Using algorithmic taxonomy to evaluate lecture workload: a case study of services application prototype in the UPM KM Portal
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 conta...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Universiti Utara Malaysia
2006
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59719/1/216.pdf http://psasir.upm.edu.my/id/eprint/59719/ |
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| Summary: | 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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