About EASEAI
In the past few years, the world has seen a tremendous digital transformation in all its areas. Consequently, 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 toward experienced practitioners. Computer science remains taught in a very classical way. At the same time, the rise of artificial intelligence facilitates a variety of tasks; e.g., providing automated support, processing and reviewing documents such as dissertations and other kinds of exercises, or providing predictions of the needs of students.
The applications of modern software engineering and artificial intelligence in human-daily life create an opportunity to foster interesting discussions among people from many different communities, including software engineering, education science, artificial intelligence, machine learning, natural language processing, etc. The common lens of how advanced software tools and techniques might be used are 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 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 or to the context of other academic fields. It is common in the industry to use techniques -- such as code versioning, testing, code smells, quality metrics, code review, continuous integration, or tools -- such as Git, SonarQube, BugFinders, Jenkins. However, integrating these techniques and tools in software engineering education is limited or uncommon. Nevertheless, efforts in this direction are beneficial in the educational field. 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 been 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 on experiences relating to agile in education would be one of the contributions to this area of the workshop.
Artificial intelligence support
The third area relates to the support that artificial intelligence can provide educators with innovative and improved pedagogical tools. Contributions to the workshop would include developments in, for example, 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, can be actively used in education. Additional areas of interest include artificial intelligence techniques designed to help improve the recommendations and 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 education at varied ranges of target audiences, from junior to senior students and from aspirant computer science specialists to a broader audience. The diverse audiences will generate fruitful conversations 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).
Call for papers
In the past few years, the world has seen a tremendous digital transformation in all its areas. Consequently, 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 toward experienced practitioners. Computer science remains taught in a very classical way.
At the same time, the rise of artificial intelligence facilitates a variety of tasks; e.g., providing automated support, processing and reviewing documents such as dissertations and other kinds of exercises, or providing predictions of the needs of students.
The applications of modern software engineering and artificial intelligence in human-daily life create an opportunity to foster interesting discussions among people from many different communities, including software engineering, education science, artificial intelligence, machine learning, natural language processing, etc. The common lens of how advanced software tools and techniques might be used are 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.
- Using technology to improve the climate in the classroom and promote diversity, equity, and inclusion in the educational environment.
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 invite authors to submit a one-page extended abstract that will not be included in the proceedings.
Important dates
- Paper submission: June 27th, 2022 July 11th, 2022 (AoE)
- Notification: August 9th, 2022 (AoE)
- Camera-ready: September 9th, 2022 (AoE)
- Workshop date: November 18th, 2022
Submission site
Submissions will be handled via HotCRP: https://easeai2022.hotcrp.com/
Workshop format
The workshop will be a hybrid organization. Each accepted paper will have 10 minutes for presentation. At the end of each presentation session, there will be a time for discussion (5 to 10 minutes per accepted paper). Discussants will be assigned to accepted papers to foster and trigger discussions. A discussant will provide the main contribution and the importance of the paper and offer opinions on the next steps for the authors to expand or improve the contribution. This approach facilitates debates and exchange of ideas, and involves active participation of all members. The discussions take place fact-to-face and/or on Slack, even beyond the workshop.
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.
Organization
Andreea Vescan
Babes-Bolyai University
Camelia Serban
Babes-Bolyai University
Julie Henry
University of Namur
Upsorn Praphamontripong
University of Virginia
Program committee
- Alexandre Bergel, University of Chile (CL)
- Alina Campan, Northern Kentucky University (US)
- Tara Chklovski, Founder and CEO of Technovation (US)
- Gianluigi Ciocca, University of Chile (CL)
- Bruno Dumas, University of Namur (BE)
- Gordon Fraser, University of Passau (DE)
- Ilenia Fronza, Free University of Bozen-Bolzano (IT)
- Christiane Gresse von Wangenheim, University of Santa Catarina (BR)
- Julie Henry, University of Namur (BE)
- Simona Motogna, Babes-Bolyai University (RO)
- Jeff Offutt, George Mason University (US)
- Daniel Port, University of Hawaii (US)
- Upsorn Praphamontripong, University of Virginia (US)
- Olaf Resch, HWR Berlin (DE)
- Camelia Serban, Babeș-Bolyai University (RO)
- Andreea Vescan, Babeș-Bolyai University (RO)
- Hironori Washizaki, Waseda University (JP)
Program
All times are UTC.
| 12:00 (UTC) | Opening |
| 12:10 (UTC) | Keynote: Evidence-Based Practices: Broadening Participation and Improving Learning in CS. Maureen Doyle, Alina Campan, Meghan Schmidt |
| In the US and other countries,
women and people of color have been underrepresented in computing
majors1 for more than twenty years.
Given this trend and research2 showing that
diverse teams are more successful, in 2017,
Northern Kentucky University's Department of Computer Science
began implementing multiple evidence-based practices to address these concerns.
New programs and practices were selected based on demonstrated improvements
in student success and increased diversity of majors.
The changes fell into two broad categories:
(1) Curriculum/Program and (2) Student Support.
New initiatives included new introductory interactive textbooks and platforms,
implementation of a peer teaching assistant program, and added requirements
for a freshmen seminar. In addition, the department was awarded
an NCWIT Extension Services grant to support faculty training
in inclusive teaching and classroom pedagogy.
The success of these programs is measured by the increase seen in majors and minors,
as well as improvements in student retention and a more diverse set of computing majors.
We will discuss a subset of the programs, their implementation,
retention and demographic enrollment results, and future work.
Maureen Doyle and Alina Campan are Professors at the College of Informatics, Northern Kentucky University. Meghan Schmidt is an Assistant Director Recruitment and Retention, in the Advising Center of the College of Informatics, Northern Kentucky University. 1https://cra.org/data/generation-cs/ 2https://hbr.org/2016/11/why-diverse-teams-are-smarter | |
| 13:00 (UTC) | Towards Automated Testing for Simple Programming Exercises. Pierre Ortegat, Benoit Vanderose, Xavier Devroey |
| 13:15 (UTC) | Mining Sorting Concept across Curriculum Levels. A Cyclic Learning Based Approach. Mariana Maier, Camelia Serban, Andrei Moisin |
| 13:30 (UTC) | Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory. Julie Henry, Bruno Dumas, Andreea Vescan, Alexandra Maria Pasca |
| 13:45 (UTC) | Break |
| 14:00 (UTC) | Implementing Microlearning and Gamification Techniques in Teaching Software Project Management Concepts. Dan Mircea Suciu |
| 14:15 (UTC) | Findings from Teaching Entrepreneurship to Undergraduate Multidisciplinary Students. Case Study. Manuela Andreea Petrescu, Diana Laura Borza, Dan Mircea Suciu |
| 14:30 (UTC) | A Pedagogical Approach in Interleaving Software Quality Concerns at an Artificial Intelligence Course. Laura Diana Cernau, Laura Dioșan, Camelia Șerban |
| 14:45 (UTC) | Closing |
Venue
The workshop will be a hybrid organization. Please see the ESEC/FSE'22 website for details about the registration.