EASEAI 2019, Tallin, Estonia, August 26, 2019 - Co-located with ESEC/FSE ‘19
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.
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.).
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.
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 years, with the development and widespread of digital technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop addresses that challenge by looking at software engineering, education, and artificial intelligence research fields to explore how these fields can cross-fertilize and benefit from each other. Specifically, this workshop brings together researchers, teachers, and practitioners who use advanced software engineering tools (such as software development tools and methods, productivity tools, software inspection and analysis tools, automated testing techniques, etc.) and artificial intelligence techniques in the education field as well as researchers and teachers in education science who tackle how to improve awareness regarding digital technologies through a transgenerational and transdisciplinary range of students.
Topics include, but are not limited to:
We invite original papers in the conference format (two columns ACM sigconf publication format) 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 2019 workshops proceedings. The review process is single-blind. Each contribution will be reviewed by at least three members of the program committee.
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. As previously published papers have been already reviewed and accepted, they will not be reviewed again for technical content. If needed, the presentations propositions will be prioritized, based on the content and structure of the sessions.
Submissions will be handled via EasyChair: https://easychair.org/conferences/?conf=easeai2019
|09:15.||Keynote: Using a learning robot to open the black box of artificial intelligence. Thomas Deneux.|
|11:00.||Session 1: Programming Education and Digital Literacy Awareness.|
|Engaging Children in the Smart City: A Participatory Design Workshop. Anthony Simonofski, Bruno Dumas, and Antoine Clarinval. Discussant: Xavier Devroey.|
|Designing Personalized Learning Environments through Monitoring and Guiding User Interactions with Code and Natural Language. Mircea Lungu. Discussant: Cédric Libert.|
|Analysis of Students’ Preconceptions of Concurrency. Cédric Libert and Wim Vanhoof. Discussant: Olaf Resch and Aglika Yankova.|
|14:00.||Session 2: Automated Feedback and Evaluation Systems.|
|Open Knowledge Interface: A Digital Assistant to Support Students in Writing Academic Assignments. Olaf Resch and Aglika Yankova. Discussant: Anthony Simonofski.|
|Towards Context-Aware Automated Writing Evaluation Systems. Pierre-André Patout and Maxime Cordy. Discussant: Mircea Lungu.|
|Application of Data Clustering for Automated Feedback Generation about Student Well-Being. Mikko Kylvaja, Pekka Kumpulainen, and Anne Konu. Discussant: Antoine Clarinval.|
|16:00.||Session 3: Teaching Advanced Software Engineering.|
|Does Learning by Doing Have a Positive Impact on Teaching Model Checking? Andreea Vescan. Discussant: Simona Motogna.|
|Artificial Intelligence Meets Software Engineering in the Classroom. Laura Diosan and Simona Motogna. Discussant: Andreea Vescan.|
|Advances in Designing a Student-Centered Learning Process using Cutting-Edge Methods, Tools, and Artificial Intelligence: An E-Learning Platform. Camelia Serban and Andreea Vescan. Discussant: Mikko Kylvaja.|
The workshop is co-located with ESEC/FSE'19. Please see the ESEC/FSE ‘19 website for details about the registration.