CSEDM 2018: Educational Data Mining in Computer Science Education Workshop The University at Buffalo New York, NY, United States, July 15-18, 2018 |
Conference website | https://sites.google.com/asu.edu/csedm-ws-edm-2018 |
Submission link | https://easychair.org/conferences/?conf=csedm2018 |
The objective of this workshop is to facilitate a discussion among our research community around Artificial Intelligence (AI) in Computer Science Education. The workshop is meant to be an interdisciplinary event. Researchers, faculty and students are encouraged to share their data mining approaches, methodologies and experiences where AI is transforming the way students learn Computer Science (CS) skills.
Submission Guidelines
Paper presentations and discussions. All papers must report on original, unpublished work and must be formatted following the International Educational Data Mining (EDM)'s template: Word or LaTeX.
- Full papers (max. 5 pages)
- Short papers (max. 2 pages)
List of Topics
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 behavioural logs, backgrounds, assessments and all sort of learning analytics. We aim to create a forum to bring together CS education researchers from adjacent fields (AIED, CSE, LAK) to identify the EDM challenges and issues in the domain-specific field, Computer Science Education.
This workshop will follow on 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 trajetory paths
- Linked Data for Computer Science knowledge mapping
- Recommender systems and in-course recommendations for Computer Science learning
Organizers
David Azcona is a PhD candidate in the Insight Centre for Data Analytics at Dublin City University in Ireland. At the moment, he is visiting Arizona State University as a Fulbright research scholar. His research focuses on personalizing Computer Science Education. His interests are representational learning for programming learning, Machine Learning & Deep Learning and Data Science for Social Good. Contact: David.Azcona@insight-centre.org
Sharon I-Han Hsiao is an Assistant Professor at the School of Computing, Informatics & Decision Systems Engineering in Arizona State University. Her research lies in the intersections of Informatics & Computational Technologies for Learning with a focus on Intelligent Tutoring Systems, Computer Science Education, Adaptive Educational Systems, Open User Modeling, Data Sciences, Visualization, Social Computing, and Learning Technologies. Contact: Sharon.Hsiao@asu.edu
Nguyen-Thinh Le studied Informatics at the University of Hamburg and received his Diploma in 2001. After several years working as software developer in the industry, he started his research career at the University of Hamburg in 2004 and received a PhD degree there. In April 2010, he joined the "Human-Centered Information Systems" research group led by Prof. Pinkwart at Clausthal University of Technology. Since October 2013, he works as a lecturer in the research group "Computer Science Education / Computer Science and Society" at Humboldt-Universität zu Berlin. Contact: nguyen-thinh.le@hu-berlin.de
John Stamper is a member of the research faculty at the Human-Computer Interaction Institute at Carnegie Mellon University in Pittsburgh, PA. He is also the Technical Director of the Pittsburgh Science of Learning Center DataShop. Contact: jstamper@cs.cmu.edu
Mikhail Yudelson is a Sr. Research Scientist at ACT, Inc. where he is working on building and evaluating mobile learning and formative assessment platforms. In 2010-2013 he was a post-doctoral Fellow at the Human-Computer Interaction Institute of Carnegie Mellon University working on issues of modeling student knowledge acquisition and learning transfer. In 2013- 2017 Mikhail worked as Research Scientist at Carnegie Learning, Inc. and at Carnegie Mellon University where he focused on the issues of student model design and scalability. Contact: michael.yudelson@act.org
Venue
The conference will be held in The University at Buffalo, New York, United States in conjunction with EDM 2018
Contact
Do not hesitate to email us at csedm.workshop@gmail.com