Theories of self-regulated learning suggest a positive link between knowledge monitoring accuracy (the ability to predict test performance) and performance on tests. Put differently, students who accurately monitor their knowledge of course content more efficiently regulate study of course materials. However, a plethora of literature indicates…

In this work, we introduce a novel oversampling technique, the theory of inheritance and Gower distance-based oversampling (TIGO) method, designed to address class imbalance issues in mixed categorical and continuous variables data set. Drawing inspiration from genetic inheritance principles, TIGO synthesizes new minority class data,…

Purpose: Academic anxiety is a common phenomenon in the college student population, which has an important impact on students' psychological health and academic performance. Therefore, by exploring the effects of college students' professional commitment and achievement goal orientation variables on academic anxiety, it helps to understand…

This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…

As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…

Across two studies, children ages 6-9 (N = 160, 82 boys, 78 girls; 75% White, 91% non-Hispanic) rated an inaccurate expert's knowledge and provided explanations for the expert's inaccurate statements. In Study 1, children's knowledge ratings decreased as he provided more inaccurate information. Ratings were predicted by age (i.e., older children…

Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning…

Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students'…

As is rapidly becoming clear, data science increasingly permeates many aspects of life. Educational research recognizes the importance and complexity of learning data science. In line with this imperative, there is a growing need to investigate the factors that influence student performance in data science tasks. In this paper, we aimed to apply…

Predicting student performance has been a critical focus of educational research. With an effective predictive model, schools can identify potentially at-risk students and implement timely interventions to support student success. Recent developments in educational data mining (EDM) have introduced several machine learning techniques that can…

Child protection social workers in England are required to make many decisions in their day-to-day work, including whether to accept a referral, undertake a child protection investigation, pursue care proceedings, or close the case. Many of these decisions involve implicit or explicit predictions about the likelihood of future actions, events, and…

The higher the level of development of higher education, the larger its contribution to socioeconomic development. In order to predict the trend of higher education development in a country more accurately, a new methodology is employed in this study. A weakening buffer operator-based GM (1, 1) model is constructed using Kazakhstan's gross…

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

Studying texts constitutes a significant part of student learning in health professions education. Key to learning from text is the ability to effectively monitor one's own cognitive performance and take appropriate regulatory steps for improvement. Inferential cues generated during a learning experience typically guide this monitoring process. It…

Recent investigations on how people derive meaning from language have focused on task-dependent shifts between two cognitive systems. The symbolic (amodal) system represents meaning as the statistical relationships between words. The embodied (modal) system represents meaning through neurocognitive simulation of perceptual or sensorimotor systems…