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Showing 1 to 15 of 122 results Save | Export
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Yiting Wang; Tong Li; Jiahui You; Xinran Zhang; Congkai Geng; Yu Liu – ACM Transactions on Computing Education, 2025
Understanding software modelers' difficulties and evaluating their performance is crucial to Model-Driven Engineering (MDE) education. The software modeling process contains fine-grained information about the modelers' analysis and thought processes. However, existing research primarily focuses on identifying obvious issues in the software…
Descriptors: Computer Software, Engineering Education, Models, Identification
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Joalise Janse van Rensburg – Discover Education, 2024
The ability to think critically is an important and valuable skill that students should develop to successfully solve problems. The process of writing requires critical thinking (CT), and the subsequent piece of text can be viewed as a product of CT. One of the strategies educators may use to develop CT is modelling. Given ChatGPT's ability to…
Descriptors: Critical Thinking, Writing Instruction, Computer Software, Artificial Intelligence
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Xieling Chen; Haoran Xie; Di Zou; Lingling Xu; Fu Lee Wang – Educational Technology & Society, 2025
In massive open online course (MOOC) environments, computer-based analysis of course reviews enables instructors and course designers to develop intervention strategies and improve instruction to support learners' learning. This study aimed to automatically and effectively identify learners' concerned topics within their written reviews. First, we…
Descriptors: Classification, MOOCs, Teaching Skills, Artificial Intelligence
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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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Guillermo Bautista Jr.; Mathias Tejera; Thierry Dana-Picard; Zsolt Lavicza – International Journal for Technology in Mathematics Education, 2023
On the one hand, mathematical software is ubiquitous in mathematics education. On the other hand, word problems are an important part of the curriculum, and they often require modelling skills. This is especially true with optimisation and extrema problems proposed to high school and undergraduate students. We propose two activities around extrema…
Descriptors: Word Problems (Mathematics), Secondary School Mathematics, College Mathematics, Computer Software
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Guozhu Ding; Xiangyi Shi; Shan Li – Education and Information Technologies, 2024
In this study, we developed a classification system of programming errors based on the historical data of 680,540 programming records collected on the Online Judge platform. The classification system described six types of programming errors (i.e., syntax, logical, type, writing, misunderstanding, and runtime errors) and their connections with…
Descriptors: Programming, Computer Science Education, Classification, Graphs
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Kostousov, Sergei A.; Simonova, Irina V. – International Association for Development of the Information Society, 2019
The purpose of the article is to identify conditions for the effective use of visual modeling tools that can help reduce the difficulty level of solving problems during the teaching high school students programming. Visual modeling tools are a type of software that allows you to create visual abstractions that reproduce concepts and objects of the…
Descriptors: Visual Aids, Models, Problem Solving, Computer Science Education
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Jian-Hong Ye; Mengmeng Zhang; Weiguaju Nong; Li Wang; Xiantong Yang – Education and Information Technologies, 2025
ChatGPT, as an example of generative artificial intelligence, possesses high-level conversational and problem-solving capabilities supported by powerful computational models and big data. However, the powerful performance of ChatGPT might enhance learner dependency. Although it has not yet been confirmed, many teachers and scholars are also…
Descriptors: Artificial Intelligence, College Students, Problem Solving, Student Attitudes
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Bowers, Jonathan; Eidin, Emanuel; Damelin, Daniel; McIntyre, Cynthia – Science Teacher, 2022
The COVID-19 crisis has demonstrated the importance of being able to understand complex computational models for everyday life. To make sense of the evolving predictive models of the COVID-19 pandemic, global citizens need to have a firm grasp of both systems thinking (ST) and computational thinking (CT). ST is the ability to understand a problem…
Descriptors: Computation, Thinking Skills, Models, Systems Approach
Du, Han; Enders, Craig; Keller, Brian; Bradbury, Thomas N.; Karney, Benjamin R. – Grantee Submission, 2022
Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as…
Descriptors: Bayesian Statistics, Data Analysis, Computer Simulation, Sample Size
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Peter Curtis; Brett Moffett; David A. Martin – Australian Primary Mathematics Classroom, 2024
In this article, the authors explore how the 3C Model can be used to integrate other curriculum areas with mathematics, namely digital technologies. To illustrate the model, they provide a practical example of a teaching sequence. T he 3C Model is designed to create opportunities for applying reasoning and problem-solving skills and learning…
Descriptors: Models, Computer Software, Problem Solving, Mathematics Instruction
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Zhihan Lv Ed. – IGI Global, 2024
The rapid adoption of deep learning models has resulted in many business services becoming model services, yet most AI systems lack the necessary automation and industrialization capabilities. This leads to heavy reliance on manual operation and maintenance, which not only consumes power but also causes resource wastage and stability issues during…
Descriptors: Artificial Intelligence, Robotics, Computer Software, Problem Solving
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Özdemir, Erdogan; Coramik, Mustafa – Physics Education, 2022
It is often necessary to enrich the teaching environment in order for students to learn optics in depth and to interpret the real optical situations with the information they have learned. In this study, a virtual teaching environment was developed using by Algodoo, a 2D simulation software. An eye model was created in order to explain the…
Descriptors: Light, Physics, Teaching Methods, Models
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Kuroki, Masanori – Journal of Economic Education, 2023
As vast amounts of data have become available in business in recent years, the demand for data scientists has been rising. The author of this article provides a tutorial on how one entry-level machine learning competition from Kaggle, an online community for data scientists, can be integrated into an undergraduate econometrics course as an…
Descriptors: Statistics Education, Teaching Methods, Competition, Prediction
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Joseph C. Y. Lau; Emily Landau; Qingcheng Zeng; Ruichun Zhang; Stephanie Crawford; Rob Voigt; Molly Losh – Autism: The International Journal of Research and Practice, 2025
Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor…
Descriptors: Artificial Intelligence, Models, Pragmatics, Language Variation
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