The study is designed to take place over two academic terms, which provides adequate time to collect meaningful data. The inclusion of an initial summer term without competitive programming establishes a baseline for comparison. The second summer term incorporates competitive programming using standardized questions, allowing assessment of this pedagogical approach. The fall term offering adds the dimension of culturally relevant questions, enabling analysis of their impact. Extending the study over multiple terms enables more robust data collection and analysis.
Promoting Active and Engaged Learning
The core content is delivered through weekly lectures focused on programming concepts. The competitive programming contests complement the lectures by providing opportunities to practice applying concepts. Weekly competitive programming contests foster active learning in several key ways. Students must apply conceptual knowledge to solve concrete programming problems. This process reinforces their understanding and helps identify knowledge gaps. The contest format adds an engaging gamification element through scoring, feedback, and peer comparison. Using standardized questions initially assesses whether baseline content needs are being met.
Introducing culturally relevant questions aims to promote better integration of concepts by relating them to students’ cultural knowledge and experiences. Having students co-create contest questions in the fall term further activates learning. They must think critically to develop culturally relevant problems that integrate with the content. This approach promotes deeper engagement with the material and encourages collaboration with classmates, allowing students to take ownership of their learning.
Addressing Teachers’ Needs
The study aims to provide teachers with insight into using competitive programming and culturally relevant pedagogy. The data collected will help determine the effectiveness of these approaches in an international educational setting. Instructors will gain an understanding of how competitive programming engages students versus standardized practice problems. They will also see whether student-created culturally relevant questions increase participation and motivation. The study addresses teachers’ needs for effective and inclusive instructional strategies. They will gain practical knowledge from the comparative data on different contest designs.
Promoting Collaborative Participation
Collaboration is encouraged through the group development of culturally relevant contest questions. Students can brainstorm and build on each other’s ideas, which fosters teamwork. Producing questions from diverse cultural perspectives requires working together. Students are also given the choice of problem-solving in teams. Students can motivate each other and strategize in groups for the competitions. Their scores are tracked on a collective leaderboard which reinforces the collaborative element. The shift from individual to team contest creation necessitates and enables productive collaboration.
The multi-term study design, interactive contest format, customized problems, and collaborative elements demonstrate an interesting pedagogical approach that promotes engaged and inclusive learning. The results should provide valuable insights for computer science educators.
Introductory programming courses are the foundation for students to gain fundamental coding abilities and analytical thinking skills required for various fields. However, programming poses unique challenges for beginners, like struggling with syntax, grasping complex concepts and developing logical reasoning. As computer science education continues to evolve, AI-powered tools like GitHub Copilot are emerging that can augment programming instruction for novices.
This blog post explores how GitHub Copilot could be effectively integrated into introductory programming courses to enhance student outcomes. Relevant education literature is drawn upon to support the recommendations.
An Introduction to GitHub Copilot
GitHub Copilot is a relatively new AI pair programmer that suggests complete lines of code, functions, and entire code blocks based on comments and the code context. It is powered by Codex, a large deep-learning model trained on billions of lines of public code from GitHub repositories. Copilot employs natural language processing, neural code synthesis, and semantic code search to generate helpful code recommendations tailored to the programmer’s intent. Copilot is a Visual Studio Code extension that integrates into the coding workflow. At its core, it transforms a natural language problem statement into executable code. It aims to boost programmer productivity by reducing boilerplate code and suggesting subsequent lines. Here’s a quick demo:
Advantages of Using Copilot for Introductory Programming Education
Copilot has several characteristics that make it well-suited for enhancing student outcomes in introductory programming courses.
Firstly, it assists with syntax. Beginners often struggle with syntax rules and tedious boilerplate code, such as import statements, which slow learning. Copilot speeds development by handling repetitive code, allowing students to concentrate on higher-level problem-solving.
Secondly, Copilot provides varied examples. Giving several possible output suggestions for a piece of code from a comment enables students to select the best code required, much like a built-in multiple-choice question. Exposure to diverse examples aids conceptual understanding according to constructivist learning theories. The numerous code suggestions from Copilot illustrate varied programming approaches, which require students to analyze each one to fit their needs, thereby promoting language learning.
Many CS curriculums fall short due to little attention given to debugging and problem-solving. As of this writing, the beta version of Copilot chat has features that fix broken code (/fix command) and explains a block of code (/explain command). This is incredibly useful for finding bugs and suggesting changes, keeping student motivation high by mitigating frustrations from debugging errors. The explanation feature helps students work comfortably with legacy code, i.e., code that is undocumented and generally difficult to read and understand. Acting much like a virtual teaching assistant increases the learning rate.
Plagiarism Concerns
Lau & Guo (2023) present findings from interviews with 20 instructors across 9 countries in early 2023, right after ChatGPT’s public release. This captures a unique snapshot before best practices have converged. In the short-term, many instructors are concerned about cheating and have reacted by banning AI tools, weighing exams more heavily, or exposing students to AI capabilities/limitations. Most agree that in the longer term, CS educators must learn to embrace these tools opinions diverge on whether to resist or embrace AI tools by integrating tools into courses to prepare students for using AI in future jobs. New assignments could have students collaborate with AI.
In my testing, I have found that manual intervention is still needed to produce functional code (as you can see from the video above). Nguyen & Nadi (2022) found that out of 33 programming problems from LeetCode, a popular competitive programming practice site, Copilot produced a correct answer 57% in Java, compared to only 27% of the time in JavaScript. This means proper tool use requires students to learn and apply the language’s nuances. We should note that this tool is made for the programmer to lessen their workload. Tools like Intelli-sense and extensions for specific languages already exist that do similar things. It is important to remind students that the goal is to learn the language; this tool will help them get there. It is still essential to learn things like modularization, design, and other programming abstractions as a CS student. Copilot is a great tool to give students a close enough answer and for them to figure out the rest of the code. This requires an understanding beyond what can be taught in the classroom.
Strategies for Productive Use of Copilot in Introductory Programming Courses
I believe Github Copilot should not be used in assessments in first-year courses due to its ease of use for basic programming problems. However, in later courses, once students become more familiar with language syntax and proper software design techniques, it can be used to solve more elaborate problems. I think Copilot will allow students in upper-division courses to express what they’ve learned rather than getting bogged down by syntax.
Still, to leverage Copilot effectively, instructors should provide guidance on integrating it into the learning objectives. Here are some best practices. First, let Copilot provide hints, not complete solutions. Encourage students to trace through Copilot code proposals line-by-line manually to build understanding rather than passively accepting suggestions. Second, students must refine and improve upon Copilot’s recommendations and learn to identify any incorrect suggestions. This practice enhances critical thinking and reinforces good practices. In teaching, we must balance Copilot usage with understanding documentation. Lastly, it’s vital to underscore that Copilot is an assistive aid, not a replacement for foundational coding skills.
Conclusion
GitHub Copilot has significant potential to be a transformative tool in introductory programming education. It offers a variety of functions that can significantly benefit beginners. Used strategically and under close supervision, Copilot can become an asset in a novice programmer’s toolkit. It can provide help and immediate feedback to increase comprehension and build confidence, much like a virtual tutor. However, it’s essential to recognize the delicate balance between assistance and over-reliance on AI tools. The challenges faced in integrating GitHub Copilot into programming education are reminiscent of those faced by mathematics educators when determining at what grade level a student should be allowed to use a calculator. Just as a calculator can aid in complex calculations but should not substitute for understanding basic mathematical principles, Copilot can be a valuable tool for code suggestions and error corrections but should not replace a solid understanding of programming concepts.
References
Nguyen, N., & Nadi, S. (2022). An empirical evaluation of GitHub copilot’s code suggestions. Proceedings of the 19th International Conference on Mining Software Repositories. https://doi.org/10.1145/3524842.3528470
