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Mousa Javidialsaadi; Scott T. Albert; Badr Moufarrej S. Al Mutairi; Jinsung Wang – npj Science of Learning, 2025
When humans encounter the same perturbation twice, they typically adapt faster the second time, a phenomenon called savings. Studies have examined savings following adaptation to a gradually introduced perturbation, with mixed results. These inconsistencies might be caused by differences in how behavior returns to its baseline state during the…
Descriptors: Psychomotor Skills, Motor Reactions, Error Patterns, Feedback (Response)
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Tanja C. Roembke; Bob McMurray – Cognitive Science, 2025
Computational and animal models suggest that the unlearning or pruning of incorrect meanings matters for word learning. However, it is currently unclear how such pruning occurs during word learning and to what extent it depends on supervised and unsupervised learning. In two experiments (N[subscript 1] = 40; N[subscript 2] = 42), adult…
Descriptors: Vocabulary Development, Computation, Models, Accuracy
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Jess Sullivan; Joseph Alvarez; Sophie Cramer-Benjamin; Sadie Holcomb; Melissa Nolan; Alex Morabito; David Barner – Journal of Numerical Cognition, 2025
When children first learn to count, what do they understand about the structure of the count system? The present study investigated English-speaking children's ability to generalize the rules that structure their count list to novel contexts. A total of N = 86 children (3;0-6;11) completed a battery of tasks aimed at measuring their understanding…
Descriptors: Computation, Young Children, Novelty (Stimulus Dimension), English
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Muntasir Hoq; Ananya Rao; Reisha Jaishankar; Krish Piryani; Nithya Janapati; Jessica Vandenberg; Bradford Mott; Narges Norouzi; James Lester; Bita Akram – International Educational Data Mining Society, 2025
In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issues students face, educators can provide targeted assistance to help them overcome obstacles and improve learning outcomes. While identifying sources of struggle, such as…
Descriptors: Computer Science Education, Programming, Misconceptions, Error Patterns
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Marc Deosdad-Díez; Josep Marco-Pallarés – npj Science of Learning, 2025
Rhythm production requires the integration of perceptual predictions and performance monitoring mechanisms to adjust actions, yet the role of auditory prediction remains underexplored. To address this, electroencephalography was recorded from 70 non-musicians as they synchronized with and reproduced rhythms containing notes of varying…
Descriptors: Prediction, Language Rhythm, Music, Auditory Perception
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Yasin Memis – Journal of Pedagogical Research, 2025
The integration of artificial intelligence (AI) into mathematical problem-solving has shown significant potential to enhance student learning and performance. However, while AI tools offer numerous benefits, they are prone to occasional conceptual and arithmetic errors that can mislead users and obscure understanding. This research examines such…
Descriptors: Artificial Intelligence, Mathematics Instruction, Problem Solving, Error Patterns
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Mansour Saleh Alabdulaziz – Educational Process: International Journal, 2025
Background/purpose: This study aimed to identify common mistakes made in master's theses on mathematics education by Saudi mathematics students at lAU in Saudi Arabia, as determined through a content analysis, and to compare these with the views of faculty members. Materials/methods: A descriptive approach (analytical and survey) was employed. The…
Descriptors: Foreign Countries, Masters Theses, Error Patterns, Mathematics Education
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Martin Abt; Katharina Loibl; Timo Leuders; Wim Van Dooren; Frank Reinhold – Educational Studies in Mathematics, 2025
In the boxplot, the box always represents -- regardless of its area -- the middle half of the data and thus a measure of variability (interquartile range). However, when students first learn about boxplots, they are usual already familiar with other forms of statistical representations (e.g., bar or circle graphs) in which a larger area represents…
Descriptors: College Students, Data Analysis, Graphs, Error Patterns
Babawande Emmanuel Olawale; Saidat Adeniji; Zizipho Mabhoza – Mathematics Education Research Group of Australasia, 2025
This paper analyses learners' common errors in simplifying algebraic problems. 102 Grade 10 learners from three rural schools in South Africa participated in the study. Following a quantitative approach, content analysis of learners' responses to algebraic tests revealed that while learners commit several errors in algebraic problems, encoding and…
Descriptors: Algebra, Grade 10, High School Students, Secondary School Mathematics
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Anna Rechtácková; Radek Pelánek; Tomáš Effenberger – ACM Transactions on Computing Education, 2025
Code quality is a critical aspect of programming, as high-quality code is easier to maintain, and code maintenance constitutes the majority of software costs. Consequently, code quality should be emphasized in programming education. While previous research has identified numerous code quality defects commonly made by students, the current state…
Descriptors: Programming, Computer Science Education, Error Patterns, Automation
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Lauren A. Mason; Abigail Miller; Gregory Hughes; Holly A. Taylor – Cognitive Research: Principles and Implications, 2025
False alarming, or detecting an error when there is not one, is a pervasive problem across numerous industries. The present study investigated the role of elaboration, or additional information about non-error differences in complex visual displays, for mitigating false error responding. In Experiment 1, learners studied errors and non-error…
Descriptors: Error Correction, Error Patterns, Evaluation Methods, Visual Aids
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Peter Baldwin; Victoria Yaneva; Kai North; Le An Ha; Yiyun Zhou; Alex J. Mechaber; Brian E. Clauser – Journal of Educational Measurement, 2025
Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…
Descriptors: Artificial Intelligence, Scoring, Computational Linguistics, Accuracy
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Masahiro Yamada; Omid Ansari; Ali Emami; Alireza Saberi Kakhki; Takehiro Iwatsuki – Journal of Motor Learning and Development, 2025
Motor performance has been shown to be superior when focusing on a physically farther environmental cue (external focus-far, EF-far) instead of a cue proximal to the body (EF-near). However, little is known about whether these foci affect bimanual tasks. Further, the effect of visual information on attentional focus is unclear. In the present…
Descriptors: Psychomotor Skills, Attention, Cues, Proximity
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Markus Dresel; Martin Daumiller; Jana Spear; Stefan Janke; Oliver Dickhäuser; Gabriele Steuer – British Journal of Educational Psychology, 2025
Background: Errors can provide informative feedback and exhibit a high potential for learning gains. Affective-motivational and action-related reactions to errors are two forms of error adaptivity that have been shown to enhance learning outcomes from errors. However, little is known regarding the development and contextual conditions of students'…
Descriptors: Secondary School Students, Error Patterns, Student Reaction, Mathematics Education
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Owen Henkel; Hannah Horne-Robinson; Libby Hills; Bill Roberts; Josh McGrane – International Journal of Artificial Intelligence in Education, 2025
This paper reports on a set of three recent experiments utilizing large-scale speech models to assess the oral reading fluency (ORF) of students in Ghana. While ORF is a well-established measure of foundational literacy, assessing it typically requires one-on-one sessions between a student and a trained rater, a process that is time-consuming and…
Descriptors: Foreign Countries, Oral Reading, Reading Fluency, Literacy
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