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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
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
Andrew Millam; Christine Bakke – Journal of Information Technology Education: Innovations in Practice, 2024
Aim/Purpose: This paper is part of a multi-case study that aims to test whether generative AI makes an effective coding assistant. Particularly, this work evaluates the ability of two AI chatbots (ChatGPT and Bing Chat) to generate concise computer code, considers ethical issues related to generative AI, and offers suggestions for how to improve…
Descriptors: Coding, Artificial Intelligence, Natural Language Processing, Computer Software
Taskeen Hasrod; Yannick B. Nuapia; Hlanganani Tutu – Journal of Chemical Education, 2024
In order to improve the accessibility and user friendliness of an accurately pretrained stacking ensemble machine learning regressor used to predict sulfate levels (mg/L) in Acid Mine Drainage (AMD), a Graphical User Interface (GUI) was developed using Python by combining human input with ChatGPT and deployed in the Jupyter Notebook environment.…
Descriptors: Artificial Intelligence, Natural Language Processing, Educational Technology, Computer Software
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
Lahiru Ariyananda – ProQuest LLC, 2022
DEVS (Discrete Event System Specification) is a formalism that was introduced in the mid-1970s by Bernard Zeigler, for modeling and analysis of discrete event systems. DEVS is essentially a formal mathematical language for specifying complex systems through models that can be simulated and has been executed in object-oriented software, DEVSJava…
Descriptors: Active Learning, Programming, Computer Software, Computer Science Education
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
Charalampos-S Charitsis – ProQuest LLC, 2023
The employment rate of software developers has risen significantly over the last 30 years. As a result, more students are considering computer science as a potential career path. Over the last 15 years, introductory programming course (CS1) enrollment has been increasing at a much faster rate than the increase in the number of CS faculty, with no…
Descriptors: Computer Science Education, Programming, Natural Language Processing, Computer Software
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
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
Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability
Corlatescu, Dragos-Georgian; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2021
Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…
Descriptors: Reading Comprehension, Memory, Inferences, Syntax
Brandon Sepulvado; Jennifer Hamilton – Society for Research on Educational Effectiveness, 2021
Background: Traditional survey efforts to gather outcome data at scale have significant limitations, including cost, time, and respondent burden. This pilot study explored new and innovative large-scale methods of collecting and validating data from publicly available sources. Taking advantage of emerging data science techniques, we leverage…
Descriptors: Automation, Data Collection, Data Analysis, Validity
Hsu, Ting-Chia; Chen, Mu-Sheng – Research and Practice in Technology Enhanced Learning, 2022
This research explored the creative thinking, learning achievement, and engagement of students when they integrated the application of the personal audio classifier (PAC) into the competition of a computational thinking (CT) board game (i.e., the experimental group), or did not integrate it into the competition but only collaborated with peers to…
Descriptors: Learner Engagement, Robotics, Games, Computation