Held in conjunction with the 22nd International Conference on Computers in Education 2014 (ICCE) in Nara, Japan

Workshop web site: https://sites.google.com/a/asu.edu/aiedcs2014-icce/

Papers due August 18, 2014

1. Motivation

Over the last two decades, Computer Science (CS) has emerged as a field of study at almost all levels of education. At the same time, the basic CS literacy has become not only an important skill required for a wide range of modern professions, but also an essential competency for everyday life. Yet, CS is still a young domain that, unlike many other subjects, cannot benefit from long years of teaching tradition. Besides, it is a very dynamic domain, where technologies, skills and even subdomains are constantly evolving and even emerging. This makes CS very challenging from the point of education.

Despite considerable progress in this field, we still observe tremendous challenges related to improving general quality of CS instruction and increasing the diversity of students in CS classes. 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 approaches to enhance computational thinking but also dedicated learning tools. The number of AI-supported tools available for primary, secondary and higher CS education is rather small and cases of real integration of AI-supported tools into teaching and learning at various education levels are still rare.

From one perspective, a better set of tools needs to be designed that address the core CS competencies, such as computation thinking and modeling, program understanding, writing, debugging, testing, etc. Such tools should rely on deeper understanding of the CS pedagogy, CS students and the CS-related domains, and advanced AI techniques to support it. From another perspective, teachers who are supposed to employ existing and emerging tools need to be provided with proper solutions for integrating them into real educational practices, customization and restructuring of excising learning activities and creation of new ones.

Not only does designing AI-supported tools for CS education present interesting challenges, but deploying such tools and developing new teaching approaches in Computer Science education may give rise to several important areas of study. By addressing these challenges and problems as a research community, we will be poised to make great strides in building intelligent, highly effective AI-supported educational tools for Computer Science and developing innovative approaches to support teaching and learning in this field.

Spurred by the growing need for intelligent teaching/learning tools that support Computer Science education, the third workshop on AI-supported Education for Computer Science (AIEDCS 2014 – ICCE) follows up on our previous workshops, and comes at an important time for solidifying this community to engage wider researchers from Asia.

2. Topic of Interests

• AI-supported methods for learning computational thinking and modeling
• Teaching approaches using AI-supported tools
• Learning approaches using AI-supported tools
• Student modeling for Computer Science learning
• Adaptation and personalization within Computer Science learning environments
• Intelligent support for collaborative Computer Science problem solving
• Affective and motivational aspects related to Computer Science learning
• Adaptive feedback for Computer Science learning
• Intelligent methods for testing and assessment of Computer Science competencies
• Discourse and dialogue research related to classroom, online, collaborative, or one-on-one learning of Computer Science
• Online learning environments for Computer Science
• Authoring support for AIEDCS tools
• Using AIEDCS approaches in a real classroom

3. Important Dates

* Paper submission: August 18, 2014

* Acceptance notification: September 2, 2014

* Camera-ready version: September 10, 2014

4. Workshop Organizers

* Sharon I-Han Hsiao, Arizona State University, USA

* Hidenobu Kunichika, Kyushu Institute of Technology, Japan

* Nguyen-Thinh Le, Humboldt Universität zu Berlin, Germany

* Sergey Sosnovsky, DFKI Saarbrücken, Germany