27 citations · 29 across the 10 of their papers we have counts for
10 papers
Analyzing the Difficulty of Programming Assignments with Interpretable Knowledge Component Metrics
Tsvetomila Mihaylova, Jing Fan, Bita Akram +4
This research paper examines how Knowledge Components (KCs) - fine-grained concepts or skills required to solve programming tasks - can be used as interpretable signals for underst…
The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance
Tsvetomila Mihaylova, Evanfiya Logacheva, Arto Hellas +6
When programming students encounter errors in their code, compiler messages or static analysis output often provide limited guidance, particularly for novice programmers. Personali…
Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education
Mragisha Jain, Tirth Bhatt, Griffin Pitts +6
Students learning algorithms often need support as they interpret traces, debug reasoning errors, and apply procedures across unfamiliar problem instances. In this paper, we presen…
An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration
Muntasir Hoq, Griffin Pitts, Bradford Mott +6
Active learning is widely recognized as an effective approach for improving learning outcomes in introductory programming courses. However, insufficient instructional support often…
The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness
Rose Niousha, Samantha Boatright Smith, Bita Akram +5
Current Artificial Intelligence (AI)-based tutoring systems (AI tutors) are primarily evaluated based on the pedagogical quality of their feedback messages. While important, pedago…
Personalized Worked Example Generation from Student Code Submissions Using Pattern-based Knowledge Components
Griffin Pitts, Muntasir Hoq, Peter Brusilovsky +4
Adaptive programming practice often relies on fixed libraries of worked examples and practice problems, which require substantial authoring effort and may not correspond well to th…
Automated Identification of Logical Errors in Programs: Advancing Scalable Analysis of Student Misconceptions
Muntasir Hoq, Ananya Rao, Reisha Jaishankar +7
In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issu…
Facilitating Instructors-LLM Collaboration for Problem Design in Introductory Programming Classrooms
Muntasir Hoq, Jessica Vandenberg, Shuyin Jiao +5
Advancements in Large Language Models (LLMs), such as ChatGPT, offer significant opportunities to enhance instructional support in introductory programming courses. While extensive…
A Knowledge-Component-Based Methodology for Evaluating AI Assistants
Laryn Qi, J. D. Zamfirescu-Pereira, Taehan Kim +3
We evaluate an automatic hint generator for CS1 programming assignments powered by GPT-4, a large language model. This system provides natural language guidance about how students…
61A Bot Report: AI Assistants in CS1 Save Students Homework Time and Reduce Demands on Staff. (Now What?)
J. D. Zamfirescu-Pereira, Laryn Qi, Björn Hartmann +2
LLM-based chatbots enable students to get immediate, interactive help on homework assignments, but even a thoughtfully-designed bot may not serve all pedagogical goals. We report h…