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Shan Li; Xiaoshan Huang; Tingting Wang; Juan Zheng; Susanne P. Lajoie – Journal of Computing in Higher Education, 2025
Coding think-aloud transcripts is time-consuming and labor-intensive. In this study, we examined the feasibility of predicting students' reasoning activities based on their think-aloud transcripts by leveraging the affordances of text mining and machine learning techniques. We collected the think-aloud data of 34 medical students as they diagnosed…
Descriptors: Information Retrieval, Artificial Intelligence, Prediction, Abstract Reasoning
Logan Sizemore; Brian Hutchinson; Emily Borda – Chemistry Education Research and Practice, 2024
Education researchers are deeply interested in understanding the way students organize their knowledge. Card sort tasks, which require students to group concepts, are one mechanism to infer a student's organizational strategy. However, the limited resolution of card sort tasks means they necessarily miss some of the nuance in a student's strategy.…
Descriptors: Artificial Intelligence, Chemistry, Cognitive Ability, Abstract Reasoning
Lewis J. Baker; Hongyue Li; Hugo Hammond; Christopher B. Jaeger; Anne Havard; Jonathan D. Lane; Caroline E. Harriott; Daniel T. Levin – Cognitive Research: Principles and Implications, 2024
As a wide variety of intelligent technologies become part of everyday life, researchers have explored how people conceptualize agents that in some ways act and think like living things but are clearly machines. Much of this work draws upon the idea that people readily default to generalizing human-like properties to such agents, and only pare back…
Descriptors: Cognitive Processes, Psychological Patterns, Abstract Reasoning, Attribution Theory
Suna-Seyma Uçar; Inigo Lopez-Gazpio; Josu Lopez-Gazpio – Education and Information Technologies, 2025
Recent advancements in large language models (LLMs) have shown potential in enhancing educational practices, particularly in technology-assisted learning environments. This study critically evaluates the reasoning capabilities of LLMs, such as ChatGPT, within the context of chemistry education. We designed targeted adversarial prompts that…
Descriptors: Abstract Reasoning, Thinking Skills, Artificial Intelligence, Technology Uses in Education
Funda Nayir; Tamer Sari; Aras Bozkurt – Journal of Educational Technology and Online Learning, 2024
From personalized advertising to economic forecasting, artificial intelligence (AI) is becoming an increasingly important element of our daily lives. These advancements raise concerns regarding the transhumanist perspective and associated discussions in the context of technology-human interaction, as well as the influence of artificial…
Descriptors: Artificial Intelligence, Technology Uses in Education, Humanism, Capacity Building
Mara Cotic; Daniel Doz; Matija Jenko; Amalija Žakelj – International Electronic Journal of Mathematics Education, 2024
The evolution of mathematics coincided with advancements in its teaching. The 19th and 20th centuries marked a pedagogical revolution in mathematics education. This paper argues that Bruner's (1966) model, Gagné's (1985) taxonomy, innovative teaching methods emphasizing research and problem-solving, and the inclusion of data analysis topics have…
Descriptors: Mathematics Education, Mathematics Instruction, Educational History, Mathematics Achievement
Zhenwen Liang – ProQuest LLC, 2024
Mathematical reasoning, a fundamental aspect of human cognition, poses significant challenges for artificial intelligence (AI) systems. Despite recent advancements in natural language processing (NLP) and large language models (LLMs), AI's ability to replicate human-like reasoning, generalization, and efficiency remains an ongoing research…
Descriptors: Mathematics Skills, Thinking Skills, Abstract Reasoning, Generalizability Theory
Karahan, Engin – International Journal of Science Education, 2023
Using video-elicitation focus group interviews, this study aims to reveal pre-service science teachers' perspectives and reasoning on artificial intelligence as a socioscientific issues-based scenario. Hence, it illustrates the ways the video data were used in the focus group elicitation interviews to understand their interpretations of how their…
Descriptors: Video Technology, Teaching Methods, Focus Groups, Interviews
Judith Galezer; Smadar Szekely – Informatics in Education, 2024
Spark, one of the products offered by MyQ (formerly Plethora), is a game-based platform meticulously designed to introduce students to the foundational concepts of computer science. By navigating through logical challenges, users delve into topics like abstraction, loops, and graph patterns. Setting itself apart from its counterparts, Spark boasts…
Descriptors: Learning Management Systems, Game Based Learning, Computer Science Education, Teaching Methods
Noawanit Songkram; Supattraporn Upapong; Heng-Yu Ku; Narongpon Aulpaijidkul; Sarun Chattunyakit; Nutthakorn Songkram – Interactive Learning Environments, 2024
This research proposes the integration of robotic education and scenario-based learning (SBL) paradigm for teaching computational thinking (CT) to enhance the computational abilities of primary school students, based on digital innovation and a teaching assistant robot acceptance model. The sample group consisted of 532 primary school teachers and…
Descriptors: Foreign Countries, Elementary School Students, Elementary School Teachers, Grade 1
McClelland, James L. – First Language, 2020
Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up…
Descriptors: Models, Language Processing, Artificial Intelligence, Language Usage
Rho, Jihyun; Rau, Martina A.; Van Veen, Barry D. – International Educational Data Mining Society, 2022
Instruction in many STEM domains heavily relies on visual representations, such as graphs, figures, and diagrams. However, students who lack representational competencies do not benefit from these visual representations. Therefore, students must learn not only content knowledge but also representational competencies. Further, as learning…
Descriptors: Learning Processes, Models, Introductory Courses, Engineering Education
Benjamin D. Nye; Aaron Shiel; Ibrahim Burak Olmez; Anirudh Mittal; Jason Latta; Daniel Auerbach; Yasemin Copur-Gencturk – Grantee Submission, 2021
Despite the critical role of teachers in the educational process, few advanced learning technologies have been developed to support teacher-instruction or professional development. This lack of support is particularly acute for middle school math teachers, where only 37% felt well prepared to scaffold instruction to address the needs of diverse…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Faculty Development, Abstract Reasoning
Yun Dai; Ziyan Lin; Ang Liu; Wenlan Wang – British Journal of Educational Technology, 2024
While AI has become more prevalent in our society than ever, many young learners are found holding various naive, erroneous conceptions of AI due to the influence of their technology and media environments. To address this issue, this study seeks to propose a novel pedagogical solution to improve upper-elementary school students' scientific…
Descriptors: Artificial Intelligence, Technology Uses in Education, Elementary Education, Elementary School Students
Mahowald, Kyle; Kachergis, George; Frank, Michael C. – First Language, 2020
Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec -- which have been extremely successful in modern natural language processing (NLP) applications -- count? Although these models often have ample parametric complexity to store exemplars from their training data, they also…
Descriptors: Models, Language Processing, Computational Linguistics, Language Acquisition
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