AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (3.8 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Course Drop-out Prediction on MOOC Platform via Clustering and Tensor Completion

Jinzhi Liao, Jiuyang Tang, Xiang Zhao( )
National University of Defense Technology, Changsha 410072, China.
Show Author Information

Abstract

As a supplement to traditional education, online courses offer people, regardless of their age, gender, or profession, the chance to access state-of-the-art knowledge. Nonetheless, despite the large number of students who choose to begin online courses, it is easy to observe that quite a few of them drop out in the middle, and information on this is vital for course organizers to improve their curriculum outlines. In this work, in order to make a precise prediction of the drop-out rate, we propose a combined method MOOP, which consists of a global tensor and local tensor to express all available feature aspects. Specifically, the global tensor structure is proposed to model the data of the online courses, while a local tensor is clustered to capture the inner connection of courses. Consequently, drop-out prediction is achieved by adopting a high-accuracy low-rank tensor completion method, equipped with a pigeon-inspired algorithm to optimize the parameters. The proposed method is empirically evaluated on real-world Massive Open Online Courses (MOOC) data, and is demonstrated to offer remarkable superiority over alternatives in terms of efficiency and accuracy.

Keywords

MOOC platformdrop-out predictiontensor completionclustering

References

[1]
C., Alario-Hoyos M., Pérez-Sanagustín C. D. Kloos, and P. J. Muñoz-Merino, Recommendations for the design and deployment of MOOCs: Insights about the MOOC digital education of the future deployed in MiríadaX, in Proc. 2nd Int. Conf. Technological Ecosystems for Enhancing Multiculturality, Salamanca, Spain, 2014, pp. 403-408.
[2]
H. Khalil and M. Ebner, MOOCs completion rates and possible methods to improve retention—A literature review, in Proc. World Conf. Educational Multimedia, Hypermedia and Telecommunications, Chesapeake, VA, USA, 2014, pp. 1236-1244.
[3]
D. F. O., Onah J. Sinclair, and R. Boyatt, Dropout rates of massive open online courses: Behavioural patterns, in Proc. 6th Int. Conf. Education and New Learning Technologies, Barcelona, Spain, 2014, pp. 5825-5834.
[4]
J. Z., Qiu J., Tang T. X., Liu J., Gong C. H., Zhang Q. Zhang, and Y. F. Xue, Modeling and predicting learning behavior in MOOCs, in Proc. 9th ACM Int. Conf. Web Search and Data Mining, San Francisco, CA, USA, 2016, pp. 93-102.
[5]
G. L., Zhang T. J., Anderson M. W. Ohland, and B. R. Thorndyke, Identifying factors influencing engineering student graduation: A longitudinal and cross-institutional study, J. Eng. Educ., vol. 93, no. 4, pp. 313-320, 2004.
Crossref Google Scholar
[6]
G., Thomas A. Henderson, and T. L. Goldfinch, The influence of university entry scores on student performance in engineering mechanics, in Proc. Australasian Association for Engineering Education Conf., Adelaide, Australia, 2009, pp. 980-985.
[7]
M. Sheard, Hardiness commitment, gender, and age differentiate university academic performance, Br. J. Educ. Psychol., vol. 79, no. 1, pp. 189-204, 2009.
Crossref Google Scholar
[8]
B. F., French J. C. Immekus, and W. C. Oakes, An examination of indicators of engineering students’ success and persistence, J. Eng. Educ., vol. 94, no. 4, pp. 419-425, 2005.
Crossref Google Scholar
[9]
A. Y. Wang and M. H. Newlin, Predictors of web-student performance: The role of self-efficacy and reasons for taking an on-line class, Comput. Hum. Behav., vol. 18, no. 2, pp. 151-163, 2002.
Crossref Google Scholar
[10]
A. Parker, A study of variables that predict dropout from distance education, Int. J. Educ. Technol., vol. 1, no. 2, pp. 1-10, 1999.
Google Scholar
[11]
N. Elouazizi, Point-of-view mining and cognitive presence in MOOCs: A (computational) linguistics perspective, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 32-37.
Crossref
[12]
S., Moon S. Potdar, and L. Martin, Identifying student leaders from MOOC discussion forums through language influence, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 15-20.
Crossref
[13]
S., Crossley D. S., McNamara R., Baker Y., Wang L., Paquette T. Barnes, and Y. Bergner, Language to completion: Success in an educational data mining massive open online class, in Proc. Int. Conf. Educational Data Mining, Madrid, Spain, 2015.
[14]
M. M., Wen D. Y. Yang, and C. P. Rosé, Sentiment analysis in MOOC discussion forums: What does it tell us? in Proc. of Educational Data Mining, London, UK, 2014.
[15]
M. M., Wen D. Y. Yang, and C. P. Rosé, Linguistic reflections of student engagement in massive open online courses, in Proc. 8th Int. AAAI Conf. Web and Social Media, Ann Arbor, MI, USA, 2014.
[16]
D., Chaplot E. Rhim, and J. Kim, Predicting student attrition in MOOCs using sentiment analysis and neural networks, in Proc. 17th Int. Conf. Artificial Intelligence in Education, Madrid, Spain, 2015.
[17]
J., Whitehill K., Mohan D., Seaton Y. Rosen, and D. Tingley, Delving deeper into MOOC student dropout prediction, arXiv preprint arXiv:1702.06404, 2017.
Google Scholar
[18]
T., Sinha P., Jermann N. Li, and P. Dillenbourg, Your click decides your fate: Inferring information processing and attrition behavior from MOOC video clickstream interactions, in Proc. 2014 Conf. on Empirical Methods in Natural Language Processing, Doha, Qatar, 2014.
[19]
T., Sinha N., Li P. Jermann, and P. Dillenbourg, Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 42-49.
[20]
M., Kloft F., Stiehler Z. L. Zheng, and N. Pinkwart, Predicting MOOC dropout over weeks using machine learning methods, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 60-65.
[21]
S., Nagrecha J. Z. Dillon, and N. V. Chawla, MOOC dropout prediction: Lessons learned from making pipelines interpretable, in Proc. 26th Int. Conf. World Wide Web Companion, Perth, Australia, 2017, pp. 351-359.
[22]
G. K. Balakrishnan and D. Coetzee, Predicting Student Retention in Massive Open Online Courses Using Hidden Markov Models, University of California, Berkeley, CA, USA, Tech. Rep. No. UCB/EEC5-2013-109, 2013.
[23]
R. F. Kizilcec and S. Halawa, Attrition and achievement gaps in online learning, in Proc. 2nd ACM Conf. Learning, Vancouver, Canada, 2015, pp. 57-66.
Crossref
[24]
C., Robinson M., Yeomans J., Reich C. Hulleman, and H. Gehlbach, Forecasting student achievement in MOOCs with natural language processing, in Proc. 6th Int. Conf. Learning Analytics & Knowledge, Edinburgh, UK, 2016, pp. 383-387.
Crossref
[25]
C., Taylor K. Veeramachaneni, and U. M. O’Reilly, Likely to stop? Predicting stopout in massive open online courses, arXiv preprint arXiv:1408.3382, 2014.
Google Scholar
[26]
C. Ye and G. Biswas, Early prediction of student dropout and performance in MOOCSs using higher granularity temporal information, J. Learn. Anal., vol. 1, no. 3, pp. 169-172, 2014.
Crossref Google Scholar
[27]
E., Acar D. M., Dunlavy T. G. Kolda, and M. Mørup, Scalable tensor factorizations for incomplete data, Chemom. Intell. Lab. Syst., vol. 106, no. 1, pp. 41-56, 2011.
Crossref Google Scholar
[28]
T. G. Kolda and B. W. Bader, Tensor decompositions and applications, SIAM Rev., vol. 51, no. 3, pp. 455-500, 2009.
Crossref Google Scholar
[29]
J., Liu P., Musialski P. Wonka, and J. P. Ye, Tensor completion for estimating missing values in visual data, IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 208-220, 2013.
Crossref Google Scholar
[30]
H. B. Duan and P. X. Qiao, Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning, Int. J. Intell. Comput. Cybern., vol. 7, no. 1, pp. 24-37, 2014.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 24 Issue 4,
August 2019
Pages 412-422
DOI: 10.26599/TST.2018.9010110
Cite this article:
Liao J, Tang J, Zhao X. Course Drop-out Prediction on MOOC Platform via Clustering and Tensor Completion. Tsinghua Science and Technology, 2019, 24(4): 412-422. https://doi.org/10.26599/TST.2018.9010110

684

Views

15

Downloads

13

Crossref

N/A

Web of Science

20

Scopus

0

CSCD

Altmetrics
Received: 15 May 2018
Accepted: 26 June 2018
Published: 07 March 2019
© The author(s) 2019
Return