34 Search Results for "Pinto, Mário"

Volume

OASIcs, Volume 133

6th International Computer Programming Education Conference (ICPEC 2025)

ICPEC 2025, July 10-11, 2025, PORTIC, Polytechnic of Porto, Portugal

Editors: Ricardo Queirós, Mário Pinto, Filipe Portela, and Alberto Simões

Volume

OASIcs, Volume 112

4th International Computer Programming Education Conference (ICPEC 2023)

ICPEC 2023, June 26-28, 2023, Vila do Conde, Portugal

Editors: Ricardo Alexandre Peixoto de Queirós and Mário Paulo Teixeira Pinto

Volume

OASIcs, Volume 94

10th Symposium on Languages, Applications and Technologies (SLATE 2021)

SLATE 2021, July 1-2, 2021, Vila do Conde/Póvoa de Varzim, Portugal

Editors: Ricardo Queirós, Mário Pinto, Alberto Simões, Filipe Portela, and Maria João Pereira

Volume

OASIcs, Volume 81

First International Computer Programming Education Conference (ICPEC 2020)

ICPEC 2020, June 25-26, 2020, ESMAD, Vila do Conde, Portugal (Virtual Conference)

Editors: Ricardo Queirós, Filipe Portela, Mário Pinto, and Alberto Simões

Volume

OASIcs, Volume 56

6th Symposium on Languages, Applications and Technologies (SLATE 2017)

SLATE 2017, June 26-27, 2017, Vila do Conde, Portugal

Editors: Ricardo Queirós, Mário Pinto, Alberto Simões, José Paulo Leal, and Maria João Varanda

Artifact
Text
DOI: 10.4230/artifacts.27132
Gamibot

Authors: José Paiva and Ricardo Queirós

Abstract
Cite as

José Paiva, Ricardo Queirós. Gamibot (Text, Project Website). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@misc{dagstuhl-artifact-27132,
   title = {{Gamibot}}, 
   author = {Paiva, Jos\'{e} and Queir\'{o}s, Ricardo},
   note = {Text (visited on 2026-07-22)},
   url = {https://inov-norte.github.io/gamibot-docs},
   doi = {10.4230/artifacts.27132},
}
Document
DOI: 10.4230/OASIcs.ICPEC.2026.17
Bridging Generative AI and Gamification in Moodle: Design and Evaluation of GamiBot

Authors: José Carlos Paiva, Ricardo Queirós, Raquel Simões de Almeida, Teresa Terroso, and Mário Pinto

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

Abstract
While Learning Management Systems (LMS) like Moodle are ubiquitous in higher education, they often lack dynamic, personalized academic support. We present GamiBot, an intelligent pedagogical agent natively integrated into Moodle to bridge this gap. GamiBot combines Large Language Model (LLM) assistance, Retrieval-Augmented Generation (RAG) grounded in course materials, adaptive quizzes, and a structured gamification backend. To evaluate its effectiveness, we conducted a two-week pilot study across two higher education institutions. Results from telemetry (773 interactions) and student surveys demonstrated high system usability (74.6% positive sentiment) and strong chatbot performance (69.4%). Students heavily favored active self-regulation, dedicating 53% of interactions to generating adaptive quizzes rather than passive content summaries. Conversely, gamification elements received mixed feedback (31.7%), underscoring the need for technical refinement and high customizability. Ultimately, GamiBot suggests that integrating generative AI directly into existing LMS workflows can provide reliable, context-aware formative support that aligns with self-regulated learning strategies.
Cite as

José Carlos Paiva, Ricardo Queirós, Raquel Simões de Almeida, Teresa Terroso, and Mário Pinto. Bridging Generative AI and Gamification in Moodle: Design and Evaluation of GamiBot. In 7th International Computer Programming Education Conference (ICPEC 2026). Open Access Series in Informatics (OASIcs), Volume 145, pp. 17:1-17:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{paiva_et_al:OASIcs.ICPEC.2026.17,
  author =	{Paiva, Jos\'{e} Carlos and Queir\'{o}s, Ricardo and de Almeida, Raquel Sim\~{o}es and Terroso, Teresa and Pinto, M\'{a}rio},
  title =	{{Bridging Generative AI and Gamification in Moodle: Design and Evaluation of GamiBot}},
  booktitle =	{7th International Computer Programming Education Conference (ICPEC 2026)},
  pages =	{17:1--17:14},
  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.2026.17},
  URN =		{urn:nbn:de:0030-drops-267545},
  doi =		{10.4230/OASIcs.ICPEC.2026.17},
  annote =	{Keywords: Generative AI, Educational chatbot, Pedagogical agent, Gamification, Moodle, Retrieval-augmented generation, Self-regulated learning}
}
Document
Research
DOI: 10.4230/TGDK.3.3.4
Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web

Authors: Florian Ruosch, Cristina Sarasua, and Abraham Bernstein

Published in: TGDK, Volume 3, Issue 3 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 3

Abstract
In Argument Mining, predicting argumentative relations between texts (or spans) remains one of the most challenging aspects, even more so in the cross-document setting. This paper makes three key contributions to advance research in this domain. We first extend an existing dataset, the Sci-Arg corpus, by annotating it with explicit inter-document argumentative relations, thereby allowing arguments to be distributed over several documents forming an Argument Web; these new annotations are published using Semantic Web technologies (RDF, OWL). Second, we explore and evaluate three automated approaches for predicting these inter-document argumentative relations, establishing critical baselines on the new dataset. We find that a simple classifier based on discourse indicators with access to context outperforms neural methods. Third, we conduct a comparative analysis of these approaches for both intra- and inter-document settings, identifying statistically significant differences in results that indicate the necessity of distinguishing between these two scenarios. Our findings highlight significant challenges in this complex domain and open crucial avenues for future research on the Argument Web of Science, particularly for those interested in leveraging Semantic Web technologies and knowledge graphs to understand scholarly discourse. With this, we provide the first stepping stones in the form of a benchmark dataset, three baseline methods, and an initial analysis for a systematic exploration of this field relevant to the Web of Data and Science.
Cite as

Florian Ruosch, Cristina Sarasua, and Abraham Bernstein. Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 3, pp. 4:1-4:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{ruosch_et_al:TGDK.3.3.4,
  author =	{Ruosch, Florian and Sarasua, Cristina and Bernstein, Abraham},
  title =	{{Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:33},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.3.4},
  URN =		{urn:nbn:de:0030-drops-252159},
  doi =		{10.4230/TGDK.3.3.4},
  annote =	{Keywords: Argument Mining, Large Language Models, Knowledge Graphs, Link Prediction}
}
Document
Complete Volume
DOI: 10.4230/OASIcs.ICPEC.2025
OASIcs, Volume 133, ICPEC 2025, Complete Volume

Authors: Ricardo Queirós, Mário Pinto, Filipe Portela, and Alberto Simões

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

Abstract
OASIcs, Volume 133, ICPEC 2025, Complete Volume
Cite as

6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 1-226, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Proceedings{queiros_et_al:OASIcs.ICPEC.2025,
  title =	{{OASIcs, Volume 133, ICPEC 2025, Complete Volume}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{1--226},
  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},
  URN =		{urn:nbn:de:0030-drops-245929},
  doi =		{10.4230/OASIcs.ICPEC.2025},
  annote =	{Keywords: OASIcs, Volume 133, ICPEC 2025, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.ICPEC.2025.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Ricardo Queirós, Mário Pinto, Filipe Portela, and Alberto Simões

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

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

6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{queiros_et_al:OASIcs.ICPEC.2025.0,
  author =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Portela, Filipe and Sim\~{o}es, Alberto},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{0:i--0:xii},
  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.2025.0},
  URN =		{urn:nbn:de:0030-drops-245910},
  doi =		{10.4230/OASIcs.ICPEC.2025.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
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.2025.1
Exploring Mutation Testing for Teaching Introductory Programming

Authors: Pedro Vasconcelos

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

Abstract
This paper proposes the use of introductory programming assignments based on mutation testing where students are asked to write tests rather than code. We believe such exercises can be used to teach code reading skills before students could write the corresponding programs on their own. Furthermore, feedback for such exercises can be automatically generated using testing tools. We have extended an existing web-based system for programming exercises with such mutation testing assignments and show some example use cases. This is on-going work that has yet to be validated in the classroom.
Cite as

Pedro Vasconcelos. Exploring Mutation Testing for Teaching Introductory Programming. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 1:1-1:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{vasconcelos:OASIcs.ICPEC.2025.1,
  author =	{Vasconcelos, Pedro},
  title =	{{Exploring Mutation Testing for Teaching Introductory Programming}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{1:1--1:8},
  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.2025.1},
  URN =		{urn:nbn:de:0030-drops-240319},
  doi =		{10.4230/OASIcs.ICPEC.2025.1},
  annote =	{Keywords: mutation testing, programming education}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2025.18
The Influence of GenAI on the Evaluation of Computer Programming Students in Higher Education

Authors: Teresa Terroso and Mário Pinto

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

Abstract
Artificial Intelligence (AI) has assumed an increasingly prominent role in education, transforming the dynamics of teaching and learning while introducing new pedagogical opportunities and challenges. In computer programming education, generative AI tools have had a particularly profound impact. Historically, computer programming education has emphasized problem-solving skills, syntax accuracy, and code efficiency. However, the emergence of generative AI models capable of supporting automatic code generation, producing high-quality code snippets and entire programs, personalized explanations and tutoring, and real-time debugging, has triggered a paradigm shift. These tools make learning processes and assessment less effective and less clear about students' true knowledge. In this context, the paper explores three key dimensions: the broader impact of AI in education, the new challenges that AI presents in teaching and learning computer programming in higher education, and the implications for student assessment, an essential element of the educational process. To investigate these topics, we conducted an online survey targeting Portuguese higher education instructors teaching programming-related courses. Our primary objective was to understand the changes introduced in evaluation methods and criteria due to the growing use of generative AI tools, particularly those focused on code generation.
Cite as

Teresa Terroso and Mário Pinto. The Influence of GenAI on the Evaluation of Computer Programming Students in Higher Education. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 18:1-18:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{terroso_et_al:OASIcs.ICPEC.2025.18,
  author =	{Terroso, Teresa and Pinto, M\'{a}rio},
  title =	{{The Influence of GenAI on the Evaluation of Computer Programming Students in Higher Education}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{18:1--18:8},
  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.2025.18},
  URN =		{urn:nbn:de:0030-drops-240482},
  doi =		{10.4230/OASIcs.ICPEC.2025.18},
  annote =	{Keywords: Generative Artificial Intelligence, Computer Programming Education, Student Assessment, Teaching and Learning, Higher Education}
}
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.2025.11
Rethinking IoT Education: Is the Concept Truly Grasped?

Authors: Tomáš Kormaník and Jaroslav Porubän

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

Abstract
This paper focuses on the topic of the Internet of Things (abbr. IoT) in the context of higher education and academic understanding of it. When briefly looking at the IoT course curriculum at our department, we suspected that the curriculum contents are not adhering to the definition of IoT. The goal of our work was to pinpoint the correct definition of IoT, which can be used to bring contents of the IoT courses as close to the truth as possible. Secondarily, we reviewed available articles and reviews of formerly and currently taught IoT or related courses and evaluated whether their approach and contents were correct when considering the definition of IoT. We summarise the issues present in existing works and identify which specific parts are problematic, according to our assessment. Improving IoT courses is crucial since it shapes a student’s understanding of the IoT paradigm and allows them to use it or even develop it in the future. Provisioning our students with a needed set of skills will make them more suitable for research, development, and industry-related futures.
Cite as

Tomáš Kormaník and Jaroslav Porubän. Rethinking IoT Education: Is the Concept Truly Grasped?. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 11:1-11:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{kormanik_et_al:OASIcs.ICPEC.2025.11,
  author =	{Korman{\'\i}k, Tom\'{a}\v{s} and Porub\"{a}n, Jaroslav},
  title =	{{Rethinking IoT Education: Is the Concept Truly Grasped?}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{11:1--11:8},
  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.2025.11},
  URN =		{urn:nbn:de:0030-drops-240411},
  doi =		{10.4230/OASIcs.ICPEC.2025.11},
  annote =	{Keywords: Internet of Things, Informatics Education, Higher Education, Computer Science Education}
}
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