Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates. In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students. Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect. Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.

Predicting students’ academic performance and the factors that significantly influence it can improve students’ completion and graduation rates, as well as reduce attrition rates. In this study, we examine the factors influencing student academic achievement. A fuzzy-neural approach is adopted to build a model that predicts and explains variations in course grades among students, based on course category, student course attendance rate, gender, high-school grade, school type, grade point average (GPA), and course delivery mode as input predictors. The neuro-fuzzy system was used because of its ability to implicitly capture the functional form between the dependent variable and input predictors. Our results indicate that the most significant predictors of course grades are student GPA, followed by course category. Using sensitivity analysis, student attendance was determined to be the most significant factor explaining the variations in course grades, followed by GPA, with course delivery mode ranked third. Our findings also indicate that a hybrid course delivery mode has positively impacted course grades as opposed to online or face-to-face course delivery alone.

The Covid-19 pandemic constrained higher education institutions to switch to online teaching, which led to major changes in students’ learning behavior, affecting their overall performance. Thus, students’ academic performance needs to be meticulously monitored to help institutions identify students at risk of academic failure, preventing them from dropping out of the program or graduating late. This paper proposes a CGPA Predicting Model (CPM) that detects poor academic performance by predicting their graduation cumulative grade point average (CGPA). The proposed model uses a two-layer process that provides students with an estimated final CGPA, given their progress in second- and third-year courses. This work allows academic advisors to make suitable remedial arrangements to improve students’ academic performance. Through extensive simulations on a data set related to students registered in undergraduate information technology program gathered over the years, we demonstrate that the CPM attains accurate performance predictions compared to benchmark methods.

Predicting student’s successful completion of academic programs and the features that influence their performance can have a significant effect on improving students’ completion, and graduation rates and reduce attrition rates. Therefore, identifying students are at risk, and the courses where improvements in content, delivery mode, pedagogy, and assessment activities can improve students’ learning experience and completion rates. In this work, we have developed a prediction and explanatory model using adaptive neuro-fuzzy inference system (ANFIS) methodology to predict the grade point average (GPA), at graduation time, of students enrolled in the information technology program at Ajman University. The approach adopted uses students’ grades in introductory and fundamental IT courses and high school grade point average (HSGPA) as predictors. Sensitivity analysis was performed on the model to quantify the relative significance of each predictor in explaining variations in graduation GPA. Our findings indicate HSGPA is the most influential factor in predicting graduation GPA, with data structures, operating systems, and software engineering coming closely in second place. On the explanatory side, we have found that discrete mathematics was the most influential course causing variations in graduation GPA, followed by software engineering, information security, and HSGPA. When we ran the model on the testing data, 77% of the predicted values fell within one root mean square error (0.29) of the actual GPA, which has a maximum of four. We have also shown that the ANFIS approach has better predictive accuracy than commonly used techniques such as multilinear regression. We recommend that IT programs at other institutions conduct comparable studies and shed some light on our findings.

Knowledge Management Systems (KMS) are vital for organizations in managing knowledge creation, sharing, and utilization. Integrating KMS with advanced technologies like knowledge graphs and semantic technologies can greatly enhance their functionality in business contexts. This research aims to systematically reviews and evaluates studies on the integration of knowledge graphs and semantic technologies. The study follows PRISMA guidelines for methodological rigor. The review includes articles published between 2005 and 2024 from databases like ScienceDirect and IEEE Xplore, focusing on keywords such as “knowledge graph” and “knowledge management systems.” From an initial 18,900 articles, 73 were selected for detailed analysis. The findings indicate that using tools like RDF, SPARQL, OWL, and SKOS enhances KMS capabilities, enabling features like semantic search and intelligent recommendations. However, challenges such as scalability, semantic disambiguation, and data privacy need to be addressed to fully realize KMS's potential in supporting organizational knowledge management.

Aim/Purpose: To examine how ethical awareness, cognitive appraisal (trust and perceived usefulness), digital competence, academic performance, and gender influence university students’ ethical use of ChatGPT and academic integrity. Background: This study explores how university students’ use of ChatGPT influences academic integrity in higher education. It responds to emerging integrity challenges posed by generative AI by empirically testing a model that links transparency, plagiarism avoidance, bias awareness, and responsible use to academic integrity outcomes across universities in the Gulf region. Methodology: PLS-SEM analysis of survey data from 318 students across five Gulf-region universities; tests direct effects of four ethical variables, mediation by trust in AI and perceived usefulness, and moderation by digital literacy and CGPA. Contribution: This study provides empirical evidence that core ethical-use dimensions significantly enhance academic integrity; clarifies the mediating roles of trust/usefulness and the moderating roles of digital literacy/CGPA; and documents gender and discipline differences in usage. Findings: All four ethical variables positively and significantly predict academic integrity: Trust in AI and perceived usefulness act as partial mediators. Digital literacy and CGPA significantly moderate several relationships. High-performing and senior students report more frequent and effective use of ChatGPT; gender and discipline differences are evident. Recommendations for Practitioners: Embed digital literacy and ethical-AI training (transparency, anti-plagiarism, bias awareness, responsible use) into curricula; implement inclusive AI policies; and guide trust calibration and verification workflows to support responsible use. Recommendation for Researchers: Extend the model across countries and disciplines; examine longitudinal effects; test additional mediators (e.g., AI anxiety, institutional policy clarity) and moderators (e.g., year of study, assessment type); compare alternative SEM and causal designs. Impact on Society: Promotes responsible AI adoption that safeguards academic ethics, supports equitable student outcomes, and informs policy for trustworthy AI use in higher education ecosystems. Future Research: Conduct multi-institutional replications, experimental interventions on ethics/digital literacy training, and studies of assessment design that balance AI use with integrity (e.g., oral/ authentic assessments).