Student's shift of attention away from a current learning task to task-unrelated thought, also called mind wandering, occurs about 30% of the time spent on education-related activities. Its frequent occurrence has a negative effect on learning outcomes across learning tasks. Automated detection of mind wandering might offer an opportunity to…

Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…

Early Warning Systems (EWS) in higher education accommodate student counsellors by identifying at-risk students and allow them to intervene in a timely manner to prevent student dropout. This study evaluates an EWS that shares student-specific risk information with student counsellors, which was implemented at a large Dutch university. A…

This study investigates the factors contributing to early school dropout in vocational and technical high schools in Turkey, utilizing machine learning techniques to analyze a dataset of personal, socio-economic, familial, and academic variables. The data was collected via a detailed survey administered to students at one of the largest Vocational…

In the present study, we designed a dynamic measure to assess emerging morphological awareness in Chinese children and examined its concurrent and longitudinal relations with character recognition. The initial question of the dynamic assessment of morphological awareness (DAMA) task asked children to judge whether the first morphemes in a pair of…

While open and distance education gains growing recognition over time, it also faces increasing drop-out rates. Consequently, the development of predictive models for early identification of students at-risk for drop-out could be critical to promote ongoing engagement. This study aims to gain insights into the dropout prediction problem in a…

The quality assurance and evaluation of primary schools requires early risk detection. This is a daunting task, not only because risks are typically rare and their origins complex, but also because governing institutions have limited resources and capacity and desire efficiency and proportionality. Many countries, including most Organisation for…

Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a…

Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for…

A central principle of programmatic assessment is that the final decision is not a surprise to the learner. To achieve this, assessments must demonstrate predictive and consequential validity, however, to date, research has only focussed on the former. The present study attempts to address this gap by examining the predictive and consequential…

The dropout of students is one of the major obstacles that ruin the improvement of higher education quality. To facilitate the study of students' dropout in Moroccan universities, this paper aims to establish a clustering approach model based on machine learning algorithms to determine Moroccan universities categories. Our objective in this…

This study investigated the development of component awareness, semantic radical identification ability, semantic radical knowledge application, Chinese character recognition, and the relationship among these abilities in nonnative speakers. A total of 139 Vietnamese undergraduates majoring in Chinese language who were sorted according to Chinese…

The early prediction of math difficulties (MD) is important as it facilitates timely support. MD are multifaceted, and several factors are involved in their manifestation. This makes the accurate early prediction of MD particularly challenging. In the present study, we aim to predict MD in Grade 6 with kindergarten-age (age 6) measures by applying…

Purpose: This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student…

Hybrid media context and the infodemic have increased the threat of disinformation, particularly among young people who mostly consume digital content. This article aims to identify the competencies needed to detect low-quality content linked to disinformation by Journalism and Communications undergraduates from Argentina, Chile, and Spain. Based…