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Showing 1 to 15 of 16 results Save | Export
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Jionghao Lin; Zifei Han; Danielle R. Thomas; Ashish Gurung; Shivang Gupta; Vincent Aleven; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2025
One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge, often necessitating the training of novice tutors (i.e., trainees) to ensure effective tutoring. Research suggests that providing timely explanatory feedback can…
Descriptors: Artificial Intelligence, Technology Uses in Education, Tutor Training, Trainees
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Nezihe Korkmaz Guler; Zeynep Gul Dertli; Elif Boran; Bahadir Yildiz – Pedagogical Research, 2024
The aim of the research is to investigate the academic achievement of ChatGPT, an artificial intelligence based chatbot, in a national mathematics exam. For this purpose, 3.5 and 4 versions of ChatGPT were asked mathematics questions in a national exam. The method of the research is a case study. In the research, 3.5 and 4 versions of ChatGPT were…
Descriptors: Mathematics Education, Artificial Intelligence, Man Machine Systems, Natural Language Processing
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Toni Taipalus; Hilkka Grahn; Saima Ritonummi; Valtteri Siitonen; Tero Vartiainen; Denis Zhidkikh – ACM Transactions on Computing Education, 2025
SQL compiler error messages are the primary way users receive feedback when they encounter syntax errors or other issues in their SQL queries. Effective error messages can enhance the user experience by providing clear, informative, and actionable feedback. Despite the age of SQL compilers, it still remains largely unclear what contributes to an…
Descriptors: Computer Science Education, Novices, Information Systems, Programming Languages
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Olaperi Okuboyejo; Sigrid Ewert; Ian Sanders – ACM Transactions on Computing Education, 2025
Regular expressions (REs) are often taught to undergraduate computer science majors in the Formal Languages and Automata (FLA) course; they are widely used to implement different software functionalities such as search mechanisms and data validation in diverse fields. Despite their importance, the difficulty of REs has been asserted many times in…
Descriptors: Automation, Feedback (Response), Error Patterns, Error Correction
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Schneider, Johannes; Richner, Robin; Riser, Micha – International Journal of Artificial Intelligence in Education, 2023
Autograding short textual answers has become much more feasible due to the rise of NLP and the increased availability of question-answer pairs brought about by a shift to online education. Autograding performance is still inferior to human grading. The statistical and black-box nature of state-of-the-art machine learning models makes them…
Descriptors: Grading, Natural Language Processing, Computer Assisted Testing, Ethics
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Norbert Noster; Sebastian Gerber; Hans-Stefan Siller – Digital Experiences in Mathematics Education, 2024
The use of large language models like ChatGPT is widely discussed for educational purposes. Using this technology requires teachers to have appropriate competences that incorporate knowledge of how to make use of this technology. In this study, we investigate pre-service teachers' knowledge through the lens of the KTMT model ("Knowledge for…
Descriptors: Preservice Teachers, Mathematics Skills, Problem Solving, Technology Uses in Education
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Hoppe, Dorothée B.; Rij, Jacolien; Hendriks, Petra; Ramscar, Michael – Cognitive Science, 2020
Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers ("premarkers," e.g., gendered articles) or succeeding category markers ("postmarkers," e.g., gendered suffixes). Given that numerous…
Descriptors: Discrimination Learning, Computational Linguistics, Natural Language Processing, Artificial Languages
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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Xinli Zhang; Ruiting Huang; Ruihua Zhang; Mingyi Li; Yun-Fang Tu; Yuchen Chen; Lailin Hu; Gwo-Jen Hwang – Journal of Computer Assisted Learning, 2025
Background: Primary school is key to developing writing skills. However, students may face challenges in identifying and revising articles due to weak language skills, organisational thinking, comprehension, and analytical skills. Therefore, improving the writing skills of primary school students with writing difficulties has become important.…
Descriptors: Artificial Intelligence, Technology Uses in Education, Revision (Written Composition), Elementary School Students
Olney, Andrew M. – Grantee Submission, 2021
In contrast to simple feedback, which provides students with the correct answer, elaborated feedback provides an explanation of the correct answer with respect to the student's error. Elaborated feedback is thus a challenge for AI in education systems because it requires dynamic explanations, which traditionally require logical reasoning and…
Descriptors: Feedback (Response), Error Patterns, Artificial Intelligence, Test Format
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Kortemeyer, Gerd – Physical Review Physics Education Research, 2023
Massive pretrained language models have garnered attention and controversy due to their ability to generate humanlike responses: Attention due to their frequent indistinguishability from human-generated phraseology and narratives and controversy due to the fact that their convincingly presented arguments and facts are frequently simply false. Just…
Descriptors: Artificial Intelligence, Physics, Science Instruction, Introductory Courses
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Liu, Chengyuan; Cui, Jialin; Shang, Ruixuan; Xiao, Yunkai; Jia, Qinjin; Gehringer, Edward – International Educational Data Mining Society, 2022
An online peer-assessment system typically allows students to give textual feedback to their peers, with the goal of helping the peers improve their work. The amount of help that students receive is highly dependent on the quality of the reviews. Previous studies have investigated using machine learning to detect characteristics of reviews (e.g.,…
Descriptors: Peer Evaluation, Feedback (Response), Computer Mediated Communication, Teaching Methods
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Cronin, Anthony; Intepe, Gizem; Shearman, Donald; Sneyd, Alison – International Journal of Mathematical Education in Science and Technology, 2019
This paper explores analysis of feedback data collected from student consultations at two mathematics support centres at universities in Australia and Ireland. Unstructured text data was collected over six years and includes qualitative data on student queries collected during the consultations from mathematics and statistics related subjects.…
Descriptors: Natural Language Processing, Feedback (Response), Mathematics Instruction, Academic Support Services
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Feng, Hui-Hsien; Saricaoglu, Aysel; Chukharev-Hudilainen, Evgeny – CALICO Journal, 2016
Thanks to natural language processing technologies, computer programs are actively being used not only for holistic scoring, but also for formative evaluation of writing. CyWrite is one such program that is under development. The program is built upon Second Language Acquisition theories and aims to assist ESL learners in higher education by…
Descriptors: Error Patterns, Grammar, Language Proficiency, English (Second Language)
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Garnier, Marie – Research-publishing.net, 2012
According to recent studies, there is a persistence of adverb placement errors in the written productions of francophone learners and users of English at an intermediate to advanced level. In this paper, we present strategies for the automatic detection and correction of errors in the placement of manner adverbs, using linguistic-based natural…
Descriptors: Form Classes (Languages), Error Correction, Natural Language Processing, Feedback (Response)
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