Unravelling student evaluations of courses and teachers

There is debate over the functional basis of student evaluations of academics, and fresh potential for looking at the data in new ways. Student evaluation data was collated over a three year period (Semester 2 2015 to Semester 1 2018). We used a General Linear Model to estimate the variation in cour...

Full description

Bibliographic Details
Main Authors: Antonio Reverter, Cristina Martinez, Phil Currey, Severine van Bommel, Nicholas J. Hudson
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Cogent Education
Subjects:
Online Access:http://dx.doi.org/10.1080/2331186X.2020.1771830
id doaj-cc2ea4a3efa344a2be66a97be84f60e0
record_format Article
spelling doaj-cc2ea4a3efa344a2be66a97be84f60e02021-04-21T16:14:27ZengTaylor & Francis GroupCogent Education2331-186X2020-01-017110.1080/2331186X.2020.17718301771830Unravelling student evaluations of courses and teachersAntonio Reverter0Cristina Martinez1Phil Currey2Severine van Bommel3Nicholas J. Hudson4Commonwealth Science and Industrial Research OrganisationThe University of QueenslandThe University of QueenslandThe University of QueenslandThe University of QueenslandThere is debate over the functional basis of student evaluations of academics, and fresh potential for looking at the data in new ways. Student evaluation data was collated over a three year period (Semester 2 2015 to Semester 1 2018). We used a General Linear Model to estimate the variation in course scores explained by a number of coordinator and course attributes. Three significant factors collectively explain 49% of the School’s variation in course scores—individual coordinator, student evaluation response rate, and mode of delivery. Next, we used hierarchical clustering to explore the inter-relationships among the eight course and teaching evaluation questions. Learning appears to be related to stimulation, whereas overall satisfaction appears to be related to quality of learning materials and course structure (i.e. aspects of course organisation). Student evaluation response rate is positively correlated to all eight course questions, but most positively to a question relating to receiving adequate feedback. This perhaps implies some reciprocity in the flow of information between student and coordinator. The overall teaching rating awarded to academics clusters most with approachability and encouragement of student input—aspects of temperament and style—and not with explanatory skill or organisational ability.http://dx.doi.org/10.1080/2331186X.2020.1771830student evaluationshierarchical clustering
collection DOAJ
language English
format Article
sources DOAJ
author Antonio Reverter
Cristina Martinez
Phil Currey
Severine van Bommel
Nicholas J. Hudson
spellingShingle Antonio Reverter
Cristina Martinez
Phil Currey
Severine van Bommel
Nicholas J. Hudson
Unravelling student evaluations of courses and teachers
Cogent Education
student evaluations
hierarchical clustering
author_facet Antonio Reverter
Cristina Martinez
Phil Currey
Severine van Bommel
Nicholas J. Hudson
author_sort Antonio Reverter
title Unravelling student evaluations of courses and teachers
title_short Unravelling student evaluations of courses and teachers
title_full Unravelling student evaluations of courses and teachers
title_fullStr Unravelling student evaluations of courses and teachers
title_full_unstemmed Unravelling student evaluations of courses and teachers
title_sort unravelling student evaluations of courses and teachers
publisher Taylor & Francis Group
series Cogent Education
issn 2331-186X
publishDate 2020-01-01
description There is debate over the functional basis of student evaluations of academics, and fresh potential for looking at the data in new ways. Student evaluation data was collated over a three year period (Semester 2 2015 to Semester 1 2018). We used a General Linear Model to estimate the variation in course scores explained by a number of coordinator and course attributes. Three significant factors collectively explain 49% of the School’s variation in course scores—individual coordinator, student evaluation response rate, and mode of delivery. Next, we used hierarchical clustering to explore the inter-relationships among the eight course and teaching evaluation questions. Learning appears to be related to stimulation, whereas overall satisfaction appears to be related to quality of learning materials and course structure (i.e. aspects of course organisation). Student evaluation response rate is positively correlated to all eight course questions, but most positively to a question relating to receiving adequate feedback. This perhaps implies some reciprocity in the flow of information between student and coordinator. The overall teaching rating awarded to academics clusters most with approachability and encouragement of student input—aspects of temperament and style—and not with explanatory skill or organisational ability.
topic student evaluations
hierarchical clustering
url http://dx.doi.org/10.1080/2331186X.2020.1771830
work_keys_str_mv AT antonioreverter unravellingstudentevaluationsofcoursesandteachers
AT cristinamartinez unravellingstudentevaluationsofcoursesandteachers
AT philcurrey unravellingstudentevaluationsofcoursesandteachers
AT severinevanbommel unravellingstudentevaluationsofcoursesandteachers
AT nicholasjhudson unravellingstudentevaluationsofcoursesandteachers
_version_ 1721516035807379456