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

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.

[Retracted] Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function‐Particle Swarm Optimization Neural Network (RBFNN‐PSO)

“…At present, many students still use the traditional evaluation mode in teaching quality evaluation, with limited content, less times of assessment, and low data coverage, the students' learning quality problems cannot be reflected truthfully and completely, and teachers cannot find the problems and short boards in the teaching process in time. In order to improve the teaching quality, it is urgent to establish a scientific evaluation system of teaching effect [ 1 ].…”

The impact of external academic accreditation of undergraduate medical program on students’ satisfaction

“…Many factors contribute to this controversy that follows the pendulum movement towards underrating and overeating its validity. From faculty point of view, the student satisfaction might be criticized for the following: variable students’ attitude, confounding effect of students’ performance, low response rate, reliability, and validity of survey as an evaluation tool for instruction, vulnerability to recall bias or bias due to instructor’s gender, personality, ethnic background, technical aspects of data collection, analysis, and construction of survey items [ 39 , 45 – 48 ].…”

“…Many factors contribute to this controversy that follows the pendulum movement towards underrating and overeating its validity. From faculty point of view, the student satisfaction might be criticized for the following: variable students' attitude, confounding effect of students' performance, low response rate, reliability, and validity of survey as an evaluation tool for instruction, vulnerability to recall bias or bias due to instructor's gender, personality, ethnic background, technical aspects of data collection, analysis, and construction of items [39,[45][46][47][48]. Moreover, along with the negative perception of students' evaluation, students may view the survey as a futile effort and burden rather than a way to improve the course instruction, particularly when the quality improvement loop is not closed appropriately.…”

Data Analysis of Educational Evaluation Using K-Means Clustering Method

“…How to properly utilize and analyze massive raw data and turn it into useable knowledge and value has become an important topic of common concern at home and abroad to tackle the difficult problems in educational data processing [ 3 ]. The introduction of information management technology to tackle data problems produced by the influx of students in the field of education has played a major role in the construction of schools [ 4 ], thanks to the rapid development of Internet technology. It is vital to acquire the necessary data for evaluating education.…”