Computing is an increasingly fundamental skill for students across disciplines. It enables them to solve complex, real, and challenging problems and make a positive impact on the world. Yet, the field of computing education is still facing a range of problems, from high failure and attrition rates to challenges in training and recruiting teachers to the under-representation of women and students of color.
Advanced learning technologies, which use data and AI to improve student learning outcomes, have the potential to address these problems. However, the domain of CS education presents novel challenges for applying these techniques. CS presents domain-specific challenges, such as helping students effectively use tools like compilers and debuggers and supporting complex, open-ended problems with many possible solutions. CS also offers unique opportunities for developing learning technologies, such as abundant and rich log data, including code traces that capture each detail of how students' solutions evolved.
These domain-specific challenges and opportunities suggest the need for a specialized community of researchers working at the intersection of AI, data mining, and computing education research. The goal of this Educational Data Mining for Computer Science Education (CSEDM) is to bring this community together to share insights for supporting and understanding learning in the domain of CS using data. This field is nascent but growing, with research in computing education increasingly using data analysis approaches and researchers in the EDM community increasingly studying CS datasets. This workshop will help these researchers learn from each other and develop the growing sub-field of CSEDM.
Preserving explainability in the age of LLMs
LLMs in Action: lessons learned for effective integration of LLMs in CS classrooms
Generative AI and Computing Education
Integrating the strength of classical ML with the power of LLMs
Predictive and descriptive modeling for CS courses
Adaptation and personalization within CS learning environments
Intelligent support for collaborative CS problem solving
Machine learning approaches to analyze massive CS datasets and courses
Online learning environments for CS: implementation, design, and best practices
Multimodal learning analytics and combination of student data sources in CS Education
Affective, self-regulation, and motivational modeling of students as related to CS learning
Adaptive feedback and adaptive testing for CS learning
Discourse and dialogue research related to classroom, online, collaborative, or one-on-one learning of CS
Teaching approaches using AI tools
Visual Learning Analytics and Dashboards for CS
Network Analysis for programming learning environments
Classification of student program code
Natural Language Processing for CS forums and discussions
Analysis of programming design and trajectory paths
Recommender systems and in-course recommendations for CS learning
Adaptive educational technology and CS pedagogy for non-majors.
Deep learning approaches for analyzing, assessing, and scaffolding programming challenges
We invite you to submit your original work for presentation and discussion. There will be three types of submissions:
Research Papers (up to 8 pages) on AI and data mining being applied to computing education courses and data, addressing any of the topics of interest above.
Position Papers or Work-in-progress Papers (up to 6 pages) on:
Critical meta-reviews of CSEDM research and practice putting forward discussions of the vision and future research and practice directions for the CSEDM community.
Original, unpublished work-in-progress papers (incomplete or ongoing work, ready for feedback, but not yet fully developed), addressing any of the topics of interest above.
Descriptions of CS Tools/Datasets/Infrastructure (up to 2 pages), such as:
Descriptions of shareable Computer Science (CS) datasets
Descriptions of data mining/analytics approaches applied to specific Computer Science datasets
Descriptions of tools or programming environments that use/produce data
Case studies of collaboration where reproducible practices were used to integrate or compose two or more data analysis tools from different teams
Descriptions of infrastructures that could collect and integrate data from multiple learning tools (e.g., forum posts, LMS activity, and programming data)
Note: You will select your submission type on easychair.
Blinding: All submitted papers should be carefully blinded for review. Take care to remove all authors' names and identifying information (e.g. grant numbers), and refer to any of your prior work in the third person (e.g. "Previously, Smith et al. did ... [1]" rather than "In our prior work [1]").
*Note: references do not count towards the page limit. Authors can also include appendices to more clearly describe datasets and tools if necessary, and these do not count toward the page limit.
As we plan to publish all proceedings with CEUR, All submissions must be formatted using the CEURART style, the CEUR Workshop Proceedings format. The files for the CEURART style are available as an Overleaf template (for LaTeX users) and as a downloadable ZIP file (for both Word and LaTeX users, as it includes both LaTex and Word templates).
Submissions are handled via EasyChair: https://easychair.org/my/conference?conf=csedm25
April 01, 2025: Open Call for Submissions
April 17, 2025 April 24, 2025: Abstract Deadline for Papers from all tracks
April 24, 2025 May 1, 2025: Paper Deadline for Papers from all tracks
May 22, 2025 May 29, 2025: Notification of acceptance for Papers from all tracks
June 5, 2025 June 12, 2025: Camera-Ready Version Deadline for Papers from all tracks
July 20, 2025: 9th CSEDM Workshop at EDM 2025
CSEDM proceedings will be published online via CEUR. CSEDM proceedings should be considered semi-archival. This means that the papers are peer-reviewed, published and citable, but are still appropriate to extend for submission elsewhere. This is similar to the status of CHI extended abstracts. Some venues may expect that work published in a workshop should be revised before republication in a conference or journal, and we expect this should be straightforward to do with feedback you get by presenting at CSEDM. For example, ACM conferences expect an extension of 25%. Authors retain the copyright to their work when published through CEUR at CSEDM.