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: 135

(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. 2, Apr. 2025

REGULAR PAPERS

VR IGLOOTM: A VR-Based Teaching-Learning Model for Distance Education

By Yu Jin Choi Hae Chan Na Yoon Sang Kim

DOI: https://doi.org/10.5815/ijmecs.2025.02.01, Pub. Date: 8 Apr. 2025

Traditional distance learning has been widely adopted for its capacity to provide educational access to a broad and diverse audience, overcoming spatial and temporal limitations. However, it needs to deliver the same immersion and learning effectiveness as face-to-face instruction, particularly in courses requiring hands-on practice, where these limitations become more pronounced. To address this, virtual reality (VR)-based distance learning has gained attention as a potential solution. Previous studies have confirmed that VR-enhanced distance learning can improve educational outcomes; however, a standardized teaching-learning model designed explicitly for VR-based distance learning has yet to be established. Consequently, instructors have often relied on conventional models, leading to variability in instructional quality. This paper proposes the VR IGLOO model, a structured VR-based teaching-learning framework tailored for distance education. For this purpose, analysis of the conventional studies and focus group interview (FGI) of the expert group were conducted. And the validity of the proposed model was verified through Delphi validation.

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Agile Intelligent Software Solution for Textual Content Authorship Identification Based on NLP, Artificial Intelligence and Machine Learning

By Zhengbing Hu Victoria Vysotska Lyubomyr Chyrun Roman Romanchuk Yuriy Ushenko Dmytro Uhryn Cennuo Hu

DOI: https://doi.org/10.5815/ijmecs.2025.02.02, Pub. Date: 8 Apr. 2025

The main goal of the work is to create an intelligent system that uses NLP methods and machine learning algorithms to analyse and classify textual content authorship. The following machine learning models for English and Ukrainian publications were tested and trained on the dataset: Support Vector Classifier, Random Forest, Naive Bayes, Logistic Regression and Neuron Networks. For English, the accuracy of the models was higher due to the more significant amount of text data available. The results for English fiction publication show that the Neuron Networks classifier outperforms the other models in all evaluated metrics, achieving the highest accuracy (0.97), recall (0.96), F1 score (0.98), and precision (0.96). It shows that Neuron Networks is particularly effective in capturing distinctive features of the writing styles of different English authors in scientific and technical texts. For the Ukrainian language,  
there is a drop in accuracy by 5-10% due to the smaller number of corpora of texts for teaching. The results for scientific and technical Ukrainian publications show that the Random Forest classifier outperforms the other models in all evaluated metrics, achieving the highest accuracy (0.88), recall (0.87), F1 score (0.87), and precision (0.87). It shows that Random Forest is particularly effective in capturing distinctive features of the writing styles of different Ukrainian authors in scientific and technical texts. Much worse accuracy results were shown by other models such as Support Vector Classifier (77%), Logistic Regression (73%) and Naive Bayes (70%). The results for the Ukrainian fiction publication show that the Random Forest classifier outperforms the other models in all evaluated metrics, achieving the highest accuracy (0.85), recall (0.84), F1 score (0.84), and precision (0.84). Much worse accuracy results were shown by other models such as Support Vector Classifier (77%), Logistic Regression (73%) and Naive Bayes (70%)

[...] Read more.
Enhancing Student Performance Prediction in E-Learning Environments: Advanced Ensemble Techniques and Robust Feature Selection

By N. S. Koti Mani Kumar Tirumanadham Thaiyalnayaki S.

DOI: https://doi.org/10.5815/ijmecs.2025.02.03, Pub. Date: 8 Apr. 2025

By means of a thorough investigation of ensemble methodologies and feature selection approaches, this work explores enhancing predictive modelling in e-learning contexts. The setting is in the growing significance of data-driven decision-making in education and tailored learning programs. The main concern is how to fairly forecast student performance in environments of digital learning. This work intends to solve gaps by investigating new ensemble models and robust feature selection techniques based on already published research. Using cutting-edge analytical techniques including hybrid BR2-2T models and the Chi-square test, the study produces remarkable accuracy surpassing known limits. The results underline the need of feature selection and ensemble methods in improving forecast accuracy and dependability. Finally, this study marks a major step in the field of e-learning predictive modelling since it helps to improve educational results and enable data-driven interventions.

[...] Read more.
Unlocking Educational Excellence: Leveraging Federated Learning for Enhanced Instructor Evaluation and Student Success

By Ariful Islam Debajyoti Karmaker Abhijit Bhowmik Md Masum Billah Md Iftekharul Mobin Noorhuzaimi Mohd Noor

DOI: https://doi.org/10.5815/ijmecs.2025.02.04, Pub. Date: 8 Apr. 2025

Federated Learning (FL) is an emerging machine learning approach with promising applications. In this paper, FL is comprehensively examined in relation to teacher performance evaluation. Through FL, teachers can be evaluated based on data-driven metrics while preserving data privacy. There are several benefits, including data privacy preservation, collaborative learning, scalability, and privacy-preserving insights. Additionally, it faces problems related to communication efficiency, system heterogeneity, and statistical heterogeneity. To address these issues, we propose a novel clustering-based technique in federated learning. The technique aims to overcome the challenges of system heterogeneity and improve communication efficiency. We provide a comprehensive review of existing research on clustering techniques in the context of federated learning, offering insights into the state of the art in this field. In addition, we emphasize the need for advanced compression methods, enhanced privacy-preserving mechanisms, and robust aggregation algorithms for future federated learning research. To address these challenges, we present a clustering-based approach to address the merits and challenges of federated learning The clustering-based approach we propose in this research demonstrates promising results in terms of reducing communication overhead and improving model convergence in federated learning. These findings suggest that incorporating clustering techniques can significantly enhance the efficiency and effectiveness of federated learning algorithms, paving the way for more scalable and privacy-preserving distributed machine learning systems. The findings of this study suggest that clustering techniques can improve the efficiency and scalability of federated learning.

[...] Read more.
Assessing Student Academic Performance with Fuzzy Expert System

By Bhupendra Kumar Pathak

DOI: https://doi.org/10.5815/ijmecs.2025.02.05, Pub. Date: 8 Apr. 2025

Nowadays, higher education institutions and universities are facing a competitive environment for enhancing the quality of students to achieve extensive knowledge with critical thinking skills and a good personality for better employment in the industry. Universities and other higher education establishments ensure that students overcome the obstacles in these cutthroat environments. In order to do this, it is necessary to analyze the academic performance of each student by determining their strengths and weaknesses. A fuzzy expert system (FES) is used in this study to evaluate student’s academic performance. This FES uses fuzzy logic to classify each student’s performance based on a variety of linguistic factors. It classifies each student’s performance by considering various linguistic factors using fuzzy logic. For this purpose, seven significant input factors have been taken into account which is Stress, Motivation, Confidence, Parent’s support & Availability, Self study hours, Punctuality, and Friend circle. Several defuzzification techniques are applied in order to examine student performance using the FES & generate more precise and measurable results. These findings could aid colleges and other educational establishments in determining the right variables that influence student’s academic performance. Additionally, a comparison of various Mamdani fuzzy defuzzification techniques, including the centroid, bisector, and mean of maxima methods, is provided in this study. After comparing all three techniques by taking different scenarios of all the external factors, it has been concluded that all of them are performing equally.

[...] Read more.
Toxicity Detection Using TextBlob Sentiment Analysis for Location-Centered Tweets

By Varun Mishra Tejaswita Garg

DOI: https://doi.org/10.5815/ijmecs.2025.02.06, Pub. Date: 8 Apr. 2025

The toxic comment detection over the internet through social networking posts found hatred comments and apply certain limitations to stop the negative impact of that information in our society. In order to perform sentiment analysis, NLP text classification approach is very effective. In this paper, we design a specific algorithm using Convolution Neural Network (CNN) approach and perform TextBlob sentiment analysis to evaluate the polarity and subjectivity analysis of posted tweets or comments. This paper can also filter the tweets collected over different locations formed Twitter dataset and then model is evaluated in terms of accuracy, precision, recall and f1-score as calculated results of 0.984, 0.887, 0.905 and 0.895 respectively for the analysis of toxic/non-toxic comment identification. Hence, our algorithm utilized NLTK and TextBlob libraries and suggests that the analyzed post can be recommended to the others or not.

[...] Read more.
AI in Education: A Decade of Global Research Trends and Future Directions

By Dedy Irfan Ronal Watrianthos Faizal Amin Nur Bin Yunus

DOI: https://doi.org/10.5815/ijmecs.2025.02.07, Pub. Date: 8 Apr. 2025

This article addresses the need for a comprehensive understanding of the rapidly evolving field of Artificial Intelligence (AI) in education, given its potential to transform teaching and learning practices. The study analyzed 1,234 articles from the Web of Science database, using bibliometric techniques and topic modeling. Quantitative analyses of publication trends, citation impacts, and collaboration patterns were conducted using the R programming language, and Latent Dirichlet Allocation (LDA) was employed to uncover latent themes and potential research gaps. The study reveals a dramatic growth in research output, with an annual growth rate of 47.9%. China and the United States emerge as dominant contributors, collectively accounting for 38% of publications. Key research themes include AI in language learning, AI ethics and policy, and AI literacy. The findings highlight the need for more inclusive and diverse research efforts to address the unique challenges and opportunities of AI in education in across socioeconomic contexts.

[...] 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.
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.

[...] Read more.
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.

[...] 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.
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.

[...] 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 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.

[...] 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.

[...] Read more.
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.
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.
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.

[...] Read more.
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.

[...] Read more.
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.
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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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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