Abstract

ICT learning systems in war-affected regions face continuous disruption caused by damaged infrastructure, unstable connectivity, mobility restrictions, and psychological stress. These conditions interrupt digital-skills development and limit access to online freelancing opportunities. This paper proposes a Digital Resilience MIS Framework designed to restore and sustain ICT learning during active conflict. The framework is developed from two ICT training programs implemented in Gaza during the 2025 war, both of which operated throughout severe instability. Analysis of program documentation shows that learning continuity depended on five interconnected pillars: resilient infrastructure; adaptable instructional workflows; structured monitoring and information flow; human-centered psychosocial support; and coordinated organizational decision-making. When combined, these elements enabled the programs to maintain attendance, progress through technical content, and complete practical projects despite external disruptions. The framework offers a practical model for institutions seeking to preserve digital learning and freelance-readiness in fragile settings and contributes to MIS scholarship by demonstrating how resilience principles can be operationalized in active conflict environments.

Abstract

The construction industry in Oman faces significant risks that influence cost, time, and overall project performance. Although risk management has been widely studied globally, research focusing specifically on Oman and the Middle Eastern context remains limited. This study manages this gap by conducting a systematic literature review on risk identification and control strategies in construction projects. Eligible studies were selected by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. SCOPUS and ScienceDirect were selected as the leading journal databases. A total of 33 articles were selected for further analysis through this procedure. The review synthesises findings from recent studies, highlighting critical risk identification and classification, including financial, environmental, operational, logistical, regulatory, legal, health and safety, and market risks, as well as strategies for mitigating them. Several recommendations were also suggested to provide the essential knowledge and information for future research. Based on the insights gained, a conceptual framework is proposed to guide risk control practices in Oman’s construction industry.

Abstract

Digital transformation is increasingly influencing English literacy education in secondary schools; nonetheless, differences in technology utilisation and its effects persist among institutions. This study investigates the correlation between students' preferences and the frequency of digital learning methodologies and technology integration in the advancement of English literacy. A quantitative descriptive–correlational cross-sectional design was utilised, employing a 20-item Likert questionnaire. The population consisted of senior high school students in Palu City, Indonesia, with a sample of 469 students from five schools chosen via purposive and convenience sampling methods. The results show a very significant link between Group 1's choice for digital methods and Group 2's impact on technology integration and literacy (r = 0.872; r² ? 0.761). The total visual correlation is r = 0.918. The average perception score was M = 76.89 (SD = 9.23). These results show that basic digital skills have a big impact on literacy outcomes. This means that teachers need more training in digital skills and schools need better infrastructure to facilitate long-term changes in education.

Abstract

The support activities of the value chain model include procurement, firm infrastructure, human resource management, and technology management. This study aims to assess the influence of retail value chain management support actions on customer satisfaction. This study is based on Michel Porter's (1985) generic value chain model, which strives to provide organizations with a competitive edge. This is an empirical study in which 500 retail customers provided primary data using a structured questionnaire covering factors related to retail value chain management support activities. These qualities were uncovered after focus group talks with merchants and retail specialists from a variety of organized retail locations. The ordered probit model was used to examine how major retail value chain management procedures affect consumer satisfaction. Based on the analysis done, the managerial and theoretical implications were discussed in detail, and a model of Support Activities of Retail Value Chain Management was proposed based on the evidence. This research outcome contributes towards SDG12 of the United Nations Sustainable Development Goals.

Abstract

Textbook Question Answering has been a central feature of educational artificial intelligence enabling curriculumaligned machine reading to support personalized learning and diagnostic testing. While there is significant advancement in English-language TQA datasets, there is still a lag in Arabic because of a lack of sufficient highquality domain-specific resources. A new bilingual English-Arabic TQA data is presented in this paper, and it was created using a hybrid translation and validation method. It combines machine translation of CK12-QA dataset with Google sheet translator. Semantic consistency was evaluated using automated metrics based on multilingual sentence embeddings and translation quality scores. Cosine similarity (0.87) and BLEU score (38.5) confirmed strong semantic equivalence and translation reliability across the bilingual dataset. These results demonstrate robust linguistic alignment and completeness. This approach is a balance between conflicting scalability and accuracy in long-standing semantic drift, morphological variation and in context misalignment issues in Arabic education datasets compared to previous efforts to use machine translation or mini-batch annotation only. Output dataset has a parallel format structure of English-Arabic question-answer pair that facilitates simple cross-lingual research in multiple-choice and textbook conditions. By focusing on K-12 science curriculum in specific subject areas, this contribution can enable improved monolingual and cross-lingual educational QA applications model training and testing. This does not only make AI-based learning more inclusive among Arabic students but also provides impetus to creation of cross-lingual transfer learning and benchmarking in TQA. The sources and information are openly published in an attempt to further increase the reproducibility, verifiable peer cooperation and further promote the development of AI in multilingual education

Abstract

Studies and research in the health fields have shown that information technology has a significant impact on managing and improving the quality, safety and efficiency of health care. It reduces clinical errors. When we build and customize a database for any system, we allow access to the content and the ease of recalling, managing and updating its data when needed. The project aims to build a database system to store, organize and retrieve data from educational clinics in the College of Dentistry in Iraqi universities. System specifications and requirements were collected through discussion, scientific expertise, and discussions with dental staff at the College of Dentistry at the University of Iraq in Baghdad, Iraq. The collected data is used to build the database model and system architecture. The system was developed to provide assistance to college teaching dental clinics in managing and maintaining their database. Data were collected from 5 clinics in various specialties affiliated with the College of Dentistry from various departments in the college, and a unique asset number was allocated to each educational clinic located in the college according to the required standards and for ease of representation. The system is built in C#. SQL Server (Structural Query Language) is used to run user information. The proposed system has the ability to manage and protect a database through a login mechanism for all members working on the system.

Abstract

Depression among college students is a growing public health concern, with existing screening methods often limited in sensitivity, scalability, and interpretability. This study developed and validated an explainable machine learning framework for early depression risk identification and tiered intervention planning in universities. We propose a Dynamic Weighted Ensemble Model (DWEM) that integrates five tree-based algorithms, with weights optimized via Bayesian search and cost-sensitive learning. Informed by the diathesis–stress framework, features were engineered and interpreted using SHAP to provide global and local explanations. The model was evaluated using stratified five-fold cross-validation with careful control of data leakage. The DWEM achieved an accuracy of 94.96% and an AUC of 98.95%, with balanced sensitivity and specificity, outperforming both single-model benchmarks and traditional questionnaire-based screening. SHAP analysis stably identified academic performance, stress-burnout, sleep problems, and protective factors as key risk determinants. Based on these outputs, a probability-based three-tier intervention framework was designed to translate risk stratification into actionable clinical support. This study demonstrates that an optimized ensemble approach, combined with theory-driven features and robust explainability, can provide a reliable, transparent, and practical tool for scalable mental health screening, supporting a shift toward proactive, data-driven prevention and efficient resource allocation in campus settings.

Abstract

The problem is how students can learn independently when no assistant teachers are present. The main objective of this research is to build an application system that can recognize the stances in Ibing Penca martial art. This research aims to help facilitate independent learning for martial arts practitioners, especially Ibing Penca, by developing a system that is able to recognize and classify the movements in 62 Ibing Penca stances. To achieve these goals, the research method used is to collect input data in the form of images or videos taken using an Orbbec camera. After the images are obtained, the next stage is data processing to detect important points on the body using landmark detection techniques. The next process is the identification of 33 keypoints on the body using the MediaPipe algorithm. From these keypoints, six important angles were calculated which included the right arm, left arm, right leg, left leg, right foot and left foot. This angle calculation is done using the angle method of the three relevant key points. The system is able to recognize the movements in Ibing Penca with a high degree of accuracy, which is very useful for learners who want to practice independently. The results of this study show that the system is able to classify 62 Ibing Penca moves with a success rate of 95.2% (58 moves), while the error rate is only 4.8% (4 moves). For future research, it is expected to develop this system by adding variations of movements and improving detection accuracy in more diverse environmental conditions.

Abstract

Given the critical importance of crude oil prices in the global economy, this study focuses on forecasting WTI and Brent crude oil futures prices. To incorporate both price series as independent variables in our predictive model, we employed Vector Autoregression (VAR) to analyze their interdependence. Results from impulse response analysis, variance decomposition, and Granger causality tests revealed a significant spillover effect between the two crude oil futures prices over the past 12 lagged periods. Consequently, the lagged data from the past 12 periods of both price series were utilized as independent variables for the deep learning framework. We developed a hybrid model integrating Empirical Mode Decomposition (EMD), Autoregressive Moving Average (ARMA), and Bidirectional Long Short-Term Memory (BiLSTM) to predict WTI and Brent prices. Our findings demonstrate that predictive performance improves with increased model complexity. Specifically, the hybrid model (EMD+ARMA+BiLSTM) outperforms both the EMD+BiLSTM model and the standalone BiLSTM model. For WTI, the hybrid model achieved a Mean Squared Error (MSE) of 57.77, Mean Absolute Error (MAE) of 5.66, and Mean Absolute Percentage Error (MAPE) of 8.78%. For Brent, the corresponding metrics were 62.15, 5.87, and10.40%, respectively. These results validate the robustness and rationality of our hybrid model, offering a reliable methodology for crude oil price prediction and providing valuable insights for researchers and practitioners in the energy markets.

Abstract

This work is concerned with the computational perspective of a specific case of derivations, known as inner derivation mapping. In this regard, we propose an algebraic method to provide inner derivations for a finite-dimensional Leibniz algebra in matrix representation. The method is applied on four-dimensional complex Leibniz algebras obtained earlier to present comprehensive descriptions of their inner derivations. Additionally, we also exhibit the span basis of outer derivatives for these algebras.

Abstract

In higher education, scientific literacy is still a significant difficulty, especially when instruction focuses on procedural knowledge without including moral principles. The purpose of this study was to create and examine Bulletin-Integrated Character Education (BICE) as a teaching tool to improve and gauge students' scientific literacy. The requirement for educational materials that simultaneously promote character development and higher-order thinking makes this research urgent. In order to produce verified educational media, this study used a Research and Development (R&D) approach based on the Hannafin and Peck paradigm. According to expert validation, the BICE media was highly reliable (Cronbach's ? = 0.88–0.92) and valid (CVI = 0.92–0.95). Strong dependability was also shown by the scientific literacy test (? = 0.92). Students who were taught PBL–BICE fared better than those who were taught PBL alone, according to inferential analysis, with a significant effect size (d = 1.12). Conceptual literacy was a strong predictor of multidimensional literacy, according to correlation and regression analyses (r = 0.71; ? = 0.48). These results suggest that in order to foster advanced scientific literacy in higher education, character-based media should be incorporated into problem-based learning.

Abstract

Accurate price volatility prediction is a cornerstone of sound investment decisions and effective dynamic risk management in financial markets. This study addresses a significant research gap: the limited number of studies exploring the systematic integration of traditional statistical models and artificial intelligence techniques within emerging financial markets, despite their high levels of instability and volatility. The research aims to develop a hybrid predictive framework that combines the flexibility of linear models, specifically ARIMA, with the ability of machine learning algorithms to grasp the complex, nonlinear patterns inherent in financial time series. Furthermore, the study highlights an application gap: the underutilization of advanced volatility estimators. The Garman-Class estimator was adopted as a more efficient and accurate alternative to traditional estimators for measuring daily volatility, due to its reliance on four-part price information (open, close, high, and low). The proposed framework was applied to data from Savola Group, listed on the TASI. The results demonstrated the superiority of the proposed hybrid model in improving forecast accuracy and reducing predictive error measures, particularly the MAE, and RMSE, compared to traditional single-model models.

The scientific value of this research lies in its contribution to bridging the knowledge gap related to the integration of statistical models and artificial intelligence techniques in the emerging markets environment. Furthermore, it provides an advanced analytical tool that can enhance asset allocation efficiency and support decision-makers and portfolio managers in navigating the dynamics of highly volatile markets.

Abstract

This study analyzes the bioinformatics skills and a selection of related experiences of student biology teachers who had varying amounts of formal training in courses on bioinformatics. Data were collected using a qualitative approach from 26 students who did and 23 students who did not take the bioinformatics course. The data collection instrument consisted of three opinion-based items and three items that tested cognitive performance. Qualitative data analyses made use of thematic analysis supported by diagramming; cognitive performances were analyzed by descriptive statistics. The results indicated descriptive differences in the skill profiles of the two groups, including tool use, reflective learning practices, perceived task difficulty, and biology knowledge scores-70.29% for students with course exposure and 63.63% for those without course exposure. As an exploratory study, these findings provide empirical insight into the distribution of bioinformatics-related competencies and learning experiences among prospective biology teachers. Therefore, future studies should consider longitudinal or experimental designs to better understand how bioinformatics learning can be optimally integrated into biology teacher education.

Abstract

Ecotheology is an integration of religious values towards awareness of environmental preservation. Indonesia’s Ministry of Religious Affairs has identified ecotheology as a strategic program, including Islamic school students. Therefore, this study aims to reveal the understanding, implementation, challenges, and opportunities of ecotheology in Islamic schools. This research applies data science and machine learning algorithms to analyze a large student dataset, 22,933 data from 32 provinces, with a 41-question validated questionnaire (Cronbach’s Alpha = 0.765, Kappa = 0.791). This research uses K-Means and PCA for clustering to group students by ecotheology awareness and implementation, Association Rules with Apriori algorithm to identify knowledge sources, obstacles, and program linkages, classification using ensemble learning with CatBoost as the best model with 98.71% accuracy, and sentiment analysis using RoBERTa-based Indonesian model on open responses. This research found that students’ understanding of ecotheology is high, with most learning from teachers and others gaining knowledge from social media and books, while implementation remains moderate due to limited programs, policies, and subject integration. In accordance with student’s understanding, the sentiment analysis revealed neutral tones in suggestions but mostly positive expectations, with students desiring more practical, Quran-linked, and community-based activities.

Abstract

This study aims to develop algorithms of real-time traffic control system based on computer vision. In addition, the developed system is capable to analyze vehicles in a traffic stream at a traffic-controlled junction. The video recording sequence was installed near an intersection to control traffic lights during traffic congestion situations. This project's scope is limited to analyzing real-time traffic feeds and developing methods that count and track moving and stopping vehicles that approach a traffic junction. The project was designed using SIMULINK, which MathWorks created. Four algorithms were proposed to analyze the video signal inputs and estimate the number of vehicles detected. Gaussian mixture model and edge detection with frame differencing method were used to detect and track arrived vehicles. An optical flow-based approach was used to determine the number of stopped vehicles. Additionally, a vehicle classification algorithm was used to detect certain types of vehicles. In the Gaussian mixture model algorithm, implementing trained mask and geometric transform on each frame improved the perception of the outputs, which is defined by counting 1100 vehicles on the approach. Also, by using color detection, more control over traffic flow was obtained by prioritizing certain cars. The obtained results showed good representation of vehicle classification for the data detected in the developed system compared with the empirical data. The estimated errors were determined by achieving RMSPE < 15%, GHE < 5 and Um < 1.

Abstract

This study aims to develop the Si Robot Pintar Edugame, an AI-based interactive tool designed to enhance Cognitive Abilities and Character Development in early childhood at Pondok Pesantren. The product development followed the Research and Development (R&D) model, adopting the ADDIE framework (Analysis, Design, Development, Implementation, Evaluation) to create a technology-driven, engaging learning environment that fosters both cognitive and moral growth. A quasi-experimental design was employed in this study with three groups: two experimental groups (one using the Si Robot Pintar Edugame and the other using interactive videos) and a control group (using worksheets). Pretest and posttest measures were used to assess improvements in cognitive skills and character development. The results revealed that the Si Robot Pintar Edugame had the greatest effect, with the experimental group showing a 40% increase in posttest scores, compared to a 15% improvement in the control group. The Si Robot Pintar Edugame group exhibited the largest effect size (0.890), followed by the interactive video group (0.875), and the control group (0.540), underscoring the superiority of this AI-based intervention. The Si Robot Pintar Edugame significantly enhanced children’s abilities in identifying emotions and controlling emotions, which are vital aspects of emotional regulation and self-regulation. The game was especially effective in promoting cognitive skills and fostering character traits such as self-discipline, empathy, and critical thinking, all of which align with the educational objectives of Pondok Pesantren and Islamic teachings. These findings suggest that integrating AI and interactive tools in Islamic educational settings can substantially improve Cognitive Abilities and Character Development in children. The study concludes that the Si Robot Pintar Edugame is an effective, innovative educational tool that plays a significant role in enhancing both cognitive and moral development in early childhood at Pondok Pesantren.

Abstract

The potential of the Metaverse to create immersive learning spaces in higher education is promising, but the implementation complexities pose an enormous challenge. There is little to no research concerning the Metaverse component readiness gap. Most of the studies that do exist are either too focused on the technological factors of the Metaverse (as in the simple extensions of VR/AR, the 3D graphical environments) or address the technological access, the user’s VR/AR attitudes, or the infrastructure in isolation, without merging the technological, organizational, and environmental dimensions of the issue into one framework. Following the guidelines of the PRISMA methodology, 32 documents, which were indexed in Scopus, were systematically identified, and thematic analysis was conducted to elaborate the factors influencing the readiness for the implementation of the Metaverse. The literature synthesis yielded ten themes or factor clusters influencing Metaverse readiness in higher education: Immersive Learning Experience (ILE), System & Service Quality (SQ), Perceived Performance Value (PPV), Ease, Simplicity & Learnability (ESL), Risk, Security & Concerns (RSC), Personal Readiness (PR), Affective & Motivational Factors (AMF), Pedagogical & Knowledge Competence (PKC), Social Influence & Norms (SIN), and Institutional Support & Facilitating Conditions (ISFC). These findings contribute to the body of relevant literature and useful frameworks to assess metaverse readiness in higher education.

Abstract

This study investigated the challenges experienced by students in an English for Public Speaking course during the implementation of blended learning instruction. This study employed a qualitative descriptive design to examine students’ challenges in the implementation of blended learning instruction in an English for Public Speaking course. Data were collected from three classes in the Business English Communication Study Program at Universitas Negeri Makassar through document analysis, classroom observations, and semi-structured interviews. The data were analyzed using the interactive model of Miles, Huberman, and Saldaña, which consists of data condensation, data display, and conclusion drawing/verification, to identify and verify the recurring challenges experienced by students in blended public speaking instruction. The findings revealed four major categories of challenges. First, students encountered linguistic and content-related difficulties, including limited vocabulary, grammatical inaccuracies, and problems in organizing coherent and persuasive arguments. Second, methodological challenges emerged from unclear instructional guidelines, limited lecturer–student interaction, and insufficiently engaging asynchronous learning activities. Third, practical challenges were associated with unstable internet connectivity, limited access to reliable digital devices, and inadequate digital literacy. Fourth, psychological challenges, particularly speaking anxiety, fear of making mistakes, and low self-confidence, negatively affected students’ public speaking performance. These challenges were interconnected and collectively shaped students’ learning experiences in the course of English for Public Speaking. This study offered a holistic understanding of students’ challenges in the implementation of blended learning instruction in an English for Public Speaking course. By highlighting the interrelationship among linguistic, methodological, practical, and psychological factors, the study contributed to the development of more responsive blended learning practices that supported students’ communicative competence and public speaking development.

Abstract

Smart attendance systems have evolved from routine administrative tools into data-driven mechanisms for monitoring, prediction, and institutional decision-making in education. However, limited attention has been given to how this transformation is understood from educational, ethical, and socio-technical perspectives. This study aims to examine the development of smart attendance system research using a bibliometric and science mapping approach. A dataset of 108 Scopus-indexed journal articles published between 2016 and 2025 was analyzed using VOSviewer and Biblioshiny. The findings reveal a clear shift from RFID-based and manual verification systems toward artificial intelligence-driven approaches, including facial recognition, deep learning, computer vision, and predictive analytics. Despite these technological advancements, the literature remains predominantly technocentric, with major emphasis on system efficiency, authentication, and automation, while issues related to privacy, consent, fairness, and surveillance receive limited attention. This study identifies an “ethical silence” in the field and suggests that attendance is increasingly redefined as a machine-verifiable construct rather than a pedagogical expression of engagement. Future research should integrate data ethics, learning analytics, and student agency to support more balanced educational practices.

Abstract

This study aims to explore teachers’ practices and challenges in implementing multimodal literacy-based instruction in EFL classrooms. The study employed a qualitative research design involving two EFL teachers from two senior high schools in South Sulawesi. The data were collected through semi-structured interviews and analyzed using Braun and Clarke’s reflexive thematic analysis. The findings revealed that teachers implemented multimodal literacy-based instruction through adaptive communication practices, collaborative classroom interaction, and the integration of various multimodal resources during learning activities. Teachers adjusted communication modes, instructional approaches, and classroom activities based on students’ learning needs and classroom situations. The findings also showed that multimodal classroom practices encouraged students’ participation through discussion, role play, presentations, and peer interaction during learning activities. In addition, the study identified several instructional and institutional challenges experienced by teachers, including difficulties related to classroom interaction, students’ participation, instructional preparation, technological adaptation, and limited classroom facilities. Overall, the findings suggest that multimodal literacy-based instruction involves not only the integration of multimodal resources but also teachers’ adaptive communication practices and continuous instructional adjustment during classroom learning activities.