International Journal of Education and Management Engineering (IJEME)

ISSN: 2305-3623 (Print)

ISSN: 2305-8463 (Online)

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

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

Published By: MECS Press

Frequency: 6 issues per year

Number(s) Available: 91

(IJEME) in Google Scholar Citations / h5-index

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

 

IJEME has been abstracted or indexed by several world class databases: Google Scholar, CrossRef, CNKI, Scilit, Baidu Scholar,  JournalTOCs, etc..

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IJEME Vol. 16, No. 4, Aug. 2026

REGULAR PAPERS

Ontological Cores of Documents: A Construction Methodology and Use Cases

By Mohammed El Ouaazizi

DOI: https://doi.org/10.5815/ijeme.2026.04.01, Pub. Date: 8 Aug. 2026

The preservation of rare documents in the form of image collections presents significant challenges regarding access to their documentary content. To enable this accessibility for software agents, this article proposes a formal representation of this type of document through a semantic description layer. This layer includes a set of descriptive metadata attached to the document, alongside the minimal and strictly necessary vocabulary required to formalize the explicit textual and visual knowledge of its documentary content. To achieve this, we present a construction methodology based on a Semantic Model of Document (SMD), where a document is treated as a core documentary resource containing a set of information resources. The semantic description of these resources, aligned with RDF framework logic, produces an Ontological Core of Document (OCD) that formally describes the document's logical structure and captures its underlying semantics. Finally, we demonstrate the practical utility of these Ontological Cores through three distinct use cases—each targeting a specific dataset level (structural, administrative, and semantic)—showing how they allow software applications to move beyond simple collection searching toward intelligent, precise information extraction directly from the documentary content.

[...] Read more.
A Comparative Analysis of Evolutionary Metaheuristics: Genetic Algorithm vs. Particle Swarm Optimization for Feature Selection in High-Accuracy Fake News Detection

By Nikita Garg Pritam Singh Negi

DOI: https://doi.org/10.5815/ijeme.2026.04.02, Pub. Date: 8 Aug. 2026

The proliferation of online misinformation demands the development of highly accurate and computationally efficient automated systems for Fake News Detection. A primary impediment to system performance is the high dimensionality of textual features derived from techniques like TF-IDF, making optimal Feature Selection a critical step. This paper presents a detailed comparative experimental study of two prominent bio-inspired evolutionary metaheuristics, the Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) used as wrapper-based FS techniques for FND. The methodologies were rigorously tested across two distinct textual datasets: the complex, large-scale FakeNewsNet corpus and a moderate-scale general news dataset. The feature sets, once optimised, were evaluated using six standard Machine Learning (ML) classifiers. The GA-based FS approach, emphasising global exploration, achieved state-of-the-art accuracy of 99.91% with the Random Forest classifier on the FakeNewsNet dataset. In contrast, the PSO-based FS approach, valued for its rapid convergence, yielded a maximum accuracy of 93.29% with the Support Vector Machine (SVM) on the general news dataset. This analysis provides empirical evidence of the intrinsic trade-off between the algorithms: GA is superior for maximising accuracy in high-dimensional, complex textual spaces, while PSO offers a more efficient and practical solution for resource-constrained or moderate-scale FND tasks. The study confirms that evolutionary computation provides a robust, effective pathway for significantly enhancing ML classifier performance in this critical domain.

[...] Read more.
Multi-Task BanglaBERT for Joint Sentiment and Fake News Detection in COVID-19 Discourse

By Arshadul Hoque

DOI: https://doi.org/10.5815/ijeme.2026.04.03, Pub. Date: 8 Aug. 2026

The COVID-19 pandemic catalyzed an unprecedented surge of misinformation on social media, frequently intertwined with emotionally charged language. Understanding both the sentiment and truthfulness of this content is critical for public health monitoring and misinformation mitigation. However, Bangla—despite being a globally prominent language—remains severely underrepresented in joint sentiment and fake news detection research, with existing studies largely restricted to single-task settings. To bridge this gap, this paper proposes a novel multi-task BanglaBERT-based framework for the simultaneous classification of sentiment and truthfulness in COVID-19 discourse. Furthermore, we introduce the first publicly available, dual-annotated Bangla corpus for this domain, comprising 35,526 textual samples aggregated from social media and news sources. Our architecture employs a shared BanglaBERT encoder with dual task-specific heads, optimized using a task-prioritized loss function that combines modified Focal Loss and weighted cross-entropy to address inherent class imbalances. Extensive experiments demonstrate that the proposed model achieves 75.1% accuracy (Macro F1: 0.707) for sentiment classification and 88.0% accuracy (Macro F1: 0.851) for truthfulness detection. Ablation studies and error analyses confirm that our tailored loss strategies significantly enhance the recognition of underrepresented and semantically ambiguous classes, particularly neutral sentiments. By releasing our dataset, code, trained models, and a Gradio-based interactive demo, this work establishes a robust benchmark for multi-task learning in low-resource Bangla NLP and provides a practical tool for fact-checking during health crises.

[...] Read more.
Shelf Space Optimization in Retail: A Study Using Linear Programming, Genetic Algorithm, and Proximal Policy Optimization

By Anitha Palakshappa Shruti J. R. Sowmya Kyathanahalli Nanjappa Ashwitha Anni Aditya Gaonkar Bhawna Botra

DOI: https://doi.org/10.5815/ijeme.2026.04.04, Pub. Date: 8 Aug. 2026

The aim is to design a comprehensive shelf space optimization framework that maximizes profitability, enhances sales forecasting, improves efficiency of inventory management, and supports effective decision-making in retail businesses. A robust and interactive analytical dashboard is developed that allows users to visualize critical sales metrics, analyze historical data trends, and accurately forecast product demand and supply requirements based on seasonal variations and sales performance. The work integrates three mathematical optimization paradigms like Linear Programming (LP), metaheuristic search via Genetic Algorithms (GA), and reinforcement learning using Proximal Policy Optimization (PPO) to support both static and adaptive allocation strategies. Experimental validation highlights the relative advantages of each method, with detailed evaluations based on forecast accuracy, inventory turnover efficiency, shelf utilization rate, and overall improvement in profitability. Unlike traditional static optimization models, the PPO-based framework continuously adapts allocation decisions using environmental feedback, improving flexibility in dynamic retail scenarios The paper uses multi-objective shelf optimization considering profitability, utilization, and customer demand simultaneously. The results demonstrate that the integration of predictive analytics and advanced optimization techniques significantly performs traditional shelf management approaches, offering retailers actionable insights and operational advantages.

[...] Read more.
Analysis and Comparison of Air Quality Index Prediction using Regression Based Machine Learning Models

By Priyanka Goyal Utkrisht Patel

DOI: https://doi.org/10.5815/ijeme.2026.04.05, Pub. Date: 8 Aug. 2026

In this research work, multiple machine learning regression techniques were used to predict the pollution and offer a comparative study to establish the optimum model for reliably predicting air quality in terms of data quantity and processing time. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used as evaluation measures to compare these regression models. Furthermore, the processing time of each algorithm was determined via standalone learning and hyper-parameter tweaking to produce the best-fit model in terms of computational time and error rate. In this paper, we have calculated the custom score which is sum of MAE, RMSE, MAPE and time processing values. The best model obtained is the custom stacked regression model has custom score of 111.41 which is very less as compared to other regression models.

[...] Read more.
Edge-First Adaptive Learning with Lightweight RL and LNN for STEM Education in Low- Resource Kenyan Schools

By David Shiala Ongoma

DOI: https://doi.org/10.5815/ijeme.2026.04.06, Pub. Date: 8 Aug. 2026

There’s a lot of promise around artificial intelligence for education to personalize learning; however, there has been very little research regarding Artificial Intelligence (AI) applications in fields with very few resources for its implementation. This paper describes a proposed AI-based adaptive learning system that aims to personalize STEM education in a low-resource school environment in Kenya. This research addresses the numerous challenges associated with such a system, such as irregular internet access, limited computer hardware in situ and no previous teacher background in both AI and education. To address these issues, an adapted reinforcement learning algorithm will personalize the content shown to students, and a modified liquid neural network is used for the prediction of student success, while not being computationally expensive. As compared to traditional adaptive systems, this adaptive learning platform supports edge computing and offline updates in order to operate in a consistently low connectivity environment. In addition, by continually adjusting the difficulty, format, and rate of delivery of STEM topics to fit the style, prior knowledge, and attention level of the individual student, this platform has been seen to improve the educational experience. An 8-month case study was done with 6 Kenyan low-resource schools and 6 comparison schools located in the city. In this case, we report a 31% increase in the level of understanding of students’ key STEM subjects, 27% reduction in student drop rate from STEM topics, and 43% increase in teacher efficiency over traditional methods. We were also able to predict the level of performance of students to a 89% success while occupying a low 1.9MB memory, making it feasible to be employed on budget Android devices. This study presents evidence that the utilization of AI for personalized adaptive learning technologies in order to minimize the disparities in the provision of STEM education in low-resource settings worldwide is possible. 

[...] Read more.
A Multi-Criteria Evaluation Framework for Performance, Sustainability, and Scalability of Moodle-Based Learning Management Systems: A Data-Driven Approach

By Mohammad Nasar Mohammad Abu Kausar

DOI: https://doi.org/10.5815/ijeme.2026.04.07, Pub. Date: 8 Aug. 2026

Learning management systems (LMS) have become an important part of the modern higher education industry, and Moodle is one of the most popular open-source systems, which have become popular on an international level. Despite its extensive application, the majority of the existing evaluation tools are inclined to examine either performance, sustainability, or scalability separately and, thus, cannot be useful in long-term institutional planning. The current study proposes an assessment model that is a rational model of a Moodle-based LMS, as all three dimensions are included in the assessment model. The framework identifies the performance of the system, resource utilization and resource management, load-adaptive scalability metric, and an adaptable and predictable algorithmic-based assessment procedure in a variety of deployment environments. The framework applies Min–Max normalization and weighted aggregation to combine the three evaluation dimensions into a unified assessment score. To make the model more realistic, it was tested on publicly available data, for example, the Open University Learning Analytics Dataset (OULAD), which simulated actual interactions between LMS users and the utilization of cloud resource traces to analyze scalability and sustainability. Experimental evaluation using the OULAD and cloud resource datasets demonstrated approximately a 10% improvement in performance under medium workload conditions, an 18% reduction in sustainability due to increased resource utilization, and a 20% improvement in scalability as workload increased. These findings demonstrate that the proposed framework provides a systematic and data-driven approach for evaluating Moodle-based learning management systems and supports informed institutional decision-making.

[...] Read more.
The Impact of AI-Powered Digital Assistants on Student Emotions, Engagement, and Academic Performance: A PLS-SEM Analysis in Higher Education

By Dennis Franscico Chandiona

DOI: https://doi.org/10.5815/ijeme.2026.04.08, Pub. Date: 8 Aug. 2026

This study investigates the impact of AI-powered digital assistants on students’ feelings, engagement, and academic success within higher education environments. The study aims to investigate post-adoption behaviour, emphasizing how service experiences, functional attributes, information quality, and ease of interaction influence emotional and behavioural results. A structured survey was used to gather data from 431 respondents in higher education at Exploits university, Malawi, and the study utilized a quantitative approach. Measurement scales were adapted from validated studies in the AI adoption and educational technology literature and contextualized for the higher education setting. Partial Least Squares Structural Equation Modelling (PLS-SEM) version 4.1.1.8 was utilized to examine the connections between variables. Common method bias was assessed using the full collinearity approach, and all VIF values were below the recommended threshold, indicating that common method bias was not a significant concern. The results indicate that Service experience leads to Positive emotions (β = 0.205, p = 0.002) and Student engagement (β = 0.242, p < 0.001), validating H1a and H1b. Quality of information → Positive feelings (β = 0.161, p = 0.005), backing H3a, whereas Functional characteristics → Student involvement (β = 0.409, p < 0.001), supporting H4b. Positive emotions → Student involvement (β = 0.190, p < 0.001) and Academic achievement (β = 0.460, p < 0.001), and Student involvement → Academic achievement (β = 0.310, p < 0.001), confirming H5–H7. Contextualization × Positive emotions → Academic performance was noteworthy (β = 0.063, p = 0.038), reinforcing H8a. Nonetheless, H2a, H2b, H3b, H4a, and H8b received no support (p > 0.05). The research advances theoretical understanding by broadening AI adoption literature to include emotional and behavioural effects, while also enhancing practical implications by highlighting service quality and system efficiency. Suggestions emphasize the importance of focusing on contextual, high-quality AI resources to enhance student engagement, emotional well-being, and educational achievement. The results demonstrate that service experience is the most influential antecedent of both emotional and behavioural outcomes, whereas the effects of functional features and information quality vary across the examined relationships.

[...] Read more.
Stressors and Stress-Coping Mechanisms of Academic Scholars in HEIs: A Basis for Stress Management Plan Formulation

By Ruth G. Luciano Mickel John Salvatierra

DOI: https://doi.org/10.5815/ijeme.2022.03.01, Pub. Date: 8 Jun. 2022

This study aims to describe the stress coping mechanism of the academic scholars from the College of Education (COEd) in one of the private higher education institutions in Cabanatuan City, Philippines. This is an action research that focuses on the assessment of the academic scholars’ stressors and their correlates. It involves systematic observations and data collection that enables the researchers to reflect, decide and develop a training plan for stress management. The findings show that monthly family income and economic-related stressors were highly correlated. This further explains that students with high family income are less likely to experience frequent stress. In contrary, students who belong to low-income families are more prone to experience frequent stress. In other words, students who belong to poor families are more vulnerable to stress. Likewise, monthly family income and physiological responses to stress had high interdependence, which means that students with higher socio-economic status are less likely to experience severe anxiety, while students belonging to low-income families tend to experience severe anxiety. The results of this quantitative analysis served as basis in designing or preparing the stress management plan for these students. 

[...] Read more.
Push Management Platform Based on Wechat Small Program and Cloud Development

By Yan Wu Fang Wang Yanying Zou Huaijin Zhang Bingsheng Chen Mengshan Li

DOI: https://doi.org/10.5815/ijeme.2020.01.03, Pub. Date: 8 Feb. 2020

On the Wechat platform, the current article push is mainly completed by the Wechat Public Account, but it is not perfect in the aspects of user information collection, user service, data storage and management. With economic development and progress of the times, people seek development in spiritual and cultural aspects. This program "One Thing One Story" uses Wechat Web Developer Tools as the medium and Wechat Small Program and Cloud Development as the platform. The purpose of push management platform is "use at any time". Small program cloud development has a relatively complete cloud background. It does not need to rebuild the server in the development cycle. Through the relevant interface, small program development can be started and time cost can be reduced. Using JavaScript, CSS style, JSON database and other technologies, we can realize user data collection, article push, push classification management, push data storage, user praise collection and other functions. This program is applied to article pushing, cultural dissemination and other aspects. Through the platform of Wechat applet, the dream of "accessible" can be realized. 

[...] Read more.
A Study on Malware and Malware Detection Techniques

By Rabia Tahir

DOI: https://doi.org/10.5815/ijeme.2018.02.03, Pub. Date: 8 Mar. 2018

The impact of malicious software are getting worse day by day. Malicious software or malwares are programs that are created to harm, interrupt or damage computers, networks and other resources associated with it. Malwares are transferred in computers without the knowledge of its owner. Mostly the medium used to spread malwares are networks and portable devices. Malwares are always been a threat to digital world but with a rapid increase in the use of internet, the impacts of the malwares become severe and cannot be ignored. A lot of malware detectors have been created, the effectiveness of these detectors depend upon the techniques being used. Although researchers are developing latest technologies for the timely detection of malwares but still malware creators always stay one step ahead. In this paper, a detailed review of malwares types are provided, malware analysis and detection techniques are studied and compared. Furthermore, malware obfuscation techniques have also been presented.

[...] Read more.
Classroom Management Strategies and Academic Performance of Junior High School Students

By Maxwell Kontor Owusu Bakari Yusuf Dramanu Mark Owusu Amponsah

DOI: https://doi.org/10.5815/ijeme.2021.06.04, Pub. Date: 8 Dec. 2021

The study examined the influence of classroom management strategies of Junior High School teachers on the academic performance of students in the Ashanti Akim North District. The descriptive survey design was used for the study. One hypothesis and two research questions were developed to guide the study. Multistage sampling technique was used to select 48 teachers and 297 year two students to respond to the Behaviour and Instructional Management Scale (BIMS). Test scores in English Language, Integrated Science, Mathematics and Social Studies were used to measure students’ academic performance. The statistical tools used to analyse the data collected were means, standard deviation, Pearson’s Product Moment Correlation Coefficient (PPMCC) and Multiple Regression. The findings revealed that both students and teachers identified good relationship and reinforcement as the mostly used classroom management strategies. It was found that a significant positive relationship existed between reinforcement and antecedent as classroom management schemes and students’ academic performance. However, good relationship and punishment as classroom management strategies did not have a positive relationship with the academic performance of students. It is recommended that teachers should use reinforcement and antecedent strategies frequently in their classrooms since they play a dual role of managing behaviour and predicting the academic performance of students. Good relationship as a classroom management strategy should be cautiously used because it could potentially be misinterpreted or abused and can lead to low academic performance. Using punishment as a classroom management strategy should be avoided as its use hinders academic performance of students.

[...] Read more.
Medicine Management System: Its Design and Development

By Ruth G. Luciano Rhoel Anthony G. Torres Edward B. Gomez Hardly Joy D. Nacino Rodmark D. Ramirez

DOI: https://doi.org/10.5815/ijeme.2023.03.02, Pub. Date: 8 Jun. 2023

The researchers conducted this study with the main purpose of helping the residents of the municipality to expedite the process of obtaining free medicine. In the current setup, an individual who needs to avail of free medicine from the barangay or municipal health center personally visits the place to request maintenance medicine. This motivated the researchers to make a research study focusing on converting the manual requisition system to something that people can access quickly and comfortably without necessarily going out of their households, especially during these challenging times – the pandemic. The researchers called it a “Medicine Management System”. The researchers aimed to speed up the requisition of medicine using this online system. The patients or qualified recipients need not consume time lining up to request medicine from the municipal health center. This system can be accessed over the internet anytime and anywhere. Users must register and upload a legit doctor’s prescription. Researchers have created this system using HTML for the system interface, XAMPP for maintaining database records, and PHP for other system functionalities.

[...] Read more.
The Application of Computer Softwares in Chemistry Teaching

By Wei Yu Lifei Chen

DOI: https://doi.org/10.5815/ijeme.2012.12.12, Pub. Date: 29 Dec. 2012

Chemistry is very interesting, but it is often regarded as a difficult subject. Computer software can make chemistry teaching easier, and keeps the students active. This paper seeks to introduce some implications of computer softwares in chemistry classroom teaching. These softwares include Powerpoint, Chemoffice, computer simulation softwares, LabVIEW software, some computational chemistry softwares, and other chemistry softwares, such as ACD/ChemSketch, ChemDB software, Chemical Reagent Calculator, Atom Builder and Atoms, Symbols and Equations. We gave the simple directions of these softwares and presented some applicable examples.

[...] Read more.
Machine Learning Applications in Algorithmic Trading: A Comprehensive Systematic Review

By Arash Salehpour Karim Samadzamini

DOI: https://doi.org/10.5815/ijeme.2023.06.05, Pub. Date: 8 Dec. 2023

This paper reviews recent advancements in machine learning (ML) driven automated trading systems (ATS). ATS has progressed from simple rule-based systems to sophisticated ML models like deep reinforcement learning, deep learning, and Q-learning that can adapt to evolving markets. These techniques have been successfully applied across various financial instruments to optimize trading strategies, forecast prices, and enhance profits. The literature indicates that ML improves ATS performance over conventional methods by identifying intricate patterns and relationships in data. However, risks like overfitting, instability, and low interpretability exist. Techniques to mitigate these limitations include cross-validation, careful model management, and utilizing more transparent algorithms. Although challenges remain, ML creates valuable opportunities for ATS via alternative data sources, advanced feature engineering, optimized adaptive strategies, and holistic market modelling. While research shows ML improves market quality through increased liquidity and efficiency, heightened volatility needs further analysis. Promising future research directions include leveraging innovations in deep learning, reinforcement learning, sentiment analysis, and hybrid systems. More work is also needed on evaluating different techniques systematically. Overall, the progress in ML-driven ATS contributes significantly to the field, but judicious application and balanced regulations are required to address risks. Further advancements in ML will enable more capable, nuanced, and profitable algorithmic trading.

[...] Read more.
The Effectiveness of the TaRL Approach on Moroccan Pupils’ Mathematics, Arabic, and French Reading Competencies

By Abdessamad Binaoui Mohammed Moubtassime Latifa Belfakir

DOI: https://doi.org/10.5815/ijeme.2023.03.01, Pub. Date: 8 Jun. 2023

Teaching at the Right Level (henceforth, TaRL) is a new trending remedial educational approach being piloted in many countries. It basically matches pedagogical content to pupils’ educational needs through various adapted activities after segmentation of pupils’ depending on their actual difficulties and needs. In this respect, Morocco has been piloting this relatively new approach during the beginning of the school year 2022-23. Therefore, this study aimed at measuring the effectiveness of the TaRL approach on Moroccan pupils’ mathematics, Arabic, and French reading competencies. An experimental study took place involving 106 pupils from 4th grade to 6th grade during a one-month remedial course (half an hour per day, one subject per day) based on TaRL guidelines. After carefully examining the data through the Wilcoxon Signed Ranks Test by comparing the baseline and endline results in all three subjects. The results showed statistically high improvements with large effect sizes in the levels of the three subjects suggesting that TaRL was effective in raising the levels of numeracy and literacy and may be, safely, further adopted throughout Moroccan primary schools.

[...] Read more.
The Digital Literacy in Teachers of the Schools of Rajouri (J&K)-India: Teachers Perspective

By M Mubasher Hassan Tabasum Mirza

DOI: https://doi.org/10.5815/ijeme.2021.01.04, Pub. Date: 8 Feb. 2021

The present age is the age of information. The globalization has affected every sphere of the life including education. In spite of availability of ICT infrastructure in schools, their potential is underutilized because of digital incompetence of the teachers.  New digital technologies are acting as a catalyst towards improvement of learning outcome and enhancing quality of education, but only introduction of such technologies in schools for producing change and innovation is not enough, it requires digitally competent teachers to facilitate the use of ICT in education. These teachers will act as facilitators and mentors to students to lead them towards problem solving and innovation to meet the new challenges of globalization. Teachers must be able to create learning environments which are student centric and foster creativity, Meta cognition, meta-literacy, collaboration and communication in learners. Mere superficial use of ICT in teaching will not yield the required learning outcome, but the integration of ICT in pedagogy is important to enhance teaching, learning process. This can be done only when teachers are competent enough to use ICT tools and facilitate ICT integrated education. In this paper, we tried to assess the teacher’s perspective about the ICT and investigate the factors responsible for resistance of teachers in using ICT in schools and suggestive measures for successful integration of ICT in the teaching process by the teachers of Rajouri district (J&K, India). The ICT skills are very important for teachers to support alternative modes of teaching, learning, i.e. e-learning, mobile learning in the present outbreak of pandemic disease caused by Coronavirus-COVID19. 

[...] Read more.
A Critical Review by Teachers on the Online Teaching-Learning during the COVID-19

By Malik Mubasher Hassan Tabasum Mirza Mirza Waseem Hussain

DOI: https://doi.org/10.5815/ijeme.2020.05.03, Pub. Date: 8 Oct. 2020

The world has witnessed a sudden change in the teaching-learning processes due to the ongoing pandemic of COVID-19. The worldwide compulsive lockdown for ensuring the preventive measures to stop the spread of this infection has equally affected education sector as other business sectors. As all of us know that quality education is the only long-term rescue for all the challenges and therefore, the need to find out the alternative solution to the traditional classroom teaching-learning is the concern of all stakeholders and the only option found is online mode of teaching-learning, which was somehow already available and had attracted an intense attention during this period. The aim of the paper is to study the teacher’s perspective in India about this mode of learning, challenges and issues faced by them in migration to online platform, experience about online tools/platforms used for instructional delivery and their suggestions to improve the process for effective teaching. This study will help in gaining insight towards the possible improvements in the ongoing mode of online teaching and in future situations also. The results obtained based on sample collection through web based questionnaire clearly gives some information, which could be an eye opener for enhancing the implementation of the online teaching-learning among the learners especially teachers, who can further help in implementation of the large. Although, the online mode was already in place and was utilized in blended form to a substantial level in the developed countries, but in developing countries like India, where teachers are not familiar with online platforms/tools, lack of knowledge and skills to handle the online ICT infrastructure in a challenging situation. The results also give an impression about the need of professional development with special focus on digital literacy skills and awareness among the teacher community about the merits of online platforms for the teaching-learning process.

[...] Read more.
Push Management Platform Based on Wechat Small Program and Cloud Development

By Yan Wu Fang Wang Yanying Zou Huaijin Zhang Bingsheng Chen Mengshan Li

DOI: https://doi.org/10.5815/ijeme.2020.01.03, Pub. Date: 8 Feb. 2020

On the Wechat platform, the current article push is mainly completed by the Wechat Public Account, but it is not perfect in the aspects of user information collection, user service, data storage and management. With economic development and progress of the times, people seek development in spiritual and cultural aspects. This program "One Thing One Story" uses Wechat Web Developer Tools as the medium and Wechat Small Program and Cloud Development as the platform. The purpose of push management platform is "use at any time". Small program cloud development has a relatively complete cloud background. It does not need to rebuild the server in the development cycle. Through the relevant interface, small program development can be started and time cost can be reduced. Using JavaScript, CSS style, JSON database and other technologies, we can realize user data collection, article push, push classification management, push data storage, user praise collection and other functions. This program is applied to article pushing, cultural dissemination and other aspects. Through the platform of Wechat applet, the dream of "accessible" can be realized. 

[...] Read more.
Stressors and Stress-Coping Mechanisms of Academic Scholars in HEIs: A Basis for Stress Management Plan Formulation

By Ruth G. Luciano Mickel John Salvatierra

DOI: https://doi.org/10.5815/ijeme.2022.03.01, Pub. Date: 8 Jun. 2022

This study aims to describe the stress coping mechanism of the academic scholars from the College of Education (COEd) in one of the private higher education institutions in Cabanatuan City, Philippines. This is an action research that focuses on the assessment of the academic scholars’ stressors and their correlates. It involves systematic observations and data collection that enables the researchers to reflect, decide and develop a training plan for stress management. The findings show that monthly family income and economic-related stressors were highly correlated. This further explains that students with high family income are less likely to experience frequent stress. In contrary, students who belong to low-income families are more prone to experience frequent stress. In other words, students who belong to poor families are more vulnerable to stress. Likewise, monthly family income and physiological responses to stress had high interdependence, which means that students with higher socio-economic status are less likely to experience severe anxiety, while students belonging to low-income families tend to experience severe anxiety. The results of this quantitative analysis served as basis in designing or preparing the stress management plan for these students. 

[...] Read more.
Medicine Management System: Its Design and Development

By Ruth G. Luciano Rhoel Anthony G. Torres Edward B. Gomez Hardly Joy D. Nacino Rodmark D. Ramirez

DOI: https://doi.org/10.5815/ijeme.2023.03.02, Pub. Date: 8 Jun. 2023

The researchers conducted this study with the main purpose of helping the residents of the municipality to expedite the process of obtaining free medicine. In the current setup, an individual who needs to avail of free medicine from the barangay or municipal health center personally visits the place to request maintenance medicine. This motivated the researchers to make a research study focusing on converting the manual requisition system to something that people can access quickly and comfortably without necessarily going out of their households, especially during these challenging times – the pandemic. The researchers called it a “Medicine Management System”. The researchers aimed to speed up the requisition of medicine using this online system. The patients or qualified recipients need not consume time lining up to request medicine from the municipal health center. This system can be accessed over the internet anytime and anywhere. Users must register and upload a legit doctor’s prescription. Researchers have created this system using HTML for the system interface, XAMPP for maintaining database records, and PHP for other system functionalities.

[...] Read more.
A Study on Malware and Malware Detection Techniques

By Rabia Tahir

DOI: https://doi.org/10.5815/ijeme.2018.02.03, Pub. Date: 8 Mar. 2018

The impact of malicious software are getting worse day by day. Malicious software or malwares are programs that are created to harm, interrupt or damage computers, networks and other resources associated with it. Malwares are transferred in computers without the knowledge of its owner. Mostly the medium used to spread malwares are networks and portable devices. Malwares are always been a threat to digital world but with a rapid increase in the use of internet, the impacts of the malwares become severe and cannot be ignored. A lot of malware detectors have been created, the effectiveness of these detectors depend upon the techniques being used. Although researchers are developing latest technologies for the timely detection of malwares but still malware creators always stay one step ahead. In this paper, a detailed review of malwares types are provided, malware analysis and detection techniques are studied and compared. Furthermore, malware obfuscation techniques have also been presented.

[...] Read more.
Machine Learning Applications in Algorithmic Trading: A Comprehensive Systematic Review

By Arash Salehpour Karim Samadzamini

DOI: https://doi.org/10.5815/ijeme.2023.06.05, Pub. Date: 8 Dec. 2023

This paper reviews recent advancements in machine learning (ML) driven automated trading systems (ATS). ATS has progressed from simple rule-based systems to sophisticated ML models like deep reinforcement learning, deep learning, and Q-learning that can adapt to evolving markets. These techniques have been successfully applied across various financial instruments to optimize trading strategies, forecast prices, and enhance profits. The literature indicates that ML improves ATS performance over conventional methods by identifying intricate patterns and relationships in data. However, risks like overfitting, instability, and low interpretability exist. Techniques to mitigate these limitations include cross-validation, careful model management, and utilizing more transparent algorithms. Although challenges remain, ML creates valuable opportunities for ATS via alternative data sources, advanced feature engineering, optimized adaptive strategies, and holistic market modelling. While research shows ML improves market quality through increased liquidity and efficiency, heightened volatility needs further analysis. Promising future research directions include leveraging innovations in deep learning, reinforcement learning, sentiment analysis, and hybrid systems. More work is also needed on evaluating different techniques systematically. Overall, the progress in ML-driven ATS contributes significantly to the field, but judicious application and balanced regulations are required to address risks. Further advancements in ML will enable more capable, nuanced, and profitable algorithmic trading.

[...] Read more.
Classroom Management Strategies and Academic Performance of Junior High School Students

By Maxwell Kontor Owusu Bakari Yusuf Dramanu Mark Owusu Amponsah

DOI: https://doi.org/10.5815/ijeme.2021.06.04, Pub. Date: 8 Dec. 2021

The study examined the influence of classroom management strategies of Junior High School teachers on the academic performance of students in the Ashanti Akim North District. The descriptive survey design was used for the study. One hypothesis and two research questions were developed to guide the study. Multistage sampling technique was used to select 48 teachers and 297 year two students to respond to the Behaviour and Instructional Management Scale (BIMS). Test scores in English Language, Integrated Science, Mathematics and Social Studies were used to measure students’ academic performance. The statistical tools used to analyse the data collected were means, standard deviation, Pearson’s Product Moment Correlation Coefficient (PPMCC) and Multiple Regression. The findings revealed that both students and teachers identified good relationship and reinforcement as the mostly used classroom management strategies. It was found that a significant positive relationship existed between reinforcement and antecedent as classroom management schemes and students’ academic performance. However, good relationship and punishment as classroom management strategies did not have a positive relationship with the academic performance of students. It is recommended that teachers should use reinforcement and antecedent strategies frequently in their classrooms since they play a dual role of managing behaviour and predicting the academic performance of students. Good relationship as a classroom management strategy should be cautiously used because it could potentially be misinterpreted or abused and can lead to low academic performance. Using punishment as a classroom management strategy should be avoided as its use hinders academic performance of students.

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The Effectiveness of the TaRL Approach on Moroccan Pupils’ Mathematics, Arabic, and French Reading Competencies

By Abdessamad Binaoui Mohammed Moubtassime Latifa Belfakir

DOI: https://doi.org/10.5815/ijeme.2023.03.01, Pub. Date: 8 Jun. 2023

Teaching at the Right Level (henceforth, TaRL) is a new trending remedial educational approach being piloted in many countries. It basically matches pedagogical content to pupils’ educational needs through various adapted activities after segmentation of pupils’ depending on their actual difficulties and needs. In this respect, Morocco has been piloting this relatively new approach during the beginning of the school year 2022-23. Therefore, this study aimed at measuring the effectiveness of the TaRL approach on Moroccan pupils’ mathematics, Arabic, and French reading competencies. An experimental study took place involving 106 pupils from 4th grade to 6th grade during a one-month remedial course (half an hour per day, one subject per day) based on TaRL guidelines. After carefully examining the data through the Wilcoxon Signed Ranks Test by comparing the baseline and endline results in all three subjects. The results showed statistically high improvements with large effect sizes in the levels of the three subjects suggesting that TaRL was effective in raising the levels of numeracy and literacy and may be, safely, further adopted throughout Moroccan primary schools.

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An Empirical Study on Make-or-buy Decision Making

By Monika Arora Anand Kumar

DOI: https://doi.org/10.5815/ijeme.2022.01.03, Pub. Date: 8 Feb. 2022

Every enterprise will be based on the other enterprise to manufacture, product items/parts, for make or buy. The make-buy decision is based on the assessment whether it should be manufactured or buy it from an outside supplier to produce a component internally or to buy it from the outside. It depends on cost and profitability. The cost for both the alternatives may be calculated and the alternative with less cost is to be chosen. The aim of any enterprise is to improve its performance that is measured in terms of profitability. There is some research that has been carried out to make the decision based on profitability of the enterprise for make or buy decision.  The strategy is based on cost, flexibility and responsiveness of work to be carried out. However, some of the research is required to maintain the relationship between profitability and make or buy decision.

The reports of this paper attempt made on how buying decision influences the performances of an enterprise. The different sectors were chosen for the study such as Manufacturing, Automobile, Food, Textile and Hospitality. The focus of the study was based on three theories such as operational control, performance management and decision. The paper reveals the current trends and make or buy decision of the components and its relationship in taking decisions. It also discusses the two techniques break even analysis and economic analysis for decision making in make or buy decision. The study discusses the advantage of outsourcing and discusses the four theories in the study for make and buys decision

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An Internet of Thing based Agribot (IOT- Agribot) for Precision Agriculture and Farm Monitoring

By Kakelli Anil Kumar Aju. D.

DOI: https://doi.org/10.5815/ijeme.2020.04.04, Pub. Date: 8 Aug. 2020

Developing nations like India have a huge potential for agricultural business and better cultivation. Because of the large size of cultivation land, improper water supply systems and lack of technology-based agricultural practices, there is a huge gap among expected and actual quantity and quality of agricultural products. Hence there is a need for significant revival in agribusiness using emerging technologies. The article proposes an intelligent water framework device called Agribot designed for the agricultural industry to minimize the water wastage and a better supply of cultivating materials using the Internet of Things (IoT). Our proposed IOT- Agribot will energize the water framework, improve the cost-effective water usage and reduce the labor force to achieve precision agriculture. The proposed IOT- Agribot has performed well for variable weather conditions, soli type, moisture content and crops.

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Essential and New Maintenance KPIs Explained

By Fatima Zohra Berrabah Chahira Belkacemi Leila Zemmouchi-Ghomari

DOI: https://doi.org/10.5815/ijeme.2022.06.02, Pub. Date: 8 Dec. 2022

Maintenance in any manufacturing organization is critical, given its significant role in ensuring business continuity. Maintenance plays a crucial role and has a significant impact on the results of industrial companies. Therefore, it is essential to manage maintenance, observe, understand, and improve actions by adopting well-chosen performance indicators according to the company's needs. These indicators are known as Maintenance KPIs or Key Performance Indicators, which allow for gathering knowledge and exploring the best means to achieve the organization's goals. Maintenance KPIs are critical to keeping track of the function, monitoring performance, and ensuring fulfillment of business expectations. In addition, KPIs drive reliability growth while guiding decisions to improve maintenance efficiency and performance. A helpful maintenance KPIs help to identify the problems causing the maintenance effect and help to select the right strategy to support or correct the actions that produced the results. They also allow to identify the causes of equipment failures (measure the influence of life cycle factors), direct what maintenance does with its time and resources (measure the efficiency and effectiveness of the maintenance group) and identify if maintenance removes failure causes ( measure the improved reliability and operational risk reduction results of maintenance effort) and help drive the business benefits provided by maintenance (measure the contribution to the business value of maintenance).

Essential maintenance KPIs are the most commonly used for maintenance management and are adopted by most industries; among these primary KPIs which are essential for maintenance management, we cite Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), and Overall Equipment (OEE). Nevertheless, it is crucial to continuously redefine and update KPIs to ensure they are appropriate for the organization's current environment, significantly when the constant market or research methodologies change. Hence, researchers and the industry propose several other maintenance KPIs outside the essential ones used in the industry according to the needs and within the performance improvement framework. These proposed KPIs aim to compensate for the lack of maintenance data, the absence of decision support, and the problems related to specific equipment, also in the context of improving the management strategy, the application of predictive maintenance, and the quality control of a maintenance process or the monitoring of systems reviews. Unfortunately, these indicators are not sufficiently known and are, therefore, not used by the industry. However, we believe that some of them should gain maturity and reach the status of widely used traditional indicators, such as the KPI of obsolescence management in maintenance operations and schedule compliance KPIs that aim to link maintenance planning with production. In addition, although not all proposed KPIs in the literature are generalizable, it has been identified that they can sometimes be specific to problematic situations, equipment categories, and even sectors of industry activity. Therefore, this work aims to inventory the most widely used maintenance KPIs and some of the KPIs proposed by researchers and the industry. In addition, we study the trends and challenges of selecting these KPIs and for what purposes they are used to help their understanding and usability. Indeed, Maintenance managers need to select relevant KPIs aligned with the maintenance strategy and the company objectives. 

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