Informatics in Education

Scrum is a widely-used framework in industry, so many schools apply it to their software engineering courses, particularly capstone courses. Due to the differences between students and industrial professionals, changing Scrum is necessary to fit capstone projects. In this paper, we suggest a decision-making process to assist instructors in developing a strategy to adapt Scrum for their course. This framework considers critical differences, such as student’s workloads and course schedules, and keeps the Agile principles and Scrum events. To evaluate the adapted Scrum, we investigated student’s learning experiences, satisfaction, and performance by quantitatively analyzing user story points and source codes and qualitatively studying instructor’s evaluations, student’s feedback, and Sprint Retrospective notes. Our two case studies about adapted Scrum showed that having daily stand-up meetings in every class was not helpful, student’s satisfaction positively correlated to the difficulty of the task they tackled, and the project provided good learning experiences.

When we “think like a computer scientist,” we are able to systematically solve problems in different fields, create software applications that support various needs, and design artefacts that model complex systems. Abstraction is a soft skill embedded in all those endeavours, being a main cornerstone of computational thinking. Our overview of abstraction is intended to be not so much systematic as thought provoking, inviting the reader to (re)think abstraction from different – and perhaps unusual – perspectives. After presenting a range of its characterisations, we will explore abstraction from a cognitive point of view. Then we will discuss the role of abstraction in a range of computer science areas, including whether and how abstraction is taught. Although it is impossible to capture the essence of abstraction in one sentence, one section or a single paper, we hope our insights into abstraction may help computer science educators to better understand, model and even dare to teach abstraction skills.

The notion of algorithm may be perceived in different levels of abstraction. In the lower levels it is an operational set of instructions. In higher levels it may be viewed as an object with properties, solving a problem with characteristics. Novices mostly relate to the lower levels. Yet, higher levels are very relevant for them as well. We unfold the importance of higher level abstractions for novices, by demonstrating the role of declarative observations of algorithmic problems, and the benefit of developing awareness of such observations in algorithmic problem solving. This is shown in a two-stage study, which first reveals the unfortunate lack of declarative observations, and then displays comparative results of experimental and control groups, which stems from different awareness and competence with declarative observations.

For over a decade, a declarative approach to problem solving based on the use of abstract data types (ADTs) has been taught to high-school students as part of the logic programming instructional unit. We conducted a study aimed at assessing students' problem-solving processes when utilizing ADTs. The findings indicated that students' strategies that diverged from the conceptual model often cause the students to develop incorrect programs. Specifically, students have difficulties in establishing correct mapping between the problem and its abstract model - the corresponding ADT, and in establishing proper connectivity between layers of abstraction related to different stages of the problem-solving processes (e.g., between distinct programming modules). These difficulties are apparently associated with general difficulties that novices encounter when learning programming, and with the cognitive load encountered when dealing with high levels of abstraction. With the intention to reduce student difficulties, we suggest using an instructional approach designed to gradually educate the students toward attaining proficiency as ``problem solvers'' through the use of integrative knowledge and autonomous problem-solving techniques. This approach should be further evaluated regarding its feasibility and applicability to reducing students' difficulties in dealing with abstraction processes.

Abstract thinking is a vital skill when learning computer science. Object technology and the concepts it is based upon make this skill even more crucial. However, previous research works show that students in top universities as well as experienced practitioners in industry encounter difficulties in thinking in abstract terms while practicing object oriented development. In this paper we suggest an iterative teaching methodology for supporting students in learning object oriented concepts. The suggested methodology is based on familiarizing students with modeling languages and tools at the early stages of their learning and iterating between model and code. We theoretically examine the contribution of modeling languages, in particular UML, to abstract thinking and consequently to the understanding of object oriented concepts and present some observations acquired during a trial execution of this methodology in a university course.