4 citations · 11 across the 12 of their papers we have counts for
12 papers
Decomposing the Doer Effect in Programming Practice: Code Writing Stands Out Among Active Practice
Arun Balajiee Lekshmi Narayanan, Gillian Gold, Jordan Barria-Pineda +3
The "doer effect" suggests that actively doing practice activities is more strongly associated with learning outcomes than passively viewing content. In the doer effect literature,…
Automated Recommendation of Programming Learning Content Using Pattern-based Knowledge Components
Muntasir Hoq, Griffin Pitts, Zhangqi Duan +5
Introductory programming instruction relies on hands-on practice and short learning activities to support mastery of foundational concepts. Although many such learning resources ex…
A Survey of LLM-Based Applications in Programming Education: Balancing Automation and Human Oversight
Griffin Pitts, Anurata Prabha Hridi, Arun-Balajiee Lekshmi-Narayanan
Novice programmers benefit from timely, personalized support that addresses individual learning gaps, yet the availability of instructors and teaching assistants is inherently limi…
Automated Knowledge Component Generation for Interpretable Knowledge Tracing in Coding Problems
Zhangqi Duan, Nigel Fernandez, Arun Balajiee Lekshmi Narayanan +5
Knowledge components (KCs) mapped to problems help model student learning, tracking their mastery levels on fine-grained skills thereby facilitating personalized learning and feedb…
A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents
Jean Vassoyan, Anan Schütt, Jill-Jênn Vie +3
Massive Open Online Courses (MOOCs) have greatly contributed to making education more accessible. However, many MOOCs maintain a rigid, one-size-fits-all structure that fails to ad…
Towards Scalable Automated Grading: Leveraging Large Language Models for Conceptual Question Evaluation in Engineering
Rujun Gao, Xiaosu Guo, Xiaodi Li +3
This study explores the feasibility of using large language models (LLMs), specifically GPT-4o (ChatGPT), for automated grading of conceptual questions in an undergraduate Mechanic…
Evaluating the Quality of Code Comments Generated by Large Language Models for Novice Programmers
Aysa Xuemo Fan, Arun Balajiee Lekshmi Narayanan, Mohammad Hassany +1
Large Language Models (LLMs) show promise in generating code comments for novice programmers, but their educational effectiveness remains under-evaluated. This study assesses the i…
Human-AI Co-Creation of Worked Examples for Programming Classes
Mohammad Hassany, Peter Brusilovsky, Jiaze Ke +2
Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among t…
Authoring Worked Examples for Java Programming with Human-AI Collaboration
Mohammad Hassany, Peter Brusilovsky, Jiaze Ke +2
Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among t…
Automatic Unit Test Generation for Deep Learning Frameworks based on API Knowledge
Arunkaleeshwaran Narayanan, Nima Shiri harzevili, Junjie Wang +3
Many automatic unit test generation tools that can generate unit test cases with high coverage over a program have been proposed. However, most of these tools are ineffective on de…
Enhancing Programming eTextbooks with ChatGPT Generated Counterfactual-Thinking-Inspired Questions
Arun Balajiee Lekshmi Narayanan, Rully Agus Hendrawan, Venktesh V
Digital textbooks have become an integral part of everyday learning tasks. In this work, we consider the use of digital textbooks for programming classes. Generally, students strug…
GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children
Arun Balajiee Lekshmi Narayanan, Ligia E. Gomez, Martha Michelle Soto Fernandez +4
When caregivers ask open--ended questions to motivate dialogue with children, it facilitates the child's reading comprehension skills.Although there is scope for use of technologic…