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Documents authored by Pedroso, João Pedro

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)

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@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.ATMOS.2021.12
A Branch-Price-And-Cut Algorithm for Stochastic Crowd Shipping Last-Mile Delivery with Correlated Marginals

Authors: Marco Silva, João Pedro Pedroso, Ana Viana, and Xenia Klimentova

Published in: OASIcs, Volume 96, 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021)

Abstract
We study last-mile delivery with the option of crowd shipping, where a company makes use of occasional drivers to complement its vehicle’s fleet in the activity of delivering products to its customers. We model it as a data-driven distributionally robust optimization approach to the capacitated vehicle routing problem. We assume the marginals of the defined uncertainty vector are known, but the joint distribution is difficult to estimate. The presence of customers and available occasional drivers can be random. We adopt a strategic planning perspective, where an optimal a priori solution is calculated before the uncertainty is revealed. Therefore, without the need for online resolution performance, we can experiment with exact solutions. Solving the problem defined above is challenging: not only the first-stage problem is already NP-Hard, but also the uncertainty and potentially the second-stage decisions are binary of high dimension, leading to non-convex optimization formulations that are complex to solve. We propose a branch-price-and-cut algorithm taking into consideration measures that exploit the intrinsic characteristics of our problem and reduce the complexity to solve it.
Cite as

Marco Silva, João Pedro Pedroso, Ana Viana, and Xenia Klimentova. A Branch-Price-And-Cut Algorithm for Stochastic Crowd Shipping Last-Mile Delivery with Correlated Marginals. In 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021). Open Access Series in Informatics (OASIcs), Volume 96, pp. 12:1-12:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{silva_et_al:OASIcs.ATMOS.2021.12,
  author =	{Silva, Marco and Pedroso, Jo\~{a}o Pedro and Viana, Ana and Klimentova, Xenia},
  title =	{{A Branch-Price-And-Cut Algorithm for Stochastic Crowd Shipping Last-Mile Delivery with Correlated Marginals}},
  booktitle =	{21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021)},
  pages =	{12:1--12:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-213-6},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{96},
  editor =	{M\"{u}ller-Hannemann, Matthias and Perea, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2021.12},
  URN =		{urn:nbn:de:0030-drops-148811},
  doi =		{10.4230/OASIcs.ATMOS.2021.12},
  annote =	{Keywords: Last-mile delivery, Stochastic Vehicle Routing Problem, Crowd shipping, Distributionally Robust Optimization, Data-driven Optimization}
}
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