In the past few years, the world has seen a tremendous digital transformation in all of its areas. In consequence, the general public needs to be able to acquire an ever-increasing amount of digital literacy and at least some level of proficiency with modern digital tools. While modern software engineering relies heavily on Computer Assisted Software Engineering (CASE) tools and development methodologies (to improve productivity, quality, and efficiency of development teams), those tools remain targeted towards experienced practitioners and computer science remains taught in a very classical way. In the same time, the rise of artificial intelligence allows more and more easily to provide automated support, automate the processing and review of documents such as dissertations and other kinds of exercises, or to provide predictions of the needs of students.

This context seems to be a perfect opportunity to foster interesting discussions in a workshop that gathers people from many different communities (software engineering, education science, artificial intelligence, machine learning, natural language processing, etc.), through the common lens of how advanced software tools and techniques might be used as a catalyst for a better way to teach various types of students.

The primary goal of this workshop is to gather researchers, teachers, and practitioners who use advanced software engineering tools and artificial intelligence techniques on a daily basis in the education field and through a transgenerational and transdisciplinary range of students. The workshop covers three main areas described in the following.

Innovative usages of software tools

The first area covered by the workshop is the use or development of innovative software tools to improve the quality of education in the fields of both computer and science and other disciplines. This theme includes the advancement in tools designed to help individuals (ranging from children to seniors) acquire better computational thinking skills (such as Scratch and Blockly) and improve their digital literacy. It also covers the development and use of tools designed to support the acquisition of scientific or technical skills (such as visualization tools in various fields of science, etc.).

Modern software engineering tools and methodologies

The second area targeted by the workshop relates to the adaptation of modern software engineering tools and methodologies to the needs of beginner computer science students and/or to the context of other academic fields. Indeed, it is common in the industry to either use techniques such as code versioning, testing, code smells, quality metrics, code review, continuous integration, etc. or tools such as Git, SonarQube, BugFinders, Jenkins, etc. However, it is neither common nor trivial to integrate these techniques and tools in software engineering education. Nevertheless, efforts in this direction have been shown to be beneficial in the field of education. For instance, tools such as Hairball or Dr Scratch have been designed to review the quality of code developed by youth or novice coders. They are essentially static code analysis tools made approachable for younger coders. Recently, these tools have gathered some interest due to their positive impact on the growth of computational thinking in young coders.

Similarly, agile development methods have become very popular in the software industry. Many of their founding principles (focus on customers, iterative appropriation of complex artifacts, self-organization of teams, etc.) might be applicable in education. Gathering and discussing feedback of experiences relating to agile in education would be one of the contributions to this area of the workshop.

Artificial intelligence support

The third discussion area of the workshop is related to the support that artificial intelligence might provide to teachers regarding the improvement of pedagogical tools. Contributions to the workshop would include developments in the field of automatic grading and feedback provided to students through machine learning. Issues addressed in this area relate to how advanced tools such as automated translation applications or replace-as-you-type spell checkers might be proactively used in education. It also discusses the use or development of artificial intelligence techniques designed to help improve the recommendations provided to support personalized curricula. And methods defined to predict the engagement and risks of dropping out of students through machine learning.

Through these areas, the workshop aims to achieve convergence between research works focusing on the education of a varied range of target audiences both from younger to senior students and from aspirant computer science specialists to a broader audience. In turn, this blending of different audiences will generate interesting discussions and future directions relating to the intricate balancing of teaching technical and specialized topics to audiences that need only a cursory yet accurate overview of the subject (e.g., the need to teach what AI is to social network users).

In the past few years, the world has seen a tremendous digital transformation in all of its areas. In consequence, the general public needs to be able to acquire an ever-increasing amount of digital literacy and at least some level of proficiency with modern digital tools.

While modern software engineering relies heavily on Computer Assisted Software Engineering (CASE) tools and development methodologies (to improve productivity, quality, and efficiency of development teams), those tools remain targeted towards experienced practitioners and computer science remains taught in a very classical way.

In the same time, the rise of artificial intelligence allows more and more easily to provide automated support, automate the processing and review of documents such as dissertations and other kinds of exercises, or to predict the needs of students.

This context seems to be a perfect opportunity to foster interesting discussions in a workshop that gathers people from many different communities (software engineering, education science, artificial intelligence, machine learning, natural language processing, etc.), through the common lens of how advanced software tools and techniques might be used as a catalyst for a better way to teach various types of students.

Topics of interest

Topics include, but are not limited to:

Tools and techniques

• Advances in automated grading of assignments.
• Advances in automated feedback and recommendations to provide support to students.
• Visualization of technical and/or scientific information in various fields (chemistry, software engineering, physics, history, sociology, etc.).
• Efforts related to the improvement of usability of advanced tools for non experienced users.
• Efforts related to inclusion of users with disabilities in the learning environment.
• Dropout prediction in a learning environment using machine learning.
• Methods with a focus on explainability and interpretability (either to students or teachers) are particularly welcome.

Methodologies

• New and unexpected usage of established software engineering practices in the education environment.
• Integration of agile methods principles in the education environment.
• Integration of software quality standards and methods dedicated to continuous improvement in the education environment.
• Application to serious game and gamification in the context of education.
• Team work or individual work in online settings or not.

Evaluations and Lessons Learned

• Feedback on case studies using cutting edge techniques, methods, and tools applied to the education environment.
• Improvement of computational thinking skills and digital literacy through software engineering.
• Improvement of critical thinking and research skills of students.
• Integration of software engineering and artificial intelligence research into teaching and training.
• Introduction to artificial intelligence and machine learning principles for younger audiences.

Types of submissions

We invite original papers in the conference format (ACM sigconf, double column) describing positions and new ideas (short papers up to 4 pages) as well as new results and reporting on innovative approaches (long papers up to 8 pages). All accepted papers will be published in the ACM digital library, together with the other ESEC/FSE workshops proceedings.

The workshop also welcomes presentations of previously peer-reviewed published papers. We will invite authors to submit a one-page extended abstract that will not be included in the proceedings.

Important dates

• Paper submission: May 14th June 1st, 2021 (AoE)
• Notification: July 1st, 2021 (AoE)
• Camera-ready: July 8th, 2021 (AoE)

Submission site

Submissions will be handled via EasyChair: https://easychair.org/conferences/?conf=easeai2021

Modalities

The workshop will run in online mode. Presenters will be invited to submit a video of their work that will be available to attendees before the workshop begins. Attendees will have the opportunity to access all available content during the week leading up to the workshop and following the end of the workshop. Lightning talk presentations will take place online during the workshop, with online question/answering and timezone mirroring.

Review process

The workshop will follow a single-blind peer review process. Each contribution will be reviewed by at least three members of the program committee. Acceptance will be jointly decided with the reviewers, based on the reviews and discussions.

As previously published papers have been already reviewed and accepted, they will not be reviewed again for technical content. If needed, the presentation’s propositions will be prioritized, based on the content and structure of the sessions.

• Efthimia Aivaloglou, Open University (NL)
• Francesca Arcelli Fontana, University of Milano-Bicocca (IT)
• Andreas Bollin, Alpen-Adria University of Klagenfurt (AT)
• Anne-sophie Collard, University of Namur (BE)
• Fabiano Cutigi Ferrari, Federal University of São Carlos (BR)
• Luis Cruz, Delft University of Technology (NL)
• Xavier Devroey, Delft University of Technology (NL)
• Ilenia Fronza, Free University of Bozen-Bolzano (IT)
• Gordon Fraser, University of Passau (DE)
• Fredrik Heintz, Linköping University (CE)
• Johan Jeuring, Utrecht University (NL)
• Grischa Liebel, Reykjavik University (IS)
• Birgitta Lindström, University of Skövde (SE)
• Mircea Lungu, IT University of Copenhagen (DK)
• Kim Mens, Université catholique de Louvain (BE)
• José Miguel Rojas, University of Leicester (GB)
• Simona Motogna, Babes-Bolyai University (RO)
• Jeff Offutt, George Mason University (US)
• Andrew Petersen, University of Toronto Mississauga (CA)
• Upsorn Praphamontripong, University of Virginia (US)
• Olaf Resch, HWR Berlin (DE)
• Camelia Serban, Babeș-Bolyai University (RO)
• Benoit Vanderose, University of Namur (BE)
• Andreea Vescan, Babeș-Bolyai University (RO)

All times are UTC.

The workshop will take place online. Please see the ESEC/FSE'21 website for details about the registration.