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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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Janet Metcalfe; Judy Xu; Matti Vuorre; Robert Siegler; Dylan Wiliam; Robert A. Bjork – British Journal of Educational Psychology, 2025
Background: Although the generation of errors has been thought, traditionally, to impair learning, recent studies indicate that, under particular feedback conditions, the commission of errors may have a beneficial effect. Aims: This study investigates the teaching strategies that facilitate learning from errors. Materials and Methods: This 2-year…
Descriptors: Error Patterns, Error Correction, Direct Instruction, Test Preparation
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Arinjoy Basak; Nicole P. Pitterson; Clifford A. Shaffer; Sneha Patel Davison; David A. Dillard; Jacob Grohs – Advances in Engineering Education, 2025
Motivated by the benefits of repeated deliberate practice, we created an interactive exercise system for use in an undergraduate engineering mechanics class that focuses on practicing learned fundamental concepts. These exercises take the form of traditional word problems commonly found in mechanics courses, involving things like selecting and…
Descriptors: Usability, Electronic Learning, Computer Mediated Communication, Engineering Education
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Conrad Borchers; Tianze Shou – Grantee Submission, 2025
Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--where student knowledge and pedagogical strategies are explicitly modeled. We propose a prompt variation framework to assess LLM-generated instructional moves' adaptivity…
Descriptors: Benchmarking, Computational Linguistics, Artificial Intelligence, Computer Software
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Christina Areizaga Barbieri; Brianna L. Devlin – Journal of Computer Assisted Learning, 2024
Background: Providing students with worked out problem solutions is a beneficial instructional technique in STEM disciplines, and studying examples that have been worked out incorrectly may be especially helpful for reducing misconceptions in students with low prior content knowledge. However, past results are inconclusive and the effects of…
Descriptors: STEM Education, Misconceptions, Fractions, Error Patterns
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Rashkovits, Rami; Lavy, Ilana – International Journal of Information and Communication Technology Education, 2020
The present study examines the difficulties novice data modelers face when asked to provide a data model addressing a given problem. In order to map these difficulties and their causes, two short data modeling problems were given to 82 students who had completed an introductory course in database modeling. Both problems involve three entity sets…
Descriptors: Models, Data, Undergraduate Students, Computer Science Education
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Allison S. Liu; Kirk Vanacore; Erin Ottmar – Grantee Submission, 2022
Feedback in educational technologies can teach and engage students in math, but questions remain on how to present failure feedback that supports positive learning behaviors. We explore how error- and reward-based feedback influenced students' choices to replay completed problems in "From Here to There!," a math game-based educational…
Descriptors: Educational Technology, Technology Uses in Education, Feedback (Response), Failure
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Zhang, Mengxue; Wang, Zichao; Baraniuk, Richard; Lan, Andrew – International Educational Data Mining Society, 2021
Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually…
Descriptors: Mathematics Instruction, Teaching Methods, Intelligent Tutoring Systems, Error Patterns
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Tacoma, Sietske; Heeren, Bastiaan; Jeuring, Johan; Drijvers, Paul – International Journal of Artificial Intelligence in Education, 2020
Hypothesis testing involves a complex stepwise procedure that is challenging for many students in introductory university statistics courses. In this paper we assess how feedback from an Intelligent Tutoring System can address the logic of hypothesis testing and whether such feedback contributes to first-year social sciences students' proficiency…
Descriptors: Hypothesis Testing, Feedback (Response), Intelligent Tutoring Systems, Introductory Courses
Park, Soyoung; Bryant, Diane Pedrotty; Dougherty, Barbara – Intervention in School and Clinic, 2021
This article presents a checklist of 10 evidence-based practices for educators to apply in mathematics instruction for students with learning disabilities. The checklist is "actionable," meaning the items on the checklist can be put into action immediately. It provides practical strategies teachers can adopt to fit their lessons…
Descriptors: Mathematics Instruction, Teaching Methods, Students with Disabilities, Evidence Based Practice
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Casey, Stephanie; Lesseig, Kristin; Monson, Debra; Krupa, Erin E. – Mathematics Teacher Education and Development, 2018
This study examined proposed teacher responses to students' work to investigate how they respond, what characteristics of a good response are more difficult than others to achieve, and whether particular student error types are more difficult to respond to appropriately. Sixteen preservice secondary mathematics teachers' proposed responses to five…
Descriptors: Secondary School Mathematics, Preservice Teacher Education, Preservice Teachers, Equations (Mathematics)
Metcalfe, Janet – Grantee Submission, 2017
Although error avoidance during learning appears to be the rule in American classrooms, laboratory studies suggest that it may be a counterproductive strategy, at least for neurologically typical students. Experimental investigations indicate that errorful learning followed by corrective feedback is beneficial to learning. Interestingly, the…
Descriptors: Error Patterns, Error Correction, Feedback (Response), Educational Benefits
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Steif, Paul S.; Fu, Luoting; Kara, Levent Burak – Interactive Learning Environments, 2016
Problems faced by engineering students involve multiple pathways to solution. Students rarely receive effective formative feedback on handwritten homework. This paper examines the potential for computer-based formative assessment of student solutions to multipath engineering problems. In particular, an intelligent tutor approach is adopted and…
Descriptors: Formative Evaluation, Engineering Education, Problem Solving, Intelligent Tutoring Systems
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Walker, Philip; Gwynllyw, D. Rhys; Henderson, Karen L. – Teaching Mathematics and Its Applications, 2015
We demonstrate how the re-marker and reporter facility of the DEWIS e-Assessment system facilitates the capture, analysis and reporting of student errors using two case studies: logarithms and indices for first-year computing students at the University of the West of England, and Sturm-Liouville problems for second-year mathematics students at…
Descriptors: Computer Assisted Testing, Error Patterns, Case Studies, College Mathematics
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Girault, Isabelle; d'Ham, Cédric – Journal of Science Education and Technology, 2014
When solving a scientific problem through experimentation, students may have the responsibility to design the experiment. When students work in a conventional condition, with paper and pencil, the designed procedures stay at a very general level. There is a need for additional scaffolds to help the students perform this complex task. We propose a…
Descriptors: Science Instruction, Problem Solving, Science Experiments, Scaffolding (Teaching Technique)
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