Students feedback analysis model using deep learning-based method and linguistic knowledge for intelligent educational systems

Journal article


Abdi, A., Sedrakyan, G., Veldkamp, B., van Hillegersberg, J. and van den Berg, S. 2023. Students feedback analysis model using deep learning-based method and linguistic knowledge for intelligent educational systems. Soft Computing. pp. 1-22. https://doi.org/10.1007/s00500-023-07926-2
AuthorsAbdi, A., Sedrakyan, G., Veldkamp, B., van Hillegersberg, J. and van den Berg, S.
Abstract

Student feedback analysis is time-consuming and laborious work if it is handled manually. This study explores the use of a new deep learning-based method to design a more accurate automated system for analysing students’ feedback
(called DTLP: deep learning and teaching process). The DTLP employs convolutional neural networks (CNNs), bidirectional LSTM (BiLSTM), and attention mechanism. To the best of our knowledge, a deep learning-based method using a unified feature set, which is representative of wordembedding, sentiment knowledge, sentiment shifter rules, linguistic and statistical knowledge, has not been thoroughly studied with regard to sentiment analysis of student feedback. Furthermore, DTLP uses multiple strategies to overcome the following drawbacks: contextual polarity; sentence types; words with similar semantic context but opposite sentiment polarity; word coverage limit of an individual lexicon; and word sense variations. To evaluate the DTLP, we conducted an experiment on a large volume of students’ feedback. The results showed (i) DTLP outperforms the existing systems in the field, (ii) DTLP that learns from this unified feature set can acquire significantly higher performance than one that learns from a feature subset, (iii) the ensemble of sentiment shifter rules, word embedding, statistical, linguistic, and sentiment knowledge allows DTLP to obtain significant performance, and (iv) an attention mechanism into CNN-BiLSTM improves the performance of DTLP. In addition, the deployed method looks for potential causes behind student feedback.

KeywordsSentiment analysis; Educational data mining; Deep learning; Student feedback; Intelligent learning environments
Year2023
JournalSoft Computing
Journal citationpp. 1-22
PublisherSpringer Nature
ISSN1433-7479
Digital Object Identifier (DOI)https://doi.org/10.1007/s00500-023-07926-2
Web address (URL)https://link.springer.com/article/10.1007/s00500-023-07926-2
Accepted author manuscript
File Access Level
Open
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online13 Mar 2023
Publication process dates
Accepted11 Feb 2023
Deposited05 Apr 2023
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https://repository.derby.ac.uk/item/9xq4q/students-feedback-analysis-model-using-deep-learning-based-method-and-linguistic-knowledge-for-intelligent-educational-systems

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License: CC BY 4.0
File access level: Open

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