2 Search Results for "Matos, Carlos"

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
DOI: 10.4230/DagSemProc.06302.9
Rule-based Model Extraction from Source Code

Authors: Rui Correia, Carlos Matos, Mohammad El-Ramly, and Reiko Heckel

Published in: Dagstuhl Seminar Proceedings, Volume 6302, Aspects For Legacy Applications (2007)

Abstract
In the context of an approach for reengineering legacy soft- ware systems at the architectural level, we present in this paper a reverse engineering methodology that uses a model de¯ned as a type graph to represent source-code subject to a code categorization process. Two al- ternative methods for referencing the source code are discussed: native vs. graphical. To represent the code, the native representation uses the abstract syntax tree while the graphical uses a programming language metamodel. Two options regarding the way that the graph can relate to the source code reference model are also considered: association model vs. direct link. The extraction of the program representation, complying to the type graph, is based on rules that categorize source code according to its purpose. The techniques to address this process, such as the code categorization rules, are shown together with examples.
Cite as

Rui Correia, Carlos Matos, Mohammad El-Ramly, and Reiko Heckel. Rule-based Model Extraction from Source Code. In Aspects For Legacy Applications. Dagstuhl Seminar Proceedings, Volume 6302, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{correia_et_al:DagSemProc.06302.9,
  author =	{Correia, Rui and Matos, Carlos and El-Ramly, Mohammad and Heckel, Reiko},
  title =	{{Rule-based Model Extraction from Source Code}},
  booktitle =	{Aspects For Legacy Applications},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{6302},
  editor =	{Siobh\'{a}n Clarke and Leon Moonen and Ganesan Ramalingam},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06302.9},
  URN =		{urn:nbn:de:0030-drops-8815},
  doi =		{10.4230/DagSemProc.06302.9},
  annote =	{Keywords: Reverse engineering, Code categorization, Program representation}
}
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