The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…

Theories of reading comprehension have widely predicted a role for syntactic skills, or the ability to understand and manipulate the structure of a sentence. Yet, these theories are based primarily on English, leaving open the question of whether this remains true across typologically different languages such as English versus Chinese. There are…

Research that employs theory provides a framework and structure in which complex phenomenon, can be understood. While many theories have been developed to study people's technology usage, the plurality of perspectives offered are complex to navigate due to the diverse range of problems and topics addressed and the varied theoretical foundations…

This systematic literature review investigates the fairness of machine learning algorithms in educational settings, focusing on recent studies and their proposed solutions to address biases. Applications analyzed include student dropout prediction, performance prediction, forum post classification, and recommender systems. We identify common…

This study presents a systematic review of the literature on the predicting student retention in higher education through machine learning algorithms based on measures such as dropout risk, attrition risk, and completion risk. A systematic review methodology was employed comprised of review protocol, requirements for study selection, and analysis…

This paper updates a review of dynamic assessment in education by the first author, published in this journal in 2003. It notes that the original review failed to examine the important conceptual distinction between dynamic testing (DT) and dynamic assessment (DA). While both approaches seek to link assessment and intervention, the former is of…

Two articles, Edelsbrunner and, Schneider (2013), and Nokelainen and Silander (2014) comment on Musso, Kyndt, Cascallar, and Dochy (2013). Several relevant issues are raised and some important clarifications are made in response to both commentaries. Predictive systems based on artificial neural networks continue to be the focus of current…

A meta-synthesis study was conducted of 60 research studies on educational data mining (EDM) and their impacts on and outcomes for improving learning outcomes. After an overview, an examination of these outcomes is provided (Romero, Ventura, Espejo, & Hervas, 2008; Romero, "et al.", 2011). Then, a review of other EDM-related research…

This article describes the ICAP framework that defines cognitive engagement activities on the basis of students' overt behaviors and proposes that engagement behaviors can be categorized and differentiated into one of four modes: "Interactive," "Constructive," "Active," and "Passive." The ICAP hypothesis…

The purpose of this review was to update previous reviews on factors related to students' responsiveness to early literacy intervention. The 14 studies in this synthesis used experimental designs, provided small-group or one-on-one reading interventions, and analyzed factors related to responsiveness to those interventions. Participants were…

Clarifies appropriateness of certain simulation approaches by distinguishing between different types of simulations--experimental, predictive, evaluative, and educational--on the basis of purpose, assumptions, procedures, and criteria for evaluating. The kinds of questions each type best responds to are discussed. (65 references) (MBR)