399 research outputs found

    Visual and Textual Programming Languages: A Systematic Review of the Literature

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    It is well documented, and has been the topic of much research, that Computer Science courses tend to have higher than average drop out rates at third level. This is a problem that needs to be addressed with urgency but also caution. The required number of Computer Science graduates is growing every year but the number of graduates is not meeting this demand and one way that this problem can be alleviated is to encourage students at an early age towards studying Computer Science courses. This paper presents a systematic literature review on the role of visual and textual programming languages when learning to program, particularly as a first programming language. The approach is systematic, in that a structured search of electronic resources has been conducted, and the results are presented and quantitatively analysed. This study will give insight into whether or not the current approaches to teaching young learners programming are viable, and examines what we can do to increase the interest and retention of these students as they progress through their education.Comment: 18 pages (including 2 bibliography pages), 3 figure

    Female performance and participation in computer science: a national picture

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    The change in the English computing curriculum and the shift towards computer science (CS) has been closely observed by other countries. Female participation remains a concern in most jurisdictions, but female attainment in CS is relatively unstudied. Using the English national pupil database, we analysed all exam results (n=5,370,064) for students taking secondary school exams in 2016, focusing on those students taking GCSE CS (n=60,736) contrasting this against ICT (n=67,359). Combining gender with ethnicity and the IDACI poverty indicator, we find that females from the poorest areas were more likely to take CS than those from the richest areas and CS was more popular amongst ethnic minority females than white females. ICT was far more equitable for females and poorer students than CS. CS females typically got better grades than their male peers. However, when controlling for average attainment in other subjects, males got 0.31 of a grade higher. Female relative underperformance in CS was most acute amongst large female cohorts and with girls studying in mixed-gender schools. Girls did significantly better than boys in English when controlling for CS scores, supporting theories around female relative strengths lying outside STEM subjects. The move to introduce CS into the English curriculum and the removal of the ICT qualifications look to be having a negative impact on female participation and attainment in computing. Using the theory of self-efficacy we argue that the shift towards CS might decrease the number of girls choosing further computing qualifications or pursuing computing as a career. Computing curriculum designers and teachers need to carefully consider the inclusive nature of their computing courses

    The Explanatory Visualization Framework: an active learning framework for teaching creative computing using explanatory visualizations

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    Visualizations are nowadays appearing in popular media and are used everyday in the workplace. This democratisation of visualization challenges educators to develop effective learning strategies, in order to train the next generation of creative visualization specialists. There is high demand for skilled individuals who can analyse a problem, consider alternative designs, develop new visualizations, and be creative and innovative. Our three-stage framework, leads the learner through a series of tasks, each designed to develop different skills necessary for coming up with creative, innovative, effective, and purposeful visualizations. For that, we get the learners to create an explanatory visualization of an algorithm of their choice. By making an algorithm choice, and by following an active-learning and project-based strategy, the learners take ownership of a particular visualization challenge. They become enthusiastic to develop good results and learn different creative skills on their learning journey

    An International Investigation into Student Concerns regarding Transition into Higher Education Computing

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    The experience of transitioning into and starting higher education is very much an individual one, with some applicants viewing the prospect of higher education as an unknown entity. For those who are first in their family or community to consider higher education, it can seem to be an "alien environment". This is just one of the issues that lead to applicants experiencing levels of concern when considering a transition into higher education. This international working group aims to answer the following research question: "What are the concerns that computing students have with regards to their transition into higher education?" A survey was administered and the results evaluated

    Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests

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    Background and Context: Over the past year, large language models (LLMs) have taken the world by storm. In computing education, like in other walks of life, many opportunities and threats have emerged as a consequence. Objectives: In this article, we explore such opportunities and threats in a specific area: responding to student programmers' help requests. More specifically, we assess how good LLMs are at identifying issues in problematic code that students request help on. Method: We collected a sample of help requests and code from an online programming course. We then prompted two different LLMs (OpenAI Codex and GPT-3.5) to identify and explain the issues in the students' code and assessed the LLM-generated answers both quantitatively and qualitatively. Findings: GPT-3.5 outperforms Codex in most respects. Both LLMs frequently find at least one actual issue in each student program (GPT-3.5 in 90% of the cases). Neither LLM excels at finding all the issues (GPT-3.5 finding them 57% of the time). False positives are common (40% chance for GPT-3.5). The advice that the LLMs provide on the issues is often sensible. The LLMs perform better on issues involving program logic rather than on output formatting. Model solutions are frequently provided even when the LLM is prompted not to. LLM responses to prompts in a non-English language are only slightly worse than responses to English prompts. Implications: Our results continue to highlight the utility of LLMs in programming education. At the same time, the results highlight the unreliability of LLMs: LLMs make some of the same mistakes that students do, perhaps especially when formatting output as required by automated assessment systems. Our study informs teachers interested in using LLMs as well as future efforts to customize LLMs for the needs of programming education.Comment: 13 pages, 1 figure. To be published in Proceedings of the 2023 ACM Conference on International Computing Education Research V.1 (ICER '23 V1

    A Systematic Review of Studies on Educational Robotics

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    There has been a steady increase in the number of studies investigating educational robotics and its impact on academic and social skills of young learners. Educational robots are used both in and out of school environments to enhance K–12 students’ interest, engagement, and academic achievement in various fields of STEM education. Some prior studies show evidence for the general benefits of educational robotics as being effective in providing impactful learning experiences. However, there appears to be a need to determine the specific benefits which have been achieved through robotics implementation in K–12 formal and informal learning settings. In this study, we present a systematic review of the literature on K–12 educational robotics. Based on our review process with specific inclusion and exclusion criteria, and a repeatable method of systematic review, we found 147 studies published from the years 2000 to 2018. We classified these studies under five themes: (1) general effectiveness of educational robotics; (2) students’ learning and transfer skills; (3) creativity and motivation; (4) diversity and broadening participation; and (5) teachers’ professional development. The study outlines the research questions, presents the synthesis of literature, and discusses findings across themes. It also provides guidelines for educators, practitioners, and researchers in areas of educational robotics and STEM education, and presents dimensions of future research

    Emergence of computing education as a research discipline

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    This thesis investigates the changing nature and status of computing education research (CER) over a number of years, specifically addressing the question of whether computing education can legitimately be considered a research discipline. The principal approach to addressing this question is an examination of the published literature in computing education conferences and journals. A classification system was devised for this literature, one goal of the system being to clearly identify some publications as research – once a suitable definition of research was established. When the system is applied to a corpus of publications, it becomes possible to determine the proportion of those publications that are classified as research, and thence to detect trends over time and similarities and differences between publication venues. The classification system has been applied to all of the papers over several years in a number of major computing education conferences and journals. Much of the classification was done by the author alone, and the remainder by a team that he formed in order to assess the inter-rater reliability of the classification system. This classification work led to two subsequent projects, led by Associate Professor Judy Sheard and Professor Lauri Malmi, that devised and applied further classification systems to examine the research approaches and methods used in the work reported in computing education publications. Classification of nearly 2000 publications over ranges of 3-10 years uncovers both strong similarities and distinct differences between publication venues. It also establishes clear evidence of a substantial growth in the proportion of research papers over the years in question. These findings are considered in the light of published perspectives on what constitutes a discipline of research, and lead to a confident assertion that computing education can now rightly be considered a discipline of research

    How teachers would help students to improve their code

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