely, the Weighted Performance Indicator (WPI) method using respondents’ opinion to determine the criteria. This study aims to compare the performance of the two methods. Testing criteria using SPPS application dan WPI method, while testing methods utilized the SAW method and the WPI method. The results of the criterion test show the Percentage of Similarity of data validity = 96.7 % witht the minimum percentage limit (MPL) = 40%. While the results of the SAW method and WPI method testing resulted in the highest score in the 13th alternative, namely SAW score (v13) = 793.76 and WP score (WP13) = 0.928, and the lowest value in the 30th alternative, SAW score (v30) = 18.60 and WP score (WP30) = 0.140. the ranking positions in these two methods show similarities. However, for other alternatives, the rating value can be different. 
The WPI method is a scientific development in the field of decision support systems that can be applied to other performance assessments, such other human resources, system performance assesment etc. 
The results of this study prove that the WPI method can be used as a performance assessment method with different characteristics from the SAW method.

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Adoption of Blended Learning in Ghanaian Senior High Schools: A Case Study in a Less Endowed School

By Ebenezer Eghan Najim Ussiph ObedAppiah

DOI: https://doi.org/10.5815/ijmecs.2023.05.06, Pub. Date: 8 Oct. 2023

During COVID-19 pandemic, most tertiary institutions in Ghana were compelled to continue delivering of lectures online using internet technologies as was in the case of other countries. Senior high schools in Ghana were, however, not asked to do same, currently, the setting of most literature on blended or online learning in Ghana is focused on tertiary education. This paper situates the blended learning model in a less endowed senior high school to unearth the prospect of its implementation. The research provides an alternative to the traditional face-to-face learning, which is faced with the challenge of inadequate infrastructure, high number of students to class ratio, less compatibility with 21st learning skills and long-life learning in Ghana.
A customed Moodle application as web application tool, hosted students online in both synchronous and asynchronous interactions. Purposive quota sampling size technique was used to select an appreciable sample size to fully go through the traditional face-face model for a term and then study through the blended learning model for another term. Students’ examination performances for both were analyzed with a paired t test statistical model. Interviews with participants were conducted to ascertain their evaluation of the blended learning model and questionnaires were also administered to discover the institutional, technological, and human resource readiness for blended learning in senior high schools. The analysis of the data gathered, proved that blended learning in senior high schools has high prospect and is better alternative to face-to-face learning in Ghana.

 

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