The Value of Design Thinking for PhD Students: A Retrospective Longitudinal Study

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

A Data Science Maturity Model Applied to Students' Modeling

“…The authors use data from the English Language and Literacy Acquisition project to demonstrate the importance of capturing the complex data structure. Victorino et al [20] investigate the impact of Design Thinking skills on Ph.D. students' academic and professional performance through a 7-year longitudinal mixed methods approach.…”

“…Educational Data Mining identifies essential factors for academic success in a Portuguese business school's bachelor program, showing the influence of prior grades and school engagement on success at enrollment and the end of the first academic year [20,23]. The study by Nunes et al [24] combines machine learning efficiency with prototype analysis to quantify the impact of various factors on academic achievement in Mathematics and the mother tongue.…”