Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor

Azab, Mahmoud; Mihalcea, Rada; Abernethy, Jacob

doi:10.1007/978-3-319-47880-7_27

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…2014; Azab et al, 2016;Cunningham-Nelson et al, 2018;Baddam et al, 2019;Sengkey et al, 2019;Hew et al, 2020), and identify topics, themes, or suggestions from student comments (Ramesh et al, 2014;Stupans et al, 2016;Gottipati et al, 2018;Unankard and Nadee, 2019;Hynninen et al, 2019). The common goal of these works is to answer some research questions (e.g., what are sentiment differences across courses and students).…”

Section: Background and Related Workmentioning

confidence: 99%

SETSum: Summarization and Visualization of Student Evaluations of Teaching

Hu¹,

Zhang²,

Sathy³

et al. 2022

Preprint

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Student Evaluations of Teaching (SETs) are widely used in colleges and universities. Typically SET results are summarized for instructors in a static PDF report. The report often includes summary statistics for quantitative ratings and an unsorted list of open-ended student comments. The lack of organization and summarization of the raw comments hinders those interpreting the reports from fully utilizing informative feedback, making accurate inferences, and designing appropriate instructional improvements. In this work, we introduce a novel system, SETSUM, that leverages sentiment analysis, aspect extraction, summarization, and visualization techniques to provide organized illustrations of SET findings to instructors and other reviewers. Ten university professors from diverse departments serve as evaluators of the system and all agree that SETSUM helps them interpret SET results more efficiently; and 6 out of 10 instructors prefer our system over the standard static PDF report (while the remaining 4 would like to have both). This demonstrates that our work holds the potential to reform the SET reporting conventions in the future.

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Section: Background and Related Workmentioning

confidence: 99%

SETSum: Summarization and Visualization of Student Evaluations of Teaching

Hu¹,

Zhang²,

Sathy³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, compared to quantitative ratings, open-ended comments are often underanalyzed or ignored completely due to labor required to provide an adequate summary (Alhija and Fresko, 2009;Hujala et al, 2020), raising the need for contemporary methods in automated text analysis. Recent works start to analyze student comments via text mining and machine learning methods such as sentiment analysis (Wen et al, 2014;Azab et al, 2016;Cunningham-Nelson et al, 2018;Baddam et al, 2019;Sengkey et al, 2019;Hew et al, 2020), and identify topics, themes, or suggestions from student comments (Ramesh et al, 2014;Stupans et al, 2016;Gottipati et al, 2018;Unankard and Nadee, 2019;Hynninen et al, 2019). The common goal of these works is to answer some research questions (e.g., what are sentiment differences across courses and students).…”

Section: Background and Related Workmentioning

confidence: 99%

“…As mentioned in Sec. 2, many existing works have conducted sentiment analysis on SET data (Wen et al, 2014;Azab et al, 2016;Baddam et al, 2019). In UNC's SET instrument, no sentiment labels are explicitly related to student comments.…”

Section: Sentiment Analysismentioning

confidence: 99%

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

2022

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Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources. In this paper, we introduce textless-lib, a PyTorch-based library aimed to facilitate research in this research area. We describe the building blocks that the library provides and demonstrate its usability by discuss three different use-case examples: (i) speaker probing, (ii) speech resynthesis and compression, and (iii) speech continuation. We believe that textless-lib substantially simplifies research the textless setting and will be handful not only for speech researchers but also for the NLP community at large. The code, documentation, and pre-trained models are available at https://github.com/ facebookresearch/textlesslib/.

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“…Therefore, compared to quantitative ratings, open-ended comments are often underanalyzed or ignored completely due to labor required to provide an adequate summary (Alhija and Fresko, 2009;Hujala et al, 2020), raising the need for contemporary methods in automated text analysis. Recent works start to analyze student comments via text mining and machine learning methods such as sentiment analysis (Wen et al, 2014;Azab et al, 2016;Cunningham-Nelson et al, 2018;Baddam et al, 2019;Sengkey et al, 2019;Hew et al, 2020), and identify topics, themes, or Figure 1: The distribution of comments across prevalent topics (aspects). On the left, it shows the aspect bubble chart, and on the right, it shows the summary of the "assignment" aspect.…”

Section: Background and Related Workmentioning

confidence: 99%

SETSum: Summarization and Visualization of Student Evaluations of Teaching

Hu¹,

Zhang²,

Sathy³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

Student Evaluations of Teaching (SETs) are widely used in colleges and universities. Typically SET results are summarized for instructors in a static PDF report. The report often includes summary statistics for quantitative ratings and an unsorted list of open-ended student comments. The lack of organization and summarization of the raw comments hinders those interpreting the reports from fully utilizing informative feedback, making accurate inferences, and designing appropriate instructional improvements. In this work, we introduce a novel system, SETSUM, that leverages sentiment analysis, aspect extraction, summarization, and visualization techniques to provide organized illustrations of SET findings to instructors and other reviewers. Ten university professors from diverse departments serve as evaluators of the system and all agree that SETSUM help them interpret SET results more efficiently; and 6 out of 10 instructors prefer our system over the standard static PDF report (while the remaining 4 would like to have both). This demonstrates that our work holds the potential of reforming the SET reporting conventions in the future.

show abstract

Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor

Cited by 11 publications

References 14 publications

SETSum: Summarization and Visualization of Student Evaluations of Teaching

SETSum: Summarization and Visualization of Student Evaluations of Teaching

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

SETSum: Summarization and Visualization of Student Evaluations of Teaching

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