Blogs (3) >>

This program is tentative and subject to change.

Sat 1 Mar 2025 10:45 - 11:03 at Meeting Rooms 317-318 - Instructional Technologies #2

Novice programmers can greatly improve their understanding of challenging programming concepts by studying worked examples that demonstrate the implementation of these concepts. Despite the extensive repositories of effective worked examples created by CS education experts, a key challenge remains: identifying the most relevant worked example for a given programming problem and the specific difficulties a student faces solving the problem. Previous studies have explored similar example recommendation approaches. Our research introduces a novel method by utilizing deep learning code representation models to generate code vectors, capturing both syntactic and semantic similarities among programming examples. Driven by the need to provide relevant and personalized examples to programming students, our approach emphasizes similarity assessment and clustering techniques to identify similar code problems, examples, and challenges. This method aims to deliver more accurate and contextually relevant recommendations based on individual learning needs. Providing tailored support to students in real-time facilitates better problem-solving strategies and enhances students’ learning experiences, contributing to the advancement of programming education.

This program is tentative and subject to change.

Sat 1 Mar

Displayed time zone: Eastern Time (US & Canada) change

10:45 - 12:00
Instructional Technologies #2Papers at Meeting Rooms 317-318
10:45
18m
Talk
An Automated Approach to Recommending Relevant Worked Examples for Programming Problems
Papers
Muntasir Hoq North Carolina State University, Atharva Patil North Carolina State University, Kamil Akhuseyinoglu University of Pittsburgh, Peter Brusilovsky University of Pittsburgh, Bita Akram North Carolina State University
11:03
18m
Talk
Instructor-Written Hints as Automated Test Suite Quality Feedback
Papers
James Perretta Northeastern University, Andrew DeOrio University of Michigan, Arjun Guha Northeastern University; Roblox, Jonathan Bell Northeastern University
11:41
18m
Talk
"Why is my code slow?" Efficiency Bugs in Student Code
Papers
Hope Dargan MIT CSAIL, Adam Gilbert-Diamond MIT CSAIL, Adam J. Hartz MIT EECS, Robert Miller MIT