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Shi, Yang; Mao, Ye; Barnes, Tiffany; Chi, Min; Price, Thomas W. – International Educational Data Mining Society, 2021
Automatically detecting bugs in student program code is critical to enable formative feedback to help students pinpoint errors and resolve them. Deep learning models especially code2vec and ASTNN have shown great success for "large-scale" code classification. It is not clear, however, whether they can be effectively used for bug…
Descriptors: Artificial Intelligence, Program Effectiveness, Coding, Computer Science Education
Way, Jennifer; Cartwright, Katherin – Mathematics Education Research Group of Australasia, 2023
This paper presents an initial analysis of 10 Preschool children's responses to a look-draw-trace-draw task. The findings suggest that figure-tracing helped half the children to produce a more accurate representation of the geometric figure presented to them, in their second drawing.
Descriptors: Freehand Drawing, Mathematics Instruction, Preschool Children, Preschool Education
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Ward, Monica; Xu, Liang; Uí Dhonnchadha, Elaine – Research-publishing.net, 2022
Game Based Language Learning (GBLL) is an approach to language learning in which learning happens in the process of playing a game. GBLL has several potential benefits including enhanced reading, writing, and cultural awareness. This paper provides a brief overview of Cipher -- Faoi Gheasa, a GBLL app for Irish and learners' feedback on the game.…
Descriptors: Game Based Learning, Second Language Learning, Irish, Error Correction
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Richter, Catlin; Bédi, Branislav; Pálsson, Ragnar; Guðnason, Jón – Research-publishing.net, 2022
We are developing a Computer-Assisted Pronunciation Training (CAPT) system for learners of Icelandic as a second and foreign language (L2). Based on pre-designed tasks in pronunciation exercises, this system will provide corrective feedback on learners' speech. One of the main features we are implementing is a new method for automatic…
Descriptors: Indo European Languages, Second Language Instruction, Pronunciation, Computer Assisted Instruction
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Sunahase, Takeru; Baba, Yukino; Kashima, Hisashi – International Educational Data Mining Society, 2019
Peer assessment is a promising solution for scaling up the grading of a large number of submissions. The reliability of evaluations is one of the critical issues in peer assessment; several probabilistic models have been proposed for obtaining reliable grades from peers. Peer correction is a similar framework, in which students are instructed to…
Descriptors: Peer Evaluation, Error Correction, Grading, Reliability
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Dilara Yilmaz-Can; Birgül Damla Baber-Elbistan; Seyma Pekgöz; Ceyda Sensin – Online Submission, 2023
The development of students' mathematical problem-solving skills is contingent upon the approaches and methods employed by primary school teachers. This research endeavors to scrutinize the effectiveness of primary school teachers in their roles within the problem-solving process, with particular attention directed toward their inquiry techniques,…
Descriptors: Mathematics Instruction, Teaching Methods, Problem Solving, Elementary School Teachers
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Veena Paliwal – North American Chapter of the International Group for the Psychology of Mathematics Education, 2023
This study was designed to examine the use of mistakes to promote students' performance in undergraduate Algebra classes by developing a growth mindset. Participants were seventy-four students from three Algebra classes and received one of the three interventions along with regular instruction: (a) growth mindset feedback on mistakes…
Descriptors: Mathematics Education, Mathematics Instruction, Teaching Methods, Algebra
Ahmed Magooda; Diane Litman; Ahmed Ashraf; Muhsin Menekse – Grantee Submission, 2022
Having students write reflections has been shown to help teachers improve their instruction and students improve their learning outcomes. With the aid of Natural Language Processing (NLP), real-time educational applications that can assess and provide feedback on reflection quality can be deployed. In this work, we first evaluate various NLP…
Descriptors: Undergraduate Students, Writing Assignments, Reflection, Natural Language Processing
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Phung, Tung; Cambronero, José; Gulwani, Sumit; Kohn, Tobias; Majumdarm, Rupak; Singla, Adish; Soares, Gustavo – International Educational Data Mining Society, 2023
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is…
Descriptors: Computational Linguistics, Feedback (Response), Programming, Computer Science Education
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Tsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
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Li, Ni; Warter-Perez, Nancy; Shen, He – Grantee Submission, 2019
Homework is considered as a substantial process of learning especially for engineering education. However, due to the fast development of network technology, students now can easily find solution manuals on the internet. While some students use solution manuals to study, there are quite a few students who just copy homework solutions and lose…
Descriptors: Self Evaluation (Individuals), Homework, Error Correction, Models
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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
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Adriana Guanuche; Osana Eiriz; Roberto Espi – International Association for Development of the Information Society, 2023
The increasing technological development of computers, tablets and smartphones has enabled a rapid increase in the adoption of mobile technology for language teaching and learning, and numerous applications that provide easy access for any learner without limitations of place and time have been developed. This paper shows the study of grammatical…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Telecommunications
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Hamel, Marie-Josée; Landry, Jill; Bibeau, Louis-David – Research-publishing.net, 2022
In this paper, we report on a study that took place during the COVID-19 pandemic and for which we interviewed ten experienced, university level, language instructors about their digital practices as they found themselves teaching in an Emergency Remote Teaching (ERT) mode. The study sought to describe how, through their professional activities and…
Descriptors: Language Teachers, College Faculty, COVID-19, Pandemics
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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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