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Documents authored by Nunes, Luis

Document
DOI: 10.4230/OASIcs.ICPEC.2024.20
Use of Programming Aids in Undergraduate Courses

Authors: Ana Rita Peixoto, André Glória, José Luís Silva, Maria Pinto-Albuquerque, Tomás Brandão, and Luís Nunes

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

Abstract
The use of external tips and applications to help with programming assignments, by novice programmers, is a double-edged sword, it can help by showing examples of problem-solving strategies, but it can also prevent learning because recognizing a good solution is not the same skill as creating one. A study was conducted during the 2superscript{nd} semester of 23/24 in the course of Object Oriented Programming to help understand the impact of the programming aids in learning. The main questions that drove this study were: Which type(s) of assistance do students use when learning to program? When / where do they use it? Does it affect grades? Results, even though with a relatively small sample, seem to indicate that students who used aids have a perception of improved learning when using advice from Colleagues, Copilot-style tools, and Large Language Models. Results of correlating average grades with the usage of tools suggest that experience in using these tools is key for its successful use, but, contrary to students' perceptions, learning gains are marginal in the end result.
Cite as

Ana Rita Peixoto, André Glória, José Luís Silva, Maria Pinto-Albuquerque, Tomás Brandão, and Luís Nunes. Use of Programming Aids in Undergraduate Courses. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 20:1-20:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{peixoto_et_al:OASIcs.ICPEC.2024.20,
  author =	{Peixoto, Ana Rita and Gl\'{o}ria, Andr\'{e} and Silva, Jos\'{e} Lu{\'\i}s and Pinto-Albuquerque, Maria and Brand\~{a}o, Tom\'{a}s and Nunes, Lu{\'\i}s},
  title =	{{Use of Programming Aids in Undergraduate Courses}},
  booktitle =	{5th International Computer Programming Education Conference (ICPEC 2024)},
  pages =	{20:1--20:9},
  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.2024.20},
  URN =		{urn:nbn:de:0030-drops-209894},
  doi =		{10.4230/OASIcs.ICPEC.2024.20},
  annote =	{Keywords: Teaching Programming, Programming aids}
}
Document
DOI: 10.4230/OASIcs.SLATE.2022.9
Classification of Public Administration Complaints

Authors: Francisco Caldeira, Luís Nunes, and Ricardo Ribeiro

Published in: OASIcs, Volume 104, 11th Symposium on Languages, Applications and Technologies (SLATE 2022)

Abstract
Complaint management is a problem faced by many organizations that is both vital to customer image and highly dependent on human resources. This work attempts to tackle a part of the problem, by classifying summaries of complaints using machine learning models in order to better redirect these to the appropriate responders. The main challenges of this task is that training datasets are often small and highly imbalanced. This can can have a big impact on the performance of classification models. The dataset analyzed in this work suffers from both of these problems, being relatively small and having labels in different proportions. In this work, two different techniques are analyzed: combining classes together to increase the number of elements of the new class; and, providing new artificial examples for some classes via translation into other languages. The classification models explored were the following: k-NN, SVM, Naïve Bayes, boosting, and Deep Learning approaches, including transformers. The paper concludes that although, as expected, the classes with little representation are hard to classify, the techniques explored helped to boost the performance, especially in the classes with a low number of elements. SVM and BERT-based models outperformed their peers.
Cite as

Francisco Caldeira, Luís Nunes, and Ricardo Ribeiro. Classification of Public Administration Complaints. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022). Open Access Series in Informatics (OASIcs), Volume 104, pp. 9:1-9:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{caldeira_et_al:OASIcs.SLATE.2022.9,
  author =	{Caldeira, Francisco and Nunes, Lu{\'\i}s and Ribeiro, Ricardo},
  title =	{{Classification of Public Administration Complaints}},
  booktitle =	{11th Symposium on Languages, Applications and Technologies (SLATE 2022)},
  pages =	{9:1--9:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-245-7},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{104},
  editor =	{Cordeiro, Jo\~{a}o and Pereira, Maria Jo\~{a}o and Rodrigues, Nuno F. and Pais, Sebasti\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2022.9},
  URN =		{urn:nbn:de:0030-drops-167555},
  doi =		{10.4230/OASIcs.SLATE.2022.9},
  annote =	{Keywords: Text Classification, Natural Language Processing, Deep Learning, BERT}
}
Document
DOI: 10.4230/OASIcs.SLATE.2013.259
Comparing Different Methods for Disfluency Structure Detection

Authors: Henrique Medeiros, Fernando Batista, Helena Moniz, Isabel Trancoso, and Luis Nunes

Published in: OASIcs, Volume 29, 2nd Symposium on Languages, Applications and Technologies (2013)

Abstract
This paper presents a number of experiments focusing on assessing the performance of different machine learning methods on the identification of disfluencies and their distinct structural regions over speech data. Several machine learning methods have been applied, namely Naive Bayes, Logistic Regression, Classification and Regression Trees (CARTs), J48 and Multilayer Perceptron. Our experiments show that CARTs outperform the other methods on the identification of the distinct structural disfluent regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32h of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. This work shows that using fully automatic prosodic features, disfluency structural regions can be reliably identified using CARTs, where the best results achieved correspond to 81.5% precision, 27.6% recall, and 41.2% F-measure. The best results concern the detection of the interregnum, followed by the detection of the interruption point.
Cite as

Henrique Medeiros, Fernando Batista, Helena Moniz, Isabel Trancoso, and Luis Nunes. Comparing Different Methods for Disfluency Structure Detection. In 2nd Symposium on Languages, Applications and Technologies. Open Access Series in Informatics (OASIcs), Volume 29, pp. 259-269, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{medeiros_et_al:OASIcs.SLATE.2013.259,
  author =	{Medeiros, Henrique and Batista, Fernando and Moniz, Helena and Trancoso, Isabel and Nunes, Luis},
  title =	{{Comparing Different Methods for Disfluency Structure Detection}},
  booktitle =	{2nd Symposium on Languages, Applications and Technologies},
  pages =	{259--269},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-52-1},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{29},
  editor =	{Leal, Jos\'{e} Paulo and Rocha, Ricardo 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.SLATE.2013.259},
  URN =		{urn:nbn:de:0030-drops-40420},
  doi =		{10.4230/OASIcs.SLATE.2013.259},
  annote =	{Keywords: Machine learning, speech processing, prosodic features, automatic detection of disfluencies}
}
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