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Publications

2020

Ten Years Exploring Service Science: Looking Back to Move Forward

Authors
Teixeira, JG; Migueis, V; Ferreira, MC; Novoa, H; Cunha, JFE;

Publication
Lecture Notes in Business Information Processing

Abstract
In celebration of the 10th anniversary of the International Conference on Exploring Service Science (IESS), this paper takes a historical look at the papers that have been published in the IESS proceedings. The analysis is focused on the development and evolution of the IESS community and of the main research topics covered by the published papers over time. The IESS community is portrayed in terms of authors, their affiliations and co-authoring network, while the topics are analyzed according to the papers’ keywords. Moreover, this paper analyzes the impact of the papers published in this decade, in terms of citations. These results are then discussed in light of the observed trends and of the evolution of the service science field, to guide the future development of the IESS conference and of research on service science. © Springer Nature Switzerland AG 2020.

2020

Prediction of academic dropout in a higher education institution using data mining [Previsão do abandono académico numa instituição de ensino superior com recurso a data mining]

Authors
Martins, MPG; Migueis, VL; Fonseca, DSB; Gouveia, PDF;

Publication
RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao

Abstract
This study proposes two predictive models of classification that allow to identify, at the end of the 1st and 2nd semesters, the undergraduate students of a higher education institution more prone to academic dropout. The proposed methodology, which combines 3 popular data mining algorithms, such as random forest, support vector machines and artificial neural networks, in addition to contributing to predictive performance, allows to identify the main factors behind academic dropout. The empirical results show that it is possible to reduce to about 1/4 the 4 tens potential predictors of dropout, and show that there are essentially two predictors, concerning student’s curriculum context, that explain this propensity. This knowledge is useful for decision-makers to adopt the most appropriate strategic measures and decisions in order to reduce student dropout rates.

2020

Quality Assurance of Doctoral Education: Current Trends and Future Developments

Authors
Cardoso, S; Rosa, MJ; Miguéis, V;

Publication
Structural and Institutional Transformations in Doctoral Education

Abstract

2020

Predicting Market Basket Additions as a Way to Enhance Customer Service Levels

Authors
Migueis, VL; Teixeira, R;

Publication
EXPLORING SERVICE SCIENCE (IESS 2020)

Abstract
It is imperative that online companies have a complete in-depth understanding of online behavior in order to provide a better service to their customers. This paper proposes a model for real-time basket addition in the e-grocery sector that includes predictors inferred from anonymous clickstream data, such as a Markov page view sequence discrimination value. This model aims at anticipating the addition and the non-addition of items to customers' market basket, in order to enable marketers to act conveniently, for example recommending more appropriate items. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of anonymous clickstream data taken from the servers of a European e-retailing company is explored. The empirical results reveal the high predictive power of the model proposed, based on the explanatory variables introduced, as well as the supremacy of random forests over logistic regression.

2019

A Data Mining Approach for Predicting Academic Success – A Case Study

Authors
Martins, MPG; Miguéis, VL; Fonseca, DSB; Alves, A;

Publication
Advances in Intelligent Systems and Computing

Abstract
The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

Supervised
thesis

2020

Use of data mining techniques to support taxi vehicles operations

Author
Ana Luísa Correia Dias Loureiro

Institution
UP-FEUP

2020

Avaliação do impacto de ações promocionais nas vendas no contexto do retalho alimentar

Author
Maria Francisca Azevedo Paupério

Institution
UP-FEUP

2020

Developing data-driven models to assess structures' health condition

Author
João Francisco Lopes Cruz de Carvalho

Institution
UP-FEUP

2020

Previsão do potencial de vendas na cadeia de pescado com base na procura censurada

Author
Mariana Teixeira de Jesus

Institution
UP-FEUP

2020

A Comparison on Statistical Methods and Long Short Term Memory Network Forecasting the Demand of Fresh Fish Products

Author
Rúben Alexandre da Fonseca Marques

Institution
UP-FEUP