473 citations · 2.3k across the 48 of their papers we have counts for
18 papers · 1 filter
AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs
Yann Hicke, Anmol Agarwal, Qianou Ma +1
Responding to the thousands of student questions on online QA platforms each semester has a considerable human cost, particularly in computing courses with rapidly growing enrollme…
Prompt Problems: A New Programming Exercise for the Generative AI Era
Paul Denny, Juho Leinonen, James Prather +4
Large Language Models (LLMs) are revolutionizing the field of computing education with their powerful code-generating capabilities. Traditional pedagogical practices have focused o…
A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends
Yan Cathy Hua, Paul Denny, Katerina Taskova +1
Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digit…
Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models
Jaromir Savelka, Paul Denny, Mark Liffiton +1
The accurate classification of student help requests with respect to the type of help being sought can enable the tailoring of effective responses. Automatically classifying such r…
Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistant
Brad Sheese, Mark Liffiton, Jaromir Savelka +1
Providing personalized assistance at scale is a long-standing challenge for computing educators, but a new generation of tools powered by large language models (LLMs) offers immens…
The Robots are Here: Navigating the Generative AI Revolution in Computing Education
James Prather, Paul Denny, Juho Leinonen +12
Recent advancements in artificial intelligence (AI) are fundamentally reshaping computing, with large language models (LLMs) now effectively being able to generate and interpret so…
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
Lin Ni, Sijie Wang, Zeyu Zhang +4
Learnersourcing offers great potential for scalable education through student content creation. However, predicting student performance on learnersourced questions, which is essent…
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
Qiming Bao, Juho Leinonen, Alex Yuxuan Peng +7
Large language models exhibit superior capabilities in processing and understanding language, yet their applications in educational contexts remain underexplored. Learnersourcing e…
CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes
Mark Liffiton, Brad Sheese, Jaromir Savelka +1
Computing educators face significant challenges in providing timely support to students, especially in large class settings. Large language models (LLMs) have emerged recently and…
Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators
Paul Denny, Juho Leinonen, James Prather +4
With their remarkable ability to generate code, large language models (LLMs) are a transformative technology for computing education practice. They have created an urgent need for…
Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources
Paul Denny, Hassan Khosravi, Arto Hellas +2
As an increasing number of students move to online learning platforms that deliver personalized learning experiences, there is a great need for the production of high-quality educa…
Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation
Hassan Khosravi, Paul Denny, Steven Moore +1
Engaging students in creating novel content, also referred to as learnersourcing, is increasingly recognised as an effective approach to promoting higher-order learning, deeply eng…
Computing Education in the Era of Generative AI
Paul Denny, James Prather, Brett A. Becker +7
The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student…
Using Sensor-Based Programming to Improve Self-Efficacy and Outcome Expectancy for Students from Underrepresented Groups
Hussel Suriyaarachchi, Alaeddin Nassani, Paul Denny +1
Knowledge of programming and computing is becoming increasingly valuable in today's world, and thus it is crucial that students from all backgrounds have the opportunity to learn.…
Comparing Code Explanations Created by Students and Large Language Models
Juho Leinonen, Paul Denny, Stephen MacNeil +5
Reasoning about code and explaining its purpose are fundamental skills for computer scientists. There has been extensive research in the field of computing education on the relatio…
Striving for Authentic and Sustained Technology Use In the Classroom: Lessons Learned from a Longitudinal Evaluation of a Sensor-based Science Education Platform
Yvonne Chua, Sankha Cooray, Juan Pablo Forero Cortes +19
Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although…
"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers
James Prather, Brent N. Reeves, Paul Denny +6
Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy. This is likel…
Many bioinformatics programming tasks can be automated with ChatGPT
Stephen R. Piccolo, Paul Denny, Andrew Luxton-Reilly +2
Computer programming is a fundamental tool for life scientists, allowing them to carry out many essential research tasks. However, despite a variety of educational efforts, learnin…