Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder
This paper presents TVAE-SSL, a novel semi-supervised learning (SSL) paradigm that involves Tabular Variational Autoencoder (TVAE)-sampled synthetic data injection into the training process to enhance model performance under low-label data conditions in Educational Data Mining tasks. The algorithm b...
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| Published in: | Algorithms Vol. 18; no. 10; p. 663 |
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| ISSN: | 1999-4893, 1999-4893 |
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| Abstract | This paper presents TVAE-SSL, a novel semi-supervised learning (SSL) paradigm that involves Tabular Variational Autoencoder (TVAE)-sampled synthetic data injection into the training process to enhance model performance under low-label data conditions in Educational Data Mining tasks. The algorithm begins with training a TVAE on the given labeled data to generate imitative synthetic samples of the underlying data distribution. These synthesized samples are treated as additional unlabeled data and combined with the original unlabeled ones in order to form an augmented training pool. A standard SSL algorithm (e.g., Self-Training) is trained using a base classifier (e.g., Random Forest) on the combined dataset. By expanding the pool of unlabeled samples with realistic synthetic data, TVAE-SSL improves training sample quantity and diversity without introducing label noise. Large-scale experiments on a variety of datasets demonstrate that TVAE-SSL can outperform baseline supervised models in the full labeled dataset in terms of accuracy, F1-score and fairness metrics. Our results demonstrate the capacity of generative augmentation to enhance the effectiveness of semi-supervised learning for tabular data. |
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| AbstractList | This paper presents TVAE-SSL, a novel semi-supervised learning (SSL) paradigm that involves Tabular Variational Autoencoder (TVAE)-sampled synthetic data injection into the training process to enhance model performance under low-label data conditions in Educational Data Mining tasks. The algorithm begins with training a TVAE on the given labeled data to generate imitative synthetic samples of the underlying data distribution. These synthesized samples are treated as additional unlabeled data and combined with the original unlabeled ones in order to form an augmented training pool. A standard SSL algorithm (e.g., Self-Training) is trained using a base classifier (e.g., Random Forest) on the combined dataset. By expanding the pool of unlabeled samples with realistic synthetic data, TVAE-SSL improves training sample quantity and diversity without introducing label noise. Large-scale experiments on a variety of datasets demonstrate that TVAE-SSL can outperform baseline supervised models in the full labeled dataset in terms of accuracy, F1-score and fairness metrics. Our results demonstrate the capacity of generative augmentation to enhance the effectiveness of semi-supervised learning for tabular data. |
| Audience | Academic |
| Author | Kotsiantis, Sotiris Fazakis, Nikos Dimakopoulos, Yiannis Kostopoulos, Georgios |
| Author_xml | – sequence: 1 givenname: Georgios orcidid: 0000-0002-7374-0099 surname: Kostopoulos fullname: Kostopoulos, Georgios – sequence: 2 givenname: Nikos orcidid: 0000-0001-7687-2380 surname: Fazakis fullname: Fazakis, Nikos – sequence: 3 givenname: Sotiris orcidid: 0000-0002-2247-3082 surname: Kotsiantis fullname: Kotsiantis, Sotiris – sequence: 4 givenname: Yiannis orcidid: 0000-0002-8671-0657 surname: Dimakopoulos fullname: Dimakopoulos, Yiannis |
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| Cites_doi | 10.1145/2090236.2090255 10.1186/s41239-024-00450-9 10.1093/nsr/nwx106 10.1109/ACCESS.2021.3134787 10.1016/j.neucom.2010.01.018 10.1002/widm.1355 10.1007/978-3-031-01548-9 10.1109/ACCESS.2022.3156073 10.1007/s10115-009-0209-z 10.1109/TNN.2009.2015974 10.1145/3287560.3287589 10.1109/TSMCC.2010.2053532 10.1023/A:1010933404324 10.1002/widm.1075 10.1145/279943.279962 10.1145/3457607 10.1007/978-3-319-23781-7_21 10.1111/bjet.12868 10.1145/2801948.2802013 10.1007/s10115-013-0706-y 10.1186/s12909-023-04856-3 10.1109/TLT.2019.2911581 10.1109/TKDE.2005.186 10.1109/IISA.2017.8316425 10.1145/2939672.2939785 10.1109/IJCNN.2016.7727598 10.1007/11430919_71 10.3390/app10238413 10.1109/TSMCA.2007.904745 10.1109/ICDMW.2008.27 10.1007/978-3-030-75178-4 10.1142/S0218213019400013 10.1145/3290605.3300830 10.1007/s44163-021-00016-y |
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| References | ref_14 Mikre (ref_1) 2011; 6 ref_13 ref_35 ref_34 ref_31 Romero (ref_4) 2020; 10 ref_19 Romero (ref_2) 2010; 40 ref_18 Zhou (ref_7) 2018; 5 ref_39 ref_16 ref_38 ref_15 Zhou (ref_41) 2005; 17 Kostopoulos (ref_32) 2021; 9 Kloos (ref_12) 2018; 12 Flanagan (ref_24) 2022; 10 Xu (ref_47) 2019; 32 Romero (ref_3) 2013; 3 Breiman (ref_42) 2001; 45 Ke (ref_44) 2017; 30 Kitto (ref_11) 2019; 50 ref_25 ref_45 ref_22 Hardt (ref_46) 2016; 29 ref_21 Rahmani (ref_30) 2024; 21 ref_43 Kostopoulos (ref_17) 2019; 28 ref_20 Kostopoulos (ref_9) 2019; 12 Kostopoulos (ref_10) 2021; Volume 2 Triguero (ref_33) 2015; 42 ref_29 Zhou (ref_36) 2010; 24 Chapelle (ref_8) 2009; 20 ref_28 ref_27 ref_26 Li (ref_37) 2007; 37 ref_5 James (ref_23) 2021; 1 Yaslan (ref_40) 2010; 73 ref_6 |
| References_xml | – ident: ref_45 doi: 10.1145/2090236.2090255 – volume: 21 start-page: 19 year: 2024 ident: ref_30 article-title: Dropout in online higher education: A systematic literature review publication-title: Int. J. Educ. Technol. High. Educ. doi: 10.1186/s41239-024-00450-9 – volume: 5 start-page: 44 year: 2018 ident: ref_7 article-title: A brief introduction to weakly supervised learning publication-title: Natl. Sci. Rev. doi: 10.1093/nsr/nwx106 – volume: Volume 2 start-page: 79 year: 2021 ident: ref_10 article-title: Exploiting semi-supervised learning in the education field: A critical survey publication-title: Advances in Machine Learning/Deep Learning-Based Technologies – volume: 9 start-page: 165881 year: 2021 ident: ref_32 article-title: Interpretable models for early prediction of certification in MOOCs: A case study on a MOOC for smart city professionals publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3134787 – ident: ref_5 – volume: 29 start-page: 1 year: 2016 ident: ref_46 article-title: Equality of opportunity in supervised learning publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_26 – volume: 73 start-page: 1652 year: 2010 ident: ref_40 article-title: Co-training with relevant random subspaces publication-title: Neurocomputing doi: 10.1016/j.neucom.2010.01.018 – volume: 10 start-page: e1355 year: 2020 ident: ref_4 article-title: Educational data mining and learning analytics: An updated survey publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1355 – ident: ref_6 doi: 10.1007/978-3-031-01548-9 – volume: 10 start-page: 26230 year: 2022 ident: ref_24 article-title: Fine grain synthetic educational data: Challenges and limitations of collaborative learning analytics publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3156073 – volume: 24 start-page: 415 year: 2010 ident: ref_36 article-title: Semi-supervised learning by disagreement publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-009-0209-z – volume: 20 start-page: 542 year: 2009 ident: ref_8 article-title: Semi-supervised learning publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2009.2015974 – ident: ref_39 – volume: 12 start-page: 384 year: 2018 ident: ref_12 article-title: Prediction in MOOCs: A review and future research directions publication-title: IEEE Trans. Learn. Technol. – ident: ref_29 doi: 10.1145/3287560.3287589 – volume: 40 start-page: 601 year: 2010 ident: ref_2 article-title: Educational data mining: A review of the state of the art publication-title: IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. doi: 10.1109/TSMCC.2010.2053532 – volume: 45 start-page: 5 year: 2001 ident: ref_42 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 3 start-page: 12 year: 2013 ident: ref_3 article-title: Data mining in education publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1075 – ident: ref_35 doi: 10.1145/279943.279962 – ident: ref_22 doi: 10.1145/3457607 – ident: ref_13 doi: 10.1007/978-3-319-23781-7_21 – volume: 50 start-page: 2855 year: 2019 ident: ref_11 article-title: Practical ethics for building learning analytics publication-title: Br. J. Educ. Technol. doi: 10.1111/bjet.12868 – ident: ref_15 doi: 10.1145/2801948.2802013 – volume: 42 start-page: 245 year: 2015 ident: ref_33 article-title: Self-labeled techniques for semi-supervised learning: Taxonomy, software and empirical study publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-013-0706-y – ident: ref_31 doi: 10.1186/s12909-023-04856-3 – volume: 12 start-page: 212 year: 2019 ident: ref_9 article-title: Multiview learning for early prognosis of academic performance: A case study publication-title: IEEE Trans. Learn. Technol. doi: 10.1109/TLT.2019.2911581 – ident: ref_25 – volume: 17 start-page: 1529 year: 2005 ident: ref_41 article-title: Tri-training: Exploiting unlabeled data using three classifiers publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2005.186 – ident: ref_27 – ident: ref_14 doi: 10.1109/IISA.2017.8316425 – ident: ref_43 doi: 10.1145/2939672.2939785 – ident: ref_16 doi: 10.1109/IJCNN.2016.7727598 – ident: ref_34 doi: 10.1007/11430919_71 – volume: 6 start-page: 109 year: 2011 ident: ref_1 article-title: The roles of information communication technologies in education: Review article with emphasis to the computer and internet publication-title: Ethiop. J. Educ. Sci. – ident: ref_18 doi: 10.3390/app10238413 – volume: 37 start-page: 1088 year: 2007 ident: ref_37 article-title: Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples publication-title: IEEE Trans. Syst. Man, Cybern.-Part A Syst. Hum. doi: 10.1109/TSMCA.2007.904745 – volume: 32 start-page: 1 year: 2019 ident: ref_47 article-title: Modeling tabular data using conditional gan publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_19 – ident: ref_38 doi: 10.1109/ICDMW.2008.27 – ident: ref_21 doi: 10.1007/978-3-030-75178-4 – ident: ref_20 – volume: 28 start-page: 1940001 year: 2019 ident: ref_17 article-title: A semi-supervised regression algorithm for grade prediction of students in distance learning courses publication-title: Int. J. Artif. Intell. Tools doi: 10.1142/S0218213019400013 – ident: ref_28 doi: 10.1145/3290605.3300830 – volume: 30 start-page: 1 year: 2017 ident: ref_44 article-title: Lightgbm: A highly efficient gradient boosting decision tree publication-title: Adv. Neural Inf. Process. Syst. – volume: 1 start-page: 15 year: 2021 ident: ref_23 article-title: Synthetic data use: Exploring use cases to optimise data utility publication-title: Discov. Artif. Intell. doi: 10.1007/s44163-021-00016-y |
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| SubjectTerms | Academic failure Accuracy Algorithms At risk students College students Data mining Datasets Distance learning Education Educational Data Mining Experiments fairness Higher education Labels Machine learning Methods Performance evaluation prediction Quality of education Samples Semi-supervised learning Synthetic data Tables (data) variational auto-encoder |
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| Title | Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder |
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