Mining opinions from instructor evaluation reviews: a deep learning approach
Abstract
Abstract 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.
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Machine learning and deep learning algorithms can help with teacher evaluation by performing sentiment analysis of student comments on teacher performance.
“Machine learning and deep learning algorithms can help with teacher evaluation by performing sentiment analysis of student comments on teacher performance (Esparza et al., 2017; Gutiérrez et al., 2018; Onan, 2020), which provides a snapshot of student attitudes toward teachers and their overall learning experiences.”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Onan, 2020
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LD-Recognition: Classroom Action Recognition Based on Passive RFID
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- Authors
- Onan, A.
- Affiliations
- Izmir Kâtip Çelebi University (TR)
- Corresponding author
- Onan, Aytuğ
- ORCID
- Onan, Aytuğ
- Venue
- Computer Applications in Engineering Education
- Publisher
- Wiley
- Published
- 2019-11-06
- Volume
- 28
- Issue
- 1
- Pages
- 117-138
- ISSN
- 1061-3773 (ISSN-L)
- 1099-0542
- OpenAlex
- W2988589399
- Cited by
- 270
- References
- 72
- Citations by year
- 2026: 6 · 2025: 21 · 2024: 38 · 2023: 80 · 2022: 69 · 2021: 44 · 2020: 12
- FWCI
- 14.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Applications in Engineering Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Education › Evaluation of Teaching Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- student evaluations of teaching, sentiment classification, sentiment analysis, recurrent neural networks, attention mechanism, word embeddings, gloves, teaching effectiveness, higher education quality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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