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Wang, Chia-Chi; Chiou, Wen-Bin – Educational Psychology, 2022
People often underestimate their completion times of future tasks or events. The phenomenon of optimistic time prediction is called the planning fallacy. Prior research has demonstrated that individuals are less likely to make optimistic predictions about events that are temporally relatively close. Furthermore, events involving relatively more…
Descriptors: Time Management, Undergraduate Students, Prediction, Time Perspective
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Maxwell, Nicholas P.; Huff, Mark J. – Metacognition and Learning, 2022
Research has shown that judgments of learning (JOLs) often produce a reactive effect on the learning of cue-target pairs in which target recall differs between participants who provide item-based JOLs at study versus those who do not. Positive reactivity, or the memory improvement found when JOLs are provided, is typically observed on related…
Descriptors: Metacognition, Memory, Associative Learning, Cues
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Stoner, James C.; Zhang, Yi Leaf – Journal of College and University Student Housing, 2022
Employees in service-oriented, relationship-based jobs are prone to experiencing feelings of burnout. As such, paraprofessional staff in college housing environments, resident assistants (RAs), are not immune to its effects. Since navigating relationships is the root cause of burnout, it makes sense to focus on the relationships between…
Descriptors: Interpersonal Relationship, Prediction, College Students, Burnout
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Ward, Emma K.; Buitelaar, Jan K.; Hunnius, Sabine – Developmental Science, 2022
Predictive Processing accounts of autism claim that autistic individuals assign higher precision to their prediction errors than non-autistic individuals, that is, autistic individuals update their predictions more readily when faced with unexpected sensory input. Since setting the level of precision is a fundamental part of perception and…
Descriptors: Incidental Learning, Preschool Children, Autism, Pervasive Developmental Disorders
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Swamy, Vinitra; Radmehr, Bahar; Krco, Natasa; Marras, Mirko; Käser, Tanja – International Educational Data Mining Society, 2022
Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in humancentric fields. We implement five state-of-the-art methodologies for explaining black-box machine learning models…
Descriptors: Artificial Intelligence, Academic Achievement, Grade Prediction, MOOCs
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Crowley, Emmet; Ng, Kwok; Mujika, Iñigo; Powell, Cormac – Measurement in Physical Education and Exercise Science, 2022
The aim of this study was to examine the trends in Olympic Games (OLY) and World Long Course Championships (WLC) across three performance categories (1st-3rd, 4th-8th and 9th-16th), and to make predictions for the 2024 OLY. Top 16 rankings were obtained for all OLY and WLC competitions between 2011 and 2019. Linear regression and forecasting…
Descriptors: Athletics, Athletes, Aquatic Sports, Performance
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Gjata, Nensi N.; Ullman, Tomer D.; Spelke, Elizabeth S.; Liu, Shari – Cognitive Science, 2022
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…
Descriptors: Decision Making, Adults, Young Children, Motivation
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Li, Yuanmin; Chen, Dexin; Zhan, Zehui – Interactive Technology and Smart Education, 2022
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…
Descriptors: MOOCs, Individualized Instruction, Models, Student Characteristics
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Lu, Yu; Chen, Penghe; Pian, Yang; Zheng, Vincent W. – IEEE Transactions on Learning Technologies, 2022
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…
Descriptors: Concept Mapping, Learning Processes, Prediction, Models
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Giannakas, Filippos; Troussas, Christos; Krouska, Akrivi; Sgouropoulou, Cleo; Voyiatzis, Ioannis – Education and Information Technologies, 2022
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…
Descriptors: Comparative Analysis, Artificial Intelligence, Prediction, Cooperative Learning
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Ke Ting Chong; Noraini Ibrahim; Sharin Hazlin Huspi; Wan Mohd Nasir Wan Kadir; Mohd Adham Isa – Journal of Information Technology Education: Research, 2025
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…
Descriptors: Literature Reviews, Meta Analysis, Artificial Intelligence, Higher Education
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Shanna Smith Jaggars; Marcos D. Rivera; Melissa T. Buelow – Journal of College Student Retention: Research, Theory & Practice, 2025
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…
Descriptors: College Transfer Students, Grade Point Average, Student Adjustment, Academic Persistence
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Henrietta Weinberg; Florian Müller; Rouwen Cañal-Bruland – Cognitive Research: Principles and Implications, 2025
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…
Descriptors: Cues, Athletics, Decision Making, Specialists
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Majdi Beseiso – TechTrends: Linking Research and Practice to Improve Learning, 2025
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…
Descriptors: Prediction, Success, Academic Achievement, Artificial Intelligence
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Lóa Björk Jóelsdóttir; Paul Andrews – International Journal of Mathematical Education in Science and Technology, 2025
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…
Descriptors: Grade 6, Elementary School Students, Mathematics Instruction, Equations (Mathematics)
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