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e-Learning in Maintenance Management Training and Competence Assessment: Development and Demonstration

  • Nikos Papathanassiou
  • Christos Emmanouilidis

Abstract

Recent advances in industrial production and manufacturing processes as well as the rapidly increasing global competition are key drivers in a growing demand for improving personnel competences in maintenance management. Most employees in the manufacturing sector are beyond the typical age of standard education, thus traditional teaching methods are not sufficient. Vocational Education and Training should be targeted to serve the specific needs for enhancing employable skills and competences. Time and place constraints, which are often a significant concern for professional training, can be mitigated by e-training. This paper presents an overview of the e-learning toolkit employed in the iLearn2Main project, a focused EU collaborative project targeting Maintenance Management Training. The learning toolkit offers customized maintenance management training, while facilitating the standardisation of competencies assessment and learning evaluation. It is based on the Moodle Learning Management System platform and comprises a series of courses on maintenance management, structured in a complete Maintenance Management curriculum, specified by taking into account stakeholder needs. Each course is delivered in short and easily completed sessions, which are followed by comprehension questions, aiming at maximizing learner engagement and understanding. A full set of assessment questions certifies that learners acquire the required knowledge and provides an immediate, reliable and automated way of trainee performance evaluation. The learning toolkit is adequately customised to offer not only interactive training but to seek to steer the learners through individual learning paths, thus offering a more engaging and efficient learning process.

Keywords

Augmented Reality Learn Management System Comprehension Question Competence Assessment Maintenance Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Nikos Papathanassiou
    • 1
  • Christos Emmanouilidis
    • 1
  1. 1.CETI/ATHENA Research & Innovation CentreAthensGreece

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