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Immersive Analytics Through HoloSENAI MOTOR

Ramos, André L. M. and Okada, Alexandra (2019). Immersive Analytics Through HoloSENAI MOTOR. In: Computing Conference 2019, 16-17 Jul 2019, London, UK, (In Press).

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Abstract

This study examines the use of HoloSENAI MOTOR as novel approach for preparing students and professionals for the industry 4.0. This new Augmented Reality technology was developed with UNIT3D and C# language for the Microsoft HoloLens®. This educational resource enables the projection of 3D scenes of a real electric motor into the natural world environment. It was used by undergraduates from an Engineering course in Brazil. Our aim is to identify the potential benefits and barriers to promote immersive analytics and authoring skills through HoloSENAI MOTOR for learning and teaching. We present Immersive Analytics as an approach that combines real-time interaction with visualization techniques for students to explore and analyze information about the motor in their physical environment. This study is based on Responsible Research and Innovation approach and supported by e-authentication and authorship verification TeSLA. It revealed that the key benefits for learners were to increase their motivation, curiosity and understanding in terms of features, properties and functionalities of the motor, including better acquisition of information and data analysis skills. They key barriers highlighted by educational technologies, were the high cost equipment, the technical development of applications and the pedagogical approaches for assessment.

Item Type: Conference or Workshop Item
Project Funding Details:
Funded Project NameProject IDFunding Body
TeSLA688520European Commission
Keywords: Microsoft HoloLens; C#; Responsible Research and Innovation; Immersive analytics; TeSLA
Academic Unit/School: Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET)
Learning and Teaching Innovation (LTI)
Research Group: OpenTEL
Related URLs:
Item ID: 58241
Depositing User: Alexandra Okada
Date Deposited: 18 Dec 2018 16:16
Last Modified: 20 Dec 2018 05:57
URI: http://oro.open.ac.uk/id/eprint/58241
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