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Muntasir Hoq; Ananya Rao; Reisha Jaishankar; Krish Piryani; Nithya Janapati; Jessica Vandenberg; Bradford Mott; Narges Norouzi; James Lester; Bita Akram – International Educational Data Mining Society, 2025
In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issues students face, educators can provide targeted assistance to help them overcome obstacles and improve learning outcomes. While identifying sources of struggle, such as…
Descriptors: Computer Science Education, Programming, Misconceptions, Error Patterns
Babawande Emmanuel Olawale; Saidat Adeniji; Zizipho Mabhoza – Mathematics Education Research Group of Australasia, 2025
This paper analyses learners' common errors in simplifying algebraic problems. 102 Grade 10 learners from three rural schools in South Africa participated in the study. Following a quantitative approach, content analysis of learners' responses to algebraic tests revealed that while learners commit several errors in algebraic problems, encoding and…
Descriptors: Algebra, Grade 10, High School Students, Secondary School Mathematics
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Yunsung Kim; Jadon Geathers; Chris Piech – International Educational Data Mining Society, 2024
"Stochastic programs," which are programs that produce probabilistic output, are a pivotal paradigm in various areas of CS education from introductory programming to machine learning and data science. Despite their importance, the problem of automatically grading such programs remains surprisingly unexplored. In this paper, we formalize…
Descriptors: Grading, Automation, Accuracy, Programming
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Tsubasa Minematsu; Atsushi Shimada – International Association for Development of the Information Society, 2024
In using large language models (LLMs) for education, such as distractors in multiple-choice questions and learning by teaching, error-containing content is used. Prompt tuning and retraining LLMs are possible ways of having LLMs generate error-containing sentences in the learning content. However, there needs to be more discussion on how to tune…
Descriptors: Educational Technology, Technology Uses in Education, Error Patterns, Sentences
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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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Ali Sartaz Khan; Tolulope Ogunremi; Ahmed Attia; Dorottya Demszky – International Educational Data Mining Society, 2025
Speaker diarization, the process of identifying "who spoke when" in audio recordings, is essential for understanding classroom dynamics. However, classroom settings present distinct challenges, including poor recording quality, high levels of background noise, overlapping speech, and the difficulty of accurately capturing children's…
Descriptors: Audio Equipment, Acoustics, Classroom Environment, Models
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Arifi Waked; Muhammad W. Ashraf; Hanadi AbdelSalam; Khadija El Alaoui; Maura Pilotti – International Society for Technology, Education, and Science, 2024
Questions exist as to whether AI tools, such as ChatGPT, can aid learning. This study examined whether in-class exercises involving error detection in text generated by ChatGPT can aid students' foreign language writing. Participants were Arabic-English speakers who were classified as ranging from modest to competent English users according to…
Descriptors: Artificial Intelligence, Computer Software, Second Language Learning, Second Language Instruction
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Selami Aydin; Maryam Zeinolabedini – Online Submission, 2024
In line with the rapid advancement in educational technology, and the application of artificial intelligence (AI) in particular, the teaching and learning of the English language has undergone a significant transformation. This paper aims to explore students' perceptions of integrating AI into the English as a foreign language (EFL) learning…
Descriptors: Artificial Intelligence, Computer Software, Second Language Instruction, Second Language Learning