CSEDM 2019: 2nd Educational Data Mining in Computer Science Education (CSEDM) Workshop |
Website | https://sites.google.com/asu.edu/csedm-ws-lak-2019 |
Submission link | https://easychair.org/conferences/?conf=csedm2019 |
Submission deadline | December 13, 2018 |
Computer Science (CS) has become ubiquitous and is part of everything we do. Studying CS enables us to solve complex, real and challenging problems and make a positive impact in the world we live in.
Yet, the field of CS education is still facing a range of problems from inefficient teaching approaches to the lack of minority students in CS classes and the absence of skilled CS teachers. One of the solutions to these problems lies with effective technology-enhanced learning and teaching approaches, and especially those enhanced with AI-based functionality.
Providing education in Computer Science requires not only specific teaching techniques but also appropriate supporting tools. The number of AI-supported tools for primary, secondary and higher CS education is small and evidence about the integration of AI-supported tools in teaching and learning at various education levels is still rare.
In order to improve our current learning environments and address new challenges we ought to implement new AI techniques, collaborate and share student data footprints in CS. Data is the driving force for innovation at this time and new approaches have been implemented in other fields of innovation and research like Computer Vision and Image Classification. New data-driven learning algorithms and machines to process them are now widely accessible such as Deep Neural Networks and Graphical Processing Units (GPUs).
We want to keep the momentum and support the Computer Science Education community by organizing a workshop focusing on how to mine the rich student digital footprint composed by behavioral logs, backgrounds, assessments and all sort of learning analytics. We aim to create a forum to bring together CS education researchers from adjacent fields (EDM, AIED, CSE) to identify the LAK challenges and issues in the domain-specific field, Computer Science Education.
This workshop will follow on Educational Data Mining in Computer Science Education (CSEDM 2018) and AI-supported Education for Computer Science (AIEDCS) 2013 and 2014 which had an increasing number of participants, submissions and presentations. These workshops and the conferences on this field such as the ACM Technical Symposium on Computer Science Education (SIGCSE) demonstrate the strength of a community that leverages AI techniques to build its innovations.
The workshop encourages contributions from the following topics of interest:
- Predictive student modelling for Computer Science courses and learning
- Adaptation and personalization within Computer Science learning environments
- Intelligent support for collaborative Computer Science problem solving
- Deep learning approaches to massive Computer Science datasets and courses
- Online learning environments for Computer Science: implementation, design and best practices
- Multimodal learning analytics and combination of student data sources in Computer Science Education
- Affective, emotional and motivational aspects related to Computer Science learning
- Explanatory predictive models in Computer Science Education
- Adaptive feedback, adaptive testing for Computer Science learning
- Discourse and dialogue research related to classroom, online, collaborative, or one-on-one learning of Computer Science
- Peer-review, peer-grading and peer-feedback in Computer Science
- Teaching approaches using AI tools
- Visual Learning Analytics and Dashboards for Computer Science
- Learning approaches using AI tools
- Network Analysis for programming learning environments
- Self-Regulated learning for Computer Science environments
- Writing and syntax analysis for programming design learning
- Natural Language Processing for Computer Science forums and discussions
- Analysis of programming design and trajectory paths
- Linked Data for Computer Science knowledge mapping
- Recommender systems and in-course recommendations for Computer Science learning
Submission Guidelines
We invite you to submit your original work for presentation and discussion. There will be three types of submissions, each having their own deadlines:
- 2-4 page* Research Papers (due Dec 3, 2018) addressing any of the topics above. Accepted papers will be published in the LAK Companion Proceedings.
- 2 page Presentation Abstracts (due Jan 15, 2019). Researchers will present their work at CSEDM in a conversational format. However, these submissions will not be published in the LAK Companion Proceedings. Presentation might include:
- Descriptions of shareable Computer Science (CS) datasets
- Calls for Conversation (i.e. Birds of a Feather)
- Descriptions of tools or programming environments that use/produce data
- Dataset Challenge Entries (due Feb 5, 2019). This year, we are introducing a new activity which attempts to discuss some challenges when doing Data Mining in Computer Science Education. We want to ask for your ideas as we prepare for this. Kindly answer the following survey. The challenge will be released on Dec 15, 2018.
*Note: references do not count towards the page limit.
All submissions must be formatted using the Learning Analytics & Knowledge (LAK)'s Companion Proceedings Template.
Important Dates
29 Oct 2018 Open Call for Submissions
13 Dec 2018 Extended: Deadline for Research Paper Submissions
15 Dec 2018 Dataset Challenge Released
04 Jan 2019 Notification of Research Paper Acceptance
15 Jan 2019 Deadline for Presentation Abstracts Submissions
30 Jan 2019 Notification of Presentation Abstracts Acceptance
05 Feb 2019 Deadline for Dataset Challenge
05 Mar 2019 2nd CSEDM Workshop
Contact
For more information, feel free to contact us at csedm.workshop@gmail.com