arXiv subjects

Neil C. C. Brown

Publications and source records attributed to Neil C. C. Brown.

3 recordsLinked to original sources

arXiv2025

How Consistent Are Humans When Grading Programming Assignments?

Providing consistent summative assessment to students is important, as the grades they are awarded affect their progression through university and future career prospects. While small cohorts are typically assessed by a single assessor, such as the module/class leader, larger cohorts are often assessed by multiple assessors, typically teaching assistants, which increases the risk of inconsistent grading. To investigate the consistency of human grading of programming assignments, we asked 28 participants to each grade 40 CS1 introductory Java assignments, providing grades and feedback for correctness, code elegance, readability and documentation; the 40 assignments were split into two batches of 20. The 28 participants were divided into seven groups of four (where each group graded the same 40 assignments) to allow us to investigate the consistency of a group of assessors. In the second batch of 20, we duplicated one assignment from the first to analyse the internal consistency of individual assessors. Our results show that human graders in our study can not agree on the grade to give a piece of student work and are often individually inconsistent, suggesting that the idea of a ``gold standard'' of human grading might be flawed. This highlights that a shared rubric alone is not enough to ensure consistency, and other aspects such as assessor training and alternative grading practices should be explored to improve the consistency of human grading further when grading programming assignments.

cs.CY↗

arXiv2024

Howzat? Appealing to Expert Judgement for Evaluating Human and AI Next-Step Hints for Novice Programmers

Motivation: Students learning to program often reach states where they are stuck and can make no forward progress. An automatically generated next-step hint can help them make forward progress and support their learning. It is important to know what makes a good hint or a bad hint, and how to generate good hints automatically in novice programming tools, for example using Large Language Models (LLMs). Method and participants: We recruited 44 Java educators from around the world to participate in an online study. We used a set of real student code states as hint-generation scenarios. Participants used a technique known as comparative judgement to rank a set of candidate next-step Java hints, which were generated by Large Language Models (LLMs) and by five human experienced educators. Participants ranked the hints without being told how they were generated. Findings: We found that LLMs had considerable variation in generating high quality next-step hints for programming novices, with GPT-4 outperforming other models tested. When used with a well-designed prompt, GPT-4 outperformed human experts in generating pedagogically valuable hints. A multi-stage prompt was the most effective LLM prompt. We found that the two most important factors of a good hint were length (80--160 words being best), and reading level (US grade 9 or below being best). Offering alternative approaches to solving the problem was considered bad, and we found no effect of sentiment. Conclusions: Automatic generation of these hints is immediately viable, given that LLMs outperformed humans -- even when the students' task is unknown. The fact that only the best prompts achieve this outcome suggests that students on their own are unlikely to be able to produce the same benefit. The prompting task, therefore, should be embedded in an expert-designed tool.

cs.CY↗

arXiv2023

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques' feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools' performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.

cs.SE↗