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Showing 1 to 15 of 28 results Save | Export
Shraddha Govind Barke – ProQuest LLC, 2024
The dream of intelligent assistants to enhance programmer productivity has now become a concrete reality, with rapid advances in artificial intelligence. Large language models (LLMs) have demonstrated impressive capabilities in various domains based on the vast amount of data used to train them. However, tasks which require structured reasoning or…
Descriptors: Artificial Intelligence, Symbolic Learning, Programming, Programming Languages
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Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages
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Ainhoa Berciano; Astrid Cuida; María-Luisa Novo – Education and Information Technologies, 2025
In the last two decades, computational thinking has gained wide relevance in international educational systems. The inclusion of this new type of thinking poses educational challenges with some underlying research questions that need to be answered to meet these challenges with quality. Thus, this study focuses on analyzing the difficulties that…
Descriptors: Coding, Translation, Programming Languages, Sequential Approach
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Phung, Tung; Cambronero, José; Gulwani, Sumit; Kohn, Tobias; Majumdarm, Rupak; Singla, Adish; Soares, Gustavo – International Educational Data Mining Society, 2023
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is…
Descriptors: Computational Linguistics, Feedback (Response), Programming, Computer Science Education
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Kahn, Ken; Winters, Niall – British Journal of Educational Technology, 2021
Constructionism, long before it had a name, was intimately tied to the field of Artificial Intelligence. Soon after the birth of Logo at BBN, Seymour Papert set up the Logo Group as part of the MIT AI Lab. Logo was based upon Lisp, the first prominent AI programming language. Many early Logo activities involved natural language processing,…
Descriptors: Artificial Intelligence, Man Machine Systems, Programming Languages, Programming
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Carvalho, Floran; Henriet, Julien; Greffier, Francoise; Betbeder, Marie-Laure; Leon-Henri, Dana – Journal of Education and e-Learning Research, 2023
This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this…
Descriptors: Learning Processes, Algorithms, Artificial Intelligence, Programming Languages
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Michelle Pauley Murphy; Woei Hung – TechTrends: Linking Research and Practice to Improve Learning, 2024
Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this…
Descriptors: Information Retrieval, Data Analysis, Pattern Recognition, Artificial Intelligence
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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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Taipalus, Toni – Journal of Information Systems Education, 2019
Structured Query Language (SQL) is still the de facto database query language widely used in industry and taught in almost all university level database courses. The role of SQL is further strengthened by the emergence of NewSQL systems which use SQL as their query language as well as some NoSQL systems, e.g., Cassandra and DynamoDB, which base…
Descriptors: Programming Languages, Programming, Computer Science Education, Databases
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Mao, Ye; Shi, Yang; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2021
As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…
Descriptors: Programming, Computer Science Education, Learning Processes, Learning Analytics
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Tahereh Firoozi; Okan Bulut; Mark J. Gierl – International Journal of Assessment Tools in Education, 2023
The proliferation of large language models represents a paradigm shift in the landscape of automated essay scoring (AES) systems, fundamentally elevating their accuracy and efficacy. This study presents an extensive examination of large language models, with a particular emphasis on the transformative influence of transformer-based models, such as…
Descriptors: Turkish, Writing Evaluation, Essays, Accuracy
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Sanosi, Abdulaziz; Abdalla, Mohamed – Australian Journal of Applied Linguistics, 2021
This study aimed to examine the potentials of the NLP approach in detecting discourse markers (DMs), namely okay, in transcribed spoken data. One hundred thirty-eight concordance lines were presented to human referees to judge the functions of okay in them as a DM or Non-DM. After that, the researchers used a Python script written according to the…
Descriptors: Natural Language Processing, Computational Linguistics, Programming Languages, Accuracy
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Mengliyev, Bakhtiyor; Shahabitdinova, Shohida; Khamroeva, Shahlo; Gulyamova, Shakhnoza; Botirova, Adiba – Journal of Language and Linguistic Studies, 2021
This article is dedicated to the issue of morphological analysis and synthesis of word forms in a linguistic analyzer, which is a significant feature of corpus linguistics. The article discourses in detail the morphological analysis, the creation of artificial language, grammar and analyzer, the general scheme of the analysis program that…
Descriptors: Morphology (Languages), Computational Linguistics, Computer Software, Artificial Languages
Velez, Martin – ProQuest LLC, 2019
Software is an integral part of our lives. It controls the cars we drive every day, the ships we send into space, and even our toasters. It is everywhere and we can easily download more. Software solves many real-world problems and satisfies many needs. Thus, unsurprisingly, there is a rising demand for software engineers to maintain existing…
Descriptors: Computer Science Education, Programming, Introductory Courses, Computer Software
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