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…

Methods to handle ordered-categorical indicators in latent variable interactions have been developed, yet they have not been widely applied. This article compares the performance of two popular latent variable interaction modeling approaches in handling ordered-categorical indicators: unconstrained product indicator (UPI) and latent moderated…

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…

The MAP® Growth™ theory of action describes key features of MAP Growth and its position in a comprehensive assessment system. The basic premise of the theory of action is that all students learn when MAP Growth is situated in a comprehensive assessment system and used for its intended purposes to yield information about student learning and enable…

School value-added models are widely applied to study, monitor, and hold schools to account for school differences in student learning. The traditional model is a mixed-effects linear regression of student current achievement on student prior achievement, background characteristics, and a school random intercept effect. The latter is referred to…

School accountability systems increasingly hold schools to account for their performances using value-added models purporting to measure the effects of schools on student learning. The most common approach is to fit a linear regression of student current achievement on student prior achievement, where the school effects are the school means of the…

This paper examines how different measures of teacher quality are related to students' long-run educational trajectories. We estimate teachers' "test-based" and "nontest" value-added (the latter based on contributions to student absences, suspensions, grade progression, and grades) and assess how these predict various student…

Purpose: The purpose of this study is to examine the effect of students' self-regulation, co-regulation and behavioral engagement on their performance in flipped learning environments in higher education. Design/methodology/approach: The subjects were college students taking an education course offered at a 4-year university in South Korea.…

Value-added metrics or models (VAMs) are an important component of the teacher evaluation process that evaluators use to determine the value teachers add to their students' academic achievement. VAMs are used to arrive at a score that is derived from the number of the teachers' students who pass and/or fail a standardized assessment. While prior…

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…