This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners' performance through ontological machine-learning techniques and, using a systematic approach, identifies the…

In the educational landscape, educational data mining has emerged as an indispensable tool for institutions seeking to deliver exceptional and high-quality education. However, education data revealed suboptimal academic performance among a significant portion of the student population, which consequently resulted in delayed graduation. This…

Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…

In the profession of education, predicting students' academic success is an essential responsibility. This study introduces a novel methodology for predicting students' pass or fail outcome in certain courses. The system utilises academic, demographic, emotional, and VLE sequence information of students. Traditional prediction methods often…

The study of educational effectiveness has become increasingly complex. Alongside methodological advancements in the investigation and measurement of educational effectiveness, meta-analyses conducted by leading researchers have shown that the field has been suffering from a significant lack of theory or from a weak theoretical basis. The present…

Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning behavior becomes especially important, when dealing with open-ended exploration environments,…

Recently, machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on…

Assessment is a central feature of student learning in higher education and has a strong influence on the student experience. Accordingly, the appropriate communication of assessment aims is a priority for all higher education institutions. This study proposes an analytical framework for the interpretation and creation of assessments across higher…

In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a…

The Expectancy-Value model has been extensively used to understand students' achievement motivation. However, recent studies propose the inclusion of cost as a separate construct from values, leading to the development of the Expectancy-Value-Cost model. This study aimed to adapt Kosovich et al.'s ("The Journal of Early Adolescence", 35,…

The analysis of exemplars of different quality is a potentially powerful tool in enabling students to understand assessment expectations and appreciate academic standards. Through a systematic review methodology, this paper synthesises exemplar-based research designs, exemplar implementation and the educational effects of exemplars. The review of…

School climate is critical to school effectiveness, but there is limited large-scale data available to examine the magnitude and nature of the relationship between school climate and school improvement. Drawing on statewide administrative data linked with unique teacher survey data in Michigan, we examine whether school climate appeared to play a…

This paper develops models to measure growth in student achievement with a focus on the possibility of differential growth in achievement for low and high-achieving students. We consider a gap-closing model that evaluates the degree to which students in a target group -- students in the bottom quartile of measured achievement -- perform better…

This study was carried out to determine the effectiveness of the Flipped Classroom Model (FCM) in an educational setting. For this purpose, a multi-complementary approach (MCA) was used including both quantitative (meta-analysis) and qualitative (thematic). MCA consists of three parts, the first of which is the pre-complementary information stage.…

Due to COVID-19, the world has encountered new challenges regarding pedagogy, learning, assessment, and evaluation. In meeting these challenges, there have been rapid changes in learning, and the gap between pedagogy and evaluation has grown. The purpose of this paper is to develop a new evaluative model suitable for the technologically enhanced,…