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Update publications page #5526

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May 16, 2024
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Update publications page #5526

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912358d
Replace Dolos proceedings abstract with journal article
rien May 13, 2024
2cab223
Add ECHO preprint
rien May 13, 2024
86c48ad
Remove Dodona conference abstract
rien May 13, 2024
e058fac
Also remove BLINK poster
rien May 16, 2024
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90 changes: 43 additions & 47 deletions app/views/pages/publications.html.erb
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<h1><%= t '.title' %></h1>
</div>

<div class="card">
<div class="card-title card-title-colored">
<h2 class="card-title-text">Mining patterns in syntax trees to automate code reviews of student solutions for programming exercise</h2>
<div class="card-subtitle-text">
Charlotte Van Petegem, Kasper Demeyere, Rien Maertens, Niko Strijbol, Bram De Wever, Bart Mesuere, Peter Dawyndt
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<div class="card-supporting-text">
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<div class="col-md-8">
<h4>Abstract</h4>
<p>
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.
</p>
<h4>Citation</h4>
<p>
Van Petegem, C., Demeyere, K., Maertens, R., Strijbol, N., De Wever, B., Mesuere, B., Dawyndt, P., 2024. arXiv preprint. <%= link_to "https://doi.org/10.48550/arXiv.2402.10853" "https://doi.org/10.48550/arXiv.2402.10853" %>
</p>
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<%= link_to "https://doi.org/10.48550/arXiv.2402.10853", class: "publication-image" do %>
<%= image_tag "publications/vanpetegem-2024.png" %>
<% end %>
</div>
</div>
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</div>

<div class="card">
<div class="card-title card-title-colored">
<h2 class="card-title-text">Blink: An educational software debugger for Scratch</h2>
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<div class="card">
<div class="card-title card-title-colored">
<h2 class="card-title-text">Dolos 2.0: Towards Seamless Source Code Plagiarism Detection in Online Learning Environments</h2>
<h2 class="card-title-text">Discovering and exploring cases of educational source code plagiarism with Dolos</h2>
<div class="card-subtitle-text">
Rien Maertens, Peter Dawyndt, Bart Mesuere
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<h4>Abstract</h4>
<p>
With the increasing demand for programming skills comes a trend towards more online programming courses and assessments.
While this allows educators to teach larger groups of students, it also opens the door to dishonest student behaviour, such as copying code from other students.
When teachers use assignments where all students write code for the same problem, source code similarity tools can help to combat plagiarism.
Unfortunately, teachers often do not use these tools to prevent such behaviour.
In response to this challenge, we have developed a new source code plagiarism detection tool named Dolos.
Dolos is open-source, supports a wide range of programming languages, and is designed to be user-friendly.
It enables teachers to detect, prove and prevent plagiarism in programming courses by using fast algorithms and powerful visualisations.
We present further enhancements to Dolos and discuss how it can be integrated into modern computing education courses to meet the challenges of online learning and assessment.
By lowering the barriers for teachers to detect, prove and prevent plagiarism in programming courses, Dolos can help protect academic integrity and ensure that students earn their grades honestly.
</p>
<h4>Citation</h4>
<p>
Maertens, R., Dawyndt, P., & Mesuere, B. (2023). Dolos 2.0: Towards Seamless Source Code Plagiarism Detection in Online Learning Environments. Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2, 632. <%= link_to "https://doi.org/10.1145/3587103.3594166", "https://doi.org/10.1145/3587103.3594166" %>
</p>
</div>
<div class="col-md-4">
<%= link_to "https://doi.org/10.1145/3587103.3594166", class: "publication-image" do %>
<%= image_tag "publications/maertens-2023.png" %>
<% end %>
</div>
</div>
</div>
</div>

<div class="card">
<div class="card-title card-title-colored">
<h2 class="card-title-text">Dodona: Learn to Code with a Virtual Co-teacher that Supports Active Learning</h2>
<div class="card-subtitle-text">
Charlotte Van Petegem, Peter Dawyndt, Bart Mesuere
Rien Maertens, Charlotte Van Petegem, Niko Strijbol, Toon Baeyens, Arne Carla Jacobs, Peter Dawyndt, Bart Mesuere
</div>
</div>
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<h4>Abstract</h4>
<p>
Dodona (dodona.ugent.be) is an intelligent tutoring system for learning computer programming, statistics and data science.
It bridges the gap between assessment and learning by providing real-time data and feedback to help students learn better, teachers teach better and educational technology become more effective.
We show how Dodona can be used as a virtual co-teacher to stimulate active learning and support challenge-based education in open and collaborative learning environments.
We also highlight some of the opportunities and challenges we have faced in practice.
Dodona is free to use and has more than 50 thousand registered users across many educational and research institutions, including 15 thousand new users in the last year.
Dodona's source code is available on GitHub under the permissive MIT open-source license.
Source code plagiarism is a significant issue in educational practice, and educators need user-friendly tools to cope with such academic dishonesty.
This article introduces the latest version of Dolos, a state-of-the-art ecosystem of tools for detecting and preventing plagiarism in educational source code.
In this new version, the primary focus has been on enhancing the user experience.
Educators can now run the entire plagiarism detection pipeline from a new web app in their browser, eliminating the need for any installation or configuration.
Completely redesigned analytics dashboards provide an instant assessment of whether a collection of source files contains suspected cases of plagiarism and how widespread plagiarism is within the collection.
The dashboards support hierarchically structured navigation to facilitate zooming in and out of suspect cases.
Clusters are an essential new component of the dashboard design, reflecting the observation that plagiarism can occur among larger groups of students.
To meet various user needs, the Dolos software stack for source code plagiarism detection now includes a self-hostable web app, a JSON application programming interface (API), a command line interface (CLI), a JavaScript library and a preconfigured Docker container.
Clear documentation and a free-to-use instance of the web app can be found at <a href="https://dolos.ugent.be">https://dolos.ugent.be</a>.
The source code is also available on GitHub.
</p>
<h4>Citation</h4>
<p>
Van Petegem, C., Dawyndt, P., & Mesuere, B. (2023). Dodona: Learn to Code with a Virtual Co-teacher that Supports Active Learning. Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2, 633. <%= link_to "https://doi.org/10.1145/3587103.3594165", "https://doi.org/10.1145/3587103.3594165" %>
Maertens, R., Van Neyghem, M., Geldhof, M., Van Petegem, C., Strijbol, N., Dawyndt, P., Mesuere, B., 2024. Discovering and exploring cases of educational source code plagiarism with Dolos. SoftwareX 26, 101755. <%= link_to "https://doi.org/10.1016/j.softx.2024.101755", "https://doi.org/10.1016/j.softx.2024.101755" %>
</p>
</div>
<div class="col-md-4">
<%= link_to "https://doi.org/10.1145/3587103.3594165", class: "publication-image" do %>
<%= image_tag "publications/vanpetegem-2023.png" %>
<%= link_to "https://doi.org/10.1016/j.softx.2024.101755", class: "publication-image" do %>
<%= image_tag "publications/maertens-2024.png" %>
<% end %>
</div>
</div>
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