About this paper

Appears in:
Pages: 8996-9002
Publication year: 2018
ISBN: 978-84-697-9480-7
ISSN: 2340-1079
doi: 10.21125/inted.2018.2190

Conference name: 12th International Technology, Education and Development Conference
Dates: 5-7 March, 2018
Location: Valencia, Spain

LEARNING SYSTEM FOR AUTOMOTIVE SPECIFIC SYSTEM ARCHITECTURE BASED ON A KNOWLEDGE BASE

N. Englisch, A. Heller, W. Hardt

Technische Universit├Ąt Chemnitz (GERMANY)
These days software development is an important topic in many different areas, like in automotive domain. With respect to more complex systems in modern vehicles which access different sensors and actuators controlling safety relevant aspects, a reliable, flexible and maintainable system architecture is necessary. Hence these architectures specified by official committees mostly deal beside modularisation of software with parametrisation of many functionally relevant properties, a smart e-Learning System is needed to teach software developers efficiently. Thereby a software developer can easily learn the specifics in this domain and the concrete system architecture. In addition a permanent and flexible training of software developers is essential because of new requirements in very short time intervals and therefore changing of more than thousand dependent parameters.

We present a concept which represents automotive specific system architecture knowledge in a database. This knowledge contains information like software modules and their dependencies, functions and its parameters, functional models and so on. Main goal is to teach software developers from industry or research institutions and students at a university efficiently in details and specific features in the automotive domain by an e-Learning System which contains graphical representation of this knowledge base. Moreover, we provide a semi-automated approach to add or update data into the knowledge base by automatic data extraction of certified master projects. Our data extraction tool considers files (configuration and source code) following official specification as good as product specific files. Furthermore, our e-Learning System supports options to store and access experiences, evaluations as well as best practices and example projects from software developers. By that, especially students can focus on most important aspects within the architecture when gaining information and important know-how from experienced developers.

It is planned to integrate this domain specific e-Learning System into the LMS (Learning Management System) OPAL (Online Platform for Academic Learning) by a LMI (Learning Tools Interoperability) interface. By that, all courses in OPAL regardless which type lecture, exercise or seminar can access the Learning System and its knowledge base whenever automotive specific system architecture knowledge is required. We use already a prototype of this e-Learning System to train new employees as well as improving it by their impressions and suggestions.
@InProceedings{ENGLISCH2018LEA,
author = {Englisch, N. and Heller, A. and Hardt, W.},
title = {LEARNING SYSTEM FOR AUTOMOTIVE SPECIFIC SYSTEM ARCHITECTURE BASED ON A KNOWLEDGE BASE},
series = {12th International Technology, Education and Development Conference},
booktitle = {INTED2018 Proceedings},
isbn = {978-84-697-9480-7},
issn = {2340-1079},
doi = {10.21125/inted.2018.2190},
url = {http://dx.doi.org/10.21125/inted.2018.2190},
publisher = {IATED},
location = {Valencia, Spain},
month = {5-7 March, 2018},
year = {2018},
pages = {8996-9002}}
TY - CONF
AU - N. Englisch AU - A. Heller AU - W. Hardt
TI - LEARNING SYSTEM FOR AUTOMOTIVE SPECIFIC SYSTEM ARCHITECTURE BASED ON A KNOWLEDGE BASE
SN - 978-84-697-9480-7/2340-1079
DO - 10.21125/inted.2018.2190
PY - 2018
Y1 - 5-7 March, 2018
CI - Valencia, Spain
JO - 12th International Technology, Education and Development Conference
JA - INTED2018 Proceedings
SP - 8996
EP - 9002
ER -
N. Englisch, A. Heller, W. Hardt (2018) LEARNING SYSTEM FOR AUTOMOTIVE SPECIFIC SYSTEM ARCHITECTURE BASED ON A KNOWLEDGE BASE, INTED2018 Proceedings, pp. 8996-9002.
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