Abstract

In programming education, providing manual feedback is essential but labour-intensive, posing challenges in consistency and timeliness. We introduce ECHO, a machine learning method to automate the reuse of feedback in educational code reviews by analysing patterns in abstract syntax trees. This study investigates two primary questions: whether ECHO can predict feedback annotations to specific lines of student code based on previously added annotations by human reviewers (RQ1), and whether its training and prediction speeds are suitable for using ECHO for real-time feedback during live code reviews by human reviewers (RQ2). Our results, based on annotations from both automated linting tools and human reviewers, show that ECHO can accurately and quickly predict appropriate feedback annotations. Its efficiency in processing and its flexibility in adapting to feedback patterns can significantly reduce the time and effort required for manual feedback provisioning in educational settings.

Abstract

Dodona (dodona.ugent.be) is an intelligent tutoring system for computer programming. It provides real-time data and feedback to help students learn better and teachers teach better. Dodona is free to use and has more than 61 thousand registered users across many educational and research institutes, including 20 thousand new users in the last year. The source code of Dodona is available on GitHub under the permissive MIT open-source license. This paper presents Dodona and its design and look-and-feel. We highlight some of the features built into Dodona that make it possible to shorten feedback loops, and discuss an example of how these features can be used in practice. We also highlight some of the research opportunities that Dodona has opened up and present some future developments.

Abstract

Debugging is an important aspect of programming. Most programming languages have some features and tools to facilitate debugging. As the debugging process is also frustrating, it requires good scaffolding, in which a debugger can be a useful tool [3]. Scratch is a visual block-based programming language that is commonly used to teach programming to children, aged 10--14 [4]. It comes with its own integrated development environment (IDE), where children can edit and run their code. This IDE misses some of the tools that are available in traditional IDEs, such as a debugger. In response to this challenge, we developed Blink. Blink is a debugger for Scratch with the aim of being usable to the young audience that typically uses Scratch. We present the currently implemented features of the debugger, and the challenges we faced while implementing those, both from a user-experience standpoint and a technical standpoint.

Abstract

In educational contexts, automated assessment tools (AAT) are commonly used to provide formative feedback on programming exercises. However, designing exercises for AAT remains a laborious task or imposes limitations on the exercises. Most AAT use either output comparison, where the generated output is compared against an expected output, or unit testing, where the tool has access to the code of the submission under test. While output comparison has the advantage of being programming language independent, the testing capabilities are limited to the output. Conversely, unit testing can generate more granular feedback, but is tightly coupled with the programming language of the submission. In this paper, we introduce TESTed, which enables the best of both worlds: combining the granular feedback of unit testing with the programming language independence of output comparison. Educators can save time by designing exercises that can be used across programming languages. Finally, we report on using TESTed in educational practice.

Abstract

We present a privacy-friendly early-detection framework to identify students at risk of failing in introductory programming courses at university. The framework was validated for two different courses with annual editions taken by higher education students (N = 2 080) and was found to be highly accurate and robust against variation in course structures, teaching and learning styles, programming exercises and classification algorithms. By using interpretable machine learning techniques, the framework also provides insight into what aspects of practising programming skills promote or inhibit learning or have no or minor effect on the learning process. Findings showed that the framework was capable of predicting students’ future success already early on in the semester.