2023
Authors
Camanho, A; Stumbriene, D; Barbosa, F; Jakaitiene, A;
Publication
EDULEARN Proceedings - EDULEARN23 Proceedings
Abstract
2023
Authors
Camanho, S; Zanella, A; Moutinho, V;
Publication
Lecture Notes in Economics and Mathematical Systems
Abstract
2023
Authors
Camanho, S; D’Inverno, G;
Publication
Lecture Notes in Economics and Mathematical Systems
Abstract
2023
Authors
Piran, FS; Camanho, S; Silva, MC; Lacerda, DP;
Publication
Lecture Notes in Economics and Mathematical Systems
Abstract
2023
Authors
Blanquet, L; Grilo, J; Strecht, P; Camanho, A;
Publication
Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao
Abstract
This study explores data mining techniques for predicting student dropout in higher education. The research compares different methodological approaches, including alternative algorithms and variations in model specifications. Additionally, we examine the impact of employing either a single model for all university programs or separate models per program. The performance of models with students grouped according to their position on the program study plan was also tested. The training datasets were explored with varying time series lengths (2, 4, 6, and 8 years) and the experiments use academic data from the University of Porto, spanning the academic years from 2012 to 2022. The algorithm that yielded the best results was XGBoost. The best predictions were obtained with models trained with two years of data, both with separate models for each program and with a single model. The findings highlight the potential of data mining approaches in predicting student dropout, offering valuable insights for higher education institutions aiming to improve student retention and success. © 2023 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.
2023
Authors
Silva E.; Beirão G.; Torres A.;
Publication
Journal of Small Business Strategy
Abstract
The recent pandemic crisis has greatly impacted startups, and some changes are expected to be long-lasting. Small businesses usually have fewer resources and are more vulnerable to losing customers and investors, especially during crises. This study investigates how startups’ business processes were affected and how entrepreneurs managed this sudden change brought by the COVID-19 outbreak. Data were analyzed using qualitative research methods through in-depth interviews with the co-founders of eighteen startups. Results show that the three core business processes affected by the COVID-19 crisis were marketing and sales, logistics and operations, and organizational support. The way to succeed is to be flexible, agile, and adaptable, with technological knowledge focusing on digital channels to find novel opportunities and innovate. Additionally, resilience, self-improvement, education, technology readiness and adoption, close relationship with customers and other stakeholders, and incubation experience seem to shield startups against pandemic crisis outbreaks.
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