Abstract
Computational intelligence approaches have proven to be effective in enhancing online learning systems. Although many studies have been conducted to reveal the learners’ satisfaction in online learning platforms, the use of machine learning in the analysis of big datasets for this aim has rarely been explored. In addition, although the analysis of online reviews on courses has been carried out in other fields, there are very few contributions in the area of online learning platforms. This study, therefore, aims to perform learner satisfaction analysis through the use of machine learning. We develop a new method using text mining and supervised learning techniques with the aid of the ensemble learning approach. A boosting approach, AdaBoost, is used in ANN for ensemble learning to improve its performance. We employ Artificial Neural Network (ANN) approach, dimensionality reduction and Latent Dirichlet Allocation (LDA) for textual data analysis. Principal Component Analysis (PCA) is used for data dimensionality reduction. We perform several experimental evaluations on the big datasets obtained from the online learning platforms. The accuracy and computation time of the proposed method are assessed on the obtained dataset. The method is compared with several machine learning approaches to show its effectiveness in big datasets analysis. The results showed that the method is effective in predicting learners’ satisfaction from online reviews. In addition, the proposed method outperform other classifiers, K-Nearest Neighbor (K-NN), Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB), in case of accuracy. The results are discussed and research implications from different perspectives are provided for future developments of educational decision support systems.
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Data Availibility
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- ANN:
-
Artificial Neural Network
- ACC:
-
Accuracy
- BERT:
-
Bidirectional Encoder Representations from Transformers
- PCA:
-
Principal Component Analysis
- MOOCs:
-
Massive Open Online Courses
- LDA:
-
Latent Dirichlet Allocation
- S-O-R:
-
Stimulus–Organism–Response
- RNN:
-
Recurrent Neural Networks
- CO:
-
Course Organization
- HE:
-
Helpfulness
- HAN:
-
Hierarchical Attention Network
- QV:
-
Quality of Video and Audio
- QL:
-
Quality of Language
- IE:
-
Instructor Expertise
- IN:
-
Interactivity
- CE:
-
Clarity of Explanation
- QC:
-
Quality of Course Material
- QA:
-
Quality of Course Assignments
- SOM:
-
Self-Organizing Map
- CNN:
-
Convolutional Neural Networks
- FP:
-
False Positive
- PR:
-
Precision
- PDCA:
-
Plan, Do, Check, Act
- FN:
-
False Negative
- TN:
-
True Negative
- TP:
-
True Positive
- RE:
-
Recall
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Acknowledgements
This work was funded by the Deanship of Scientific Research at Jouf University under grant No (DSR-2021-02-0357).
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Appendices
Appendix A Linear correlation among the input variables and input variables and output
Y | X | r | \({\hbox {r}}^{2}\) |
---|---|---|---|
Course organization | Helpfulness | 0.0683 | 0.0047 |
Course organization | Quality of video and audio | 0.0638 | 0.0041 |
Course organization | Quality of language | 0.0870 | 0.0076 |
Course organization | Instructor expertise | 0.0683 | 0.0047 |
Course organization | Interactivity | 0.0701 | 0.0049 |
Course organization | Clarity of explanation | 0.0677 | 0.0046 |
Course organization | Quality of course material | 0.0637 | 0.0041 |
Course organization | Quality of course assignments | 0.0845 | 0.0071 |
Helpfulness | Quality of video and audio | 0.0700 | 0.0049 |
Helpfulness | Quality of language | 0.0855 | 0.0073 |
Helpfulness | Instructor expertise | 0.0640 | 0.0041 |
Helpfulness | Interactivity | 0.0776 | 0.0060 |
Helpfulness | Clarity of explanation | 0.0644 | 0.0041 |
Helpfulness | Quality of course material | 0.0717 | 0.0051 |
Helpfulness | Quality of course assignments | 0.0676 | 0.0046 |
Quality of video and audio | Quality of language | 0.0663 | 0.0044 |
Quality of video and audio | Instructor expertise | 0.0887 | 0.0079 |
Quality of video and audio | Interactivity | 0.0590 | 0.0035 |
Quality of video and audio | Clarity of explanation | 0.0640 | 0.0041 |
Quality of video and audio | Quality of course material | 0.0775 | 0.0060 |
Quality of video and audio | Quality of course assignments | 0.0695 | 0.0048 |
Quality of language | Instructor expertise | 0.0688 | 0.0047 |
Quality of language | Interactivity | 0.0594 | 0.0035 |
Quality of language | Clarity of explanation | 0.0665 | 0.0044 |
Quality of language | Quality of course material | 0.0710 | 0.0050 |
Quality of language | Quality of course assignments | 0.0843 | 0.0071 |
Instructor expertise | Interactivity | 0.0856 | 0.0073 |
Instructor expertise | Clarity of explanation | 0.0655 | 0.0043 |
Instructor expertise | Quality of course material | 0.0716 | 0.0051 |
Instructor expertise | Quality of course assignments | 0.0776 | 0.0060 |
Interactivity | Clarity of explanation | 0.0822 | 0.0068 |
Interactivity | Quality of course material | 0.0610 | 0.0037 |
Interactivity | Quality of Course assignments | 0.0558 | 0.0031 |
Clarity of explanation | Quality of course material | 0.0664 | 0.0044 |
Clarity of explanation | Quality of course assignments | 0.0775 | 0.0060 |
Quality of course Material | Quality of course assignments | 0.0711 | 0.0051 |
Overall Rating (satisfaction) | Course organization | 0.2776 | 0.0771 |
Overall rating (satisfaction) | Helpfulness | 0.2732 | 0.0746 |
Overall rating (satisfaction) | Quality of video and audio | 0.2749 | 0.0756 |
Overall rating (satisfaction) | Quality of language | 0.2834 | 0.0803 |
Overall rating (satisfaction) | Instructor expertise | 0.2854 | 0.0815 |
Overall rating (satisfaction) | Interactivity | 0.2840 | 0.0807 |
Overall rating (satisfaction) | Clarity of explanation | 0.2699 | 0.0728 |
Overall rating (satisfaction) | Quality of course material | 0.2735 | 0.0748 |
Overall rating (satisfaction) | Quality of course assignments | 0.2875 | 0.0826 |
Appendix B ANN Results
See Appendix Tables 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 and 20
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Alsayat, A., Ahmadi, H. A Hybrid Method Using Ensembles of Neural Network and Text Mining for Learner Satisfaction Analysis from Big Datasets in Online Learning Platform. Neural Process Lett 55, 3267–3303 (2023). https://doi.org/10.1007/s11063-022-11009-y
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DOI: https://doi.org/10.1007/s11063-022-11009-y