Publication Type:

Conference Paper

Source:

Proceedings - IEEE 18th International Conference on Advanced Learning Technologies, ICALT 2018, Institute of Electrical and Electronics Engineers Inc. (2018)

ISBN:

9781538660492

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052499922&doi=10.1109%2fICALT.2018.00022&partnerID=40&md5=ad2393e49ad3ea349c63ea5c7893750f

Keywords:

Applied machine learning, Clustering algorithms, Computer aided instruction, Construction training, Image segmentation, Intelligent systems, Learning environments, Learning experiences, Learning systems, Models, Personalized feedback, Personalized learning, Personnel training, Simulators, Time series, Time-series segmentation, Vocational training

Abstract:

Simulators have been increasingly used at many industries to provide skill training to its trainees or employees. A haptics based re-bar bending simulator is being used at leading construction training centers in India. The simulator collects the real-Time training data and uses it to generate accurate assessment reports similar to the manually generated reports. To enhance the learning environment, the simulator should learn to recognize the differences between the trainee performances and should be capable to model the successive stages of skill learning. In this paper, we discuss how the time-series segmentation technique can be applied to extract process relevant data from the training history collected by the re-bar bending simulator. We applied machine learning methods, on this transformed data to model the varying levels of expertise. In future, these models can be utilized to enhance the simulator to provide personalized feedback and learning experiences. © 2018 IEEE.

Cite this Research Publication

P. Aswathi, Menon, B. M., and Rao R. Bhavani, “Performance categorization for personalized learning in vocational training simulators”, in Proceedings - IEEE 18th International Conference on Advanced Learning Technologies, ICALT 2018, 2018.