34 Search Results for "Pinto-Albuquerque, Maria"

Volume

OASIcs, Volume 122

5th International Computer Programming Education Conference (ICPEC 2024)

ICPEC 2024, June 27-28, 2024, Lisbon, Portugal

Editors: André L. Santos and Maria Pinto-Albuquerque

Document
Short Paper
DOI: 10.4230/OASIcs.ICPEC.2026.16
Catching What the Borrow Checker Can't: Towards Serious Game-Based Security Training for Rust Developers (Short Paper)

Authors: Frederico d'Abreu, Tiago Espinha Gasiba, Sathwik Amburi, and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 145, 7th International Computer Programming Education Conference (ICPEC 2026)

Abstract
Cybersecurity incidents cause significant financial damage to organizations, making secure software development a critical industry priority. Although Rust offers strong memory safety guarantees, developers can still introduce security vulnerabilities through improper coding practices. In this paper, we present the preliminary design of a serious game for secure Rust coding training in an industrial setting. We investigate real-world Rust vulnerabilities, map (five of) them to CWEs, and propose challenge scenarios alongside a structured evaluation protocol for validation by industry security experts. Our design incorporates defense-oriented coding tasks, a three-level AI-powered hint system, and automated backend evaluation, grounded in established pedagogical principles such as situated learning and cognitive load management. This work provides a foundation for future implementation and empirical evaluation of learning outcomes with professional software developers.
Cite as

Frederico d'Abreu, Tiago Espinha Gasiba, Sathwik Amburi, and Maria Pinto-Albuquerque. Catching What the Borrow Checker Can't: Towards Serious Game-Based Security Training for Rust Developers (Short Paper). In 7th International Computer Programming Education Conference (ICPEC 2026). Open Access Series in Informatics (OASIcs), Volume 145, pp. 16:1-16:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{dabreu_et_al:OASIcs.ICPEC.2026.16,
  author =	{d'Abreu, Frederico and Espinha Gasiba, Tiago and Amburi, Sathwik and Pinto-Albuquerque, Maria},
  title =	{{Catching What the Borrow Checker Can't: Towards Serious Game-Based Security Training for Rust Developers}},
  booktitle =	{7th International Computer Programming Education Conference (ICPEC 2026)},
  pages =	{16:1--16:8},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-443-7},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{145},
  editor =	{Portela, Filipe and Matos, Lu{\'\i}s and Guimar\~{a}es, Tiago},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2026.16},
  URN =		{urn:nbn:de:0030-drops-267535},
  doi =		{10.4230/OASIcs.ICPEC.2026.16},
  annote =	{Keywords: Rust, Secure Coding Training, Secure Software Development, Serious Games, Gamification, Industry}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2025.2
Enabling Secure Coding: Exploring GenAI for Developer Training and Education

Authors: Sathwik Amburi, Tiago Espinha Gasiba, Ulrike Lechner, and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 133, 6th International Computer Programming Education Conference (ICPEC 2025)

Abstract
The rapid adoption of GenAI for code generation presents unprecedented opportunities and significant security challenges. Raising awareness about secure coding is critical for preventing software vulnerabilities. To investigate how Generative AI can best support secure coding, we built an AI Secure Coding platform, an interactive training environment that embeds a GPT-4 based chatbot directly into a structured challenge workflow. The platform comprises a landing page, a challenges page with three AI-generated tasks, and a challenge page where participants work with code snippets. In each challenge, developers (1) identify vulnerabilities by reviewing code and adding comments, (2) ask the AI for help via a chat based interface, (3) review and refine comments based on AI feedback, and (4) fix vulnerabilities by submitting secure patches. The study involved 18 industry developers tackling three challenges. Participants used the AI Secure Coding Platform to detect and remediate vulnerabilities and then completed a survey to capture their opinions and comfort level with AI assisted platform for secure coding. Results show that AI assistance can boost productivity, reduce errors, and uncover more defects when treated as a "second pair of eyes," but it can also foster over-reliance. This study introduces the AI Secure Coding platform, presents preliminary results from a initial study, and shows that embedding GenAI into a structured secure-coding workflow can both enable and challenge developers. This work also opens the door to a new research field: leveraging GenAI to enable secure software development.
Cite as

Sathwik Amburi, Tiago Espinha Gasiba, Ulrike Lechner, and Maria Pinto-Albuquerque. Enabling Secure Coding: Exploring GenAI for Developer Training and Education. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{amburi_et_al:OASIcs.ICPEC.2025.2,
  author =	{Amburi, Sathwik and Espinha Gasiba, Tiago and Lechner, Ulrike and Pinto-Albuquerque, Maria},
  title =	{{Enabling Secure Coding: Exploring GenAI for Developer Training and Education}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{2:1--2:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-393-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{133},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Portela, Filipe and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2025.2},
  URN =		{urn:nbn:de:0030-drops-240321},
  doi =		{10.4230/OASIcs.ICPEC.2025.2},
  annote =	{Keywords: Secure Coding, Industry, Software Development, Generative AI, Large Language Models, Teaching}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2025.4
Can Open Large Language Models Catch Vulnerabilities?

Authors: Diogo Gaspar Lopes, Tiago Espinha Gasiba, Sathwik Amburi, and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 133, 6th International Computer Programming Education Conference (ICPEC 2025)

Abstract
As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In this work, we conduct a systematic evaluation of three state-of-the-art LLMs - Llama3, Codestral, and Deepseek R1 - using a carefully filtered subset of the Big-Vul dataset annotated with eight representative Common Weakness Enumeration categories. Adopting a closed-world classification setup, we assess each model’s performance in both identifying the presence of vulnerabilities and mapping them to the correct CWE label. Our findings reveal a sharp contrast between high detection rates and markedly poor classification accuracy, with frequent overgeneralization and misclassification. Moreover, we analyze model-specific biases and common failure modes, shedding light on the limitations of current LLMs in performing fine-grained security reasoning.These insights are especially relevant in educational contexts, where LLMs are being adopted as learning aids despite their limitations. A nuanced understanding of their behaviour is essential to prevent the propagation of misconceptions among students. Our results expose key challenges that must be addressed before LLMs can be reliably deployed in security-sensitive environments.
Cite as

Diogo Gaspar Lopes, Tiago Espinha Gasiba, Sathwik Amburi, and Maria Pinto-Albuquerque. Can Open Large Language Models Catch Vulnerabilities?. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{gasparlopes_et_al:OASIcs.ICPEC.2025.4,
  author =	{Gaspar Lopes, Diogo and Espinha Gasiba, Tiago and Amburi, Sathwik and Pinto-Albuquerque, Maria},
  title =	{{Can Open Large Language Models Catch Vulnerabilities?}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{4:1--4:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-393-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{133},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Portela, Filipe and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2025.4},
  URN =		{urn:nbn:de:0030-drops-240340},
  doi =		{10.4230/OASIcs.ICPEC.2025.4},
  annote =	{Keywords: Large Language Models (LLMs), Secure Coding, CWE Classification, Machine Learning, Software Vulnerability Detection, Artificial Intelligence, Code Analysis, Big-Vul Dataset}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2025.9
Are We There Yet? On Security Vulnerabilities Produced by Open Source Generative AI Models and Its Implications for Security Education

Authors: Maria Camila Santos Galeano, Tiago Espinha Gasiba, Sathwik Amburi, and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 133, 6th International Computer Programming Education Conference (ICPEC 2025)

Abstract
With the increasing integration of large language models (LLMs) into software development and programming education, concerns have emerged about the security of AI-generated code. This study investigates the security of three open source code generation models. Codestral, DeepSeek R1, and LLaMA 3.3 70B using structured prompts in Python, C, and Java. Some prompts were designed to explicitly trigger known vulnerability patterns, such as unsanitized input handling or unsafe memory operations, in order to assess how each model responds to security-sensitive tasks. The findings reveal recurring issues, including command execution vulnerabilities, insecure memory handling, and insufficient input validation. In response, we propose a set of recommendations for integrating secure prompt design and code auditing practices into developer training. These guidelines aim to help future developers generate safer code and better identify flaws in GenAI-generated output. This work offers an initial analysis of the limitations of GenAI-assisted code generation and provides actionable strategies to support the more secure and responsible use of these tools in professional and educational contexts.
Cite as

Maria Camila Santos Galeano, Tiago Espinha Gasiba, Sathwik Amburi, and Maria Pinto-Albuquerque. Are We There Yet? On Security Vulnerabilities Produced by Open Source Generative AI Models and Its Implications for Security Education. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 9:1-9:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{santosgaleano_et_al:OASIcs.ICPEC.2025.9,
  author =	{Santos Galeano, Maria Camila and Espinha Gasiba, Tiago and Amburi, Sathwik and Pinto-Albuquerque, Maria},
  title =	{{Are We There Yet? On Security Vulnerabilities Produced by Open Source Generative AI Models and Its Implications for Security Education}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{9:1--9:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-393-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{133},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Portela, Filipe and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2025.9},
  URN =		{urn:nbn:de:0030-drops-240395},
  doi =		{10.4230/OASIcs.ICPEC.2025.9},
  annote =	{Keywords: Generative AI, Code Security, Programming Education, Prompt Engineering, Secure Coding, Static Analysis}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2025.13
Interactive Evaluation of Complex Programming Assignments Using LLM Assistant

Authors: Tomáš Kormaník, Viktória Lukáčová, and Jaroslav Porubän

Published in: OASIcs, Volume 133, 6th International Computer Programming Education Conference (ICPEC 2025)

Abstract
Generative language models present significant advancements in artificial intelligence with increasing applications in software engineering education. This paper explores the potential of customized generative dialogue models for automated assessment of programming assignments. The research introduces KP Assistant, a tailored implementation based on GPT-4o developed for a Component Programming university course. The research evaluated the effectiveness of this approach in generating relevant questions about source code, assessing student understanding, and providing objective feedback through a series of experiments with various game implementations and student testing. The findings demonstrate the feasibility of integrating such models into educational workflows while also acknowledging their present limitations. The study provides a framework for implementing similar systems in programming education, showing how generative AI can augment traditional assessment methods while maintaining pedagogical integrity.
Cite as

Tomáš Kormaník, Viktória Lukáčová, and Jaroslav Porubän. Interactive Evaluation of Complex Programming Assignments Using LLM Assistant. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 13:1-13:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{kormanik_et_al:OASIcs.ICPEC.2025.13,
  author =	{Korman{\'\i}k, Tom\'{a}\v{s} and Luk\'{a}\v{c}ov\'{a}, Vikt\'{o}ria and Porub\"{a}n, Jaroslav},
  title =	{{Interactive Evaluation of Complex Programming Assignments Using LLM Assistant}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{13:1--13:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-393-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{133},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Portela, Filipe and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2025.13},
  URN =		{urn:nbn:de:0030-drops-240438},
  doi =		{10.4230/OASIcs.ICPEC.2025.13},
  annote =	{Keywords: Artificial Intelligence, Generative Models, Programming Assessment, Software Engineering Education}
}
Document
Complete Volume
DOI: 10.4230/OASIcs.ICPEC.2024
OASIcs, Volume 122, ICPEC 2024, Complete Volume

Authors: André L. Santos and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
OASIcs, Volume 122, ICPEC 2024, Complete Volume
Cite as

5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 1-238, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Proceedings{santos_et_al:OASIcs.ICPEC.2024,
  title =	{{OASIcs, Volume 122, ICPEC 2024, Complete Volume}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{1--238},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024},
  URN =		{urn:nbn:de:0030-drops-209684},
  doi =		{10.4230/OASIcs.ICPEC.2024},
  annote =	{Keywords: OASIcs, Volume 122, ICPEC 2024, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.ICPEC.2024.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: André L. Santos and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{santos_et_al:OASIcs.ICPEC.2024.0,
  author =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{0:i--0:xii},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024.0},
  URN =		{urn:nbn:de:0030-drops-209694},
  doi =		{10.4230/OASIcs.ICPEC.2024.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Invited Talk
DOI: 10.4230/OASIcs.ICPEC.2024.1
Hedy: An Inclusive, Multi-Lingual, and Gradual Programming Language (Invited Talk)

Authors: Felienne Hermans

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
Software is playing an increasing role in everyone’s lives, and therefore it is important (and fun!) for kids to become creators in the digital world. However, existing programming languages are not necessarily designed for learnability, with cryptic error messages and a lack of easily accessible resources. In this talk, Felienne will outline what issues existing tools have, and how these issues disproportionally affect underrepresented minorities in programming including girls, kids with disabilities and non-English learners. She will then outline her story of inventing and creating Hedy, an inclusive, multi-lingual and gradual programming language for learners. Hedy is open source, runs in the browser, is free to use, and is available in 54 different languages (Including English, Spanish, Chinese, Arabic and Hindi). Hedy was launched in early 2020 and now serves about 500,000 monthly users.
Cite as

Felienne Hermans. Hedy: An Inclusive, Multi-Lingual, and Gradual Programming Language (Invited Talk). In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, p. 1:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{hermans:OASIcs.ICPEC.2024.1,
  author =	{Hermans, Felienne},
  title =	{{Hedy: An Inclusive, Multi-Lingual, and Gradual Programming Language}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{1:1--1:1},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024.1},
  URN =		{urn:nbn:de:0030-drops-209704},
  doi =		{10.4230/OASIcs.ICPEC.2024.1},
  annote =	{Keywords: programming education, gradual programming, outreach, Hedy}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2024.2
A Domain-Specific Language for Dynamic White-Box Evaluation of Java Assignments

Authors: Afonso B. Caniço and André L. Santos

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
Programming exercises involving algorithms typically involve time and spatial constraints. Automated assessments for such implementations are often carried out in a black-box manner or through static analysis of the code, without considering the internal execution properties, which could lead to falsely positive evaluations of students' solutions. We present Witter, a domain-specific language for defining white-box test cases for the Java language. We evaluated programming assignment submissions from a Data Structures and Algorithms course against Witter’s test cases to determine if our approach could offer additional insight regarding incomplete algorithmic behaviour requirements. We found that a significant amount of student solutions fail to meet the desired algorithmic behavior (approx. 21%), despite passing black-box tests. Hence, we conclude that white-box tests are useful to achieve a thorough automated evaluation of this kind of exercises.
Cite as

Afonso B. Caniço and André L. Santos. A Domain-Specific Language for Dynamic White-Box Evaluation of Java Assignments. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{b.canico_et_al:OASIcs.ICPEC.2024.2,
  author =	{B. Cani\c{c}o, Afonso and Santos, Andr\'{e} L.},
  title =	{{A Domain-Specific Language for Dynamic White-Box Evaluation of Java Assignments}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{2:1--2:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024.2},
  URN =		{urn:nbn:de:0030-drops-209715},
  doi =		{10.4230/OASIcs.ICPEC.2024.2},
  annote =	{Keywords: White-box assessment, student assessment, programming education}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2024.3
Seven Years Later: Lessons Learned in Automated Assessment

Authors: Bruno Pereira Cipriano and Pedro Alves

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
Automatic assessment tools (AATs) are software systems used in teaching environments to automatically evaluate code written by students. We have been using such a system since 2017, in multiple courses and across multiple evaluation types. This paper presents a set of lessons learned from our experience of using said system. These recommendations should help other teachers and instructors who wish to use or already use AATs in creating assessments which give students useful feedback in terms of improving their work and reduce the likelihood of unfair evaluations.
Cite as

Bruno Pereira Cipriano and Pedro Alves. Seven Years Later: Lessons Learned in Automated Assessment. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 3:1-3:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{cipriano_et_al:OASIcs.ICPEC.2024.3,
  author =	{Cipriano, Bruno Pereira and Alves, Pedro},
  title =	{{Seven Years Later: Lessons Learned in Automated Assessment}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{3:1--3:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024.3},
  URN =		{urn:nbn:de:0030-drops-209725},
  doi =		{10.4230/OASIcs.ICPEC.2024.3},
  annote =	{Keywords: learning to program, automatic assessment tools, unit testing, feedback, large language models}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2024.4
Adaptation of Automated Assessment System for Large Programming Courses

Authors: Marek Horváth, Tomáš Kormaník, and Jaroslav Porubän

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
This paper presents a new automated assessment system tailored for programming courses, addressing the challenge of evaluating a large number of students in extensive courses at the Technical University of Košice. The primary issue with current systems is their inability to handle massive course loads while ensuring objective evaluation and timely feedback. Our proposed system enhances the scalability of the assessment process, allowing for the simultaneous handling of a greater volume of assignments. It is designed to provide regular and systematic feedback to students, supporting their continuous learning and improvement. To ensure the objectivity of evaluations, the system utilizes a variety of unit test suites, selecting them randomly in each assessment to discourage students from hardcoding solutions. This approach not only supports fair and precise assessments but also significantly reduces the administrative burden on educators, enabling them to meet a wide range of educational demands.
Cite as

Marek Horváth, Tomáš Kormaník, and Jaroslav Porubän. Adaptation of Automated Assessment System for Large Programming Courses. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 4:1-4:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{horvath_et_al:OASIcs.ICPEC.2024.4,
  author =	{Horv\'{a}th, Marek and Korman{\'\i}k, Tom\'{a}\v{s} and Porub\"{a}n, Jaroslav},
  title =	{{Adaptation of Automated Assessment System for Large Programming Courses}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{4:1--4:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024.4},
  URN =		{urn:nbn:de:0030-drops-209735},
  doi =		{10.4230/OASIcs.ICPEC.2024.4},
  annote =	{Keywords: Automated Assessment, Informatics Education, Programming Feedback Systems, Continuous Integration in Education, Code Quality Analysis, Educational Technology, Computer Science Education}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2024.5
Kumon-Inspired Approach to Teaching Programming Fundamentals

Authors: Ivone Amorim, Pedro Baltazar Vasconcelos, and João Pedro Pedroso

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
Integration of introductory programming into higher education programs beyond computer science has lead to an increase in the failure and drop out rates of programming courses. In this context, programming instructors have explored new methodologies by introducing dynamic elements in the teaching-learning process, such as automatic code evaluation systems and gamification. Even though these methods have shown to be successful in improving students' engagement, they do not address all the existing problems and new strategies should be explored. In this work, we propose a new approach that combines the strengths of the Kumon method for personalized learning and progressive skill acquisition with the ability of online judge systems to provide automated assessment and immediate feedback. This approach has been used in teaching Programming I to students in several bachelor degrees and led to a 10% increase in exam approval rates compared to the baseline editions in which our Kumon-inspired methodology was not implemented.
Cite as

Ivone Amorim, Pedro Baltazar Vasconcelos, and João Pedro Pedroso. Kumon-Inspired Approach to Teaching Programming Fundamentals. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 5:1-5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{amorim_et_al:OASIcs.ICPEC.2024.5,
  author =	{Amorim, Ivone and Vasconcelos, Pedro Baltazar and Pedroso, Jo\~{a}o Pedro},
  title =	{{Kumon-Inspired Approach to Teaching Programming Fundamentals}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{5:1--5:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024.5},
  URN =		{urn:nbn:de:0030-drops-209749},
  doi =		{10.4230/OASIcs.ICPEC.2024.5},
  annote =	{Keywords: Programming teaching, Programming education, Kumon method, Progressive learning, Online judge system}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2024.6
An Experience with Adaptive Formative Assessment for Motivating Novices in Introductory Programming Learning

Authors: Jagadeeswaran Thangaraj, Monica Ward, and Fiona O’Riordan

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
This study presents empirical research that uses adaptive formative assessment framework in addition to traditional lectures to motivate novice students in an introductory programming course. The primary goal of this work is to provide guidance for the creation of adaptive formative assessments in Python programming language to inspire novice students. The experiment is based on lessons learned from the literature and pedagogical theories that support learning through assessment and scaffolding. This study investigates how the experiment helped the novices, whether it increased their confidence, whether it assisted in identifying and correcting common errors, and whether it covered the material on learning modular programming components. It report on extensive survey results of over 265 attempts of 90 students taking CS1 (introductory programming) that included five quizzes covering fundamental concepts. The students responded favorably to the experiment, and results are also included.
Cite as

Jagadeeswaran Thangaraj, Monica Ward, and Fiona O’Riordan. An Experience with Adaptive Formative Assessment for Motivating Novices in Introductory Programming Learning. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 6:1-6:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{thangaraj_et_al:OASIcs.ICPEC.2024.6,
  author =	{Thangaraj, Jagadeeswaran and Ward, Monica and O’Riordan, Fiona},
  title =	{{An Experience with Adaptive Formative Assessment for Motivating Novices in Introductory Programming Learning}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{6:1--6:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024.6},
  URN =		{urn:nbn:de:0030-drops-209755},
  doi =		{10.4230/OASIcs.ICPEC.2024.6},
  annote =	{Keywords: Assessment and feedback, Computer programming, CS1, Formative assessment, Introductory programming, Novice students}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2024.7
Promoting Deep Learning Through a Concept Map-Building Collaborative Activity in an Introductory Programming Course

Authors: João Paulo Barros

Published in: OASIcs, Volume 122, 5th International Computer Programming Education Conference (ICPEC 2024)

Abstract
Programming courses focus heavily on problem-solving and coding practice. However, students also face numerous interrelated concepts that should be given more attention to foster more effective and comprehensive learning. Often, students only get an incomplete knowledge of those concepts and their relations as no adequate reflection is promoted or even seen as necessary. The result is a superficial surface learning about essential programming concepts and their relations. This experience report presents a learning activity to promote deep learning of concepts and their relations. The activity challenges students to specify relations between concepts. Students search definitions for a given set of concepts and define relations between those concepts in textual form. To that end, they use a freely available tool that produces a graph from textual descriptions. This tool dramatically simplifies and speeds up the creation of readable graphical representations. Although many different courses can take advantage of the presented activity, we present the activity’s application to an introductory object-oriented programming course. We also present and discuss the student’s feedback, which was highly positive. In the end, we provide recommendations, including possible variations. These can help educators to effectively foster active learning of concepts and their relations in their classrooms.
Cite as

João Paulo Barros. Promoting Deep Learning Through a Concept Map-Building Collaborative Activity in an Introductory Programming Course. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 7:1-7:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{barros:OASIcs.ICPEC.2024.7,
  author =	{Barros, Jo\~{a}o Paulo},
  title =	{{Promoting Deep Learning Through a Concept Map-Building Collaborative Activity in an Introductory Programming Course}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{7:1--7:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-347-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{122},
  editor =	{Santos, Andr\'{e} L. and Pinto-Albuquerque, Maria},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2024.7},
  URN =		{urn:nbn:de:0030-drops-209767},
  doi =		{10.4230/OASIcs.ICPEC.2024.7},
  annote =	{Keywords: active-learning, ontologies, concepts, concept maps, learning activity, object-oriented programming, oop, pedagogy, education}
}
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