The issue of Student Evaluation of Teaching has been explored by a large literature across many decades. However, the role of social influence factors in determining teachers’ responses to a given incentive and evaluation framework has been left basically unexplored. This paper makes a first attempt in this vein by considering an evolutionary game-theoretic context where teachers face a two-stage process in which their rating depends on both students’ evaluation of their course and on retrospective students’ evaluation of their teaching output in view of students’ performance in a related follow-up course. We find that both high effort (difficult course offered) and low effort (easy course offered) outcomes may emerge, leading either to a socially optimal outcome for teachers or not, according to cases. Moreover, there may be a potential conflict between the optimal outcome for students and for teachers. We also consider possible ways to generalize our model in future research.

Student evaluation of teaching, social influence dynamics, and teachers’ choices: An evolutionary model

Brunetti I.;
2020-01-01

Abstract

The issue of Student Evaluation of Teaching has been explored by a large literature across many decades. However, the role of social influence factors in determining teachers’ responses to a given incentive and evaluation framework has been left basically unexplored. This paper makes a first attempt in this vein by considering an evolutionary game-theoretic context where teachers face a two-stage process in which their rating depends on both students’ evaluation of their course and on retrospective students’ evaluation of their teaching output in view of students’ performance in a related follow-up course. We find that both high effort (difficult course offered) and low effort (easy course offered) outcomes may emerge, leading either to a socially optimal outcome for teachers or not, according to cases. Moreover, there may be a potential conflict between the optimal outcome for students and for teachers. We also consider possible ways to generalize our model in future research.
2020
Antoci, A.; Brunetti, I.; Sacco, P.; Sodini, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1069171
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