Using algorithmic taxonomy to evaluate lecturer workload
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Leadership Alliance
2006
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59720/1/Using%20algorithmic%20taxonomy%20to%20evaluate%20lecturer%20workload.pdf http://psasir.upm.edu.my/id/eprint/59720/ http://www.tlainc.com/jkmpv7n206.htm |
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| Summary: | 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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