Characterising code quality is a challenge that was addressed by a previous ITiCSE Working Group (Börstler et al., 2017). As emerged from that study, educators, developers, and students have different perceptions of the aspects involved. The perception of code quality by CS1 students develops from the feedback they receive when submitting practical work. As a consequence of increasingly large classes and the widespread use of autograders, student code is predominantly assessed based on functional correctness, emphasising a machine-oriented perspective with scarce or no feedback given about human-oriented aspects of code quality. Such limited perception of code quality may negatively impact how students understand, create, and interact with code artefacts. Although Börstler et al. concluded that "code quality should be discussed more thoroughly in educational programs", the lack of materials and time constraints have slowed down progress in that regard. The goal of this Working Group is to support CS1 instructors who want to introduce a broader perspective on code quality in their classroom, by providing a curated list of examples and activities suitable for novices. In order to achieve this goal, we have extracted from the CS education literature a range of examples and activities, which have then been analysed and organised in terms of code quality dimensions. We have also mapped the topics covered in those materials to existing taxonomies relevant to code quality in CS1. Based on this work, we provide: (1) a catalogue of examples that illustrates the range of quality defects that could be addressed at CS1 level; and (2) a sample set of activities devised to introduce code quality to CS1 students. These materials have the potential to help educators address the subject in more depth.

The aim of this work is to describe a simple and cost effective way to integrate generative AI into GitHub to support course specific scenarios. We are motivated by helping teachers realise their creative AI use cases in spite of technical barriers and also to ensure that students have a blessed and fair way to access AI services without needing to sign-up, prompt or pay. First we will describe a scenario that we have implemented for our own CS1 course, then we will describe the technical requirements for implementation. We finish off with our early thoughts on where these types of scenarios might be heading in terms of supporting computing education.

Characterising code quality is a challenge that was addressed by Börstler et al.’s working group in 2017. As emerged from their study, educators, developers and students have different perceptions of the manifold aspects involved, and a major conclusion of that WG was that “code quality should be discussed more thoroughly in educational programs” [2, p. 70]. However, the lack of materials and the time constraints have slowed down progress in that regard. The goal of this working group is to propose manageable ways to address code quality in the CS1 classroom, with a particular focus on activities that help students become aware of and improve the quality of their code. To achieve this goal, we will (a) extract from the literature a comprehensive set of quality issues which will then be classified according to the appropriate strategies to fix them; and (b) circulate a survey to explore the instructors’ views on code quality issues and the way they deal with (or ignore) them. Based on this work we aim to produce: (1) a taxonomy of code quality issues with associated examples, as well as (2) a sample set of teaching materials to introduce those issues to CS1 students.

This study is the first to investigate how an online course consisting solely of multiple-choice questions and detailed formative feedback comparesto an online course format that was previously shown to be highlyeffective. Specifically, a pure question-based learning (pQBL) version ofa course was compared to a question-based learning course (QBL)which consisted of the same questions and feedback as the pQBLcourse, but also included ordinary texts about the subject. To explorehow pQBL and QBL compared in terms of learning outcomes andcompletion time, 492 employees at the Swedish Employment Agencywere randomized to either a pQBL or a QBL version of a course aboutIT security. The results indicate that the pQBL course resulted in equallygood or better learning outcomes compared to the QBL course. Thisresult was robust to changes in how course quality was defined. Inaddition, participants completed the pQBL course slightly faster.Because a pQBL course requires less resources to produce, there arebenefits to relying on the pQBL method when teachers or studentshave limited time. Further benefits that come with the flexibility of thepQBL method are discussed

We have evaluated a new pedagogical approach, pure question-based learning, or rather, a modern, digitized version of a really old approach: the Socratic method of learning. The pedagogical approach was evaluated and improved using a design-based research methodology. An online course was developed with pure question-based learning to explain its predecessor: question-based learning. The course was successively taken by students, researchers, and practitioners, and discussed in four group seminars. Feedback from each iteration was integrated into the next version and the course is still in use, see link below. Results from the design-based research process were positive ((Formula presented.) participants, over four iterations) with the main negative results coming from the unfamiliarity of the format and feelings of exam-like stress during the first encounter. While pure question-based learning is new, it builds upon well-tested pedagogical methods. The method has several potential advantages: learning can be broken down into smaller modules, there is less passive learning for the students, less learning material needs to be created and AI could be used for this creation.

This paper presents SOBO, a bot we designed to automatically provide feedback on code quality to undergraduate students. SOBO has been deployed in a course at the KTH Royal Institute of Technology in Sweden with more than 130 students.

Low code quality incurs a significant cost upon the software industry. Despite this, little serious effort has been devoted to the topic at the most basic levels of computing science education. Where studies have been conducted, the results are disappointing in terms of code quality. In this work, four iterations of a medium to large CS1 and CS2 course (n=200 students) were analyzed through the lens of code violations to better understand (1) what code violations occur most frequently, (2) how does their occurrence vary throughout the course, and (3) to what extent do teaching assistants have an effect upon code quality. Results showed a statistically significant improvement in code quality over 19 assignments in all four course iterations. In particular, when a single violation was the focus of a learning outcome, the effects were both a dramatic fall and sustained low occurrence. However, this improvement was mostly focused on the three most frequently occurring violations (> 70%), which masked an increase in lesser occurring violations throughout the course. Finally, the effects from different teaching assistants were found to be random at best, contradicting expectations that their influence would be easily detected. Given that explicit learning outcomes targeted at code quality had an effect and teaching assistant influence had no effect, a path forward to improving code quality might combine these forces without creating too many additional demands upon the already stretched teaching resources with CS1 and CS2 courses.

A good source of data for learning analytics is the online questions and answers posed to the learners during the course. However, there are several reports on the challenges of question construction as these questions are time consuming to construct and verify, especially if you go beyond multiple-choice questions. One remedy to this is learnersourcing that includes the learners in the generation and verification of these questions for both pedagogical reasons and resource conservation. Learnersourcing is one solution to producing and improving learning activities at scale by leveraging student effort. It also creates a new layer of learner data to analyse: first, in terms of the behaviour traces from producing learning activities; and second in terms of the impressions that other learners and teachers have from taking or reviewing those activities. This chapter will introduce basic concepts and examples of learnersourcing along with examples and approaches to applying a learning analytics lens upon the data traces that are produced.

Contribution: An intensive three-month educational program can be used for rapid integration of foreign-born people into the IT industry. A novel method for integrating industrial needs with the practical parts of a bachelor's Computer Science program. Background: The program was motivated by (1) the societal need to increase the meaningful integration of immigrants into the workforce, and (2) the demand for IT specialists in the IT labor market. Intended outcomes: An effective intensive software developer program with a high level of industrial integration and a working matching model for employment. Application design: The program consists of three different phases; recruitment of participants, training and job matching. The training is divided into six modules using five different teaching methods. An evaluation model, based on passive and active data, is implemented with fast learning loops for teachers and participants. Findings: The program has been run seven times with 263 unemployed participants of different nationalities. On average 82.6 percent of the participants found employment in the IT industry within 5 months of the course ending. Female participants are in the majority and are more successful in securing employment. The findings suggest that it was possible to rapidly prototype and deliver an advanced reskilling program within a university setting and use it as a positive method to support newcomers find meaningful work that has a direct benefit for the local IT industry, as well as for the wider society.

Developing good version control skills is important for students to master. This work focuses on helping students integrate good commit behaviour using a scaffolding process that happens with their regular assignments. A test group of students (n=30) was required to make a minimum of three commits in the first week. In the second week, students were trained to write better commit messages and worked together on a commit plan. In the final week, students worked alone on their commit plan. Commit behaviour was analysed for assignments occurring before, during and after the process. Results showed that students improved their commit behaviour in terms of number of commits, starting earlier with their assignments and writing more meaningful commit messages when compared to the rest of their cohort and the previous year's cohort (m=350). Qualitative results showed that students were mostly positive towards developing better commit behaviour and felt that the extra effort to think in commits delivered proportionally more benefits for their work.