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: 128

ICV: 2014 8.09

SJR: 2021 0.37

(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. 16, No. 1, Feb. 2024

REGULAR PAPERS

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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Explainable Fake News Detection Based on BERT and SHAP Applied to COVID-19

By Xiuping Men Vladimir Y. Mariano

DOI: https://doi.org/10.5815/ijmecs.2024.01.02, Pub. Date: 8 Feb. 2024

Fake news detection has become a significant research top in natural language processing. Since the outbreak of the covid-19 epidemic, a large amount of fake news about covid-19 has spread on social media, making the detection of fake news a challenging task. Applying deep learning models may improve predictions. However, their lack of explainability poses a challenge to their widespread adoption and use in practical applications. This work aims to design a deep learning framework for accurate and explainable prediction of covid-19 fake news. First, we choose BiLSTM as the base model and improve the classification performance of the BiLSTM model by incorporating BERT-based distillation. Then, a post-hoc interpretation method SHAP is used to explain the classification results of the model to improve the transparency of the model and increase people's confidence in the practical application. Finally, utilizing visual interpretation methods, such as significance plots, to analyze specific sample classification results for gaining insights into the key terms that influence the model’s decisions. Ablation experiments demonstrated the reliability of the explainable method.

[...] Read more.
Information Technology for Generating Lyrics for Song Extensions Based on Transformers

By Oleksandr Mediakov Victoria Vysotska Dmytro Uhryn Yuriy Ushenko Cennuo Hu

DOI: https://doi.org/10.5815/ijmecs.2024.01.03, Pub. Date: 8 Feb. 2024

The article develops technology for generating song lyrics extensions using large language models, in particular the T5 model, to speed up, supplement, and increase the flexibility of the process of writing lyrics to songs with/without taking into account the style of a particular author. To create the data, 10 different artists were selected, and then their lyrics were selected. A total of 626 unique songs were obtained. After splitting each song into several pairs of input-output tapes, 1874 training instances and 465 test instances were obtained. Two language models, NSA and SA, were retrained for the task of generating song lyrics. For both models, t5-base was chosen as the base model. This version of T5 contains 223 million parameters. The analysis of the original data showed that the NSA model has less degraded results, and for the SA model, it is necessary to balance the amount of text for each author. Several text metrics such as BLEU, RougeL, and RougeN were calculated to quantitatively compare the results of the models and generation strategies. The value of the BLEU metric is the most diverse, and its value varies significantly depending on the strategy. At the same time, Rouge metrics have less variability and a smaller range of values. In total, for comparison, we used 8 different decoding methods for text generation supported by the transformers library, including Greedy search, Beam search, Diverse beam search, Multinomial sampling, Beam-search multinomial sampling, Top-k sampling, Top-p sampling, and Contrastive search. All the results of the lyrics comparison show that the best method for generating lyrics is beam search and its variations, including ray sampling. The contrastive search usually outperformed the usual greedy approach. The top-p and top-k methods do not have a clear advantage over each other, and in different situations, they produced different results.

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A Proposed Algorithm for Assessing and Grading Automatically Student UML Diagrams

By Rhaydae Jebli Jaber El Bouhdidi Mohamed Yassin Chkouri

DOI: https://doi.org/10.5815/ijmecs.2024.01.04, Pub. Date: 8 Feb. 2024

Digital technologies and innovative methods have shown a significant impact on educational systems, and have made work easier for both learners and teachers. Additionally, they have improved the quality and the capability to digitize the assessment of student work produced during a learning process. Assessing and scoring students’ UML diagrams has become a challenging task for teachers, especially with the growing number of students, as well as the necessity to better manage their time. Consequently, there will be a necessity to automate the assessment of these learners. This paper presents an approach for assessing and grading automatically the student’s UML diagrams. The approach uses an algorithm implemented in Java, which takes the tutor's and student's solution diagrams as input, then provides the student's scores and identifies differences and errors made. Our algorithm was tested and evaluated in a real case within a web platform, and the results obtained demonstrate the effectiveness of our solution.

[...] Read more.
A Comprehensive Meta-Analysis of Blended Learning Adoption and Technological Acceptance in Higher Education

By S. Porkodi Bassam Khalil Hamdan Tabash

DOI: https://doi.org/10.5815/ijmecs.2024.01.05, Pub. Date: 8 Feb. 2024

Numerous facets of life are impacted by the efficient application of technologies. Education, like all other fields, is a major area where technology is used to teach and learn effectively. One of these technologies that instructors and educators have recently become interested in is blended learning. This article aims at identifying the main constructs that highly influence the adoption of blended learning in higher education through meta-analytic literature review and proposing new technology acceptance model that is suitable for digital education tools. About 32 quantitative studies published since 2007 in journals and conferences are selected for performing weight computation and meta-analysis of constructs with a total sample size of 8,168. Moreover, the study also conceptualises the new technology acceptance model for digital education tools, considering both students and instructors as end users. The descriptive statistics indicate that there has been an increase in the number of publications since the year 2020. The results show that perceived ease of use, attitude toward usage, and perceived usefulness on intention to use are good predictors concerning student respondents, while performance expectancy on behavioural intention is found to be a good predictor concerning instructors. The results of the meta-analysis highlight the significance of blended learning in government and educational institutions that prioritises student ease of use and instructor performance expectancy and facilitating conditions. The results prove the effectiveness of blended learning in enhancing student learning experiences while improving educational practises and outcomes. In addition to identifying key factors influencing blended learning adoption in higher education, the study also suggests a novel model for implementing digital education tools, considering both student and instructor perspectives, thereby addressing a critical need in educational technology research. It offers actionable recommendations for policymakers and educational institutions to enhance the quality of higher learning.

[...] Read more.
Effectiveness of English Online Learning Based on Dual Channel Based Capsnet

By Raghavendra Kulkarni Indrajit Patra Neelam Sharma Tribhuwan Kumar Avula Pavani M. Kavitha

DOI: https://doi.org/10.5815/ijmecs.2024.01.06, Pub. Date: 8 Feb. 2024

Web-based learning systems have quickly developed, by giving students a broader access to wide range of courses. However, when presented with a huge number of courses, it might be difficult for users to rapidly discover the ones they are interested in, from a large amount of online educational resources. As a result, a course recommendation system is crucial to increase users' learning benefit. Presently, numerous online learning platforms have developed a variety of recommender systems using conventional data mining techniques. Still, these methods have several shortcomings, like adaptability and sparsity. To solve this problem, this study provides a deep learning based English course recommendation system with the extraction of features using a dual channel based capsule network (CapsNet). This network extracts all the important features about the courses and learners and suggests suitable courses for the learners. To evaluate the proposed model’s performance, several investigations are performed on a real-world dataset (XuetangX) and outperforms existing recommendation approaches with an average of 91% precision, 45% recall, 55% f1-score, 0.798 RMSE, and 0.671 MSE. According to the experimental findings, the proposed model provides better and more reliable recommendation performance than the conventional approaches. According to the experimental findings, the proposed model provides better and more reliable recommendation performance than the conventional approaches.

[...] Read more.
Design and Validity of an Instrument to Measure Digital Literacy among Pre-service Teachers involved in Inclusive Education

By Wu Miaomiao Dorothy De Witt Nor Nazrina Mohamad Nazry Norlidah Alias Lee Leh Hong Alijah Ujang

DOI: https://doi.org/10.5815/ijmecs.2024.01.07, Pub. Date: 8 Feb. 2024

Assessing pre-service teachers’ digital literacy is challenging, particularly in inclusive education. Reliable and valid testing instruments are required to measure the digital literacy pre-service teachers possess in inclusive education. The entire research process comprises three phases. The first stage was to develop the assessment instrument, the second stage was to validate its content validity, and a pilot study was then conducted to test the reliability and construct validity of the instrument. The results of this study showed that item-level and scale-level content validity scores were both 1.0. The Kaiser-Meyer-Olkin is equal to 0.865. Five factors were extracted, explaining 54.40% of the total variance. The model fits were also all satisfactory. Standardized factor loadings of the instrument’ s 28 items were above 0.5. The values of Cronbach’s are higher than 0.7 for the five factors and the whole instrument. It can be summarized that the instrument had good reliability and validity and can be used to assess the digital literacy of pre-service teachers in inclusive education. There has been research into developing tools to evaluate the digital literacy of pre-service teachers. Still, few studies have addressed pre-service teachers of inclusive education, and this study fills this research gap. The subsequent phase involves evaluating it using a more extensive sample.

[...] 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.
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.
OCR for Printed Bangla Characters Using Neural Network

By Asif Isthiaq Najoa Asreen Saif

DOI: https://doi.org/10.5815/ijmecs.2020.02.03, Pub. Date: 8 Apr. 2020

Optical Character recognition is a buzzword in the field of computing. Artificial neural networks are being used to recognize characters for a long time. ANN has the ability to learn and model non-linear and complex relationships, which is really important because in real life, many of the relationships between inputs and outputs are non-linear as well as complex. Research in the field of OCR with Bangla language is not as vast as the English language. So, there is a scope of research in this area. It can be used to search and scan hundreds of Bangla documents within seconds and can easily manipulate the data. It is developed for various purpose like for vision impaired person where OCR software can help turn books, magazines and other printed documents into accessible files that they can listen. The limitation of traditional OCR are sufficient dataset is not available, all different font of characters are not available and there are lots of complex and similar shape characters for which accuracy not good. In our research, we first tried to make a dataset large enough so that we can train our neural network as they require big data to train. We built our own dataset of 2,97,898 Bangla single character images of different fonts . Then for implementing neural network we used Scikit-learn’s multi-layer perceptron classifier and we also implemented our own multi-layer feed forward back propagation neural network using a machine learning framework named Tensorflow. We have also built a GUI application to demonstrate the recognition of Bangla single character images.

[...] 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.

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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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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.
A Systematic Review of 3D Metaphoric Information Visualization

By A.S.K. Wijayawardena Ruvan Abeysekera M.W.P Maduranga

DOI: https://doi.org/10.5815/ijmecs.2023.01.06, Pub. Date: 8 Feb. 2023

Today, large volumes of complex data are collected in many application domains such as health, finance and business. However, using traditional data visualization techniques, it is challenging to visualize abstract information to gain valuable insights into complex multidimensional datasets. One major challenge is the higher cognitive load in interpreting information. In this context, 3D metaphor-based information visualization has become a key research area in helping to gain useful insight into abstract data. Therefore, it has become critical to investigate the evolution of 3D metaphors with HCI techniques to minimize the cognitive load on the human brain. However, there are only a few recent reviews can be found for 3D metaphor-based data visualization. Therefore, this paper provides a comprehensive review of multidimensional data visualization by investigating the evolution of 3D metaphoric data visualization and interaction techniques to minimize the cognitive load on the human brain. Complying with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines this paper performs a systematic review of 3D metaphor-based data visualizations. This paper contributes to advancing the present state of knowledge in 3D metaphoric data visualization by critically analyzing the evolution of interactive 3D metaphors for information visualization. Further, this review identifies six main 3D metaphor categories and ten cognitive load minimizing techniques used in modern data visualization. In addition, this paper contributes three taxonomies by synthesizing the literature with a critical review of the strengths and weaknesses of metaphors. Finally, the paper discusses potential exploration paths for future research improvements.

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Extended Reality Model for Accessibility in Learning for Deaf and Hearing Students (Programming Logic Case)

By Martha Segura Ramiro Osorio Adriana Zavala

DOI: https://doi.org/10.5815/ijmecs.2023.04.01, Pub. Date: 8 Aug. 2023

A group of researchers and developers from Colombia and Mexico have recognised that the development of state-of-the-art Extended Reality software, a key technology for the Metaverse, has great potential to improve teaching-learning processes in educational institutions. However, the development process does not take into account accessibility, universal design and inclusion, especially for the deaf student community. An extended reality model is proposed for the creation of this type of software as a tool to support access to knowledge, based on information gathering, requirements analysis, user-centred design and video game programming, including the ludic and didactic. The aim is to minimise the barriers that limit the learning of programming logic by students with hearing disabilities through the use of new technologies, creating spaces in virtual worlds that are understandable, usable and practical in conditions of safety, comfort and as much autonomy as possible. To validate the model, a mixed reality software prototype was designed and programmed to train students in programming logic, both deaf and hearing. User and heuristic tests were carried out, showing how immersion can improve knowledge acquisition processes and develop skills in higher education students.

[...] Read more.
Optimised MLP Neural Network Model for Optimum Prognostic Learning of out of School Children Trend in Africa: Implication for Guidance and Counselling

By Edith Edimo Joseph Joseph Isabona Odaro Osayande Ikechi Irisi

DOI: https://doi.org/10.5815/ijmecs.2023.01.01, Pub. Date: 8 Feb. 2023

One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 - 0.310 during training and 0.034 - 0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.

[...] 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.
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.
Optimised MLP Neural Network Model for Optimum Prognostic Learning of out of School Children Trend in Africa: Implication for Guidance and Counselling

By Edith Edimo Joseph Joseph Isabona Odaro Osayande Ikechi Irisi

DOI: https://doi.org/10.5815/ijmecs.2023.01.01, Pub. Date: 8 Feb. 2023

One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 - 0.310 during training and 0.034 - 0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.

[...] Read more.
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.

[...] Read more.
A Systematic Review of 3D Metaphoric Information Visualization

By A.S.K. Wijayawardena Ruvan Abeysekera M.W.P Maduranga

DOI: https://doi.org/10.5815/ijmecs.2023.01.06, Pub. Date: 8 Feb. 2023

Today, large volumes of complex data are collected in many application domains such as health, finance and business. However, using traditional data visualization techniques, it is challenging to visualize abstract information to gain valuable insights into complex multidimensional datasets. One major challenge is the higher cognitive load in interpreting information. In this context, 3D metaphor-based information visualization has become a key research area in helping to gain useful insight into abstract data. Therefore, it has become critical to investigate the evolution of 3D metaphors with HCI techniques to minimize the cognitive load on the human brain. However, there are only a few recent reviews can be found for 3D metaphor-based data visualization. Therefore, this paper provides a comprehensive review of multidimensional data visualization by investigating the evolution of 3D metaphoric data visualization and interaction techniques to minimize the cognitive load on the human brain. Complying with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines this paper performs a systematic review of 3D metaphor-based data visualizations. This paper contributes to advancing the present state of knowledge in 3D metaphoric data visualization by critically analyzing the evolution of interactive 3D metaphors for information visualization. Further, this review identifies six main 3D metaphor categories and ten cognitive load minimizing techniques used in modern data visualization. In addition, this paper contributes three taxonomies by synthesizing the literature with a critical review of the strengths and weaknesses of metaphors. Finally, the paper discusses potential exploration paths for future research improvements.

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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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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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An Empirical Research on the Effectiveness online and Offline Classes of English Language Learning based on Student’s Perception in Telangana Schools

By K. Kashinath R. L. N. Raju

DOI: https://doi.org/10.5815/ijmecs.2023.02.04, Pub. Date: 8 Apr. 2023

Learning practices commenced to shift from face-to-face offline class learning to online classes with technological networks specifically on sudden COVID-19 crises. . This sort of variation in their learning method sparks question about students' perception of the new learning system. The objective of the study was to compare English language learning, between online classes and Offline-classes and it explicates different students' perceptions of such learning practices regarding the benefits, improvements, and drawbacks of online and offline modes. The research approach of study, proceeds with a quantitative study, using statistical analysis through questionnaire distribution. The participants of the study were the school students, obtained from Government and private schools in Telangana. The quality of the study stands outstanding in addressing the effectiveness of blended learning both online and offline learning and aids to study nature of the approach if integration of learning modes including face-to face and online learning incorporated and the consideration to improvise qualities learning experiences of students. With those aspects, the research is significant to prove the preference of students to elucidate that offline classroom learning is more preferable than online English learning. The value of the research is recognised that it aids the educators, leadership authorities and researchers to understand parameters leading to efficient learning practices, enhanced collaborative student performance outcomes assisting to select the appropriate technologies in case of any pandemic crisis and to inhibit collaborative learning in and out of classroom.  The most general obstacles faced by students in online English learning are materials insufficiency, lack of communicative skills training, lacking reading activities participation, absence of interaction, the inability of queries or doubts clarification, and exercise exposure are addressed by the analysis outcomes. The comparative perception outcomes explicated that Offline English language learning stands out as more efficient than the online learning method. 

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Information Technologies for Decision Support in Industry-Specific Geographic Information Systems based on Swarm Intelligence

By Vasyl Lytvyn Olga Lozynska Dmytro Uhryn Myroslava Vovk Yuriy Ushenko Zhengbing Hu

DOI: https://doi.org/10.5815/ijmecs.2023.02.06, Pub. Date: 8 Apr. 2023

A method of choosing swarm optimization algorithms and using swarm intelligence for solving a certain class of optimization tasks in industry-specific geographic information systems was developed considering the stationarity characteristic of such systems. The method consists of 8 stages. Classes of swarm algorithms were studied. It is shown which classes of swarm algorithms should be used depending on the stationarity, quasi-stationarity or dynamics of the task solved by an industry geographic information system. An information model of geodata that consists in a formalized combination of their spatial and attributive components, which allows considering the relational, semantic and frame models of knowledge representation of the attributive component, was developed. A method of choosing optimization methods designed to work as part of a decision support system within an industry-specific geographic information system was developed. It includes conceptual information modeling, optimization criteria selection, and objective function analysis and modeling. This method allows choosing the most suitable swarm optimization method (or a set of methods). 

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Enhancing Emotion Detection with Adversarial Transfer Learning in Text Classification

By Ashritha R Murthy Anil Kumar K. M. Abdulbasit A. Darem

DOI: https://doi.org/10.5815/ijmecs.2023.05.03, Pub. Date: 8 Oct. 2023

Emotion detection in text-based content, such as opinions, comments, and textual interactions, holds pivotal significance in enabling computers to comprehend human emotions. This symbiotic understanding between machines and human languages, powered by technological advancements like Natural Language Processing and artificial intelligence, has revolutionized the dynamics of human-computer interaction. The complexity of emotion detection, although challenging, has surged in importance across diverse domains, encompassing customer service, healthcare, and surveillance of social media interactions. Within the realm of text analysis, the quest for accurate emotion detection necessitates a profound exploration of cutting-edge methodologies. This pursuit is further intensified by the imperative to fortify models against adversarial attacks, a pressing concern in deep learning-based approaches. To address this critical challenge, this paper introduces a pioneering technique—adversarial transfer learning—specifically tailored for emotion classification in text analysis. By infusing adversarial training into the model architecture, the proposed approach emerges a solution that not only mitigates the vulnerabilities of existing methods but also fortifies the model against adversarial intrusions. In realizing the potential of the proposed approach, a diverse array of datasets is harnessed for comprehensive training. The empirical results vividly demonstrate the efficacy of this approach, showcasing its superior performance when compared to state-of-the-art methodologies. Notably, the suggested approach yields in advancements in classification accuracy. In particular, the deployment of the Adversarial transfer learning methodology has increased in accuracy of 17.35%. This study, therefore, encapsulates a dual achievement: the introduction of an innovative approach that leverages adversarial transfer learning for emotion classification, and the subsequent empirical validation of its unparalleled efficiency. The implications reverberate across multiple sectors, extending the horizons of accurate emotion detection and laying a foundation for the next stride in human-computer interaction and emotion analysis.

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