The instructor's optimal mix of teaching methods
Author(s)
Guest, Ross
Griffith University Author(s)
Year published
2001
Metadata
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This paper presents a model for determining the lecturer's optimal mix of teaching methods. The optimal mix balances the greater time cost of more active teaching methods against the increase in the quality of the learning outcomes that result. In the case of two students in a class, one active learner and one passive learner, the optimal teaching mix and the time that each student chooses to spend learning are jointly determined. The paper also shows that the response of the optimal teaching mix to changes in the learning technology depends on the instructor's (or the university's) utility function. A Benthemite utility ...
View more >This paper presents a model for determining the lecturer's optimal mix of teaching methods. The optimal mix balances the greater time cost of more active teaching methods against the increase in the quality of the learning outcomes that result. In the case of two students in a class, one active learner and one passive learner, the optimal teaching mix and the time that each student chooses to spend learning are jointly determined. The paper also shows that the response of the optimal teaching mix to changes in the learning technology depends on the instructor's (or the university's) utility function. A Benthemite utility function implies equal weighting for additional learning outcomes of 'academic' and 'non-academic' students. A Rawlsian utility function implies a higher weighting of additional learning outcomes of 'non-academic' students. These and other utility functions imply different optimal teaching mixes.
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View more >This paper presents a model for determining the lecturer's optimal mix of teaching methods. The optimal mix balances the greater time cost of more active teaching methods against the increase in the quality of the learning outcomes that result. In the case of two students in a class, one active learner and one passive learner, the optimal teaching mix and the time that each student chooses to spend learning are jointly determined. The paper also shows that the response of the optimal teaching mix to changes in the learning technology depends on the instructor's (or the university's) utility function. A Benthemite utility function implies equal weighting for additional learning outcomes of 'academic' and 'non-academic' students. A Rawlsian utility function implies a higher weighting of additional learning outcomes of 'non-academic' students. These and other utility functions imply different optimal teaching mixes.
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Journal Title
Education economics
Volume
9
Issue
3
Subject
Specialist Studies in Education
Applied Economics