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Abigail R. Vild; Maggie E. Wilson; Christopher A. Was – Journal of Research in Education, 2025
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…
Descriptors: Student Satisfaction, Undergraduate Students, Scores, Prediction
Chow, Julie C.; Hormozdiari, Fereydoun – Journal of Autism and Developmental Disorders, 2023
The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an…
Descriptors: Prediction, Neurodevelopmental Disorders, Identification, Genetics
Weihao Wang – ProQuest LLC, 2024
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,…
Descriptors: Sampling, Statistics Education, Data Analysis, Prediction
Tong Zhang; Ermei Lu; Quanming Liao; Deliang Sun – Journal of Psychoeducational Assessment, 2025
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…
Descriptors: College Students, Anxiety, Academic Achievement, Student Attitudes
Mahmoud Abdasalam; Ahmad Alzubi; Kolawole Iyiola – Education and Information Technologies, 2025
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…
Descriptors: Grades (Scholastic), Prediction, Accuracy, Artificial Intelligence
Zhenchang Xia; Nan Dong; Jia Wu; Chuanguo Ma – IEEE Transactions on Learning Technologies, 2024
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…
Descriptors: Graphs, Artificial Intelligence, Multivariate Analysis, Prediction
Allison J. Williams; Judith H. Danovitch – Child Development, 2024
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…
Descriptors: Accuracy, Child Development, Decision Making, Children
Xiang Feng; Keyi Yuan; Xiu Guan; Longhui Qiu – Interactive Learning Environments, 2024
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…
Descriptors: MOOCs, Psychological Patterns, Artificial Intelligence, Prediction
Caihong Feng; Jingyu Liu; Jianhua Wang; Yunhong Ding; Weidong Ji – Education and Information Technologies, 2025
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'…
Descriptors: Academic Achievement, Prediction, Models, Student Behavior
Tenzin Doleck; Pedram Agand; Dylan Pirrotta – Education and Information Technologies, 2025
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…
Descriptors: Prediction, Data Science, Performance, Data Analysis
Yaosheng Lou; Kimberly F. Colvin – Discover Education, 2025
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…
Descriptors: Educational Research, Data Collection, Performance, Prediction
Melissa Meindl; David Wilkins – Child Care in Practice, 2025
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…
Descriptors: Foreign Countries, Caseworkers, Social Work, Prediction
Burton, Olivia R.; Bodner, Glen E.; Williamson, Paul; Arnold, Michelle M. – Metacognition and Learning, 2023
Meta-reasoning requires monitoring and controlling one's reasoning processes, and it often begins with an assessment of problem solvability. We explored whether "Judgments of Solvability (JOS)" for solvable and unsolvable anagrams discriminate and predict later problem-solving outcomes once anagrams solved during the JOS task are…
Descriptors: Accuracy, Prediction, Problem Solving, Thinking Skills
Senthil Kumaran, V.; Malar, B. – Interactive Learning Environments, 2023
Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled…
Descriptors: Electronic Learning, Dropouts, Accuracy, Classification
Sha, Lele; Rakovic, Mladen; Das, Angel; Gasevic, Dragan; Chen, Guanliang – IEEE Transactions on Learning Technologies, 2022
Predictive modeling is a core technique used in tackling various tasks in learning analytics research, e.g., classifying educational forum posts, predicting learning performance, and identifying at-risk students. When applying a predictive model, it is often treated as the first priority to improve its prediction accuracy as much as possible.…
Descriptors: Prediction, Models, Accuracy, Mathematics