Vol-3796
urn:nbn:de:0074-3796-6

Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

CSEDM 2024
Eucational Data Mining in Computer Science Education Workshop 2024

Proceedings of 8th Educational Data Mining in Computer Science Education Workshop (CSEDM 2024)
co-located with the 17th Educational Data Mining Conference (EDM 2024)

Atlanta, GA, USA, July 14, 2024.

Edited by

Yang Shi (1)
Peter Brusilovsky (2)
Bita Akram (3)
Thomas Price (3)
Juho Leinonen (4)
Ken Koedinger (5)
Andrew Lan (6)

(1) Utah State University, Department of Computer Science, Logan, UT, USA.
(2) University of Pittsburgh, School of Computing and Information, Pittsburgh, PA, USA
(3) North Carolina State University, Department of Computer Science, Raleigh, NC, USA.
(4) Aalto University, School of Electrical Engineering, Espoo, Finland
(5) Carnegie Mellon University, Human-Computer Interaction Institute, Pittsburgh, PA, USA
(6) UMass Amherst, Manning College of Information and Computer Sciences, Amherst, MA, USA

Table of Contents

Preface

Preface
Yang Shi, Peter Brusilovsky, Bita Akram, Thomas Price, Juho Leinonen, Ken Koedinger, Andrew Lan

Full Papers

A Preliminary Analysis of Students’ Help Requests with an LLM-powered Chatbot when Completing CS1 Assignments
Ruiwei Xiao, Xinying Hou, Harsh Kumar, Steven Moore, John Stamper, Michael Liut
Using Intelligent Texts in a Computer Science Classroom: Findings from an iTELL Deployment
Scott Crossley, Joon Suh Choi, Wesley Morris, Langdon Holmes, David Joyner, Vaibhav Gupta

Short Papers

Evaluating Correctness of Student Code Explanations: Challenges and Solutions
Arun-Balajiee Lekshmi-Narayanan, Peter Brusilvosky
Learning Analytics Based on the Number of Synchronous Viewings of Online Teaching Materials in a Face-to-Face Blended Course
Konomu Dobashi, Curtis P Ho, Catherine P Fulford, Christina Higa, Kris Hara
Applying Sequence Analysis to Understand the Debugging Process of Novice Programmers
Qianhui Liu, Luc Paquette
Automated Identification of Relevant Worked Examples for Programming Problems
Muntasir Hoq, Atharva Patil, Kamil Akhuseyinoglu, Bita Akram, Peter Brusilovsky
Measuring Students’ Programming Skill via Online Practice
Hongwen Guo, Mo Zhang, Amy J Ko, Min Li, Benjamin Zhou, Jared Lim, Paul Pham, Chen Li

Descriptions

An Approach to Detect Abnormal Submissions for CodeWorkout Dataset
Alex Hicks, Yang Shi, Arun-Balajiee Lekshmi-Narayanan, Wei Yan, Samiha Marwan

2024-09-27: submitted by Yang Shi, metadata incl. bibliographic data published under Creative Commons CC0
2024-10-22: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|