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Colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potential for predictive analytics, two major barriers exist to their adoption in higher education: (a) the lack of…

Studies have indicated that personality is related to achievement, and several personality assessment models have been developed. However, most are either questionnaires or based on marker systems, which entails limitations. We proposed a…

While acute stress has been shown to have both positive and negative effects on performance, not much is known about the impacts of stress on students grades during examinations. To answer this question, we examined whether a correlation…

There has been strong interest among higher education institution in implementing technology-enhanced peer assessment as a tool for enhancing students' learning. However, little is known on how to use the peer assessment system in…

The accuracy of machine learning systems is a widely studied research topic. Established techniques such as cross-validation predict the accuracy on unseen data of the classifier produced by applying a given learning method to a given…

Having observed low success rates among first-year university students in both Belgium and France, we develop prediction models in this paper in order to identify, at the earliest possible stage, those students who are at risk of failing at…

The use of deep learning methods to automatically detect students' classroom behavior is a promising approach for analyzing their class performance and improving teaching effectiveness. However, the lack of publicly available datasets on…

With the advancing technology, the hardware gain of computers and the increase in the processing capacity of processors have facilitated the processing of instantaneous and real-time images. Face recognition processes are also studies in…

Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal student ability distribution, overlooking the heterogeneous nature of student…

In high-dimensional prediction settings, it remains challenging to reliably estimate the test performance. To address this challenge, a novel performance estimation framework is presented. This framework, called Learn2Evaluate, is based on…

The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it…

Student performance prediction is a critical research problem to understand the students' needs, present proper learning opportunities/resources, and develop the teaching quality. However, traditional machine learning methods fail to…

This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning,…

A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized test scores to organize large applicant pools into viable…

Understanding socio-academic and economic factors influencing students' performance is crucial for effective educational interventions. This study employs several machine learning techniques and causal analysis to predict and elucidate the…

This study is motivated by the magnitude of the problem of Louisiana high school dropout and its negative impacts on individual and public well-being. Our goal is to predict students who are at risk of high school dropout, by examining…

Mobile applications and other integration of information and communication technology (ICT) have become well-known in education to monitor teaching and learning activities. The analysis of student learning through evaluation is a growing…

Most multi-class classifiers make their prediction for a test sample by scoring the classes and selecting the one with the highest score. Analyzing these prediction scores is useful to understand the classifier behavior and to assess its…

Language model-based instruction-following systems have lately shown increasing performance on many benchmark tasks, demonstrating the capability of adapting to a broad variety of instructions. However, such systems are often not designed…

Sequence classification is the task of predicting a class label given a sequence of observations. In many applications such as healthcare monitoring or intrusion detection, early classification is crucial to prompt intervention. In this…