Analysis of Students' Peer Reviews to Crowdsourced Programming Assignments

We have used a tool called CrowdSorcerer that allows students to create programming assignments. The students are given a topic by a teacher, after which the students design a programming assignment: the assignment description, the code template, a model solution and a set of input-output -tests. The created assignments are peer reviewed by other students on the course. We study students' peer reviews to these student-generated assignments, focusing on examining the differences between novice and experienced programmers. We then analyze whether the exercises created by experienced programmers are rated better quality-wise than those created by novices. Additionally, we investigate the differences between novices and experienced programmers as peer reviewers: can novices review assignments as well as experienced programmers?Peer reviewe

Comparing Code Explanations Created by Students and Large Language Models

Reasoning about code and explaining its purpose are fundamental skills for computer scientists. There has been extensive research in the field of computing education on the relationship between a student's ability to explain code and other skills such as writing and tracing code. In particular, the ability to describe at a high-level of abstraction how code will behave over all possible inputs correlates strongly with code writing skills. However, developing the expertise to comprehend and explain code accurately and succinctly is a challenge for many students. Existing pedagogical approaches that scaffold the ability to explain code, such as producing exemplar code explanations on demand, do not currently scale well to large classrooms. The recent emergence of powerful large language models (LLMs) may offer a solution. In this paper, we explore the potential of LLMs in generating explanations that can serve as examples to scaffold students' ability to understand and explain code. To evaluate LLM-created explanations, we compare them with explanations created by students in a large course ($n \approx 1000$) with respect to accuracy, understandability and length. We find that LLM-created explanations, which can be produced automatically on demand, are rated as being significantly easier to understand and more accurate summaries of code than student-created explanations. We discuss the significance of this finding, and suggest how such models can be incorporated into introductory programming education.Comment: 8 pages, 3 figures. To be published in Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V.

Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book

Advances in natural language processing have resulted in large language models (LLMs) that are capable of generating understandable and sensible written text. Recent versions of these models, such as OpenAI Codex and GPT-3, can generate code and code explanations. However, it is unclear whether and how students might engage with such explanations. In this paper, we report on our experiences generating multiple code explanation types using LLMs and integrating them into an interactive e-book on web software development. We modified the e-book to make LLM-generated code explanations accessible through buttons next to code snippets in the materials, which allowed us to track the use of the explanations as well as to ask for feedback on their utility. Three different types of explanations were available for students for each explainable code snippet; a line-by-line explanation, a list of important concepts, and a high-level summary of the code. Our preliminary results show that all varieties of explanations were viewed by students and that the majority of students perceived the code explanations as helpful to them. However, student engagement appeared to vary by code snippet complexity, explanation type, and code snippet length. Drawing on our experiences, we discuss future directions for integrating explanations generated by LLMs into existing computer science classrooms

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

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

What Do We Think We Think We Are Doing?: Metacognition and Self-Regulation in Programming

Metacognition and self-regulation are popular areas of interest in programming education, and they have been extensively researched outside of computing. While computing education researchers should draw upon this prior work, programming education is unique enough that we should explore the extent to which prior work applies to our context. The goal of this systematic review is to support research on metacognition and self-regulation in programming education by synthesizing relevant theories, measurements, and prior work on these topics. By reviewing papers that mention metacognition or self-regulation in the context of programming, we aim to provide a benchmark of our current progress towards understanding these topics and recommendations for future research. In our results, we discuss eight common theories that are widely used outside of computing education research, half of which are commonly used in computing education research. We also highlight 11 theories on related constructs (e.g., self-efficacy) that have been used successfully to understand programming education. Towards measuring metacognition and self-regulation in learners, we discuss seven instruments and protocols that have been used and highlight their strengths and weaknesses. To benchmark the current state of research, we examined papers that primarily studied metacognition and self-regulation in programming education and synthesize the reported interventions used and results from that research. While the primary intended contribution of this paper is to support research, readers will also learn about developing and supporting metacognition and self-regulation of students in programming courses

Programming environment for teaching introductory programming for secondary school

Programming subject was become one of the syllabus in Malaysia national school start from 2017. To introduce youth to programming, suitable programming environment to teach introductory programming should be determined. Many initiatives are proceeding to bring powerful ideas of computing into classroom around the world. A popular strategy being employed in this effort is the use of block-based programming environment. This environment found to be effective among younger learners. Their suitability in high school context is an open question. The existing tools was analysed to identify the suitable environment to teach introductory programming in high school. An experiment involving 30 participants was conducted to get their perception on three different programming environments; text-based, block-based and hybrid. Findings from the study reveal that participants in hybrid group scoring highest in content assessment and reporting higher level in enjoyment and engagement to traditional programming structure. After the completion of literature and exploratory research, a bidirectional hybrid programming environment was developed. This environment combines features of block-based and text-based interface to provides the platform and engagement of block-based tools with the power and authenticity of textbased introductory environment. A traditional hybrid programming creates a gap between block-based and text-based programming. It was be used to run in evaluation workshop involving of 13 students aged 16 - 17 years old. The evaluation of enhanced programming environment was determined by using triangulation of data; students’ perception and result from their assessment using an enhanced hybrid programming environment. Participants have positive perception on confidence and understanding of programming concept. Besides, they agreed that bidirectional hybrid programming environment offered a more effective way of introductory programming subject compared to existing environment they are using in classroom. Suggestions for future work are outlined and intended that this research will assist the development and use a bidirectional hybrid environment in teaching introductory programming

CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs

Timely, personalized feedback is essential for students learning programming. LLM-powered tools like ChatGPT offer instant support, but reveal direct answers with code, which may hinder deep conceptual engagement. We developed CodeAid, an LLM-powered programming assistant delivering helpful, technically correct responses, without revealing code solutions. CodeAid answers conceptual questions, generates pseudo-code with line-by-line explanations, and annotates student's incorrect code with fix suggestions. We deployed CodeAid in a programming class of 700 students for a 12-week semester. A thematic analysis of 8,000 usages of CodeAid was performed, further enriched by weekly surveys, and 22 student interviews. We then interviewed eight programming educators to gain further insights. Our findings reveal four design considerations for future educational AI assistants: D1) exploiting AI's unique benefits; D2) simplifying query formulation while promoting cognitive engagement; D3) avoiding direct responses while encouraging motivated learning; and D4) maintaining transparency and control for students to asses and steer AI responses.Comment: CHI 2024 Paper - The paper includes 17 pages, 8 figures, 2 tables, along with a 2-page appendi

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Perceptual Learning Modules (PLM) in CS1: a Negative Result and a Methodological Warning

Perceptual Learning Modules (PLMs) is a variation of Perceptual Learning based on multiple-choice questionnaires. There exists successful research of the use of PLMs in math and flight training. The possibility of designing and adopting PLMs in Introductory Programming Courses (CS1) is still an open area of study. The goal of this study is to test whether students that received a PLM training on recognising segments of programs will perform better at writing programs. Two PLM interventions were administered to students. The first intervention was a nonrandom controlled experiment, in which students opted to answer the PLM questionnaire (N=40), while the control group consisted of students that did not answer it (N=629). The second intervention was a randomized controlled experiment with a placebo, in which students were randomly assigned to perform either the PLM questionnaire (N=51) or another a placebo activity (N=51). The different forms of analysis of the first experiment results yielded Cohen’s d ranging from 0.23 to 0.34 in favor of the PLM intervention. For the second experiment, the effect size was d = -0.11 against the PLM intervention, but the two results were significant. We believe that the cautious conclusion is that there is a null effect in using a PLM activity as part of a CS1 course. The paper is also of interest because of the methodological decisions and techniques used

The Example Guru: Suggesting Examples to Novice Programmers in an Artifact-Based Context

Programmers in artifact-based contexts could likely benefit from skills that they do not realize exist. We define artifact-based contexts as contexts where programmers have a goal project, like an application or game, which they must figure out how to accomplish and can change along the way. Artifact-based contexts do not have quantifiable goal states, like the solution to a puzzle or the resolution of a bug in task-based contexts. Currently, programmers in artifact-based contexts have to seek out information, but may be unaware of useful information or choose not to seek out new skills. This is especially problematic for young novice programmers in blocks programming environments. Blocks programming environments often lack even minimal in-context support, such as auto-complete or in-context documentation. Novices programming independently in these blocks-based programming environments often plateau in the programming skills and API methods they use. This work aims to encourage novices in artifact-based programming contexts to explore new API methods and skills. One way to support novices may be with examples, as examples are effective for learning and highly available. In order to better understand how to use examples for supporting novice programmers, I first ran two studies exploring novices\u27 use and focus on example code. I used those results to design a system called the Example Guru. The Example Guru suggests example snippets to novice programmers that contain previously unused API methods or code concepts. Finally, I present an approach for semi-automatically generating content for this type of suggestion system. This approach reduces the amount of expert effort required to create suggestions. This work contains three contributions: 1) a better understanding of difficulties novices have using example code, 2) a system that encourages exploration and use of new programming skills, and 3) an approach for generating content for a suggestion system with less expert effort