Industrial training assessment of engineering students using Rasch measurement model

Azrilah Abdul Aziz, Nuraini Khatimin, Mohd. Zaidi Omar, Azami Zaharim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Higher learning institutions are trying their level best in providing the necessary knowledge and skill for their students. This is to better prepare them for the real working environment after they graduated. Besides providing the technical knowledge and skill through examinations and quizzes, these students are given the opportunity to apply what they have learned and expand their knowledge whilst equipping them with the invaluable on-the-job experience through industrial training. This paper discusses the assessment of engineering students in UKM Malaysian on their 20 weeks industrial training. An instrument of a criterion based 5-point Likert style rating, were given to the employers to rate their perception towards the students' achievement after the completion of the industrial training. The findings indicates that the most difficult task to be performed was the capability to take the responsibility as a group leader while the least difficult task is the ability to follow the employer's instructions.

Original languageEnglish
Title of host publicationICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences"
Pages452-456
Number of pages5
DOIs
Publication statusPublished - 2012
Event2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012 - Langkawi, Kedah
Duration: 10 Sep 201212 Sep 2012

Other

Other2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012
CityLangkawi, Kedah
Period10/9/1212/9/12

Fingerprint

Engineering
Expand
Completion
Model
Necessary
Knowledge
Training
Skills
Experience
Style
Learning
Perception

Keywords

  • competence criteria
  • engineering
  • industrial training programme
  • performance evaluation
  • Rasch measurement model

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Aziz, A. A., Khatimin, N., Omar, M. Z., & Zaharim, A. (2012). Industrial training assessment of engineering students using Rasch measurement model. In ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences" (pp. 452-456). [6396606] https://doi.org/10.1109/ICSSBE.2012.6396606

Industrial training assessment of engineering students using Rasch measurement model. / Aziz, Azrilah Abdul; Khatimin, Nuraini; Omar, Mohd. Zaidi; Zaharim, Azami.

ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences". 2012. p. 452-456 6396606.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aziz, AA, Khatimin, N, Omar, MZ & Zaharim, A 2012, Industrial training assessment of engineering students using Rasch measurement model. in ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences"., 6396606, pp. 452-456, 2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012, Langkawi, Kedah, 10/9/12. https://doi.org/10.1109/ICSSBE.2012.6396606
Aziz AA, Khatimin N, Omar MZ, Zaharim A. Industrial training assessment of engineering students using Rasch measurement model. In ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences". 2012. p. 452-456. 6396606 https://doi.org/10.1109/ICSSBE.2012.6396606
Aziz, Azrilah Abdul ; Khatimin, Nuraini ; Omar, Mohd. Zaidi ; Zaharim, Azami. / Industrial training assessment of engineering students using Rasch measurement model. ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences". 2012. pp. 452-456
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