Bowei Hong, Zhiqiang Wei, and Yongquan Yang
[1] M.A.A. Dewan, L. Fuhua, W. Dunwei, and Kinshuk, Predictingdropout-prone students in e-learning education system, Uic-Atc-Scalcom-Cbdcom-Iop, Beijing, China, 2015, 1735–1740. [2] L. Harasim, Shift happens: Online education as a new paradigmin learning, The Internet and Higher Education, 3(1–2), 2000,41–61. doi:10.1016/S1096-7516(00)00032-4. [3] K. Swan, Building learning communities in online courses: Theimportance of interaction, Education, Communication & Information, 2(1), 2002, 23–49. doi:10.1080/1463631022000005016. [4] T. V¨aljataga, P. Hans, and L. Mart, Open online courses:Responding to design challenges, Stanford University, H-STARInstitute, USA; to Associate Professor Jukka M. Laitam{¨a}ki,from New York University, USA, and to Professor Yngve TroyeNordkvelle from Lillehammer University, 2011, 68. [5] C. Tekin and M. van der Schaar, eTutor: Online learning forpersonalized education, arXiv Preprint arXiv:1410.3617, 2014. [6] D. Shah, By the numbers: MOOCS in 2016, Class Central,2016. https://www.class-central.com/report/mooc-stats-2016/(accessed Dec. 25, 2016). [7] C.G. Brinton and C. Mung, Social learning networks: Abrief survey, 2014 48th Annual Conf. on Information Sciences and Systems (CISS), Princeton, NJ, USA, 2014, 1–6.doi:10.1109/CISS.2014.6814139. [8] L. Yuan and P. Stephen, MOOCs and open education: Implications for higher education, Centre for Educational Technology& Interoperability Standards, 4(4), 2013, 206–7. [9] C.E. Hmelo-silver and S.B. Howard, Goals and strategies ofa problem-based learning facilitator, Interdisciplinary Journalof Problem-Based Learning, 1(1), 2006, 21–39. [10] C.E. Hmelo-Silver, C.P. Ros´e, and J. Levy, Fostering a learningcommunity in MOOCs, CEUR Workshop Proc., vol. 1137,2014. [11] D. Ng, Andrew and Koller, The online revolution: edu-cation at scale, 2012 conference of the American Association for the Advancement of Artificial Intelligence, 2012.http://repository.alt.ac.uk/2224/ (accessed Aug. 14, 2012). [12] C.G. Brinton, R. Rill, S. Ha, M. Chiang, R. Smith, and W. Ju,Individualization for education at scale: MIIC design andpreliminary evaluation, IEEE Transactions on Learning Technologies, 8(1), 2015, 136–48. doi:10.1109/TLT.2014.2370635. [13] C. Zhao, J. Yang, J. Liang, et al, Discover learning behaviorpatterns to predict certification, 2016 11th Int. Conf. onComputer Science Education (ICCSE), Nagoya, Japan, 2016,69–73. doi: 10.1109/ ICCSE.2016.7581557. [14] J. Liang, L. Li, and Z. Li, Machine learning applicationin MOOCs: Dropout prediction, 2016 11th Int. Conf. onComputer Science Education (ICCSE), Nagoya, Japan, 2016,52–57. doi: 10.1109/ ICCSE.2016.7581554. [15] M. Kloft, S. Felix, Z. Zhilin, and P. Niels, Predicting MOOCdropout over weeks using machine learning methods, EMNLP2014 Workshop on Analysis of Large Scale Social Interactionin MOOCs, Doha, Qatar, 2014, 60–65. [16] C. Rose, S. George, and P.R. Carolyn, Shared task on predictionof dropout over time in massively open online courses, EMNLP2014 Workshop on Analysis of Large Scale Social Interactionin MOOCs, Doha, Qatar, 2014, 39–41. [17] A. Cohen and U. Shimony, Dropout prediction in a massiveopen online course using learning analytics, Proceedings ofE-Learn: World Conference on E-Learning, Washington, DC,2016, 616–625. [18] D. Yang, S. Tanmay, A. David, and P.R. Carolyn, ‘TurnOn, Tune In, Drop Out’: Anticipating student dropouts inmassive open online courses, NIPS Workshop on Data DrivenEducation, Lake Tahoe, NV, USA, 2013. [19] W. Hong, Z. Siting, and W. Huan, Dynamic recommendation in e-recruitment system, Control and Intelligent Systems,Lausanne, Switzerland, 42(1), 2014, 3–8. [20] G.W. Dekker, M. Pechenizkiy, and J.M. Vleeshouwers, Predicting students drop out: a case study, Proceedings of the 2ndInternational Conference on Educational Data Mining, EDM2009, Cordoba, Spain, 2009, 41–50. [21] C. Piech, J. Huang, Z. Chen, C. Do, A. Ng, and D. Koller, Tunedmodels of peer assessment in MOOCs, Computer Science, 2013,arXiv:1307.2579v1. [22] A. Ramesh, D. Goldwasser, B. Huang, H. Daum, and L. Getoor,Modeling Learner Engagement in MOOCs using ProbabilisticSoft Logic, 2013. [23] J. Cheng, K. Chinmay, and K. Scott, Tools for predicting drop-off in large online classes, CSCW ’13 Proc. of the 2013 Conf. onComputer Supported Cooperative Work Companion, San Antonio, Texas, USA, 2013, 121–24. doi:10.1145/2441955.2441987. [24] Y.H. Hu, C.L. Lo, and S.P. Shih, Developing early warning systems to predict students’ online learning performance,Comput. Human Behav., vol. 36, 2014, 469–478. [25] J. Kittler, H. Mohamad, P.W.D. Robert, and M. Jiri, Oncombining classifiers, IEEE Transactions on Pattern Analysisand Machine Intelligence, 20(3), 1998, 226–239. [26] A. Liaw and W. Matthew, Classification and regression byrandom forest, R News, 2(3), 2002, 18–22. http://cran.r-project.org/doc/Rnews/. [27] C.-C. Chang, and C.-J. Lin. {LIBSVM}: A library for supportvector machines, ACM Transactions on Intelligent Systemsand Technology, 2(3), 2011, 27:1–27:27. [28] A.J. Dobson, An introduction to generalized linear models,Technometrics, 98(464), 2001, 1086–1087. [29] L. Breiman, Random forest, Machine Learning, 45, 2001, 5–32. [30] C. Cortes and V. Vladimir, Support-vector networks, MachineLearning, 20(3), 1995, 273–297. [31] F.M. Harper, M. Daniel, and A.K. Joseph, Facts or friends?Distinguishing informational and conversational questions insocial Q and A Sites, Proc. of the SIGCHI Conf. on HumanFactors in Computing Systems, CHI ’09. New York, NY, USA:ACM, 2009, 759–768. doi:10.1145/1518701.1518819. [32] XuetangX, KDD Cup 2015 – predicting dropouts in MOOC,Biendata.com, 2015. https://kddcup2015.com/competition/kddcup2015/. [33] N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer,SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16(1), 2002, 321–357.doi:10.1613/jair.953.
Important Links:
Go Back
