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Application of rasch- based espegs model in measuring generic skills of engineering students: a new paradigm

A. Rashid, Rozeha and Abdullah, Razimah and Ahmad Ghulman, Hamzah and Masodi, Mohd. Saifudin (2008) Application of rasch- based espegs model in measuring generic skills of engineering students: a new paradigm. WSEAS Transactions on Advances in Engineering Education, 8 (5). 591 - 602. ISSN 1790-1979

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Abstract

The Faculty of Electrical Engineering, Universiti Teknologi Malaysia (FKE) teaching and learning processes was certified to ISO 9001:2000 and now seeks the Engineering Accreditation Council of Malaysia (EAC) approval. One of FKE’s top management commit ment is to ensure the programme accreditation requirements are met. EAC adopts American Accred itation Board of Engineering and Technology 2000 (ABET) principles which promote outcome based education (OBE) learning process. OBE calls for the evaluation of the course learning outcomes (CLO) as specified in each Course Outline. Performance measurement has been largely dependent on students’ pe rformance in carrying out tasks such as tests, quizzes or submission of assignments. Evaluation on the perfo rmance outputs; categorised as technical knowledge and generic skills, gives an indication on the achievement of the subject’s expected CLO. This paper describes a computational model which can be used to measure a subject CLO in an undergraduate electrical engineering program. An overview of the measurement model and its key concepts are presented. ESPEGS Model is the acronym for Engineering Student Performance Evaluation on Generic Skills. This model of measurement is developed based on students’ marks entries and together with Rasch Measurement Model, it can be used to improve the students’ assessment method on the CLO of each subject. Results obtained were assessed against the CLO maps for consistency and used as a guide for future improvement of the teaching method. The study shows that this model of measurement, which adopts Rasch Model based on Logistic Regression Model, can classify students learning ability more accurately with only very few randomly selected students and dimensions as compared to the traditional CGPA method .

Item Type:Article
Subjects:Unspecified
Divisions:Electrical Engineering
ID Code:18882
Deposited By: Kamariah Mohamed Jong
Deposited On:15 Feb 2017 06:54
Last Modified:20 Oct 2017 02:43

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