A Conceptual Framework for Multidisciplinary Data Science Education Based on IoT Projects

  • The necessity for professionals to make predictions using digital data has been recognized for many decades. With its roots in statistical analysis and later implemented as data analytics, Data Science emerged as a widely accepted discipline. The data science life cycle begins by setting a goal and ends with reporting/reflecting on the gained results. It incorporates known analytical techniques, including modern artificial intelligence and machine learning techniques. While often considered an independent discipline, both engineering and liberal arts colleges have recognized the need for an understanding of data science in the general workforce. The literature still suggests a disconnect between the study fields and how the Internet of Things could be leveraged in Project-Based Learning (PBL) style data science education. In this paper, the authors propose a conceptual framework to teach multidisciplinary data science at university level to engineering and liberal arts students using data from real-world Internet of Things (IoT)The necessity for professionals to make predictions using digital data has been recognized for many decades. With its roots in statistical analysis and later implemented as data analytics, Data Science emerged as a widely accepted discipline. The data science life cycle begins by setting a goal and ends with reporting/reflecting on the gained results. It incorporates known analytical techniques, including modern artificial intelligence and machine learning techniques. While often considered an independent discipline, both engineering and liberal arts colleges have recognized the need for an understanding of data science in the general workforce. The literature still suggests a disconnect between the study fields and how the Internet of Things could be leveraged in Project-Based Learning (PBL) style data science education. In this paper, the authors propose a conceptual framework to teach multidisciplinary data science at university level to engineering and liberal arts students using data from real-world Internet of Things (IoT) projects.show moreshow less

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Metadaten
Author:Daniel Moritz MarutschkeORCiD, Patricia BrockmannORCiD
DOI:https://doi.org/10.1109/WAIE63876.2024.00065
Parent Title (English):International Workshop on Artificial Intelligence and Education (WAIE)
Publisher:IEEE Xplore
Document Type:conference proceeding (article)
Language:English
Reviewed:Begutachtet/Reviewed
Release Date:2025/05/28
Tag:Data Science; Educational Technology; Higher Education; Internet of Things
Volume:2024
Pagenumber:6
First Page:331
Last Page:335
institutes:Fakultät Informatik
Research Themes:Digitalisierung & Künstliche Intelligenz
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