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About

About

I was born in Portugal in 1964. I graduated in mathematics from the Faculty of Sciences of the University of Porto and earned a Ph.D. in Computer Science from the same institution.
My current position is auxiliary professor at the Computer Science department of the Faculty of Sciences of the University of Porto. I am also affiliated with the Center for Research in Advanced Computing Systems (CRACS), an R&D unit of INESCTEC Research Laboratory, where I am an effective member.
My main research interests are technology enhanced learning, web adaptability, and semantic web.

Interest
Topics
Details

Details

  • Name

    José Paulo Leal
  • Role

    Senior Researcher
  • Since

    01st January 2009
003
Publications

2023

PROGpedia: Collection of source-code submitted to introductory programming assignments

Authors
Paiva, JC; Leal, JP; Figueira, A;

Publication
DATA IN BRIEF

Abstract
Learning how to program is a difficult task. To acquire the re-quired skills, novice programmers must solve a broad range of programming activities, always supported with timely, rich, and accurate feedback. Automated assessment tools play a major role in fulfilling these needs, being a common pres-ence in introductory programming courses. As programming exercises are not easy to produce and those loaded into these tools must adhere to specific format requirements, teachers often opt for reusing them for several years. There-fore, most automated assessment tools, particularly Mooshak, store hundreds of submissions to the same programming ex-ercises, as these need to be kept after automatically pro-cessed for possible subsequent manual revision. Our dataset consists of the submissions to 16 programming exercises in Mooshak proposed in multiple years within the 2003-2020 timespan to undergraduate Computer Science students at the Faculty of Sciences from the University of Porto. In particular, we extract their code property graphs and store them as CSV files. The analysis of this data can enable, for instance, the generation of more concise and personalized feedback based on similar accepted submissions in the past, the identifica-tion of different strategies to solve a problem, the under -standing of a student's thinking process, among many other findings.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

2023

Bibliometric Analysis of Automated Assessment in Programming Education: A Deeper Insight into Feedback

Authors
Paiva, JC; Figueira, A; Leal, JP;

Publication
ELECTRONICS

Abstract
Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.

2023

Summarization of Massive RDF Graphs Using Identifier Classification

Authors
dos Santos, AF; Leal, JP;

Publication
Computational Science - ICCS 2023 - 23rd International Conference, Prague, Czech Republic, July 3-5, 2023, Proceedings, Part I

Abstract
The size of massive knowledge graphs (KGs) and the lack of prior information regarding the schemas, ontologies and vocabularies they use frequently makes them hard to understand and visualize. Graph summarization techniques can help by abstracting details of the original graph to produce a reduced summary that can more easily be explored. Identifiers often carry latent information which could be used for classification of the entities they represent. Particularly, IRI namespaces can be used to classify RDF resources. Namespaces, used in some RDF serialization formats as a shortening mechanism for resource IRIs, have no role in the semantics of RDF. Nevertheless, there is often a hidden meaning behind the decision of grouping resources under a common prefix and assigning an alias to it. We improved on previous work on a namespace-based approach to KG summarization that classifies resources using their namespaces. Producing the summary graph is fast, light on computing resources and requires no previous domain knowledge. The summary graph can be used to analyze the namespace inter-dependencies of the original graph. We also present chilon, a tool for calculating namespace-based KG summaries. Namespaces are gathered from explicit declarations in the graph serialization, community contributions or resource IRI prefix analysis. We applied chilon to publicly available KGs, used it to generate interactive visualizations of the summaries, and discuss the results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

A Game with a Purpose for Building Crowdsourced Semantic Relations Datasets for Named Entities

Authors
dos Santos, AF; Leal, JP;

Publication
Lecture Notes in Networks and Systems

Abstract
Semantic measures evaluate and compare the strength of relations between entities. To assess their accuracy, semantic measures are compared against human-generated gold standards. Existing semantic gold standards are mainly focused on concepts. Nevertheless, semantic measures are frequently applied both to concepts and instances. Games with a purpose are used to offload to humans computational or data collection needs, improving results by using entertainment as motivation for higher engagement. We present Grettir, a system which allows the creation of crowdsourced semantic relations datasets for named entities through a game with a purpose where participants are asked to compare pairs of entities. We describe the system architecture, the algorithms and implementation decisions, the first implemented instance – dedicated to the comparison of music artists – and the results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

Automated Assessment of Simple Web Applications (Short Paper)

Authors
Costa, LM; Leal, JP; Queirós, R;

Publication
4th International Computer Programming Education Conference, ICPEC 2023, June 26-28, 2023, Vila do Conde, Portugal

Abstract
Web programming education is an important component of modern computer science curricula. Assessing students’ web programming skills can be time-consuming and challenging for educators. This paper introduces Webpal, an automated assessment tool for web programming exercises in entry-level courses. Webpal evaluates web applications coded in HTML, CSS, and Javascript, and provides feedback to students. This tool integrates with Virtual Learning Environments (VLEs) through an API, allowing the creation, storage, and access to exercises while assessing student attempts based on the created exercises. The evaluation process comprises various subcomponents: static assessment, interface matching, functional testing, and feedback management. This approach aims to provide feedback that helps students overcome their challenges in web programming assignments. This paper also presents a demo showcasing the tool’s features and functionality in a simulated VLE environment. © Luís Maia Costa, José Paulo Leal, and Ricardo Queirós; licensed under Creative Commons License CC-BY 4.0.

Supervised
thesis

2022

Transportation Mode Detection for Real Mobile Crowdsourced Datasets

Author
Akilu Rilwan Muhammad

Institution
UP-FEUP

2021

Local Electricity Market design:P2P Trade, Pool Based and Real Time Walrasian Auctions for energy and flexibility services provision

Author
João Moreira Schneider de Mello

Institution
UP-FEUP

2021

Growth Hacking: A data-driven approach to achieve business growth

Author
Diogo Almeida Castro

Institution
UP-FEUP

2021

Eficiência operacional na indústria do calçado

Author
Paulo Roberto Otaviano Tavares Melo Júnior

Institution
UP-FEUP

2021

Industrial Reporting Using Power BI

Author
Luís Miguel Ramos Silva

Institution
UP-FEUP