About the Workshop

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 in the world. Yet, the field of computing education is still facing a range of problems from high failure and attrition rates, to challenges 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 presents 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 over time. These domain-specific challenge 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 the Educational Data Mining for Computer Science Education (CSEDM) workshop is to bring this community together to share insights for how to support and understand 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 increasing studying CS datasets. This workshop will help these researchers learn from each other, and develop the growing sub-field of CSEDM.

The workshop encourages contributions from the following topics of interest:

• Predictive and descriptive modelling 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
• Multi-modal learning analytics and combination of student data sources in CS Education
• Affective, emotional and motivational aspects related to CS learning
• Adaptive feedback, adaptive testing for CS learning
• Discourse and dialogue research related to classroom, online, collaborative, or one-on-one learning of CS
• Peer-review, peer-grading and peer-feedback in CS
• Teaching approaches using AI tools
• Visual Learning Analytics and Dashboards for CS
• Network Analysis for programming learning environments
• Self-Regulated learning for CS environments
• Writing and syntax analysis for programming design learning
• Natural Language Processing for CS forums and discussions
• Analysis of programming design and trajectory paths
• Recommender systems and in-course recommendations for CS learning

Submission Guidelines

We invite you to submit your original work for presentation and discussion. There will be two types of submissions:

• 4-8 page* Research Papers (due 6/15; abstracts by 6/8) addressing any of the topics above.
• 2-3 page* Presentation Abstracts (due 6/15; abstracts by 6/8). Researchers will present their work at CSEDM in a conversational format. Presentation might include:
  • Descriptions of shareable Computer Science (CS) datasets
  • Descriptions of data mining / analytics approaches applied to specifically 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)
  • Calls for Conversation (i.e. Birds of a Feather)

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.

All submissions must be formatted using the EDM proceedings format.

Submissions are handled via EasyChair: https://easychair.org/conferences/?conf=csedm2020

Important Dates

April, 2020: Open Call for Submissions

June 08, 2020: Abstract Deadline for Research Papers and Presentation Abstract Submissions

June 15, 2020: Paper Deadline for Research Papers and Presentation Abstract Submissions

June 29, 2020: Notification of acceptance for Research Papers and Presentation Abstract Submissions

July 10, 2020: 4th CSEDM Virtual Workshop at EDM

Publication

CSEDM proceedings will be published online via CEUR.