Student Feedback on Online Learning by Using Sentiment Analysis: A Literature Review

Ulfa, Saida; Bringula, Rex P.; Kurniawan, Citra; Fadhli, Muhibuddin

doi:10.1109/icet51153.2020.9276578

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…Numerous studies have shown that Twitter sentiment analysis is more efficient when used in certain domains including healthcare [2][3], banking sector [4], marketing [5][6], tourism [7], and politics [8]. Moreover, sentiment analysis is used in the education field to improve the quality of the educational institution's services [9][10], enhance the learning process by analyzing students' feelings [11] and orientation [12], and extract useful information about the teaching methodology of a teacher. In particular, recent studies have shifted their focus to sentiment analysis to detect the strengths and weaknesses of courses in higher education by analyzing students' online opinions [13] and measuring specific university indicators such as university reputation from social media for constructing a ranking mechanism [14].…”

Section: Introductionmentioning

confidence: 99%

Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

Lasri

Riadsolh

Elbelkacemi

2023

Int. J. Emerg. Technol. Learn.

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In recent years, sentiment analysis (SA) has raised the interest of researchers in several domains, including higher education. It can be applied to measure the quality of the services supplied by the higher education institution and construct a university ranking mechanism from social media like Twitter. Hence, this study presents a novel system for Twitter sentiment prediction on Moroccan public universities in real-time. It consists of two phases: offline sentiment analysis phase and real-time prediction phase. In the offline phase, the collected French tweets about twelve Moroccan universities were classified according to their sentiment into ‘positive’, ‘negative’, or ‘neutral’ using six machine learning algorithms (random forest, multinomial Naive Bayes classifier, logistic regression, decision tree, linear support vector classifier, and extreme gradient boosting) with the term frequency-inverse document frequency (TF-IDF) and count vectorizer feature extraction techniques. The results reveal that random forest classifier coupled with TF-IDF has obtained the best test accuracy of 90%. This model was then applied on real-time tweets. The real-time prediction pipeline comprises Twitter streaming API for data collection, Apache Kafka for data ingestion, Apache Spark for real-time sentiment analysis, Elasticsearch for real-time data exploration, and Kibana for data visualization. The obtained results can be used by the Ministry of higher education, scientific research and innovation of Morocco for decision-making process.

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Section: Introductionmentioning

confidence: 99%

Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

Lasri

Riadsolh

Elbelkacemi

2023

Int. J. Emerg. Technol. Learn.

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“…While this method provides a particular perspective on the trend in a particular field, the core of our research is to analyse the literature from the perspective of the educational context and the technique of the methods used. For example, Ulfa et al [8] analysed 12 articles between 2014 and 2019 to investigate the analysis of online student feedback using sentiment analysis. The term sentiment is used in the study as an umbrella term for polarity, emotion and opinion mining.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the authors focus on what algorithms are used with what goal for implementing sentiment analysis for student feedback in online learning. Our study differs from [8], we focus on all types of classroom settings, i.e. online, face-toface, hybrid and not only on purpose and method but also on aspects related to data and tools.…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing of Student's Feedback to Instructors: A Systematic Review

Sunar,

Khalid

2024

IEEE Trans. Learning Technol.

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“…SA algorithms have been developed to handle text data collected from various sources, including social media, educational online sites, healthcare records, and student reviews [3]. Feedback represents the response of end-users, whether online or offline, regarding provided services or their level of satisfaction [4]. SA techniques play a vital role in the development and enhancement of both commercial [5] and educational services [6].…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of student feedback using attention-based RNN and transformer embedding

Zyout,

Zyout

2024

IJ-AI

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Sentiment analysis systems aim to assess people’s opinions across various domains by collecting and categorizing feedback and reviews. In our study, researchers put forward a sentiment analysis system that leverages three distinct embedding techniques: automatic, global vectors (GloVe) for word representation, and bidirectional encoder representations from transformers (BERT). This system features an attention layer, with the best model chosen through rigorous comparisons. In developing the sentiment analysis model, we employed a hybrid dataset comprising students’ feedback and comments. This dataset comprises 3,820 comments, including 2,773 from formal evaluations and 1,047 generated by ChatGPT and prompting engineering. Our main motivation for integrating generative AI was to balance both positive and negative comments. We also explored recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM), with and without pre-trained GloVe embedding. These techniques produced F-scores ranging from 67% to 69%. On the other hand, the sentiment model based on BERT, particularly its KERAS implementation, achieved higher F-scores ranging from 83% to 87%. The Bi-LSTM architecture outperformed other models and the inclusion of an attention layer further enhanced the performance, resulting in F-scores of 89% and 88% from the Bi-LSTM-BERT sentiment models, respectively.

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Student Feedback on Online Learning by Using Sentiment Analysis: A Literature Review

Cited by 15 publications

References 20 publications

Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

Natural Language Processing of Student's Feedback to Instructors: A Systematic Review

Sentiment analysis of student feedback using attention-based RNN and transformer embedding

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