Code Interviews: Design and Evaluation of a More Authentic Assessment for Introductory Programming Assignments

• URL: http://arxiv.org/abs/2410.01010v1
• Date: Tue, 1 Oct 2024 19:01:41 GMT
• Title: Code Interviews: Design and Evaluation of a More Authentic Assessment for Introductory Programming Assignments
• Authors: Suhas Kannam, Yuri Yang, Aarya Dharm, Kevin Lin,
• Abstract summary: We describe code interviews as a more authentic assessment method for take-home programming assignments. Code interviews pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights. We conclude by discussing the different decisions about the design of code interviews with implications for student experience, academic integrity, and teaching workload.
• Score: 15.295438618760164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative artificial intelligence poses new challenges around assessment and academic integrity, increasingly driving introductory programming educators to employ invigilated exams often conducted in-person on pencil-and-paper. But the structure of exams often fails to accommodate authentic programming experiences that involve planning, implementing, and debugging programs with computer interaction. In this experience report, we describe code interviews: a more authentic assessment method for take-home programming assignments. Through action research, we experimented with varying the number and type of questions as well as whether interviews were conducted individually or with groups of students. To scale the program, we converted most of our weekly teaching assistant (TA) sections to conduct code interviews on 5 major weekly take-home programming assignments. By triangulating data from 5 sources, we identified 4 themes. Code interviews (1) pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights; (2) enabled peer learning, reducing stress in some ways but increasing stress in other ways; (3) scaled with TA-led sections, replacing familiar practice with an unfamiliar assessment; (4) focused on student contributions, limiting opportunities for TAs to give guidance and feedback. We conclude by discussing the different decisions about the design of code interviews with implications for student experience, academic integrity, and teaching workload.

Related papers

• Integrating Natural Language Prompting Tasks in Introductory Programming Courses [3.907735250728617]
  This report explores the inclusion of two prompt-focused activities in an introductory programming course. The first requires students to solve computational problems by writing natural language prompts, emphasizing problem-solving over syntax. The second involves students crafting prompts to generate code equivalent to provided fragments, to foster an understanding of the relationship between prompts and code.
  arXiv  Detail & Related papers  (2024-10-04T01:03:25Z)
• Evaluating Contextually Personalized Programming Exercises Created with Generative AI [4.046163999707179]
  This article reports on a user study conducted in an elective programming course that included contextually personalized programming exercises created with GPT-4. The results demonstrate that the quality of exercises generated with GPT-4 was generally high. This suggests that AI-generated programming problems can be a worthwhile addition to introductory programming courses.
  arXiv  Detail & Related papers  (2024-06-11T12:59:52Z)
• Editing Personality for Large Language Models [73.59001811199823]
  This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs) We construct PersonalityEdit, a new benchmark dataset to address this task.
  arXiv  Detail & Related papers  (2023-10-03T16:02:36Z)
• Exploring the Potential of Large Language Models to Generate Formative Programming Feedback [0.5371337604556311]
  We explore the potential of large language models (LLMs) for computing educators and learners. To achieve these goals, we used students' programming sequences from a dataset gathered within a CS1 course as input for ChatGPT. Results show that ChatGPT performs reasonably well for some of the introductory programming tasks and student errors. However, educators should provide guidance on how to use the provided feedback, as it can contain misleading information for novices.
  arXiv  Detail & Related papers  (2023-08-31T15:22:11Z)
• LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning [64.55001982176226]
  LIBERO is a novel benchmark of lifelong learning for robot manipulation. We focus on how to efficiently transfer declarative knowledge, procedural knowledge, or the mixture of both. We develop an extendible procedural generation pipeline that can in principle generate infinitely many tasks.
  arXiv  Detail & Related papers  (2023-06-05T23:32:26Z)
• Giving Feedback on Interactive Student Programs with Meta-Exploration [74.5597783609281]
  Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. Standard approaches require instructors to manually grade student-implemented interactive programs. Online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs.
  arXiv  Detail & Related papers  (2022-11-16T10:00:23Z)
• What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys [63.51903260461746]
  We propose a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge. We then propose a two-staged knowledge-driven model for the task, which generates informative and coherent follow-up questions.
  arXiv  Detail & Related papers  (2022-05-23T00:57:33Z)
• CodeReviewer: Pre-Training for Automating Code Review Activities [36.40557768557425]
  This research focuses on utilizing pre-training techniques for the tasks in the code review scenario. We collect a large-scale dataset of real world code changes and code reviews from open-source projects in nine of the most popular programming languages. To better understand code diffs and reviews, we propose CodeReviewer, a pre-trained model that utilizes four pre-training tasks tailored specifically for the code review senario.
  arXiv  Detail & Related papers  (2022-03-17T05:40:13Z)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)
• Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering [80.60605604261416]
  We propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks. We vary the set of language models, training regimes, knowledge sources, and data generation strategies, and measure their impact across tasks. We show that, while an individual knowledge graph is better suited for specific tasks, a global knowledge graph brings consistent gains across different tasks.
  arXiv  Detail & Related papers  (2020-11-07T22:52:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.