Sentiment Analysis of Student Evaluations of Teaching Using Deep Learning Approach

Sutoyo, Edi; Almaarif, Ahmad; Yanto, Iwan Tri Riyadi

doi:10.1007/978-3-030-80216-5_20

“…Ibrahim et al [121] proposed a data mining framework to analyze student feedback with classification algorithms like Naive Bayes, SVM, decision tree, and random forest. Sutoyo et al [122] proposed a feedback questionnaire for lecturer evaluation based on student feedback to the questionnaire. Sentiment was performed on the student feedback and classified them into positive or negative sentiment using CNN model.…”

Section: ) Sentiment Annotationmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik,

Tao,

Li

et al. 2023

Preprint

0

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Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

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“…It indicated more negative sentiment towards courses than to the teachers. Li et al [66] designed BERT-CNN model to perform sentiment analysis over the learning comments. They used BERT-CNN with a self-attention process which performed same as the classical BERT model even with significantly reduced design parameters.…”

Section: Transformersmentioning

confidence: 99%

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

B A,

K N

2023

JMC

19

0

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The high pace rising global competitions across education sector has forced institutions to enhance aforesaid aspects, which require assessing students or related stakeholders’ perception and opinion towards the learning materials, courses, learning methods or pedagogies, etc. To achieve it, the use of reviews by students can of paramount significance; yet, annotating student’s opinion over huge heterogenous and unstructured data remains a tedious task. Though, the artificial intelligence (AI) and natural language processing (NLP) techniques can play decisive role; yet the conventional unsupervised lexicon, corpus-based solutions, and machine learning and/or deep driven approaches are found limited due to the different issues like class-imbalance, lack of contextual details, lack of long-term dependency, convergence, local minima etc. The aforesaid challenges can be severe over large inputs in Big Data ecosystems. In this reference, this paper proposed an outlier resilient semantic featuring deep driven sentiment analysis model (ORDSAENet) for educational domain sentiment annotations. To address data heterogeneity and unstructured-ness over unpredictable digital media, the ORDSAENet applies varied pre-processing methods including missing value removal, Unicode normalization, Emoji and Website link removal, removal of the words with numeric values, punctuations removal, lower case conversion, stop-word removal, lemmatization, and tokenization. Moreover, it applies a text size-constrained criteria to remove outlier texts from the input and hence improve ROI-specific learning for accurate annotation. The tokenized data was processed for Word2Vec assisted continuous bag-of-words (CBOW) semantic embedding followed by synthetic minority over-sampling with edited nearest neighbor (SMOTE-ENN) resampling. The resampled embedding matrix was then processed for Bi-LSTM feature extraction and learning that retains both local as well as contextual features to achieve efficient learning and classification. Executing ORDSAENet model over educational review dataset encompassing both qualitative reviews as well as quantitative ratings for the online courses, revealed that the proposed approach achieves average sentiment annotation accuracy, precision, recall, and F-Measure of 95.87%, 95.26%, 95.06% and 95.15%, respectively, which is higher than the LSTM driven standalone feature learning solutions and other state-of-arts. The overall simulation results and allied inferences confirm robustness of the ORDSAENet model towards real-time educational sentiment annotation solution.

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“…Ibrahim et al [121] proposed a data mining framework to analyze student feedback with classification algorithms like Naive Bayes, SVM, decision tree, and random forest. Sutoyo et al [122] proposed a feedback questionnaire for lecturer evaluation based on student feedback to the questionnaire. Sentiment analysis was performed on the student feedback and classified them into positive or negative sentiment using CNN model.…”

Section: Figure 7 Sample Knowledge Graphmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik

¹

,

Tao

²

,

Li

³

et al. 2022

View full text Add to dashboard Cite

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

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Sentiment Analysis of Student Evaluations of Teaching Using Deep Learning Approach

Cited by 7 publications

References 19 publications

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

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