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Machine Learning- An Educational Revolution? A Scenario Analysis of the Future Role of Machine Learning in the Public Primary School in Gothenburg

Abstract
The Swedish students’ results have gradually decreased in international comparisons, wherefore the educational industry is experiencing an augmenting pressure for change. Simultaneously, the demand and supply of tools based on the Machine Learning (ML) technology is beginning to augment within the educational sector. However, due to the scarcity and ambiguity of research regarding the outcomes of digitizing the educational sector, industrial changes occur slowly, even though the technology of ML has the potential to disrupt the educational industry. The purpose of this research has been to study the role of ML within the field of the public primary school in the city of Gothenburg in the coming five years by using the framework of Scenario Planning. The study was conducted by identifying trends that will influence the future role of ML and their level of impact, uncertainty and correlation. The research employed mixed methods by conducting and analyzing qualitative interviews supported by a quantitative survey including a broad spectrum of stakeholders active within the industry of ML, Educational Technology (EdTech) and the public primary school in Gothenburg. This resulted in 11 identified trends, acknowledged to affect the future role of ML. Eight trends were identified as certain and three as uncertain. Based on the trends, four scenarios were developed. This study concludes that a widespread diffusion of the ML technology in the public primary school in Gothenburg will not occur within a five-year period. However, the future development of the technical infrastructure and the availability and comprehensibility of knowledge and research will greatly impact the future role of the ML technology. Lastly, an extensive knowledge gap has been identified between the schools’ needs and the companies’ supply, which might decrease with increased knowledge and with regulations focusing on solving fundamental difficulties currently present in the Swedish schools.
Degree
Master 2-years
URI
http://hdl.handle.net/2077/61089
Collections
  • Master theses
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gupea_2077_61089_1.pdf (6.417Mb)
Date
2019-07-09
Author
Emanuelsson, Lisa
von Braun, Louise
Keywords
Scenario Planning
Machine Learning
Public Primary Education
Trends
Uncertainties
Series/Report no.
Master Degree Project
2019:48
Language
eng
Metadata
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