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Vivian Maanu; Ebenezer Bonyah; Seth Amoako Atta; Lauren Jeneva Clark – African Educational Research Journal, 2024
The study aimed to explore the perception of pre-service teachers regarding the use of the red pen for corrections. A non-random sample of twelve (12) respondents, consisting of three experienced tutors from the Mathematics, Science, and English Departments, along with nine pre-service teachers, were interviewed. The data was analysed…
Descriptors: Foreign Countries, Preservice Teachers, Teacher Attitudes, Scoring
Vince Mitchell; Erica Borgstrom; Sam Murphy; Charlene Campbell; Sandy Sieminski; Sandy Fraser – Assessment & Evaluation in Higher Education, 2024
This study explores the experiences of five distance learners at a UK university who needed support to resubmit their final assignment following a fail result. Having received written feedback on the submission, we asked how this could be best delivered to inform and motivate a successful resubmission. Written feedback alone was found to be…
Descriptors: Distance Education, Foreign Countries, Academic Failure, Assignments
Simonian, Michael J.; Brand, Denys – Journal of Applied Behavior Analysis, 2022
Feedback is an effective strategy for improving performance and consists of multiple characteristics. One characteristic that can influence feedback efficacy is its nature (whether feedback is positive or corrective) and little is known about the conditions under which individuals may prefer corrective over positive feedback. Thus, the purpose of…
Descriptors: Feedback (Response), Error Correction, Positive Reinforcement, Preferences
Emmanuel Fokides; Eirini Peristeraki – Education and Information Technologies, 2025
This research analyzed the efficacy of ChatGPT as a tool for the correction and provision of feedback on primary school students' short essays written in both the English and Greek languages. The accuracy and qualitative aspects of ChatGPT-generated corrections and feedback were compared to that of educators. For the essays written in English, it…
Descriptors: Artificial Intelligence, Error Correction, Feedback (Response), Elementary School Students
Megan Waller; Daniel Yurovsky; Nazbanou Nozari – Cognitive Science, 2024
For both adults and children, learning from one's mistakes (error-based learning) has been shown to be advantageous over avoiding errors altogether (errorless learning) in pedagogical settings. However, it remains unclear whether this advantage carries over to nonpedagogical settings in children, who mostly learn language in such settings. Using…
Descriptors: Preschool Children, Language Acquisition, Error Correction, Error Analysis (Language)
Xiangyi Liao; Daniel M Bolt – Educational Measurement: Issues and Practice, 2024
Traditional approaches to the modeling of multiple-choice item response data (e.g., 3PL, 4PL models) emphasize slips and guesses as random events. In this paper, an item response model is presented that characterizes both disjunctively interacting guessing and conjunctively interacting slipping processes as proficiency-related phenomena. We show…
Descriptors: Item Response Theory, Test Items, Error Correction, Guessing (Tests)
Erik S. Godinez; Denys Brand; Caio F. Miguel; Becky Penrod – Journal of Applied Behavior Analysis, 2024
Although feedback is a widely used intervention for improving performance, it is unclear what characteristics individuals prefer and what is necessary for it to be effective. The purpose of this study was to systematically extend Simonian and Brand (2022) by addressing the limitations of the study and adding a best-treatment phase. During an…
Descriptors: Feedback (Response), Error Correction, Games, Preferences
Bulut, Okan; Bulut, Hatice Cigdem; Cormier, Damien C.; Ilgun Dibek, Munevver; Sahin Kursad, Merve – Educational Assessment, 2023
Some statewide testing programs allow students to receive corrective feedback and revise their answers during testing. Despite its pedagogical benefits, the effects of providing revision opportunities remain unknown in the context of alternate assessments. Therefore, this study examined student data from a large-scale alternate assessment that…
Descriptors: Error Correction, Alternative Assessment, Feedback (Response), Multiple Choice Tests
Yajing Zhang; Thi Kim Truc Huynh; Benjamin James Dyson – npj Science of Learning, 2023
We argue that the feedback traditionally used to indicate negative outcomes causes future detrimental performance because of the default goal of "win maximization." In gaming paradigms where participants intentionally performed as well ("win maximization") and as poorly ("loss maximization") as possible, we showed a…
Descriptors: Feedback (Response), Gamification, Goal Orientation, Success
Vasalou, Asimina; Benton, Laura; Ibrahim, Seray; Sumner, Emma; Joye, Nelly; Herbert, Elisabeth – British Journal of Educational Technology, 2021
This paper examines how primary aged children with reading difficulties attend to, understand and act upon different types of feedback within a digital literacy game. A systematic and structured video analysis of twenty-six children's game play was carried out focussing on moments where children made an error and were followed by in-game feedback.…
Descriptors: Children, Reading Difficulties, Educational Games, Game Based Learning
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
Yi-Ching Chen; Gwo-Ching Chang; Wei-Min Huang; Ing-Shiou Hwang – npj Science of Learning, 2023
This study investigated behavioral and cortical mechanisms for short-term postural training with error amplification (EA) feedback in the elderly. Thirty-six elderly subjects (65.7 ± 2.2 years) were grouped (control and EA, n = 18) for training in stabilometer balance under visual guidance. During the training session (8 training rounds of 60 s in…
Descriptors: Skill Development, Feedback (Response), Older Adults, Error Correction
Rachatasumrit, Napol; Koedinger, Kenneth R. – International Educational Data Mining Society, 2021
Student modeling is useful in educational research and technology development due to a capability to estimate latent student attributes. Widely used approaches, such as the Additive Factors Model (AFM), have shown satisfactory results, but they can only handle binary outcomes, which may yield potential information loss. In this work, we propose a…
Descriptors: Models, Student Characteristics, Feedback (Response), Error Correction
Jussi S. Jauhiainen; Agustin Bernardo Garagorry Guerra – Journal of Information Technology Education: Innovations in Practice, 2025
Aim/Purpose: This article investigates the process of identifying and correcting hallucinations in ChatGPT-4's recall of student-written responses as well as its evaluation of these responses, and provision of feedback. Effective prompting is examined to enhance the pre-evaluation, evaluation, and post-evaluation stages. Background: Advanced Large…
Descriptors: Artificial Intelligence, Student Evaluation, Writing Evaluation, Feedback (Response)
Ole Eggers Bjaelde; David Boud; Annika Büchert Lindberg – Active Learning in Higher Education, 2025
Many students struggle with making sense of feedback information and in applying and transferring it to new contexts. Research literature suggests that low-performing students are especially at risk because they often do not understand assessment criteria and cannot utilise information they receive. This paper addresses this problem through…
Descriptors: Undergraduate Students, Academic Achievement, Feedback (Response), Evaluation Methods