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Showing 1 to 15 of 262 results Save | Export
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Christina Glasauer; Martin K. Yeh; Lois Anne DeLong; Yu Yan; Yanyan Zhuang – Computer Science Education, 2025
Background and Context: Feedback on one's progress is essential to new programming language learners, particularly in out-of-classroom settings. Though many study materials offer assessment mechanisms, most do not examine the accuracy of the feedback they deliver, nor give evidence on its validity. Objective: We investigate the potential use of a…
Descriptors: Novices, Computer Science Education, Programming, Accuracy
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Daniel M. K. Lam – ELT Journal, 2025
Feedback penetrates many walks of our lives, and its importance in L2 teaching and assessment is well recognised. However, while corrective feedback and writing feedback have been the focus of much L2 research and classroom practice, there seems relatively little attention to feedback on spoken interactional skills. Concomitantly, translating…
Descriptors: Feedback (Response), Peer Evaluation, Oral Language, Interaction
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Guozhu Ding; Mailin Li; Shan Li; Hao Wu – Asia Pacific Education Review, 2025
This study investigated the optimal feedback intervals for tasks of varying difficulty levels in online testing and whether task difficulty moderates the effect of feedback intervals on student performance. A pre-experimental study with 36 students was conducted to determine the delayed time for providing feedback based on student behavioral data.…
Descriptors: Feedback (Response), Academic Achievement, Computer Assisted Testing, Intervals
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Jesús Pérez; Eladio Dapena; Jose Aguilar – Education and Information Technologies, 2024
In tutoring systems, a pedagogical policy, which decides the next action for the tutor to take, is important because it determines how well students will learn. An effective pedagogical policy must adapt its actions according to the student's features, such as knowledge, error patterns, and emotions. For adapting difficulty, it is common to…
Descriptors: Feedback (Response), Intelligent Tutoring Systems, Reinforcement, Difficulty Level
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Marc P. Janson; Oliver Dickhäuser – Journal of Experimental Education, 2025
Feedback significantly impacts learning outcomes, yet interindividual differences in feedback preferences remain understudied. We postulate and test a fitting feedback framework assuming that feedback framings matching personal preferences produce positive effects. We conducted two learning experiments including feedback representing different…
Descriptors: Cognitive Processes, Difficulty Level, Feedback (Response), Preferences
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Clariana, Roy B.; Park, Eunsung – Educational Technology Research and Development, 2021
Cognitive and metacognitive processes during learning depend on accurate monitoring, this investigation examines the influence of immediate item-level knowledge of correct response feedback on cognition monitoring accuracy. In an optional end-of-course computer-based review lesson, participants (n = 68) were randomly assigned to groups to receive…
Descriptors: Feedback (Response), Cognitive Processes, Accuracy, Difficulty Level
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Ted Peterson – Journal of Education for Business, 2024
This article delves into student course feedback using publicly available data from the University of North Texas. It examines factors influencing elevated student course evaluation ratings in the Information Technology and Decision Sciences department. The study reveals a positive relationship between higher response rates and better evaluation…
Descriptors: Course Evaluation, Feedback (Response), College Students, Influences
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Janea J. Thibodeaux; Pierce M. Taylor; Janelle K. Bacotti; Samuel L. Morris – Journal of Applied Behavior Analysis, 2025
Many researchers have evaluated how characteristics of feedback may influence trainee performance, but relatively little attention has been allocated to directly assessing trainee preference for feedback characteristics and its relation to performance. Thus, the primary purpose of this study was to use a within-subject experimental design to…
Descriptors: Undergraduate Students, Feedback (Response), Difficulty Level, Learning Strategies
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Haiyang Yu; Entai Wang; Qi Lang; Jianan Wang – IEEE Transactions on Learning Technologies, 2024
The latest technologies in natural language processing provide creative, knowledge retrieval, and question-answering technologies in the design of intelligent education, which can provide learners with personalized feedback and expert guidance. Entrepreneurship education aims to cultivate and develop the innovative thinking and entrepreneurial…
Descriptors: Entrepreneurship, Comprehension, Questioning Techniques, Information Retrieval
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Hauenstein, Clifford E.; Embretson, Susan E. – Journal of Cognitive Education and Psychology, 2020
The Concept Formation subtest of the Woodcock Johnson Tests of Cognitive Abilities represents a dynamic test due to continual provision of feedback from examiner to examinee. Yet, the original scoring protocol for the test largely ignores this dynamic structure. The current analysis applies a dynamic adaptation of an explanatory item response…
Descriptors: Test Items, Difficulty Level, Cognitive Tests, Cognitive Ability
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Liang, Garston; Sloane, Jennifer F.; Donkin, Christopher; Newell, Ben R. – Cognitive Research: Principles and Implications, 2022
In three experiments, we sought to understand when and why people use an algorithm decision aid. Distinct from recent approaches, we explicitly enumerate the algorithm's accuracy while also providing summary feedback and training that allowed participants to assess their own skills. Our results highlight that such direct performance comparisons…
Descriptors: Decision Support Systems, Accuracy, Difficulty Level, Feedback (Response)
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Sonja Dieterich; Stefan Rumann; Marc Rodemer – Educational Psychology Review, 2025
Example-based learning is a well-known instructional method for effective cognitive skill acquisition in complex domains. "(Contrasting) erroneous examples" are a promising extension that embed errors in instructional material, potentially fostering not only positive but negative knowledge. However, the mechanisms and conditions for…
Descriptors: Learning Processes, Teaching Methods, Instructional Effectiveness, Models
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Enming Zhang; Yinghua Ye; Shuqian Ni – Asia-Pacific Education Researcher, 2025
Subject (e.g., math) problems often have deep rationales and concepts underlying them. Accordingly, when students solve these problems, they are prone to making errors that expose their misunderstandings and are difficult to correct. Previous research has shown the benefits of teacher feedback in learning from errors, but the effectiveness of…
Descriptors: Feedback (Response), Error Patterns, Error Correction, Cognitive Processes
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Kang, Sangmi – Journal of Research in Music Education, 2023
The purpose of this study was to examine music teachers' experiences with flow while performing and teaching music. A model with four flow antecedents (Challenge, Skills, Goal Clarity, and Feedback) and three dimensions of flow state (Absorption, Enjoyment, and Intrinsic Motivation) was adopted to investigate music teachers' flow experiences in…
Descriptors: Music Teachers, Musicians, Music Education, Performance
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Arthur William Fodouop Kouam – Discover Education, 2024
This study investigates the effectiveness of Intelligent Tutoring Systems (ITS) in supporting students with varying levels of programming experience. Through a mixed-methods research design, the study explores the impact of ITS on student performance, adaptability to different skill levels, and best practices for utilizing ITS in heterogeneous…
Descriptors: Intelligent Tutoring Systems, Instructional Effectiveness, Programming, Skill Development
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