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Using algorithmic taxonomy to evaluate lecturer workload


Citation

Hashim, Ruhil Hayati and Abdul Hamid, Jamaliah and Selamat, Mohd Hasan and Ibrahim, Hamidah and Abdullah, Rusli and Mohayidin, Mohd Ghazali (2006) Using algorithmic taxonomy to evaluate lecturer workload. Journal of Knowledge Management Practice, 7 (2). ISSN 1705-9232

Abstract

Lecturer workload at universities includes three major categories: teaching, research and services. Teaching workload is influenced by various factors such as level of taught courses, number of student, credit and contact hour and off campus or on campus course design. Universiti Putra Malaysia (UPM) has a Knowledge Management Portal that contains sets of metadata on lecturer profile and knowledge assets. The Lecturer profile contains information of lecturer teaching load, research, publication and many more. We constructed an algorithmic taxonomy based on 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: Article
Divisions: Faculty of Computer Science and Information Technology
Publisher: The Leadership Alliance
Keywords: Taxonomy; Algorithmic taxonomy; Service workload; Scoring; University portal; Knowledge management
Depositing User: Nabilah Mustapa
Date Deposited: 21 Mar 2018 01:34
Last Modified: 21 Mar 2018 01:47
URI: http://psasir.upm.edu.my/id/eprint/59720
Statistic Details: View Download Statistic

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