1 |
L. Zhao, J. Chen, F. Chen, W. Wang, C.T. Lu, and N. Ramakrishnan, "Simnest: Social Media Nested Epidemic Simulation Via Online Semisupervised Deep Learning," Proceeding of 2015 IEEE International Conference on Data Mining, pp. 639-648, 2015.
|
2 |
K.M. Adal, D. Sidibe, S. Ali, E. Chaum, T.P. Karnowski, and F. Meriaudeau, "Automated Detection of Microaneurysms Using Scaleadapted Blob Analysis and Semi-supervised Learning," Computer Methods and Programs in Biomedicine, Vol. 114, No. 1, pp. 1-10, 2014.
DOI
|
3 |
F.H.H. Mahyoub, M.A. Siddiqui, and M.Y. Dahab, "Building an Arabic Sentiment Lexicon Using Semi-supervised Learning," Journal of King Saud University-Computer and Information Sciences, Vol. 26, No. 4, pp. 417-424, 2014.
DOI
|
4 |
Y. Cheng, W. Xu, Z. He, W. He, H. Wu, M. Sun, and Y. Liu, "Semi-supervised Learning for Neural Machine Translation," Proceeding of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1965-1974, 2016.
|
5 |
V. Tam, E.Y. Lam, S.T. Fung, W.W.T. Fok, and A.H. Yuen, "Enhancing Educational Data Mining Techniques on Online Educational Resources with a Semi-supervised Learning Approach," Proceeding of 2015 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, pp. 203-206, 2015.
|
6 |
Y. Wang, H. Fang, Q. Jin, and J. Ma, "SSPA: An Effective Semi-supervised Peer Assessment Method for Large Scale MOOCs," Interactive Learning Environments, pp. 1-19, 2019.
|
7 |
I.E. Livieris, K. Drakopoulou, V.T. Tampakas, T. Mikropoulos, and P. Pintelas, "Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach," Journal of Educational Computing Research, Vol. 57, No. 2, pp. 448-470, 2019.
DOI
|
8 |
S. Klingler, R. Wampfler, T. Kaser, B. Solenthaler, and M.H. Gross, "Efficient Feature Embeddings for Student Classification with Variational Auto-encoders," Proceedings of the 10th International Conference on Educational Data Mining, pp. 72-79, 2017.
|
9 |
Scikit-learn, https://scikit-learn.org/stable/ (accessed October 25, 2019).
|
10 |
KDD Cup 2015, https://biendata.com/competition/kddcup2015/ (accessed October 25, 2019).
|
11 |
Tensorflow, https://www.tensorflow.org/ (accessed October 25, 2019).
|
12 |
D.P. Kingma and J. Ba, "Adam: A method for Stochastic Optimization," arXiv Preprint arXiv: 1412.6980, 2014.
|
13 |
R.S. Baker, A.T. Corbett, and K.R. Koedinger, "Detecting Student Misuse of Intelligent Tutoring Systems," Proceedings of the International Conference on Intelligent Tutoring Systems, pp. 531-540, 2004.
|
14 |
D.P. Kingma and M. Welling, "Auto-encoding Variational Bayes," arXiv Preprint arXiv: 1312.6114, 2013.
|
15 |
SmartTutor, http://www.smarttutor.com/ (accessed October 25, 2019).
|
16 |
AutoTutor, www.autotutor.org/ (accessed October 25, 2019).
|
17 |
Tutor.com, www.tutor.com/ (accessed October 25, 2019).
|
18 |
S. Adjei, K. Ostrow, E. Erickson, and N.T. Heffernan, "Clustering Students in ASSIS Tments: Exploring System-and School-Level Traits to Advance Personalization," Proceedings of the 10th International Conference on Educational Data Mining, pp. 340-341, 2017.
|
19 |
S. Lee and S. Lee, “Data Augmentation for DNN- based Speech Enhancement,” Journal of Korea Multimedia Society, Vol. 22, No. 7, pp. 749-758, 2019.
DOI
|