When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people's decisions? In this paper, we investigated whether adults, who have many years…

Purpose: The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC) personalized recommendation method to help learners efficiently obtain MOOC resources. Design/methodology/approach: This study introduced ontology construction technology and a new semantic association algorithm…

In this article, we advocate for and propose a novel concept map driven knowledge tracing (CMKT) model, which utilizes educational concept map for learner modeling. This article particularly addresses the issue of learner data sparseness caused by the unwillingness to practice and irregular learning behaviors on the learner side. CMKT considers…

Working in groups is an important collaboration activity in the educational context, where a variety of factors can influence the prediction of the teams' performance. In the pertinent bibliography, several machine learning models are available for delivering predictions. In this sense, the main goal of the current research is to assess 28…

Aim/Purpose: The purpose of this study is to review and categorize current trends in student engagement and performance prediction using machine learning techniques during online learning in higher education. The goal is to gain a better understanding of student engagement prediction research that is important for current educational planning and…

As they navigate the social and academic expectations of a new college, transfer students commonly suffer "transfer shock," or a sudden drop in GPA. However, little is known about why some students suffer transfer shock, why some bounce back, and the consequences in terms of student retention. This analysis of over 25,000 transfer…

Due to severe time constraints, goalkeepers regularly face the challenging task to make decisions within just a few hundred milliseconds. A key finding of anticipation research is that experts outperform novices by using advanced cues which can be derived from either kinematic or contextual information. Yet, how context modulates decision-making…

Predicting students' success is crucial in educational settings to improve academic performance and prevent dropouts. This study aimed to improve student performance prediction by combining advanced machine learning (ML) approaches. Convolutional Neural Networks (CNNs) and attention mechanisms were used for extracting relevant features from…

In this paper we present a novel adaptation of a tri-phase assessment tool, originally devised to investigate students' linear equations-related strategy flexibility, to evaluate Danish grade-six students' multidigit arithmetic-related strategy adaptivity and flexibility. Participants, 731 students, median age 12 years and drawn from 20…

This study investigated the relationships between school resilience strength, cognitive flexibility, emotion regulation, and school engagement in high school students. A hypothetical model test was conducted for this purpose. This study was designed according to a predictive correlational model. Through convenience sampling, this study collected…

This study tested whether encouraging prediction enhances prediction in second language (L2) speakers. L2 English speakers listened to English sentences like "The woman … will read/buy one of the newspapers" while viewing the target (a newspaper) and distractor objects (a rose, a bowl, and a mango) on a screen and clicked on the target…

This study highlights how health insurance predicts the job performance of secondary school teachers in Cross River State, Nigeria. Using a descriptive survey design, data were collected from 190 public secondary school teachers and 72 principals, vice-principals across the three senatorial districts. Instruments employed were structured…

Digital textbooks are widely used in various educational contexts, such as university courses and online lectures. Such textbooks yield learning log data that have been used in numerous educational data mining (EDM) studies for student behavior analysis and performance prediction. However, these studies have faced challenges in integrating…

The proliferation of digital learning platforms has revolutionized the generation, accessibility, and dissemination of educational resources, fostered collaborative learning environments and producing vast amounts of interaction data. Machine learning (ML) algorithms have emerged as powerful tools for analyzing these complex datasets, uncovering…

While high-accuracy machine learning (ML) models for predicting student learning performance have been widely explored, their deployment in real educational settings can lead to unintended harm if the predictions are biased. This study systematically examines the trade-offs between prediction accuracy and fairness in ML models trained on the…