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…

Self-regulated learning (SRL) theory comprises cognitive, metacognitive, and affective aspects that enable learners to autonomously manage their learning processes. This article presents a systematic literature review on the measurement of SRL in digital platforms, that compiles the 53 most relevant empirical studies published between 2015 and…

Clinical prediction models are widely used in modern clinical practice. Such models are often developed using individual patient data (IPD) from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies,…

Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of educational datasets further catalyse this interestingness, especially in…

This systematic literature review critically evaluates 14 empirical studies published over a 14 years span (2006-2019) to answer questions about the models and the effects of transformational school leadership on student academic achievement. The analysis of the related literature utilized vote counting and narrative synthesis to delineate the…

Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…

The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data…

The purpose of this research was to compare the performances of the Fixed Effect Model (FEM) and the Random Effects Model (REM) in the meta-analysis studies conducted through 5, 10, 20 and 40 studies with an outlier and 4, 9, 19 and 39 studies without an outlier in terms of estimated common effect size, confidence interval coverage rate and…

Preferences for schools are important determinants of equitable access to high-quality education, effects of expanded choice on school improvement and school choice mechanism design. Standard methods for estimating consumer preferences are not applicable in education markets because students do not always get their first choice school. This review…

Connecting teachers' perspectives with their practice is an enduring challenge shaping what and how we understand teaching. Researchers tend to bifurcate teachers' work between their private and their public lives. These "worlds" bring particular meanings that are rendered through the analyses of visual documentations of teaching and…

The performance of statistical methods is often evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, simulations are not currently available for many practically relevant settings. We perform a simulation study for sparse networks of trials under between-trial heterogeneity and including multi-arm…

The growth of Learning Analytics (LA) as a research field has been extensively documented since its beginnings. This paper provides a broad overview of the publications that Latin American authors have published in the last years by performing a quantitative review of the literature (from 2011 to 2019). A total of 282 papers were collected and…

Meta-analyses of clinical trials targeting rare events face particular challenges when the data lack adequate numbers of events for all treatment arms. Especially when the number of studies is low, standard random-effects meta-analysis methods can lead to serious distortions because of such data sparsity. To overcome this, we suggest the use of…

Purpose: Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business…

Learning Analytics (LA) and adaptive learning are inextricably linked since they both foster technology-supported learner-centred education. This study identifies developments focusing on their interplay and emphasises insufficiently investigated directions which display a higher innovation potential. Twenty-one peer-reviewed studies are…