Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. This paper has two broad aims. Firstly, to develop and tune several…

Due to the growing demand for information technology skills, programming education has received increasing attention. Predicting students' programming performance helps teachers realize their teaching effect and students' learning status in time to provide support for students. However, few of the existing researches have taken the code that…

Understanding attrition risks specific to online student populations is crucial for the long-term success of online programs. Online programs allow place-based working professionals access to education needed for professional development and career advancement. This study was conducted to determine if educational preparation, student…

Higher education practitioners and researchers in the STEM field continue seeking ways to effectively identify and understand student challenges as part of an effort to support student success, retention, and persistence. These efforts have led researchers to explore non-cognitive personality factors such as perfectionism as a way of understanding…

This study aimed to determine the relationship between primary school students' critical thinking and entrepreneurial tendencies. The relational screening model was used. Three hundred seventy-three primary school students participated in the research. The data were collected with the "Critical Thinking Tendency Scale for Primary School…

Purpose: The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC) personalized recommendation method to help learners efficiently obtain MOOC resources. Design/methodology/approach: This study introduced ontology construction technology and a new semantic association algorithm…

Predicting students' success is crucial in educational settings to improve academic performance and prevent dropouts. This study aimed to improve student performance prediction by combining advanced machine learning (ML) approaches. Convolutional Neural Networks (CNNs) and attention mechanisms were used for extracting relevant features from…

As they navigate the social and academic expectations of a new college, transfer students commonly suffer "transfer shock," or a sudden drop in GPA. However, little is known about why some students suffer transfer shock, why some bounce back, and the consequences in terms of student retention. This analysis of over 25,000 transfer…

In England, students apply to universities using teacher-predicted grades instead of their final end-of-school A-level examination results. Predicted rather than achieved grades therefore determine how ambitiously students apply to and receive offers from the most selective courses. The Universities and Colleges Admissions Service (UCAS)…

Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that…

The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for…

Accurately forecasting course enrollment rates in higher education is of great concern in order to minimize unnecessary administrative costs as well as burden to both students and faculty. This research aimed to first recreate course enrollment predictions based on a conditional probability analysis using student data from San Diego State…

Recent research has paid considerable attention to the role of university support in explaining student entrepreneurship, with several studies presenting empirical evidence of the moderating effects of individual traits on the relationship between university support and entrepreneurial intentions. However, the moderating effect of entrepreneurial…

About one-third of college students drop out before finishing their degree. The majority of those remaining will take longer than 4 years to complete their degree at "4-year" institutions. This problem emphasizes the need to identify students who may benefit from support to encourage timely graduation. Here we empirically develop machine…

Properly predicting students' academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to…