Learner support in MOOCs: Identifying variables linked to completion

Computers and Education

Volumn 122, Issue , 2018, Pages 153-168

  Gregori, Elena Barberà ^a,b   Zhang, Jingjing ^b   Galván Fernández, Cristina ^c   Fernández Navarro, Francisco de Asís ^d  

^a UNIVERSITAT OBERTA DE CATALUNYA   (Spain)

^b BEIJING NORMAL UNIVERSITY   (China)

^c UNIVERSITY OF BARCELONA   (Spain)

^d UNIVERSIDAD LOYOLA ANDALUCÍA   (Spain)

Author keywords

Distance education; Dropouts; Learner support; Learning strategies; Telelearning

Indexed keywords

COMPUTER AIDED INSTRUCTION; DISTANCE EDUCATION; EDUCATION COMPUTING; LEARNING SYSTEMS; SENSITIVITY ANALYSIS; STUDENTS;

DROPOUTS; EXTREME LEARNING MACHINE; GLOBAL SENSITIVITY ANALYSIS; LEARNING STRATEGY; MASSIVE OPEN ONLINE COURSE; PEDAGOGICAL APPROACH; STUDENT BEHAVIOURS; TELELEARNING;

TEACHING;

EID: 85045033124     PISSN: 03601315     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compedu.2018.03.014     Document Type: Article

References (74)

1. Abeer, W., Miri, B., Students'Preferences and views about learning in a MOOC. Procedia - Social and Behavioral Sciences 152 (2014), 318–323, 10.1016/j.sbspro.2014.09.203.
2. Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J., Engaging with massive online courses. Proceedings of the 23rd international conference on World wide web, 2014, 687–698.
3. Bates, T., Map of research into e-learning. Online learning and distance education resources. 2007 http://www.tonybates.ca/wp-content/uploads/2008/07/map-of-research3.pdf.
4. Bates, T., What's right and what's wrong about Coursera-style MOOCs. Online learning and distance education resources. 2012.
5. Brindley, J.E., Learner support in online distance education: Essential and evolving. Zawacki-Richter, O., Terry, A., (eds.) Online distance education. Towards a research agenda, 2014, AU Press, Edmonton, 287–310.
6. Brinton, C.G., Buccapatnam, S., Chiang, M., Poor, H.V., Mining MOOC clickstreams: On the relationship between learner video-watching behavior and performance. ACD conference on knowledge discovery and data mining (SIGKDD), 2015.
7. Brinton, C.G., Chiang, M., Jain, S., Lam, H., Liu, Z., Wong, F.M.F., Learning about social learning in MOOCs: From statistical analysis to generative model. IEEE Transactions on Learning Technologies 7:4 (2014), 346–359.
8. Brown, M., Costello, E., Donlon, E., Giolla-mhichil, M.N., A strategic response to MOOCs: How one european university is approaching the challenge. International Review of Research in Open and Distance Learning 16:6 (2015), 98–115.
9. Castaño, A., Fernández-Navarro, F., Hervás-Martínez, C., PCA-elm: A robust and pruned extreme learning machine approach based on principal component analysis. Neural Processing Letters 37:3 (2013), 377–392.
10. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16 (2002), 321–357.
11. Chen, Y., Investigating MOOCs through blog mining. The International Review of Research in Open and Distributed Learning, 15(2), 2014 Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1695/2832.
12. Clark, D., MOOCs: Taxonomy of 8 types of MOOC. 2013 Donald Clark Plan B. Retrieved from: http://goo.gl/P4Hb1g.
13. Coetzee, D., Fox, A., Hearst, M.A., Hartmann, B., Should your MOOC forum use a reputation system?. CSCW ‘14, 2014, 1176–1187.
14. Conole, G., MOOCs as disruptive technologies: Strategies for enhancing the learner experience and quality MOOCs. Revista de Educación a Distancia, 39, 2013 Retrieved from: http://www.um.es/ead/red/39/conole.pdf.
15. st century. Keynote paper presented at the 18th annual conference of the asian association of open universities, shanghai, 2004 Retrieved from: http://www.col.org/resources/speeches/2004presentations/Pages/2004-11-30.aspx.
16. Daniel, J., Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education, 3, 2012 Retrieved from: http://www-jime.open.ac.uk/jime/article/viewArticle/2012-18/html.
17. Davis, D., Chen, G., van der Zee, T., Hauff, C., Houben, G.J., Retrieval practice and study planning in MOOCs: Exploring classroom-based self-regulated learning strategies at scale. EC-TEL ‘16, 2016, Springer, 57–71.
18. DeBoer, J., Ho, A., Stump, G., Breslow, L., Changing “Course”: Reconceptualizing educational variables for massive open online courses. Educational Researcher 43 (2014), 74–84.
19. European Commission, Distribution of MOOCs by country. 2015, Open Education Europa Retrieved from: http://www.openeducationeuropa.eu/en/european_scoreboard_moocs.
20. Eynon, R., Hjoth, I., Yasseri, T., Gillani, N., Understanding communication patterns in MOOCs: Combining data mining and qualitative methods. ElAtia, S., Ipperciel, D., Zai¨ane, O., (eds.) Data mining and learning Analytics: Applications in educational research, 2016, Wiley.
21. Fernández-Navarro, F., Carbonero, M., Becerra, D., Torres, M., Global sensitivity estimates for neural networks classifiers. IEEE Transactions on Neural Networks and Learning Systems 28:11 (2017 Nov), 2592–2604.
22. Fernández-Navarro, F., Hervás-Martínez, C., García-Alonso, C., Torres-Jiménez, M., Determination of relative agrarian technical efficiency by a dynamic over-sampling procedure guided by minimum sensitivity. Expert Systems with Applications 38:10 (2011), 12483–12490.
23. Fernández-Navarro, F., Hervás-Martínez, C., Gutiérrez, P.A., A dynamic over-sampling procedure based on sensitivity for multi-class problems. Pattern Recognition 44:8 (2011), 1821–1833.
24. de Freitas, S.I., Morgan, J., Gibson, D., Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision. British Journal of Educational Technology 46:3 (2015), 455–471.
25. Gillani, N., Eynon, R., Communication patterns in massively open online courses. The Internet and Higher Education 23 (2014), 18–26.
26. Gillani, N., Yasseri, T., Eynon, R., Hjorth, I., Structural limitations of learning in a crowd: Communication vulnerability and information diffusion in MOOCs. Scientific Reports, 2014.
27. Guo, P.J., Kim, J., Rubin, R., How video production affects student engagement: An empirical study of MOOC videos. Proceedings of the first ACM conference on Learning@ scale conference, 2014, ACM, 41–50.
28. Holmberg, B., Theory and practice of distance education. 2005, Routledge, Chicago.
29. Huang, G.B., Chen, L., Siew, C.-K., Universal approximation using incremental constructive feedforward networks with random hidden nodes. Neural Networks, IEEE Transactions on 17:4 (2006), 879–892.
30. Huang, G.B., Song, S., Gupta, J.N., Wu, C., Semi-supervised and unsupervised extreme learning machines. IEEE transactions on cybernetics 44:12 (2014), 2405–2417.
31. Huang, G.B., Zhou, H., Ding, X., Zhang, R., Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42:2 (2012), 513–529.
32. Huang, G.B., Zhu, Q., Siew, C., Extreme learning machine: Theory and applications. Neurocomputing 70:1–3 (2006), 489–501.
33. Jordan, K., Initial trends in enrolment and completion of massive open online courses. International Review of Research in Open and Distance Learning, 15(1), 2014.
34. Kalz, M., Kreijns, K., Walhout, J., Castaño-Muñoz, J., Espasa, A., Tovar, E., Establishing a European cross-provider data collection about open online courses. International Review of Research in Open and Distance Learning 16:6 (2015), 62–77.
35. Keegan, D., On defining distance education. Distance Education 1:1 (1980), 13–36.
36. Khalil, H., Ebner, M., MOOCs completion rates and possible methods to improve retention-A literature review. World conference on educational multimedia, hypermedia and telecommunications, 2014, 1305–1313 No. 1.
37. Kizilcec, R.F., Schneider, E., Cohen, G.L., McFarland, D.A., Encouraging forum participation in online courses with collectivist, individualist and neutral motivational framings. In eLearning Papers, 2014, 17–26.
38. Koedinger, K.R., Kim, J., Jia, J.Z., McLaughlin, E.A., Bier, N.L., Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. Proceedings of the second (2015) ACM conference on learning@ scale, 2015, ACM, 111–120.
39. Koseoglu, S., Koutropoulos, A., Teaching presence in MOOCs: Perspectives and learning design strategies. Paper presented at the 10th international conference on networked learning 2016, Lancaster, 2016, May.
40. Kulkarni, C.E., Socher, R., Bernstein, M.S., Klemmer, S.R., Scaling short-answer grading by combining peer assessment with algorithmic scoring. Proceedings of the first ACM conference on Learning@ scale conference, 2014, ACM, 99–108.
41. Labarthe, H., Bouchet, F., Bachelet, R., Yacef, K., Does a peer recommender foster students’ engagement in MOOCs?. EDM ‘16, 2016, 418–423.
42. Laurillard, D., The educational problem that MOOCs could solve: Professional development for teachers of disadvantaged students. Research in Learning Technology, 24, 2016, 29369.
43. Li, L., Tsai, C., Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education 114 (2017), 286–297 (April).
44. Lowenthal, P.R., Hodges, C.B., In search of Quality: Using quality matters to analyze the quality of massive, open. Online Courses (MOOCs) 16:5 (2015), 83–101.
45. Ludwig-Hardman, S., Dunlap, J.C., Learner support services for online students: Scaffolding for success. International Review of Research in Open and Distance Learning, 4(1), 2003.
46. Margaryan, A., Bianco, M., Littlejohn, A., Instructional quality of massive open online courses (MOOCs). Computers & Education 80 (2015), 77–83.
47. McLoughlin, C., Learner support in distance and networked learning environments: Ten dimensions for successful design. Distance Education 23:2 (2002), 149–162.
48. Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., Lendasse, A., OP-ELM: Optimally pruned extreme learning machine. IEEE Transactions on Neural Networks 21:1 (2010), 158–162.
49. Mills, R., Tait, A., (eds.) Rethinking learner support in distance education: Change and continuity in an international context, 2004, Routledge, Chicago.
50. MoE, Opinion of the Ministry of education on strengthening the application and management of the construction of online open courses in Colleges and Universities. 2015, Ministry of Education of the People's Republic of China Retrieved from: http://www.moe.gov.cn/srcsite/A08/s7056/201504/t20150416_189454.html.
51. Morrison, D., How not to design a MOOC: The disaster at coursera and how to fix it. Online learning insights. 2013, 2 de Febrero Retrieved from: http://onlinelearninginsights.wordpress.com/2013/02/01/how-not-to-design-a-mooc-the-disaster-at-coursera-and-how-to-fix-it/.
52. Onah, D.F., Sinclair, J., Boyatt, R., Dropout rates of massive open online courses: Behavioral patterns. EDULEARN14 Proceedings, 2014, 5825–5834.
53. Ramesh, A., Goldwasser, D., Huang, B., Daum, H. III, Getoor, L., Modeling learner engagement in MOOCs using probabilistic soft logic. NIPS workshop on data driven education, 2013.
54. Rullán, M., Valderrama, E., Deschamps, J., Terés, L., Bañeres, D., Saiz, J., CIDUI Congrés Internacional de Docència Universitària i Innovació Hacia un nuevo modelo docente: Curso ‘blended’ de sistemas digitales, Vol. 2, 2014, 1–15.
55. Ross, J., Sinclair, C., Knox, J., Macleod, H., Teacher experiences and academic identity: The missing components of MOOC pedagogy. Journal of Online Learning and Teaching, 10(1), 2014, 57.
56. Sánchez-Monedero, J., Gutiérrez, P.A., Fernández-Navarro, F., Hervás-Martínez, C., Weighting efficient accuracy and minimum sensitivity for evolving multi-class classifiers. Neural Processing Letters 34:2 (2011), 101–116.
57. Sangrà A., Vlachopoulos, D., Cabrera, N., The conceptual framework of e-learning: A view from inside. The International Journal of Learning 18:4 (2012), 93–104.
58. Selwyn, N., Bulfin, S., Pangrazio, L., Massive open online change? Exploring the discursive construction of the ‘MOOC’ in newspapers. Higher Education Quarterly 69:2 (2015), 175–192.
59. Sewart, D., Creating an information base for an individualized support system in distance education. Distance Education 1:2 (1980), 171–187.
60. Simpson, O., Motivating learners in open and distance learning: do we need a new theory of learner support?. Open Learning 23:3 (2008), 159–170.
61. Sinha, T., Cassell, J., Connecting the dots: Predicting student grade sequences from bursty MOOC interactions over time. Proceedings of the second (2015) ACM conference on learning@ scale, 2015, ACM, 249–252.
62. Sinha, T., Jermann, P., Li, N., Dillenbourg, P., Your click decides your fate: Inferring information processing and attrition behavior from mooc video clickstream interactions. 2014 arXiv preprint arXiv:1407.7131.
63. Sinha, T., Li, N., Jermann, P., Dillenbourg, P., Capturing” attrition intensifying” structural traits from didactic interaction sequences of MOOC learners. arXiv preprint arXiv:1409.5887, 2014 Sep 20.
64. Spector, J.M., Remarks on MOOCS and mini-MOOCS. Educational Technology Research & Development 62:3 (2014), 385–392, 10.1007/s11423-014-9339-4.
65. Taylor, C., Veeramachaneni, K., O'Reilly, U.M., . arXiv preprint arXiv:1408.3382 Likely to stop? Predicting stopout in massive open online courses, 2014.
66. Thistoll, T., Yates, A., Improving course completions in distance education: An institutional case study. Distance Education 37:2 (2016), 180–195.
67. Thorpe, M., Rethinking learner support: The challenge of collaborative online learning. Open learning 17:2 (2002), 105–119.
68. Tomkin, J.H., Charlevoix, D., Do professors matter?: Using an a/b test to evaluate the impact of instructor involvement on MOOC student outcomes. L@S ‘14, 2014, 71–78.
69. Tresman, S., Towards a strategy for improved student retention in programmes of open, distance education: A case study from the open university UK. International Review of Research in Open and Distance Learning, 3(1), 2002.
70. Van Rosmalen, P., Sloep, P., Kester, L., Brouns, F., De Croock, M., Pannekeet, K., et al. A learner support model based on peer tutor selection. Journal of Computer Assisted Learning 24:1 (2008), 74–86.
71. Wise, S., Meyer, M., Teaching and learning with technology report. ASSETT CU-boulder faculty: Colorado., 2015 Retrieved from: http://www.colorado.edu/assett/sites/default/files/attached-files/final-fac-survey-full-report.pdf.
72. Yang, D., Sinha, T., Adamson, D., Rosé C.P., Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. Proceedings of the 2013 NIPS Data-driven education workshop, Vol. 11, 2013, December, 14.
73. Yang, D., Wen, M., Rose, C., Peer influence on attrition in massively open online courses. Educational data mining 2014, 2014, July.
74. Zhang, J., Skryabin, M., Song, X., Understanding the dynamics of MOOC discussion forums with simulation investigation for empirical network analysis (SIENA). The Journal of Distance Education, 37(3), 2016.