Sobral, Sónia Rolland

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Sobral

Primeiro Nome

Sónia Rolland

Nome

Sónia Rolland Sobral

Biografia

Licenciada em Informática de Gestão, mestre em Engenharia Eletrotécnica e de Computadores, doutora em Tecnologias e Sistemas de Informação e possui o título de agregado em Ciências da Informação. Desde 1993 é docente da Universidade Portucalense (UPT), sendo atualmente professora associada com agregação. Lecionou em diversos cursos como Engenharia Informática e Engenharia e Gestão Industrial, em diversas instituições como Lodz University of Technology e a Universidade de Aveiro, e em diversos países como Angola e Cabo Verde. Participou em diferentes órgãos, tendo sido presidente do Conselho Pedagógico da UPT. Pertence à comissão de várias conferências internacionais e revistas científicas. É autora de uma centena de publicações, a sua maioria indexadas na SCOPUS e/ou WoS. É membro integrado no REMIT – Research on Economics, Management and Information Technologies, sendo atualmente coordenadora de um dos dois grupos de investigação (Transformação Digital e Inovação nas Organizações). Afiliação: REMIT – Research on Economics, Management and Information Technologies. DCT - Departamento de Ciência e Tecnologia.

Identificadores

0000-0002-5041-3597
ED15-C9EC-5996
37091626900
G-2227-2014

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REMIT – Research on Economics, Management and Information Technologies
Centro de investigação que que tem como objetivo principal produzir e disseminar conhecimento teórico e aplicado que possibilite uma maior compreensão das dinâmicas e tendências económicas, empresariais, territoriais e tecnológicas do mundo contemporâneo e dos seus efeitos socioeconómicos. O REMIT adota uma perspetiva multidisciplinar que integra vários domínios científicos: Economia e Gestão; Ciências e Tecnologia; Turismo, Património e Cultura. Founded in 2017, REMIT – Research on Economics, Management and Information Technologies is a research unit of Portucalense University. Based on a multidisciplinary and interdisciplinary perspective it aims at responding to social challenges through a holistic approach involving a wide range of scientific fields such as Economics, Management, Science, Technology, Tourism, Heritage and Culture. Grounded on the production of advanced scientific knowledge, REMIT has a special focus on its application to the resolution of real issues and challenges, having as strategic orientations: - the understanding of local, national and international environment; - the development of activities oriented to professional practice, namely in the business world.

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Autor: Oliveira, Catarina Félix de × Assunto: Educational data mining ×

Resultados da pesquisa

A mostrar 1 - 3 de 3
  • Miniatura
    PublicaçãoAcesso Aberto
    How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review
    2021-11-04 - Oliveira, Catarina Félix de; Sobral, Sónia Rolland; Ferreira, Maria João; Moreira, Fernando
    Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.
  • Miniatura
    PublicaçãoAcesso Aberto
    Predicting students' performance using survey data
    2020 - Oliveira, Catarina Félix de; Sobral, Sónia Rolland
    The acquisition of competences for the development of computer programs is one of the main challenges faced by computer science students. As a result of not being able to develop the abilities needed (for example, abstraction), students drop out the subjects and sometimes even the course. There is a need to study the causes of student success (or failure) in introductory curricular units to check for behaviours or characteristics that may be determinant and thus try to prevent and change said causes. The students of one programming curricular unit were invited to answer four surveys. We use machine learning techniques to try to predict the students’ grades based on the answers obtained on the surveys. The results obtained enable us to plan the semester accordingly, by anticipating how many students might need extra support. We hope to increase the students’ motivation and, with this, increase their interest on the subject. This way we aim to accomplish our ultimate goal: reducing the drop out and increasing the overall average student performance.
  • Miniatura
    PublicaçãoAcesso Aberto
    Predicting students performance in introductory programming courses: a literature review
    2021 - Oliveira, Catarina Félix de; Sobral, Sónia Rolland
    The teaching-learning process in programming in university with freshmen is often associated with high failure and dropout rates. These outcomes frustrate both students and teachers and there is a need to verify the causes of these failures. By predicting the causes of these problems, we can try to control them, or at least try to plan the courses to try to avoid failure in the identified cases. The purpose of this paper is to analyze the scientific production concerning the prediction of students’ performance in introductory programming courses. This analysis regards articles indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. The sample includes a total of 30 articles. The results obtained by bibliometric analysis show when and where those documents were published, who are the authors and what is the focus of said articles. We also analyzed the most cited documents. We made a summary of the articles. We were able to obtain a global overview of the theme, obtaining a strong analysis that is useful for teachers in the process of helping students achieve success in introductory programming courses at universities.