Using algorithmic taxonomy to evaluate lecture workload: a case study of services application prototype in the UPM KM Portal

Citation

Abdul Hamid, Jamaliah and Mohayidin, Mohd Ghazali and Selamat, Mohd Hasan and Ibrahim, Hamidah and Abdullah, Rusli and Hashim, Ruhil Hayati (2006) Using algorithmic taxonomy to evaluate lecture workload: a case study of services application prototype in the UPM KM Portal. In: Knowledge Management International Conference & Exhibition (KMICE 2006), 6-8 June 2006, Legend Hotel Kuala Lumpur, Malaysia. (pp. 216-224).

Abstract

Lecturer workload at universities includes three major categories: teaching, research and services. Teaching workload is influence by various factors such as level taught courses, number of student, credit and contact hour and off campus or on campus course design. The UPM has a KM Portal that contains sets of metadata on lecturer profile and knowledge assets. The Lecturer profile contains information lecturer teaching, research, publication and many more. We constructed an algorithmic taxonomy based at the lecturer profile data to measure lecturer teaching workload. This method measures the lecturer teaching workload. The taxonomy is a dynamic hierarchy that extracts validated parameters from the dataset. Results of the study highlight the contributions of this algorithmic method in better evaluation of teaching workload for lecture.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Computer Science and Information Technology
Publisher:	Universiti Utara Malaysia
Keywords:	Taxonomy; Algorithmic taxonomy; Service workload; Scoring; University portal
Depositing User:	Nabilah Mustapa
Date Deposited:	21 Mar 2018 01:22
Last Modified:	21 Mar 2018 01:47
URI:	http://psasir.upm.edu.my/id/eprint/59719
Statistic Details:	View Download Statistic

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