The Effect of Civic Knowledge and Attitudes on CS Student Work Preferences

We present an investigation in the connection between computing students' civic knowledge, attitude, or self-efficacy and their willingness to work on civic technologies. Early results indicate that these factors are related to a willingness to accept government work in technology but not non-profit work focused on civic technologies

Social Worked-Examples Technique to Enhance Student Engagement in Program Visualization

يعد تعلم البرمجة من بين أهم التحديات في تعليم علوم الكمبيوتر. حاليا، يتم استخدام تصوير البرامج ) PV ( كأداة للتغلب علىمعدلات الفشل والتسرب العالية في مادة اساسيات البرمجة. ومع ذلك، هناك مخاوف متزايدة بشأن فعالية أدوات تصوير البرامج الحالية استناداالى النتائج المختلطة المستمدة من الدراسات المختلفة. تعتبر مشاركة الطلاب أيضًا عاملاً حيويًا في بناء PV ناجحًا، كما تعد أيضًا جزءًا مهمًامن عملية التعلم بشكل عام. تم إدخال العديد من التقنيات لتعزيز المشاركة في أدوات تصوير البرامج؛ ومع ذلك، فإن مشاركة الطلاب في PVلا يزال يمثل تحديًا كبيراً. استخدمت هذه الورقة ثلاث نظريات مختلفة: البنيوية، والبناء الاجتماعي، والحمل المعرفي لاقتراح تقنية لتعزيزمشاركة الطلاب في استخدام أدوات تصوير البرامج. تعمل تقنية الأمثلة المكتملة الاجتماعية ) SWE ( على تحويل المثال المكتمل التقليدي إلىنشاط اجتماعي ، حيث يتم التركيز بشكل أكبر على دور التعاون في بناء معرفة الطلاب. حددت هذه الدراسة ثلاثة مبادئ يمكن أن تعززمشاركة الطلاب من خلال تقنية SWE : التعلم النشط والتعاون الاجتماعي والأنشطة ذاتس التحميل المنخفض.Learning programming is among the top challenges in computer science education. A part of that, program visualization (PV) is used as a tool to overcome the high failure and drop-out rates in an introductory programming course. Nevertheless, there are rising concerns about the effectiveness of the existing PV tools following the mixed results derived from various studies. Student engagement is also considered a vital factor in building a successful PV, while it is also an important part of the learning process in general. Several techniques have been introduced to enhance PV engagement; however, student engagement with PV is still challenging. This paper employed three theories—constructivism, social constructivism and cognitive load to propose a technique for enhancing student engagement with program visualisation. The social worked-examples (SWE) technique transforms the traditional worked-example into a social activity, whereby a greater focus is placed on the collaboration role in constructing students’ knowledge. This study identified three principles that could enhance student engagement through the SWE technique: active learning, social collaboration and low-load activity

Developing an Inclusive K-12 Outreach Model

This paper outlines the longitudinal development of a K-12 outreachmodel, to promote Computer Science in Ireland. Over a three-yearperiod, it has been piloted to just under 9700 K-12 students fromalmost every county in Ireland. The model consists of a two-hourcamp that introduces students to a range of Computer Sciencetopics: addressing computing perceptions, introduction to codingand exploration of computational thinking. The model incorporateson-site school delivery and is available at no cost to any interestedschool across Ireland. The pilot study so far collected over 3400surveys (pre- and post-outreach delivery).Schools from all over Ireland self-selected to participate, includ-ing male only, female only and mixed schools. The no-cost natureof the model meant schools deemed disadvantaged , to privatefee-paying schools participated. Initial findings are very positive,including the balance of male and female participants, where in the2017-18 academic year it was 56:44 and in 2019-20 (to date), it is35:65 respectively. Once the model is validated and tweaked (basedon survey data), the model will be published (open access) for otherinstitutions to implement the model locally. In addition, the authorsintend to link schools (that the team have worked with over thethree years) with local institutions, thus developing a sustainableecosystem for the program to continue. This paper describes themodel structure and outlines early finding

A Systematic Mapping Study of Code Quality in Education -- with Complete Bibliography

While functionality and correctness of code has traditionally been the main focus of computing educators, quality aspects of code are getting increasingly more attention. High-quality code contributes to the maintainability of software systems, and should therefore be a central aspect of computing education. We have conducted a systematic mapping study to give a broad overview of the research conducted in the field of code quality in an educational context. The study investigates paper characteristics, topics, research methods, and the targeted programming languages. We found 195 publications (1976-2022) on the topic in multiple databases, which we systematically coded to answer the research questions. This paper reports on the results and identifies developments, trends, and new opportunities for research in the field of code quality in computing education

Elements of AI : Busting AI Myths on a Global Scale

Considering the quick technological development in our society, as well as the continuous demand for workforce equipped with sufficient technology skills, self-paced online learning materials on hot topics like artificial intelligence (AI) play an important role in filling knowledge gaps and supporting life-long learning. In addition to technology professionals, citizens with all kinds of backgrounds have the right to gain a basic understanding in ground-breaking technologies, in order to have an equal and balanced say in what kind of technological solutions we should have in the future. Furthermore, the need for quality distance learning has been accelerated by the COVID-19 pandemic, and massive, open online courses (MOOCs) have the capability to cater for both academic students and the general public searching for reskilling and upskilling opportunities.Peer reviewe

An International Pilot Study of K-12 Teachers’Computer Science Self-Esteem

Computer Science (CS) is a new subject area for many K-12 teachersaround the world, requiring new disciplinary knowledge and skills.Teacher social-behavioral factors (e.g. self-esteem) have been foundto impact learning and teaching, and a key part of CS curriculumimplementation will need to ensure teachers feel confident to de-liver CS. However, studies about CS teacher self-esteem are lacking.This paper presents an analysis of publicly available data (n=219)from a pilot study using a Teacher CS Self-Esteem scale. Analy-sis revealed significant differences, including 1) females reportedsignificantly lower CS self-esteem than males, 2) primary teachersreported lower levels of CS self-esteem than secondary teachers, 3)those with no CS teaching experience reported significantly lowerCS self-esteem, 4) teachers with 0-3 years experience had a neg-ative CS self-esteem, but after four years, teachers had a positiveCS self-esteem, and 5) teachers who lived further from metropol-itan areas and in some countries reported lower CS self-esteem.These initial findings suggest a pressing need for future researchto look further into teacher CS self-esteem to inform teacher CSprofessional development

How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment

As Large Language Models (LLMs) gain in popularity, it is important to understand how novice programmers use them. We present a thematic analysis of 33 learners, aged 10-17, independently learning Python through 45 code-authoring tasks using Codex, an LLM-based code generator. We explore several questions related to how learners used these code generators and provide an analysis of the properties of the written prompts and the generated code. Specifically, we explore (A) the context in which learners use Codex, (B) what learners are asking from Codex, (C) properties of their prompts in terms of relation to task description, language, and clarity, and prompt crafting patterns, (D) the correctness, complexity, and accuracy of the AI-generated code, and (E) how learners utilize AI-generated code in terms of placement, verification, and manual modifications. Furthermore, our analysis reveals four distinct coding approaches when writing code with an AI code generator: AI Single Prompt, where learners prompted Codex once to generate the entire solution to a task; AI Step-by-Step, where learners divided the problem into parts and used Codex to generate each part; Hybrid, where learners wrote some of the code themselves and used Codex to generate others; and Manual coding, where learners wrote the code themselves. The AI Single Prompt approach resulted in the highest correctness scores on code-authoring tasks, but the lowest correctness scores on subsequent code-modification tasks during training. Our results provide initial insight into how novice learners use AI code generators and the challenges and opportunities associated with integrating them into self-paced learning environments. We conclude with various signs of over-reliance and self-regulation, as well as opportunities for curriculum and tool development.Comment: 12 pages, Peer-Reviewed, Accepted for publication in the proceedings of the 2023 ACM Koli Calling International Conference on Computing Education Researc