EASEAI 2020 – Proceedings

Welcome from the Chairs
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 they can be combined. Specifically, this workshop brings together researchers, teachers, and practitioners who use advanced software engineering tools and artificial intelligence techniques in the education field and through a transgenerational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions.

Papers

Adapting to Online Teaching in Software Engineering Courses
Simona Motogna, Andrian Marcus, and Arthur-Jozsef Molnar
(Babes-Bolyai University, Romania; University of Texas at Dallas, USA)
The COVID-19 worldwide pandemic caused sudden and unexpected changes in how we teach software engineering and other university courses. This paper presents an empirical study that aims to improve our understanding on how the assessment of student learning changed, in response to the transition from in-class to online courses. A questionnaire was distributed to instructors across the globe. The results indicate that the evaluation methodologies for most reported learning objectives have changed. Not surprising, in-class oral presentations and in-class exams are no longer used by the instructors for evaluations. We observed a trend of having fewer exams and more project-related evaluations after the transition. Not all instructors changed the way they evaluated student learning after the transition, however the majority reported their effort in student learning assessment increased after the transition, whether they made changes in methodologies or not.

Publisher's Version

Using Static Analysis Tools to Assist Student Project Evaluation
Arthur-Jozsef Molnar, Simona Motogna, and Cristina Vlad
(Babes-Bolyai University, Romania)
Code review and static analysis tools are acknowledged as important instruments in software quality control and are used in the industry on a daily basis. In this exploratory study we examine how a well-known static analysis tool can be employed to assess the quality of student solutions to coding assignments. We examine all student solutions submitted to fulfill coding assignments required as part of an introductory programming course taught using Python. We show how teaching staff can evaluate the progress of individual students and how coding mistakes common to many students can be highlighted. We also show how teaching staff can improve their own understanding of perceived assignment complexity by evaluating the aggregate quality of student submitted source code.

Publisher's Version

Facilitating Model Checking Learning through Experiential Learning
Andreea Vescan and Camelia Serban
(Babes-Bolyai University, Romania)
In this ever-changing world when our students are being prepared for jobs that are not being invented yet, the educational system is forced to step into a new era of education over night. To gain effectiveness, the new approach in education should design the learning process in such a way that will reinforce students motivation for learning and improve their learning experience.
Considering the context described above, this paper outlines how the experiential learning approach was applied to facilitate learning in computer science education at a course taught in a ``Software engineering'' master program at the Babes-Bolyai University. Further, it is illustrated how this approach in teaching model checking was constructed and conducted, both theoretical concepts and practical based activities.
The results of quantitative and qualitative analyses acknowledge the effectiveness and efficiency of the experiential learning approach in teaching model checking. The perceptions and opinions of the students were collected through surveys and an independent t-test statistical test that compares grades with different teaching strategies was used.

Publisher's Version

Developing Smart Edutainment for Preschoolers: A Multidisciplinary Approach
Adriana-Mihaela Guran, Grigoreta Sofia Cojocar, and Laura Silvia Dioşan
(Babes-Bolyai University, Romania)
Education has suffered multiple changes due to technological progress. Even though the current generation of preschoolers (aged 3 to 6 years in our country) is called digital native, there is a lack of focus on introducing technology enabled learning tools for them. This paper presents our approach to designing smart learning experiences for a fringe users group, the preschoolers. We present our method proposal for designing edutainment applications for preschoolers, based on a User Centered Design process and how we may integrate Artificial Intelligence to complete and support the capabilities of our little users.

Publisher's Version

Agile and Cyclic Learning in Teaching Parallel and Distributed Computing
Virginia Niculescu, Adrian Sterca, and Darius Bufnea
(Babes-Bolyai University, Romania)
Agile and cyclic learning are methodologies that have been recently proposed to be used in teaching Computer Science. This paper investigates their usage for the undergraduate studies on parallel and distributed computing (PDC). The aim of this analysis is to evaluate their effectiveness, and also to evaluate to which extent we have to go with the knowledge related to PDC at the undergraduate level. Also, we intended to find out the pace in which agile and cyclic learning enforces the best knowledge transfer of PDC concepts. The analysis takes into consideration several courses spread on the entire curricula, students auto-evaluation based on questionnaires, and grade results. The analysis emphasizes the fact that the tendency is to introduce more and more information and this is facilitated by an agile approach, but in the same time this should be moderated if the final goal is to assure also a good and deep understanding of associated knowledge.

Publisher's Version

Engaging Families in a 10-Week, AI Global Competition
Tara Chklovski, Richard Jung, and Kathryn Young
(Technovation, USA)
The 2019 season of Technovation Families engaged ~9200 under-resourced 3rd-8th grade students, parents and educators from 13 countries. To increase program sustainability, partner implementation stipends were reduced from 2018, and the program length reduced from 15 to 10 weeks. These changes resulted in decreased participation (-14%), retention (-6%) and learning gains; the latter was most likely due to participants’ higher socio-economic levels with greater access to knowledge and technology. Successes included identifying factors for effective mentor engagement to provide social and technical capital and deployment of an effective curriculum on responsible AI inventions.

Publisher's Version

EASEAI 2020 – Proceedings

2nd ACM SIGSOFT International Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI 2020)

Frontmatter

Papers