Evaluating students’ evaluations of professors

Braga, Michela; Paccagnella, Marco; Pellizzari, Michele

doi:10.1016/j.econedurev.2014.04.002

Cited by 309 publications

(164 citation statements)

References 48 publications

Supporting

Mentioning

140

Contrasting

Unclassified

Order By: Relevance

“…In terms of rigor, students have been shown to reward faculty on end-of-semester course evaluations for awarding higher grades (Nimmer and Stone, 1991; Rodabaugh and Kravitz, 1994; Sailor et al ., 1997; Carrell and West, 2010). However, two studies that followed up on these same students in subsequent courses found a negative correlation between how they rated faculty and achievement in later courses (Carrell and West, 2010; Braga et al ., 2014), implying that students may be negatively rating the most rigorous, demanding, but yet effective instructors. Alternatively, it is possible that faculty do not provide critical feedback for fear of negative repercussions to the faculty being observed.…”

Section: Discussionmentioning

confidence: 99%

Making the Grade: Using Instructional Feedback and Evaluation to Inspire Evidence-Based Teaching

Gormally

Martella

2016

LSE

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Making the Grade: Using Instructional Feedback and Evaluation to Inspire Evidence-Based Teaching

Gormally

Martella

2016

LSE

View full text Add to dashboard Cite

show abstract

“…10 SETs should not be the only tool to assess teaching effectiveness, but should be combined with other methods of evaluations, including peer assessment and self-assessment. 2,6 The weight that SETs’ input carries in career decisions may be great and justifies the need to enhance their usefulness. 10,11,15 An administrative commitment to provide the appropriate human and monetary resources to support all aspects of faculty assessment is essential.…”

Section: Viewpoint 1: Formal Faculty Assessment Should Include Studenmentioning

confidence: 99%

“…20 In a third study, high SET ratings and student performance in a downstream related course were in fact negatively correlated. 2 …”

Section: Viewpoint 2: Student Evaluation Of Teaching Should Not Be Pamentioning

confidence: 99%

“…24 Not surprisingly, high SET ratings do in fact correlate with narrower grade curves. 2,25 SETs may thus compromise the very thing that they are intended to assess: teaching effectiveness and student learning.…”

Section: Viewpoint 2: Student Evaluation Of Teaching Should Not Be Pamentioning

confidence: 99%

“…Indeed, multiple studies have shown a lack of correlation (or even an inverse correlation) between SET ratings and student learning. 2,19,20 As consumers of education, students are more likely to rate instructors based on factors that have little to do with teaching effectiveness, such as their level of entertainment in the course. Numerous studies have also demonstrated that subjective biases (gender, racial, age) significantly impact SET ratings, even for “objective” categories, such as speed of grading 31 or organization.…”

Section: Viewpoint 2: Student Evaluation Of Teaching Should Not Be Pamentioning

confidence: 99%

See 2 more Smart Citations

Should Student Evaluation of Teaching Play a Significant Role in the Formal Assessment of Dental Faculty? Two Viewpoints

Rowan

Newness²,

Tetradis

et al. 2017

Journal of Dental Education

View full text Add to dashboard Cite

Student evaluation of teaching (SET) is often used in the assessment of faculty members’ job performance and promotion and tenure decisions, but debate over this use of student evaluations has centered on the validity, reliability, and application of the data in assessing teaching performance. Additionally, the fear of student criticism has the potential of influencing course content delivery and testing measures. This Point/Counterpoint article reviews the potential utility of and controversy surrounding the use of SETs in the formal assessment of dental school faculty. Viewpoint 1 supports the view that SETs are reliable and should be included in those formal assessments. Proponents of this opinion contend that SETs serve to measure a school’s effectiveness in support of its core mission, are valid measures based on feedback from the recipients of educational delivery, and provide formative feedback to improve faculty accountability to the institution. Viewpoint 2 argues that SETs should not be used for promotion and tenure decisions, asserting that higher SET ratings do not correlate with improved student learning. The advocates of this viewpoint contend that faculty members may be influenced to focus on student satisfaction rather than pedagogy, resulting in grade inflation. They also argue that SETs are prone to gender and racial biases and that SET results are frequently misinterpreted by administrators. Low response rates and monotonic response patterns are other factors that compromise the reliability of SETs.

show abstract

Mining opinions from instructor evaluation reviews: A deep learning approach

Onan

2019

Comp Applic In Engineering

230

104

View full text Add to dashboard Cite

Student evaluations of teaching (SET) provides potentially essential source of information to achieve educational quality objectives of higher educational institutions. The findings can be utilized as a measure of teaching effectiveness and they may aid the administrative decision‐making process. The purpose of our research is to establish an efficient sentiment classification scheme on instructor evaluation reviews by pursuing the paradigm of deep learning. Deep learning is a recent research direction of machine learning, which seeks to identify a classification scheme with higher predictive performance based on multiple layers of nonlinear information processing. In this study, we present a recurrent neural network (RNN) based model for opinion mining on instructor evaluation reviews. We analyze a corpus containing 154,000 such reviews, with the use of conventional machine learning algorithms, ensemble learning methods, and deep learning architectures. In the empirical analysis, three conventional text representation schemes (namely, term‐presence, term‐frequency [TF], and TF‐inverse document frequency schemes) and four word embedding schemes (namely, word2vec, global vector [GloVe], fastText, and LDA2Vec) have been taken into consideration. The predictive performance of supervised machine learning methods (such as, Naïve Bayes, support vector machines, logistic regression, K‐nearest neighbor, and random forest) and three ensemble learning methods have been examined on word embedding schemes. The extensive empirical analysis indicates that deep learning‐based architectures outperform the conventional machine learning classifiers for the task of sentiment classification on instructor reviews. For the RNN with attention mechanism in conjunction with GloVe word embedding scheme‐based representation a classification accuracy of 98.29% has been obtained.

show abstract

Evaluating students’ evaluations of professors

Cited by 309 publications

References 48 publications

Making the Grade: Using Instructional Feedback and Evaluation to Inspire Evidence-Based Teaching

Making the Grade: Using Instructional Feedback and Evaluation to Inspire Evidence-Based Teaching

Should Student Evaluation of Teaching Play a Significant Role in the Formal Assessment of Dental Faculty? Two Viewpoints

Mining opinions from instructor evaluation reviews: A deep learning approach

Contact Info

Product

Resources

About