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Annalisa Soncini; Maria Cristina Matteucci; Carlo Tomasetto; Fabrizio Butera – British Journal of Educational Psychology, 2025
Background: Although it is well established that students' adaptive reactions towards errors promote learning outcomes, little is still known about the role of error feedback in promoting these reactions. Aim: Through a targeted intervention based on an online teaching unit, this study aimed at testing whether supportive error feedback promotes…
Descriptors: Error Patterns, Feedback (Response), Student Reaction, Intervention
Julie Y. L. Chow; Jessica C. Lee; Peter F. Lovibond – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2024
People often rely on the covariation between events to infer causality. However, covariation between cues and outcomes may change over time. In the associative learning literature, extinction provides a model to study updating of causal beliefs when a previously established relationship no longer holds. Prediction error theories can explain both…
Descriptors: Beliefs, Learning Processes, Foreign Countries, Attribution Theory
Jana Spear; Maria Tulis; Markus Dresel – Educational Psychology, 2024
Adaptive action-related reactions to errors, i.e. (meta-)cognitive processes and behaviours directly aimed at overcoming an error, have been proposed to benefit learning outcomes. However, causally interpretable findings are sparse in the current literature. Addressing this research deficit, the present study aimed at investigating whether…
Descriptors: Error Patterns, Error Correction, Student Reaction, Undergraduate Students
Maria Tulis; Markus Dresel – British Journal of Educational Psychology, 2025
Background: Interest in the potential of learning from errors to benefit innovation and organizational and personal growth is currently increasing. In practice, individuals frequently do not appear to learn spontaneously from errors and setbacks without support. Based on prior work, this paper considers antecedents and consequences of adaptive…
Descriptors: Undergraduate Students, Student Attitudes, Beliefs, Student Motivation
Chi Hong Leung; Winslet Ting Yan Chan – Asian Journal of Contemporary Education, 2025
This paper explores the efficacy of ChatGPT, a generative artificial intelligence in educational contexts, particularly concerning its potential to assist students in overcoming academic challenges while highlighting its limitations. ChatGPT is suitable for solving general problems. When a student comes across academic challenges, ChatGPT may…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Error Patterns
Kwaku Adu-Gyamfi; Kayla Chandler; Anthony Thompson – School Science and Mathematics, 2025
The challenge posed by algebra story problems creates a significant hurdle for many students, transcending both the mathematical content of the problem and the specific instructional background received. This study offers a distinctive contribution to the existing literature by focusing on the cognitive conditions essential for comprehension in…
Descriptors: Algebra, Mathematics Instruction, Barriers, Cognitive Processes
Dyre, Liv; Grierson, Lawrence; Rasmussen, Kasper Møller Boje; Ringsted, Charlotte; Tolsgaard, Martin G. – Advances in Health Sciences Education, 2022
The purpose of this scoping review was to explore how errors are conceptualized in medical education contexts by examining different error perspectives and practices. This review used a scoping methodology with a systematic search strategy to identify relevant studies, written in English, and published before January 2021. Four medical education…
Descriptors: Error Patterns, Medical Education, Medical Students, Learning Processes
Kristen Vroom; Tenchita Alzaga Elizondo – International Journal of Research in Undergraduate Mathematics Education, 2024
Undergraduate students are expected to produce and comprehend constructive existence proofs; yet, these proofs are notoriously difficult for students. This study investigates students' thinking about these proofs by asking students to validate two arguments for the existence of a mathematical object. The first argument featured a common structural…
Descriptors: Mathematics Instruction, Validity, Mathematical Logic, Student Attitudes
Falakfarsa, Galan; Brand, Denys; Bensemann, Joshua; Jones, Lea; Miguel, Caio F.; Heinicke, Megan R.; Mason, Makenna A. – Journal of Applied Behavior Analysis, 2023
Procedural fidelity is defined as the extent to which the independent variable is implemented as prescribed. Research using computerized tasks has shown that fidelity errors involving consequences for behavior can hinder skill acquisition. However, studies examining the effects of these errors once skills have been mastered are lacking. Thus, this…
Descriptors: Fidelity, Error Patterns, Mastery Learning, Task Analysis
Pham Sy Nam; Ngoc-Giang Nguyen; Hoa Anh Tuong; Ben Haas; Zsolt Lavicza; Yves Kreis – International Journal for Technology in Mathematics Education, 2023
Problem-based learning puts students in situations that suggest problems without providing instructions and available knowledge. Therefore, when using problem-based learning, students need to be flexible, self-disciplined, active and self-occupied with knowledge and turn the knowledge the teacher intends to impart into their knowledge. For locus…
Descriptors: Computer Software, Mathematics Instruction, Teaching Methods, Problem Based Learning
Isaac N. Treves; Jonathan Cannon; Eren Shin; Cindy E. Li; Lindsay Bungert; Amanda O'Brien; Annie Cardinaux; Pawan Sinha; John D. E. Gabrieli – Journal of Autism and Developmental Disorders, 2024
Some theories have proposed that autistic individuals have difficulty learning predictive relationships. We tested this hypothesis using a serial reaction time task in which participants learned to predict the locations of a repeating sequence of target locations. We conducted a large-sample online study with 61 autistic and 71 neurotypical…
Descriptors: Autism Spectrum Disorders, Adults, Learning Processes, Visual Perception
Eva Viviani; Michael Ramscar; Elizabeth Wonnacott – Cognitive Science, 2024
Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error-driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of…
Descriptors: Symbolic Learning, Learning Processes, Artificial Intelligence, Prediction
Erdin Mujezinovic; Vsevolod Kapatsinski; Ruben van de Vijver – Cognitive Science, 2024
A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney,…
Descriptors: Morphology (Languages), Phonology, Morphemes, Cues
Eglington, Luke G.; Pavlik, Philip I., Jr. – International Journal of Artificial Intelligence in Education, 2023
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Rahel Schmid; Robbert Smit; Nicolas Robin; Alexander Strahl – British Journal of Educational Psychology, 2025
Background: Students make many errors in visual programming. In order to learn from these, it is important that students regulate their emotions and view errors as learning opportunities. Aims: This study aimed to explore to what extent momentary emotions, specifically enjoyment, anxiety and boredom, as well as the error learning orientation of…
Descriptors: Psychological Patterns, Emotional Response, Learning Processes, Error Patterns