Data Mining of Undergraduate Course Evaluations

In this paper, we take a new look at the problem of analyzing course evaluations. We examine ten years of undergraduate course evaluations from a large Engineering faculty. To the best of our knowledge, our data set is an order of magnitude larger than those used by previous work on this topic, at o...

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Main Authors: Yuheng Helen JIANG, Sohail Syed JAVAAD, Lukasz GOLAB GOLAB
Format: Article
Language:English
Published: Vilnius University 2016-04-01
Series:Informatics in Education
Subjects:
Online Access:https://infedu.vu.lt/doi/10.15388/infedu.2016.05
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author Yuheng Helen JIANG
Sohail Syed JAVAAD
Lukasz GOLAB GOLAB
author_facet Yuheng Helen JIANG
Sohail Syed JAVAAD
Lukasz GOLAB GOLAB
author_sort Yuheng Helen JIANG
collection DOAJ
description In this paper, we take a new look at the problem of analyzing course evaluations. We examine ten years of undergraduate course evaluations from a large Engineering faculty. To the best of our knowledge, our data set is an order of magnitude larger than those used by previous work on this topic, at over 250,000 student evaluations of over 5,000 courses taught by over 2,000 distinct instructors. We build linear regression models to study the factors affecting course and instructor appraisals, and we perform a novel information-theoretic study to determine when some classmates rate a course and/or its instructor highly but others poorly. In addition to confirming the results of previous regression studies, we report a number of new observations that can help improve teaching and course quality.
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spelling doaj.art-5d7fca74f3c749e298bd370c153cfac72022-12-22T03:17:15ZengVilnius UniversityInformatics in Education1648-58312335-89712016-04-011518510210.15388/infedu.2016.05Data Mining of Undergraduate Course EvaluationsYuheng Helen JIANG0Sohail Syed JAVAAD1Lukasz GOLAB GOLAB2University of Waterloo Waterloo, Ontario, N2L 3G1, CanadaUniversity of Waterloo Waterloo, Ontario, N2L 3G1, CanadaUniversity of Waterloo Waterloo, Ontario, N2L 3G1, CanadaIn this paper, we take a new look at the problem of analyzing course evaluations. We examine ten years of undergraduate course evaluations from a large Engineering faculty. To the best of our knowledge, our data set is an order of magnitude larger than those used by previous work on this topic, at over 250,000 student evaluations of over 5,000 courses taught by over 2,000 distinct instructors. We build linear regression models to study the factors affecting course and instructor appraisals, and we perform a novel information-theoretic study to determine when some classmates rate a course and/or its instructor highly but others poorly. In addition to confirming the results of previous regression studies, we report a number of new observations that can help improve teaching and course quality.https://infedu.vu.lt/doi/10.15388/infedu.2016.05course evaluationentropyregression
spellingShingle Yuheng Helen JIANG
Sohail Syed JAVAAD
Lukasz GOLAB GOLAB
Data Mining of Undergraduate Course Evaluations
Informatics in Education
course evaluation
entropy
regression
title Data Mining of Undergraduate Course Evaluations
title_full Data Mining of Undergraduate Course Evaluations
title_fullStr Data Mining of Undergraduate Course Evaluations
title_full_unstemmed Data Mining of Undergraduate Course Evaluations
title_short Data Mining of Undergraduate Course Evaluations
title_sort data mining of undergraduate course evaluations
topic course evaluation
entropy
regression
url https://infedu.vu.lt/doi/10.15388/infedu.2016.05
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AT sohailsyedjavaad dataminingofundergraduatecourseevaluations
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