Learning Analytics: The Emergence of a Discipline

American Behavioral Scientist

Volumn 57, Issue 10, 2013, Pages 1380-1400

  Siemens, George ^a  

^a ATHABASCA UNIVERSITY   (Canada)

Author keywords

distributed learning; learning analytics; models

Indexed keywords

EID: 84883762729     PISSN: 00027642     EISSN: 15523381     Source Type: Journal    
DOI: 10.1177/0002764213498851     Document Type: Article

References (64)

1. Anderson J. R.,Corbett A. T.,Koedinger K. R.,Pelletier R.Cognitive tutors: Lessons learned.Journal of the Learning Sciences. 1995;4 (2): 167-207.
2. Anderson P. W.More is different.Science. 1972;177 (4047): 393-396.
3. The theory and practice of online learning. 2008). The theory and practice of online learning (2nd ed.). Athabasca, Canada: Athabasca University Press.Anderson T., ed. Athabasca, Canada: Athabasca University Press; 2008:.
4. Handbook of e-learning research. Andrews R.Haythornthwaite C., ed. London, UK: Sage; 2007:.
5. ArnoldK. (2010). Signals: Applying academic analytics. EDUCAUSE Review. Retrieved from http://www.educause.edu/ero/article/signals-applying-academic-analytics.
6. BakerR. S. J. d.YacefK. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1). Retrieved from http://www.educationaldatamining.org/JEDM/images/articles/vol1/issue1/JEDMVol1Issue1_BakerYacef.pdf.
7. BienkowskiM.FengM.MeansB. (2012). Enhancing teaching and learning through educational data mining and learning analytics. Washington, DC: U.S. Department of Education. Retrieved from http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf.
8. Börner K.Plug-and-play macroscopes.Communications of the ACM. 2011;54 (3): 60-69.
9. Börner K.,Chen C.,Boyack K. W.Visualizing domains of knowledge.Annual Review of Information Science and Technology. 2003;37 (1): 179-255.
10. Brusilovsky P.Adaptive hypermedia: From intelligent tutoring systems to web-based education.User Modeling and User-Adapted Interaction. 2001;11 (1/2): 87-110.
11. Buckingham ShumS. (2012). Learning analytics. UNESCO policy brief. Retrieved from http://iite.unesco.org/pics/publications/en/files/3214711.pdf.
12. Buckingham Shum S.,Ferguson R.Social learning analytics.Educational Technology and Science. 2012;15 (3): 3-26.
13. BurnsH. L. (1989). Foundations of intelligent tutoring systems: An introduction. In RichardsonJ. J.PolsonM. C. (Eds.), Proceedings of the Air Force Forum for Intelligent Tutoring Systems. Retrieved from http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA207096#page=16.
14. Chen C.,Paul R.Visualizing a knowledge domain's intellectual structure.Computer. 2001;34 (3): 65-71.
15. Choudhury T.,Pentland A.Sensing and modeling human networks using the sociometer. Paper presented at the Seventh IEEE International Symposium on Wearable Computers (ISWC'03); 2003White Plains, NY; 2003.
16. Chronicle of Higher Education. (2012). What you need to know about MOOCs. Chronicle of Higher Education. Retrieved http://chronicle.com/article/What-You-Need-to-Know-About/133475/.
17. ClowD.MakriyannisE. (2011). iSpot analysed: Participatory learning and reputation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge. Retrieved from http://dl.acm.org/citation.cfm?id=2090121&CFID=82269174&CFTOKEN=35344405.
18. Contractor N. S.,Monge P. R.,Leonardi P. M.Multidimensional networks and the dynamics of sociomateriality: Bringing technology inside the network.International Journal of Communication. 2011;5:682-720.
19. CooperA. (2012a). A brief history of analytics. JISC CETIS Analytics Series, 1(9). Retrieved from http://publications.cetis.ac.uk/wp-content/uploads/2012/12/Analytics-Brief-History-Vol-1-No9.pdf.
20. CooperA. (2012b). What is analytics? Definitions and essential characteristics. JISC CETIS Analytics Series, 1(5). Retrieved from http://publications.cetis.ac.uk/wp-content/uploads/2012/11/What-is-Analytics-Vol1-No-5.pdf.
21. Dawson S.,Bakharia A.,Heathcote E.SNAPP: Realising the affordances of real-time SNA within networked learning environments. Paper presented at Networked Learning-Seventh International Conference; 2010Aalborg, Denmark; 2010.
22. DenleyT. (2012). Austin Peay State University: Degree Compass. EDUCAUSE Review. Retrieved from http://www.educause.edu/ero/article/austin-peay-state-university-degree-compass.
23. DownesS. (2005). E-learning 2.0. eLearn Magazine. Retrieved from http://elearnmag.acm.org/featured.cfm?aid=1104968.
24. Ellul J.The technological society. New York, NY: Vintage Books; 1964:.
25. Fayyad U.,Piatetsky-Shapiro G.,Smyth P.From data mining to knowledge discovery in databases.American Association for Artificial Intelligence. 1996;17 (30): 37-54.
26. Fischer G.User modeling in human-computer interactions.User Modeling and User-Adapted Interaction. 2001;11:65-86.
27. FritzJ. (2010). Video demo of UMBC's "Check My Activity" tool for students. EDUCAUSE Review. Retrieved from http://www.educause.edu/ero/article/video-demo-umbcs-check-my-activity-tool-students.
28. Garfield E.Citation indexes for science: A new dimension in documentation through association of ideas.Science. 1955;122 (3159): 108-111.
29. GoldsteinP. J. (2005). Academic analytics: The uses of management information and technology in higher education. ECAR Key Findings. Retrieved from http://net.educause.edu/ir/library/pdf/ERS0508/ekf0508.pdf.
30. Granovetter M.The strength of weak ties.American Journal of Sociology. 1973;78 (6): 1360-1380.
31. Greller W.,Drachsler H.Translating learning into numbers: A generic framework for learning analytics.Educational Technology and Society. 2102;19 (3): 42-57.
32. GruzdA. (2010). Automated discovery and analysis of online social networks. Retrieved from https://www.mitacs.ca/events/images/stories/focusperiods/social-presentations/gruzd-mitacs.pdf.
33. HalevyA.NorvigP.PereiraF. (2009). The unreasonable effectives of data. IEEE Intelligent Systems, 24(2), 8-12. doi:ieeecomputersociety.org/10.1109/MIS.2009.36.
34. HawkseyM. (2012). CFHE12 analysis: Summary of Twitter activity. Retrieved from http://mashe.hawksey.info/2012/11/cfhe12-analysis-summary-of-twitter-activity/.
35. Haythornthwaite C.Strong, weak, and latent ties and the impact of new media.Information Society. 2002;18:285-401.
36. Haythornthwaite C.,Andrews R.E-learning theory and practice. London, UK: Sage; 2011:.
37. Hendler J.,Berners-Lee T.From the semantic web to social machines: A research challenge for AI on the World Wide Web.Artificial Intelligence. 2010;174 (2): 156-161.
38. Herskovitz,Baker R. S. J.,Gobert J.,Wixon M.,Pedro M. S.Discovery with models: A case study on carelessness in computer-based science inquiry.American Behavioral Scientist. 2013;:.
39. HeyT.TansleyS.TolleK. (Eds.) (2009). The fourth paradigm: Data-intensive scientific discovery. Microsoft Research. Retrieved from http://research.microsoft.com/en-us/collaboration/fourthparadigm/4th_paradigm_book_complete_lr.pdf.
40. HirstT. (2013). Exploring ONCourse data a little more. Retrieved from http://blog.ouseful.info/2013/01/19/exploring-oncourse-data-a-little-more/.
41. KayD.KornN.OppenheimC. (2012). Legal, risk, and ethical aspects of analytics in higher education. JISC CETIS Analytics Series, 1(6). Retrieved from http://publications.cetis.ac.uk/wp-content/uploads/2012/11/Legal-Risk-and-Ethical-Aspects-of-Analytics-in-Higher-Education-Vol1-No6.pdf.
42. Kim M.,Lee E.A multidimensional analysis tool for visualizing online interactions.Educational Technology and Society. 2012;15 (3): 89-102.
43. KironD.ShockleyR.KruschwitzN.FinchG.HaydockM. (2011). Analytics: The widening gap. MIT Sloan Management Review. Retrieved from http://sloanreview.mit.edu/reports/analytics-advantage/.
44. Kuhn T.The structure of scientific revolutions. 1996). The structure of scientific revolutions (3rd ed.). Chicago, IL: University of Chicago Press.Chicago, IL: University of Chicago Press; 1996:.
45. LatourB. (2008). Beware, your imagination leaves digital traces. Retrieved from http://www.bruno-latour.fr/sites/default/files/P-129-THES-GB.pdf.
46. Lee V. R.,Thomas J. M.Integrating physical activity data technologies into elementary classrooms.Technology Research and Development. 2011;59:865-884.
47. Macfadyen L.,Dawson S.Mining LMS data to develop an "early warning system" for educators: A proof of concept.Computers and Education. 2010;54 (2): 588-599.
48. Macfadyen L. P.,Dawson S.Numbers are not enough: Why e-learning analytics fail to inform an institutional strategic plan.Educational Technology and Society. 2012;15 (3): 149-163.
49. MadrigalA. (2012). How Google builds its maps-and what it means for the future of everything. Atlantic. Retrieved from http://www.theatlantic.com/technology/archive/2012/09/how-google-builds-its-maps-and-what-it-means-for-the-future-of-everything/261913/.
50. ManyikaJ.ChuiM.BrownB.BughinJ.DobbsR.RoxburghC.ByersA. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. Retrieved from http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation.
51. Milgram S.The small world problem.Psychology Today. 1967;2:60-67.
52. Morin E.On complexity. Cresskill, NJ: Hampton Press; 2008:.
53. Morris L. V.,Finnegan C.,Wu S.Tracking student behavior, persistence, and achievement in online courses.Internet and Higher Education. 2005;8:221-231.
54. Mumford L.The automation of knowledge.AV Communication Review. 1964;12 (3): 261-276.
55. Nielsen M.Reinventing discovery. Princeton, NJ: Princeton University Press; 2012:.
56. PageL.BrinS.MotwaniR.WinogradT. (1999). The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab. Retrieved from http://ilpubs.stanford.edu:8090/422/.
57. Rich E.User modeling via stereotypes.Cognitive Science. 1979;3:329-354.
58. Siemens G.,Baker R. S. J.d.Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. New York, NY: ACM; 2012:252-254.
59. Slade S.,Prinsloo P.Learning analytics: Ethical issues and dilemmas.American Behavioral Scientist. 2013;:.
60. Suthers D.,Rosen D.Proceedings of the 1st International Conference on Learning Analytics. New York, NY: ACM; 2011:64-74.
61. Verbert K.,Manouselis N.,Drachsler H.,Duval E.Dataset-driven research to support learning and knowledge analytics.Educational Technology and Society. 2012;15 (3): 133-148.
62. Wellman B.Networks in the global village: Life in contemporary communities. Boulder, CO: Westview Press; 1999:.
63. Wolpers M.,Jehad N.,Verbert K.,Duval E.Tracking actual usage: The attention metadata.Educational Technology and Society. 2007;10 (3): 106-121.
64. World Economic Forum. (2011). Personal data: The emergence of a new asset class. Retrieved from http://www3.weforum.org/docs/WEF_ITTC_PersonalDataNewAsset_Report_2011.pdf.