Although the modern education system is highly developed, educators have never stopped looking for new ways to improve it. After entering the 21st century, more and more educational data are stored, and data mining techniques have developed rapidly....
Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching a...
This study examines the extent to which incorporating social media data enhances the predictive accuracy of models forecasting international students’ arrivals. Private social media data collected from a public university, along with collected...
The integration of Student Response Systems (SRSs) into classroom teaching is a pioneering progression in social sciences research that has shown potential in boosting student engagement and elevating academic success. However, no extensive study has...
Student participation in educational decision making, for example, through data-informed decision making, can have a positive effect on student well-being, engagement, and performance. Teachers play a crucial role in student participation, and leader...
University dropout poses academic, social, and economic challenges that call for effective prevention strategies. The objective was to identify determining factors of student dropout through educational data mining and machine learning models. A surv...
Predicting student dropout is a challenging problem in the education sector. This is due to an imbalance in student dropout data, mainly because the number of registered students is always higher than the number of dropout students. Developing a mode...
Analyzing student performance in Introductory Programming courses in Higher Education is critical for early intervention and improved learning outcomes. This study explores the potential of a cognitive test for student success in an Introductory Prog...
Predicting undergraduate student success is critical for informing timely interventions and improving outcomes in higher education. This study leverages over a decade of historical data from Louisiana State University (LSU) to forecast graduation out...
A critical component of environmental education is to ensure student understanding and use of available technologies to better experience and analyze spatially distributed features of the environment. Combining mobile technologies with geographic inf...
Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at...
Student retention has become a major challenge for higher education institutions due to the influence that academic, socioeconomic, family, and motivational factors exert on students’ academic continuity. In this context, understanding the dete...
In recent years, the informatization of the educational system has caused a substantial increase in educational data. Educational data mining can assist in identifying the factors influencing students’ performance. However, two challenges have...
Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understandin...
This study investigates the use of educational data mining (EDM) techniques to predict student performance and enhance learning outcomes in higher education. Leveraging data from Moodle, a widely used learning management system (LMS), we analyzed 450...
Understanding how students’ online homework behaviors relate to their academic success is increasingly important, especially in elementary education where such research is still emerging. In this study, we examined three years of online homewor...
Student achievement prediction is one of the most important research directions in educational data mining. Student achievement directly reflects students’ course mastery and lecturers’ teaching level. Especially for the achievement predi...
As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of studen...
Student dropout rates continue to present major difficulties for educational institutions, leading to academic, operational, and financial impacts. Educational Data Mining (EDM) methods, particularly those combining clustering techniques with predict...
Student dropout remains a critical challenge in higher education, especially within public universities that serve diverse and vulnerable populations. This research presents the design and evaluation of an early warning system based on an XGBoost cla...
High psychological stress and ineffective study habits induce academic pressure and low learning efficiency, respectively. When these psycho-behavioral dimensions compound poor performance, the student profile exhibits high vulnerability to dropout....
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COVID-19-related school closures caused unprecedented and prolonged disruption to daily life, education, and social and physical activities. This disruption in the life course affected the well-being of students from different age groups. This study...
The progress of technology has played a crucial role in enhancing various fields such as education. Universities in Saudi Arabia offer free education to students and follow specific admission policies. These policies usually focus on features and sco...
This study investigates how student teachers engaged with data science and machine learning (ML) through a collaborative, scenario-based project in a graduate-level online course, AI in STEAM Education. The study focuses on the pilot implementation o...
Student success is a multifaceted outcome influenced by academic, behavioral, contextual, and socio-environmental factors. With the growing availability of educational data, machine learning (ML) offers promising tools to model complex, nonlinear rel...
This paper describes how plan-do-study-act cycles engaged a classroom mentor teacher and student teacher in a professional collaboration that resulted in two inquiry activities for high-school geometry classes. The PDSA cycles were carried out in fou...
A robust and scientifically grounded teaching evaluation system holds significant importance in modern education, serving as a crucial metric that reflects the quality of classroom instruction. However, current methodologies within smart classroom en...
By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of d...
This study presents a fuzzy-Apriori model that analyses student background data, along with end-of-lesson student-generated questions, to identify interpretable rules. After linguistic and semantic preprocessing, questions are represented in a fuzzy...
Student dropout remains a persistent challenge in higher education, with significant academic, social and institutional implications. Although machine learning (ML) models for dropout prediction have proliferated, most select input variables based on...
Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Re...
The research studies describe that students utilize Information and Communication Technology (ICT) widely to improve their academic performance. In the classroom, students use ICT assistive technologies via laptops and smartphones for academic and no...
Background: The humanization of care is increasingly recognized as a core component of healthcare quality; however, its meaning remains complex and strongly shaped by organizational, professional, and educational contexts. Nursing students, as future...
Student applications for admission to the University of Georgia College of Veterinary Medicine include the following information: undergraduate grade point average (GPA), GPA in science courses (GPAScience), GPA for the last 45 credit hours (GPALast4...
The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplo...
As the media landscape continuously evolves in response to the increasing dominance of data, it is important to understand how future journalists perceive and respond to the emerging discipline of data journalism. This paper explores the impact of an...
This paper uses the technology acceptance model (TAM) framework for the research of economic and geography students’ attitudes towards interdisciplinary knowledge. Based on the SmartPLS Structural equation modelling SEM variance-based method, r...
Two highly efficient commercial organic photosensitizers—azure A (AA) and 5-(4-aminophenyl)-10,15,20-(triphenyl)porphyrin (APTPP)—were covalently attached to the glass surface to form a photoactive monolayer. The proposed straightforward strategy con...
Background: The growing use of big data and algorithmic personalisation in political communication has intensified concerns about surveillance, privacy, and manipulation. Although previous research has examined these issues among the general public,...
The academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which invo...
With the development of university campus informatization, effective information mined from fragmented data can greatly improve the management levels of universities and the quality of student training. Academic performances are important in campus l...
This paper explores the affordances and constraints of STEM faculty members’ instructional data-use practices and how they engage students (or not) in reflection around their own learning data. We found faculty used a wide variety of instructional da...
Background/Objectives: Digital technologies and data-driven approaches are rapidly transforming healthcare practice and enabling more personalized and preventive care. As personal health data becomes increasingly embedded in healthcare systems, under...
The study explores the application of data analytics and machine learning to forecast academic outcomes, with the aim of ensuring effective and sustainable e-learning. Technological study programs in universities often experience high dropout rates,...
Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become compete...
In the United States, teachers are often expected to use student assessment data to inform their instructional decisions. This study explores how teachers’ collective reflections on students’ mathematical thinking are influenced by the sp...
The growing demand for data science competencies across professional fields creates a need for curricula that enable management students to develop analytical skills while navigating increasingly complex technological environments. This study examine...
As time goes by, information and communication technology continue to advance. Since the pandemic, the need for information and communication technology has risen to aid us in working and studying from home. One of the forms of information and commun...
This commentary is based on the experience of teaching and observations of how pharmacy technician students can expand their perspective on patient safety by using real-life student-gathered patient data collected from community pharmacies. Pharmacy...
This study explores the application of artificial intelligence (AI) in higher education to enable the early identification of at-risk students using only engagement data from learning management systems (LMS). Unlike many existing early-warning model...
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