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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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Cai, Zhiqiang; Marquart, Cody; Shaffer, David W. – International Educational Data Mining Society, 2022
Regular expression (regex) coding has advantages for text analysis. Humans are often able to quickly construct intelligible coding rules with high precision. That is, researchers can identify words and word patterns that correctly classify examples of a particular concept. And, it is often easy to identify false positives and improve the regex…
Descriptors: Coding, Classification, Artificial Intelligence, Engineering Education
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Jahnke, Maximilian; Höppner, Frank – International Educational Data Mining Society, 2022
The value of an instructor is that she exactly recognizes what the learner is struggling with and provides constructive feedback straight to the point. This work aims at a step towards this type of feedback in the context of an introductory programming course, where students perform program execution tracing to align their understanding of Java…
Descriptors: Programming, Coding, Computer Science Education, Error Patterns
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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
Conrad Borchers; Paulo F. Carvalho; Meng Xia; Pinyang Liu; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2023
In numerous studies, intelligent tutoring systems (ITSs) have proven effective in helping students learn mathematics. Prior work posits that their effectiveness derives from efficiently providing eventually-correct practice opportunities. Yet, there is little empirical evidence on how learning processes with ITSs compare to other forms of…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Mathematics Education, Learning Processes
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Rodic, Dušica; Horvat, Saša; Roncevic, Tamara; Babic-Kekez, Snežana – International Baltic Symposium on Science and Technology Education, 2021
Examining students' inclinations to use algorithms and rules to solve a task was a fruitful area of research in chemical education in the last four decades. This research aimed to examine whether students read the task request carefully, considering its meaningfulness, or they approach it mechanically, applying a set of algorithms by default. The…
Descriptors: Mathematics, Problem Solving, Chemistry, Science Education
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Gorgun, Guher; Yildirim-Erbasli, Seyma N.; Epp, Carrie Demmans – International Educational Data Mining Society, 2022
The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for…
Descriptors: Online Courses, Group Discussion, Learner Engagement, Student Participation
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2022
Automated scoring of student language is a complex task that requires systems to emulate complex and multi-faceted human evaluation criteria. Summary scoring brings an additional layer of complexity to automated scoring because it involves two texts of differing lengths that must be compared. In this study, we present our approach to automate…
Descriptors: Automation, Scoring, Documentation, Likert Scales
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Espino, Alejandro Ecos; Núñez, Joffré Huamán; Moscoso, Braulio Barzola; Chávez, Zoraida Manrique; Alvitez, Alejandro Rumaja; Cajo, Oscar García – International Association for Development of the Information Society, 2020
The study of the variational behavior of functions constitutes an important element in the understanding of the change of phenomena in real life. His understanding is an essential axis in the mathematical training of university students, especially those who pursue engineering careers. This article presents the results of a study whose objective…
Descriptors: Engineering Education, College Students, Error Patterns, Mathematics
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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
Ashish Gurung; Morgan P. Lee; Sami Baral; Adam C. Sales; Kirk P. Vanacore; Andrew A. McReynolds; Hilary Kreisberg; Cristina Heffernan; Aaron Haim; Neil T. Heffernan – Grantee Submission, 2023
Solving mathematical problems is cognitively complex, involving strategy formulation, solution development, and the application of learned concepts. However, gaps in students' knowledge or weakly grasped concepts can lead to errors. Teachers play a crucial role in predicting and addressing these difficulties, which directly influence learning…
Descriptors: Error Patterns, Mathematical Applications, Grade 6, Grade 7
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