ALGORITMI Research Center

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Publications

Data analysis techniques applied to the mathE database

Publication . Azevedo, Beatriz Flamia; Romanenko, Sofia; Pacheco, Maria F.; Fernandes, Florbela P.; Pereira, Ana I.

MathE is an international online platform that aims to provide a resource for in-class support as well as an alternative instrument to teach and study mathematics. This work focuses on the investigations of the students’ behavior when answering the training questions available in the platform. In order to draw conclusions about the value of the platform, the ways in which the students use it and what are the most wanted mathematical topics, thus deepening the knowledge about the difficulties faced by the users and finding how to make the platform more efficient, the data collected since the it was launched (3 years ago) is analyzed through the use of data mining and machine learning techniques. In a first moment, a general analysis was performed in order to identify the students’ behavior as well as the topics that require reorganization; it was followed by a second iteration, according to the students’ country of origin, in order to identify the existence of differences in the behavior of students from distinct countries. The results point out that the advanced level of the platform’s questions is not adequate and that the questions should be reorganized in order to ensure a more consistent support for the students’ learning process. Besides, with this analysis it was possible to identify the topics that require more attention through the addition of more questions. Furthermore, it was not possible to identify significant disparities in the students behavior in what concerns the students’ country of origin.

2022Conference object

Open access

NLP/AI based techniques for programming exercises generation

Publication . Freitas, Tiago; Neto, Álvaro; Pereira, Maria João; Henriques, Pedro

This paper focuses on the enhancement of computer programming exercises generation to the benefit of both students and teachers. By exploring Natural Language Processing (NLP) and Machine Learning (ML) methods for automatic generation of text and source code, it is possible to semiautomatically construct programming exercises, aiding teachers to reduce redundant work and more easily apply active learning methodologies. This would not only allow them to still play a leading role in the teaching-learning process, but also provide students a better and more interactive learning experience. If embedded in a widely accessible website, an exercises generator with these Artificial Intelligence (AI) methods might be used directly by students, in order to obtain randomised lists of exercises for their own study, at their own time. The emergence of new and increasingly powerful technologies, such as the ones utilised by ChatGPT, raises the discussion about their use for exercise generation. Albeit highly capable, monetary and computational costs are still obstacles for wider adoption, as well as the possibility of incorrect results. This paper describes the characteristics and behaviour of several ML models applied and trained for text and code generation and their use to generate computer programming exercises. Finally, an analysis based on correctness and coherence of the resulting exercise statements and complementary source codes generated/produced is presented, and the role that this type of technology can play in a programming exercise automatic generation system is discussed.

2023Conference object

Open access

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Publication . Azevedo, Beatriz Flamia; Rocha, Ana Maria A.C.; Pereira, Ana I.

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.

2024Journal article

Open access

A systematic literature review on home health care management

Publication . Alves, Filipe; Rocha, Ana Maria A.C.; Pereira, Ana I.; Leitão, Paulo

This work provides a systematic literature review based on an up-to-date assessment of the concepts, procedures and operational management in home health care (HHC). Worldwide, and especially in Portugal, there is a growing demand for HHC services, whether health or social, often supported by private or public institutions of social solidarity or health. The methodology was developed to, in the first phase, collect a set of potentially relevant articles, from which some journal papers with a high degree of citation were analyzed. The searched databases were Scopus and Web of Science. In a second phase, all identified documents were pre-processed with a bibliometric analysis between 2010 and 2021 to support the current state of HHC. The main contributions of this work are a summary update of the literature dealing with HHC routing and scheduling management, some discussions on current past and actual trends, and some future research directions.

2023Conference object

Open access

Active methodologies in incoming programming classes

Publication . Aires, Joao Paulo; Aires, Simone Bello Kaminski; Pereira, Maria João; Alves, Luís M.

Innovative approaches in teaching programming have been required to improve the success of incoming programming students. This work presents the initial results of a teaching strategy implemented in the Algorithms subject of a Computer Science course. Ninety-five students, enrolled in this subject during the first semester of the course, participated in the research. The reported activity is related with active methodologies of teaching and Problem-Based Learning, being developed on the first day of class in groups of up to five students. The activity was based in two actions: 1) answering a questionnaire associating computing elements to daily life routines; and, 2) even without programming concepts knowledge, develop a smartphone application. Each group received a questionnaire containing 19 questions, divided into four blocks. What can be perceived with the accomplishment of this work, was the enthusiasm, motivation and engagement of the students who, even being unknown from each other, organized themselves in the groups and researched the necessary strategies to complete the challenge. The teacher acted as an advisor in the teaching process, conducting the experiment in order to lead students to find the solution.

2021Conference object

Open access