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All Journal Emerging Science Journal
Henriques, Roberto
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Environmental Science

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The Value of Design Thinking for PhD Students: A Retrospective Longitudinal Study Victorino, Guilherme; Coelho, Pedro S.; Henriques, Roberto
Emerging Science Journal Vol. 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SIED2-02

Abstract

Doctoral studies are changing worldwide, with growing concerns about doctoral graduates' employability and ability to develop relevant links with industrial challenges. The present study aims examine the impact of Design Thinking skills on PhD students on their future academic and professional performance. Drawing on 7 years of pedagogical experimentation, we conducted a mixed methods longitudinal study to investigate the perceptions of students who attended a two-day Design Thinking workshop. Two questionnaires, with a total of 40 items measuring the quality and course impact dimensions, were given to 415 and 41 students, respectively. Finally, 12 students were chosen for in-depth interviews to learn more about how they applied their newly acquired design thinking skills in their research and work. Our findings show that developing Design Thinking skills impacts the professional lives of students of all fields of knowledge, ages, and stages of their PhD. The primary outcomes mentioned are associated with increased creative confidence and collaboration abilities. This study focuses on relevant dimensions for designing and delivering Design Thinking skills within doctoral programmes, as well as the impact of design thinking on the quality of PhD education and student employability opportunities. Doi: 10.28991/ESJ-2023-SIED2-02 Full Text: PDF
Educational Data Mining to Predict Bachelors Students' Success Jacob, David; Henriques, Roberto
Emerging Science Journal Vol. 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SIED2-013

Abstract

Predicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countries' economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelor's historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural network's best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the student's previous education and engagement with the school environment are decisive in achieving academic success. Doi: 10.28991/ESJ-2023-SIED2-013 Full Text: PDF
The Impact of Team-Based Learning on Anxiety Among Graduate Students in a Data Science Master's Program Marques, Andrea; Anastasiadou, Maria; Henriques, Roberto
Emerging Science Journal Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-SIED1-01

Abstract

Objectives: This study aims to explore the impact of Team-Based Learning (TBL) on anxiety levels (AL) among master's students in Data Science, particularly how TBL influences students' anxiety in academic settings and contributes to their engagement, skill development, and learning awareness. Methods/Analysis: The study implemented TBL in a Data Science master's course for one semester, employing both qualitative and quantitative methods. Data were collected through surveys and individual interviews, followed by exploratory and statistical analyses conducted in Jupyter Notebook, SPSS Statistics, and Excel. This mixed-methods approach provided comprehensive insights into students' experiences and perceptions regarding TBL and anxiety. Findings: The analysis revealed that TBL positively affects students' anxiety levels, contributing to enhanced engagement, individual and group skills, and awareness of the learning process. However, certain TBL elements were found to potentially increase anxiety, suggesting a need for tailored adjustments to the approach. Novelty/Improvement: This study underscores TBL's potential to reduce anxiety and foster active learning, emphasizing the importance of student-centered teaching. It highlights specific TBL components that may require modification to minimize negative impacts on student anxiety, offering valuable guidance for educators aiming to create a supportive and effective learning environment. Doi: 10.28991/ESJ-2025-SIED1-01 Full Text: PDF
Co-Authors Anastasiadou, Maria Coelho, Pedro S. Jacob, David Marques, Andrea Victorino, Guilherme