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Wole Michael Olatokun; Oluyemi Folorunso Ayanbode; Sunday Oluwadare Oladipo – Education and Information Technologies, 2025
This study examined the data science career preference, data science skills, and core competencies of 416 students from fourteen Nigerian universities using a Google Forms-created structured online questionnaire. A convenience sampling technique was adopted to select the participants. Data were analysed using both descriptive and inferential…
Descriptors: Data Science, Preferences, College Students, Foreign Countries
Maria Eftychia Angelaki; Fragkiskos Bersimis; Theodoros Karvounidis; Christos Douligeris – IEEE Transactions on Education, 2024
Contributions: This article explores the impact of environmental education interventions about e-waste recycling and management practices as well as about the energy usage of data centers (DCs) into the Information and Communication Technologies (ICTs) university curricula. Intended Outcomes: An education program was implemented aiming to raise…
Descriptors: Information Technology, Communications, Conservation Education, Sanitation
Toshiya Arakawa; Haruki Miyakawa – Technology, Knowledge and Learning, 2025
Data science education in Japan extends from elementary to high school students. However, some studies show that this has not enhanced interest or curiosity in data science. Therefore, gamification appears to be an efficient method for encouraging high school students' interest in data science, with research indicating that video games are…
Descriptors: Data Science, Educational Games, Statistics Education, Foreign Countries
Ostblom, Joel; Timbers, Tiffany – Journal of Statistics and Data Science Education, 2022
In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example…
Descriptors: Statistics Education, Data Science, Teaching Methods, Replication (Evaluation)
Azzah Al-Maskari; Thuraya Al Riyami; Sami Ghnimi – Journal of Applied Research in Higher Education, 2024
Purpose: Knowing the students' readiness for the fourth industrial revolution (4IR) is essential to producing competent, knowledgeable and skilled graduates who can contribute to the skilled workforce in the country. This will assist the Higher Education Institutions (HEIs) to ensure that their graduates own skill sets needed to work in the 4IR…
Descriptors: Career Readiness, Technological Literacy, Student Attitudes, Information Technology
Joao Alberto Arantes do Amaral; Izabel Patricia Meister; Valeria Sperduti Lima; Gisele Grinevicius Garbe – Journal of Problem Based Learning in Higher Education, 2023
In this article, we presented our findings regarding an online project-based learning course, delivered to 64 students from the Federal University of Sao Paulo, Brazil, during the COVID-19 pandemic, in the second semester of 2021. The course had the goal of teaching Project Management by means of a competition (the Data Science Olympics). Our goal…
Descriptors: Competition, Active Learning, Student Projects, Data Science