Evidence supports offering research experiences for undergraduate computing science students as a means of broadening participation in computing[7, 10, 11]. However, student perceptions about computing science research, how students become interested in these research experiences, and the details of effective design and delivery of programs capable of attracting and retaining this interest are less explored. In this study, we investigate the design and delivery of undergraduate research programs. We expand on and explore several factors, including but not limited to cultural relevance, the presence of a cross-disciplinary high-level view, task assignment, entry point, and support elements of a program.

The ITiCSE '23 final keynote raised teaching soft skills, or professional dispositions, to help students face challenges in modern programming. This project addresses helping computing students develop professional dispositions through collaborative learning (CL) since some in the industry observe entry-level engineers struggling due to their fragile professional dispositions. We are motivated to understand professional expectations from entry-level engineers and present the academia-industry gap to support practitioners and researchers in advancing CL in Computing Education, encouraging positive curricula and policy changes that promote DEIA. We will present CL practices alongside their supported professional dispositions to assist practitioners in adoption. We will present the academia-industry gap in CL for future research opportunities, helping researchers advance CL practices to integrate professional dispositions the industry expects from entry-level engineers.

Many novice programmers struggle to write code from scratch and get frustrated when their code does not work. Parsons problems can reduce the difficulty of a coding problem by providing mixed-up blocks that the learner assembles in the correct order. Parsons problems can also include distractor blocks that are not needed in a correct solution, but which may help students learn to recognize and fix errors. Evidence indicates that students find Parsons problems engaging, easier than writing code from scratch, useful for learning patterns, and typically faster to solve than writing code from scratch with equivalent learning gains. This working group leverages the work of the 2022 ITiCSE working group which published an extensive literature review of Parsons problems and designed and piloted several studies based on the gaps identified by the literature review. The 2023 working group is revising, conducting, and creating new studies. We will analyze the data from these multi-institutional and multi-national studies and publish the results as well as recommendations for future working groups.

Blended learning is the combination of in-person teaching and online activities. For example, combining face-to-face lectures/tutorials with online videos and assessments. With the adoption of non-traditional, not fully on-campus courses, such as degree and graduate apprenticeships in the UK, CPD, re/upskilling, and alternative teaching methods being required due to the pandemic, many different teaching strategies have been explored. Blended learning is one of these strategies and has proven popular for both universities and students.

It is not unusual for a software development organization to expend 40% of total project effort on testing, which call be a very laborious and time-consuming process. Therefore, there is a big necessity for test automation. This paper describes an approach to automatically generate test-data for 00 software exploiting a Genetic Algorithm (GA) to achieve high levels of data-flow (d-u) coverage. A proof-of-concept tool is presented. The experimental results from testing six Java classes helped us identify three categories of problematic test targets, and suggest that in the future full d-u coverage with a reasonable computational cost may be possible if we overcome these obstacles.