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Expert Competence in Remote Diagnostics - Industrial Interests, Educational Goals, Flipped Classroom & Laboratory Settings
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Linnéuniversitetet, SWE.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
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2018 (English)In: ONLINE ENGINEERING & INTERNET OF THINGS / [ed] Auer, ME Zutin, DG, 2018, p. 438-451Conference paper, Published paper (Refereed)
Abstract [en]

The manufacturing industry are dependent of engineering expertise. Currently the ability to supply the industry with engineering graduates and staff that have an up-to-date and relevant competences might be considered as a challenge for the society. In this paper an education approach is presented where academia - industry - research institutes cooperate around the development and implementation of master level courses. The methods applied to reach the educational goals, concerning expert competence within remote diagnostics, have been on site and remote lectures given by engineering, medical and metrology experts. The pedagogical approach utilized has been flipped classroom. The main results show that academic courses developed in cooperation with industry requires flexibility, time and effort from the involved partners. The evaluation interviews indicate that student are satisfied with the courses and pedagogical approach but suggests more reconciliation meetings for course development. Labs early in the course was considered good, and division of labs at the system and the component level. However further long-term studies of evaluation of impact is necessary.

Place, publisher, year, edition, pages
2018. p. 438-451
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370 ; 22
Keywords [en]
Engineering education; Flipped classroom; Smart home and health; Diabetes; Scientific literacy; Engineering competence; Academia - industry; Expert competence; Metrology; Internet-of-Things
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-17620DOI: 10.1007/978-3-319-64352-6_41ISI: 000456875500041ISBN: 9783319643526 OAI: oai:DiVA.org:bth-17620DiVA, id: diva2:1290623
Conference
14th International Conference on Remote Engineering and Virtual Instrumentation (REV), MAR 15-17, 2017, Columbia Univ, New York, NY
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-04-18Bibliographically approved

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Claesson, LenaZackrisson, JohanJohansson, Sven

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