Download Compiled Bibliography for EDUCAUSE 10-21-15 38

Bibliography for EDUCAUSE
Learning Analytics – Must Read Bibliography
Assembled by James Willis, Ph.D., Kimberly Arnold, Patsy Moskal, Ed.D.
With assistance from Andrea Hermsdorfer, & Genny DeCantis
Baepler, P. and Murdoch, C.J. 2010. Academic analytics and data mining in higher education.
International Journal for the Scholarship of Teaching and Learning 4, 2 (2010) 17.
Retrieved from:
http://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1237&context=ij
-sotl
Baker, R.S.J.D. and Yacef, K. 2009. The state of educational data mining in 2009: A review and
future visions. Journal of Educational Data Mining 1, 1 (Fall 2009), 3-17.
Buckingham-Shum, S. and Ferguson, R. 2012. Social learning analytics. Journal of Educational
Technology & Society 15, 3 (July 2012), 3-26.
Chatti, M.A., Dyckhoff, A.L., Schroeder, U., and Thüs, H. 2012. A reference model for learning
analytics. International Journal of Technology Enhanced Learning 4, 5/6 (2012), 318331.
De Laat, M. and Prinsen, F. 2014. Social learning analytics: Navigating the changing settings of
higher education. Research and Practice in Assessment 9 (Winter 2014), 51-60.
Retrieved from:
http://dspace.ou.nl/bitstream/1820/5870/1/Social%20Learning%20Analytics%20Navigati
ng%20the%20changing%20settings%20of%20HE%20final.pdf
Ferguson, R. 2012. Learning analytics: Drivers, developments, and challenges. International
Journal of Technology Enhanced Learning 4, 5/6 (2012), 304-317. Retrieved from:
http://oro.open.ac.uk/36374/1/IJTEL40501_Ferguson%2520Jan%25202013.pdf
Greller, W. and Draschler, H. 2012. Translating learning into numbers: A generic framework for
learning analytics. Journal of Educational Technology & Society 15, 3 (July 2012), 4257. Retrieved from: http://ifets.info/journals/15_3/4.pdf
Hickey, D.T., Kelley, T.A., and Shen, X. 2014. Small to big before massive: Scaling up
participatory learning analytics. In Proceedings of the Fourth International Conference
on Learning Analytics and Knowledge (Indianapolis, IN, 24-28 March 2014) LAK14.
ACM, New York, NY, 93-97.
Hickey, D.T., Quick, J.D., and Shen, X. 2015. Formative and summative analyses of disciplinary
engagement and learning in a big open online course. In Proceedings of the Fifth
International Conference on Learning Analytics and Knowledge (Poughkeepsie, NY 1620 March 2015) LAK15. ACM, New York, NY, 310-314.
Iglesias-Pradas, S., Ruiz-de-Azcárate, C. and Agudo-Peregrina, A.F. 2015. Assessing the
suitability of student interactions from Moodle data logs as predictors of cross-curricular
competencies. Computers in Human Behavior 47 (June 2015), 81-89.
Norris, D., Baer, L. (nd). A toolkit for building organizational capacity for analytics. Strategic
Initiatives: Herndon, Virginia. Retrieved from:
https://docs.google.com/file/d/0B0CqeHHcp-YzMEZPY2hCRHpyLVU/edit.
Norris, D., Baer, L., Leonard, J., Pugliese, L., and Lefrere, P. 2008. Action analytics: Measuring
and improving performance that matters in higher education. EDUCAUSE Review 43, 1
(2008), 42.
Piety, P J. 2013. Assessing the Educational Data Movement. Teachers College Press, New York,
NY.
Piety, P.J., Hickey, D.T., and Bishop, M.J. 2014. Educational data sciences: Framing emergent
practices for analytics of learning, organizations, and systems. In Proceedings of the
Fourth International Conference on Learning Analytics and Knowledge (Indianapolis,
IN, 24-28 March 2014) LAK14. ACM, New York, NY, 193-202.
Siemens, G. 2013. Learning analytics: The emergence of a discipline. American Behavioral
Scientist 57, 10 (September 2013), 1-21.
van Barneveld, A., Arnold, K.E., and Campbell, J.P. 2012. Analytics in higher education:
Establishing a common language. EDUCAUSE Learning Initiative 1 (2012), 1-11.
Learning Analytics and Ethics
Campbell, J.P., DeBlois, P.B., and Oblinger, D.G. 2007. Academic analytics: A new tool for a
new era. EDUCAUSE Review.
Ferguson, R. 2012. Learning analytics: Drivers, developments and challenges. International
Journal of Technology Enhanced Learning 4 (5/6), 304-317.
Greller, W. and Drachsler, H. 2012. Translating learning into numbers: A generic framework for
learning analytics. Journal of Educational Technology & Society 15, 3 (July 2012), 4257.
Guta, A., Nixon, S.A., Wilson, M.G. 2013. Resisting the seduction of ‘ethics creep’: Using
Foucault to surface complexity and contradiction in research ethics review. Social
Science & Medicine 98 (2013), 301-310.
Hack, C. 2015. Applying learning analytics to smart learning – ethics and policy. In Middleton,
A. (ed.) Smart learning: Teaching and learning with smartphones and tablets in post
compulsory education. Media-Enhanced Learning Special Interest Group and Sheffield
Hallam University.
Johnson, J.A. 2012. Ethics of data mining and predictive analytics in higher education. In
Proceedings of the Rocky Mountain Association for Institutional Research Conference
(Laramie, Wyoming, October 5, 2012).
Macfadyn, L.P. and Dawson, S. 2012. Numbers are not enough. Why e-learning analytics failed
to inform an institutional strategic plan. Educational Technology & Society 15, 3 (July
2012), 149-163.
Pardo, A. and Siemens, G. 2014. Ethical and privacy principles for learning analytics. British
Journal of Educational Technology 45, 3, 438-450. DOI= 10.1111/bjet.12152
Prinsloo, P. and Slade, S. 2013. An evaluation of policy frameworks for addressing ethical
considerations in learning analytics. In Proceedings of the Third International
Conference on Learning Analytics and Knowledge (Leuven, Belgium, 8-12 April 2013)
LAK13. ACM, New York, NY, 240-244.
Prinsloo, P. and Slade, S. 2014. Educational triage in higher open education: Walking a moral
tightrope. In Proceedings of EDEN 2014 Annual Conference: E-Learning at Work and
the Workplace (Zagreb, Croatia, 10-13 June 2014), European Distance and E-Learning
Network, 1-6.
Prinsloo, P. and Slade, S. 2014. Student data privacy and institutional accountability in an age of
surveillance. In Menon, M.E., Terkla, D.G., and Gibbs, P. (eds.) Using Data to Improve
Higher Education. Rotterdam, The Netherlands: Sense Publishers.
Prinsloo, P. and Slade, S. 2015. Student privacy self-management: Implications for learning
analytics. In Proceedings of the Fifth International Conference on Learning Analytics
and Knowledge (Poughkeepsie, NY, 16-20 March 2015) LAK15. ACM, New York,
NY, 83-92.
Slade, S. and Prinsloo, P. 2013. Learning analytics: Ethical issues and dilemmas. American
Behavioral Scientist 57, 1, 1510-1529. DOI= 10.1177/0002764213479366
Swenson, J. 2014. Establishing an ethical literacy for learning analytics. In Proceedings of the
Learning Analytics and Knowledge conference (Indianapolis, IN, March 24-28, 2014).
LAK ’14. ACM, New York, NY, 246-250.
Wall, A.F., Hursh, D., Rodgers, III, J.W. 2014. Assessment for whom: Repositioning higher
education assessment as an ethical and value-focused social practice. Research and
Practice in Assessment 9, 1 (Summer, 2014), 5-17.
Willis, J.E. 2014. Learning analytics and ethics: A framework beyond utilitarianism.
EDUCAUSE Review.
Willis, J.E., Campbell, J., Pistilli, M. 2013. Ethics, big data, and analytics: A model for
application. EDUCAUSE Review.
Willis, J.E., Quick, J.D., and Hickey, D.T. 2015. Digital badges and ethics: The uses of
individual learning data in social contexts. In Proceedings of the Open Badges in
Education workshop (Poughkeepsie, NY, March 16, 2015). OBIE ’15, ACM, New York,
NY, 1-5.
Willis, J. & Strunk, V. (2015). Ethical responsibilities of preserving academicians in an age of
mechanized learning: Balancing the demands of educating at capacity and preserving
human interactivity. In J. White (Ed.), Rethinking Machine Ethics in the Age of
Ubiquitous Technology. IGI Global: Advances in Human and Social Aspects of
Technology series.
Learning Analytics General Bibliography
Abdullah, Z., Herawan, T., Ahmad, N., & Deris, M. M. (2011). Extracting highly positive
association rules from students’ enrollment data. Social and Behavioral Sciences, 28,
107-111. Retrieved from
http://www.sciencedirect.com/science/article/pii/S187704281102461X
Ali, L., Hatala, M., Gasevic, D., & Jovanovic, J. (2011). A qualitative evaluation of evolution of
a learning analytics tool. Computers & Education, 58, 470-489.
Analytics series: Vol. 1, No. 3. Analytics for learning and teaching by Mark van Harmelen and
David Workman. Retrieved from
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.257.3748&rep=rep1&type=pdf
Arnold, K. E., Lonn, S., & Pistilli, M. D. (2014, March). An exercise in institutional reflection:
The learning analytics readiness instrument (LARI). In Proceedings of the Fourth
International Conference on Learning Analytics and Knowledge (pp. 163-167). ACM.
Arnold, K. E. & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to
increase student success. In Proceedings of the 2nd International Conference on Learning
Analytics and Knowledge (pp. 267–270). Retrieved from
http://www.itap.purdue.edu/learning/docs/research/Arnold_PistilliPurdue_University_Course_Signals-2012.pdf
Arnold, K. E. (2010) Signals: Applying academic analytics. EDUCAUSE Review Online.
Retrieved from http://er.educause.edu/articles/2010/3/signals-applying-academicanalytics
Baepler, P. & Murdoch, C. J. (2010). Academic analytics and data mining in higher education.
International Journal for the Scholarship of Teaching and Learning, 4(2). Retrieved from
http://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1237&context=ij
-sotl
Bakharia, A. Heathcote, E., & Dawson, S. (2009). Social networks adapting pedagogical
practice: SNAPP. Poster at Auckland 2009.
Barber, R. & Sharkey, M. (2012). Course correction: using analytics to predict course success. In
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
(pp. 259–262).
Bichsel, J. (2012). Analytics in Higher Education: Benefits, Barriers, Progress, and
Recommendations. EDUCAUSE Center for Applied Research. Retrieved from
https://net.educause.edu/ir/library/pdf/ERS1207/ers1207.pdf
Boyd, d. & Crawford, K. (2011, September, 21). Six provocations for big data (Presentation on
Paper). Oxford Internets Institute’s .Retrieved from
http://softwarestudies.com/cultural_analytics/Six_Provocations_for_Big_Data.pdf
Bramucci, R. & Gaston, J. (2012). Sherpa: increasing student success with a recommendation
engine. In Proceedings of the 2nd International Conference on Learning Analytics and
Knowledge (pp. 82–83).
Campbell, J. P. Oblinger, D. G. (2007, October). Academic Analytics. EDUCAUSE. Retrieved
from https://net.educause.edu/ir/library/pdf/PUB6101.pdf
Campbell, J. P., DeBlois, P. B. & Oblinger, D. G. (2007). Academic analytics: A new tool for a
new era. EDUCAUSE Review, 40-56. Retrieved from
http://er.educause.edu/articles/2007/7/academic-analytics-a-new-tool-for-a-new-era
Dawson, S., Bakharia, S., Heathcote, E. (2010). SNAPP: Realizing the affordances of real-time
SNA within networked learning environments. 7th International Conference on
Networked Learning. Retrieved from
http://www.networkedlearningconference.org.uk/past/nlc2010/abstracts/PDFs/Dawson.pd
f
Diaz, V. & Brown, M. (2012, May). Learning analytics: A report on the ELI focus session.
EDUCAUSE Learning Initiative. Retrieved from
https://net.educause.edu/ir/library/PDF/ELI3027.pdf
Diaz, V. & Fowler, S. (2012, November). Leadership and learning analytics. EDUCAUSE
Learning Initiative Brief. Retrieved from
https://net.educause.edu/ir/library/pdf/ELIB1205.pdf
Dietz-Uhler, B. & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student
success: A faculty perspective. Journal of Interactive Online Learning, 12(1). Retrieved
from http://www.ncolr.org/jiol/issues/pdf/12.1.2.pdf
Duval, E. (2011). Attention please! Learning analytics for visualization and recommendation. In
Proceedings of LAK11: 1st International Conference on Learning Analytics and
Knowledge (pp. 9–17). Retrieved from
https://lirias.kuleuven.be/bitstream/123456789/315113/1/
Ferguson, R (2012). Learning analytics: drivers, developments and challenges. International
Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317. Retrieved from
http://oro.open.ac.uk/36374/1/IJTEL40501_Ferguson%20Jan%202013.pdf
Ferguson, R. & Shum, S. B. (2012). Social learning analytics: five approaches. In Proceedings of
the 2nd International Conference on Learning Analytics and Knowledge (pp. 23–33).
Retrieved from http://oro.open.ac.uk/32910/1/LAK2012-RF-SBS.pdf
Ferguson, R. (2012, March). The state of learning analytics 2012: A review and future challenges
(Technical Report). Knowledge Media Institute. Retrieved from
http://kmi.open.ac.uk/publications/pdf/kmi-12-01.pdf
Forsythe, Jr., R.G., Chacon, F.J., Spicer, D.Z., & Valbuena, A. (2012). Two case studies of
learner analytics in the university system of Maryland. Educause Review Online.
Retrieved from http://www.educause.edu/ero/article/two-case-studies-learner-analyticsuniversity-system-maryland
Frankfort, J., Salim, K., Carmean, C., & Haynie, T. (2012). Analytics, nudges, and learner
persistence. Educause Review Online. Retrieved from
www.educause.edu/ero/article/analytics-nudges-and-learner-persistence
Fritz, J. (2011). Classroom walls that talk: Using online course activity data of successful student
to raise self-awareness of underperforming peers. Internet and Higher Education, 14, 8997. Retrieved from
http://www.sciencedirect.com/science/article/pii/S109675161000062X
Fritz, J. (2013, April 30). Using analytics at UMBS: Encouraging student responsibility and
identifying effective course designs. EDUCAUSE Research Bulletin. Retrieved from
https://net.educause.edu/ir/library/pdf/ERB1304.pdf
From insight to action: A case study on using predictive analytics to impact student outcomes
(Research Report). Civitas Learning & University of Maryland University College.
Retrieved from http://www.civitaslearning.com/wpcontent/uploads/2015/07/UMUC_CaseStudy.pdf
Goldstein, P. J. (2005, December). Academic analytics: The used of management information
and technology in higher education. ECAR Key Findings. Retrieved from
https://net.educause.edu/ir/library/pdf/ecar_so/ers/ers0508/EKF0508.pdf
Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information
and technology in higher education. ECAR, 8. Retrieved from
https://net.educause.edu/ir/library/pdf/ERS0508/ekf0508.pdf
Grajek, S. (2011). Research and data services for higher education information technology: Past,
present, and future. EDUCAUSE Review. Retrieved from
http://er.educause.edu/~/media/files/article-downloads/erm1162.pdf
Heuer, Jr., R. J. (2008, March). Taxonomy of Structured Analytic Techniques (Presentation on
Paper). International Studies Association 2008 Annual Convention. Retrieved from
http://www.pherson.org/wp-content/uploads/2013/06/03.-Taxonomy-of-StructuredAnalytic-Techniques_FINAL.pdf
Ice, P., Díaz, S., Swan, K., Burgess, M., Sharkey, M., Sherrill, J., & Okimoto, H. (2012). The
PAR Framework proof of concept: Initial findings from a multi-institutional analysis of
federated postsecondary data. Online Learning Journal, 16(3). Retrieved from:
http://olj.onlinelearningconsortium.org/index.php/olj/article/view/277
Kilgannon, C. (2014, March 27). Without tenure or a home. The New York Times. Retrieved
from http://www.nytimes.com/2014/03/30/nyregion/without-tenure-or-a-home.html?_r=3
Long, P. & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education.
EDUCAUSE Review, 31-40. Retrieved from
http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-andeducation
Macfadyen, L. P. & Dawson, S. (2009). Mining LMS data to develop an “early warning system”
for educators: A proof of concept. Computers & Education, 54(2) 588-599. Retrieved
from
http://www.researchgate.net/profile/Leah_Macfadyen/publication/220139967_Mining_L
MS_data_to_develop_an_early_warning_system_for_educators_A_proof_of_concept/lin
ks/5449997a0cf2f6388084ceff.pdf
Macfadyen, L. P., Dawson, S., Pardo, A., & Gasevic, D. (2014). Embracing big data complex
educational systems: The learning analytics imperative and the policy challenge.
Research & Practice in Assessment, 9, 17-28. Retrieved from
http://www.rpajournal.com/dev/wp-content/uploads/2014/10/A2.pdf
Morris, L. V., Finnegan, C., & Wu, S. (2005). Tracking student behavior, persistence, and
achievement in online courses. The Internet and Higher Education, 8(3), 221-231.
Retrieved from http://ac.els-cdn.com/S1096751605000412/1-s2.0-S1096751605000412main.pdf?_tid=24cbb53c-6798-11e5-a4ce00000aab0f27&acdnat=1443634065_ed78772803a419e7950d95216a20c6bd
Norris, D., Baer, L., Leonard, J., Pugliese, L., and Lefrere, P. 2008. Framing action analytics and
putting them to work. EDUCAUSE Review Online 43, 1 (January / February 2008).
Norris, D., Baer, L. Pugliese, L., & Lefrere, P. (2008). Action analytics: Measuring and
improving performance that matters in higher education. EDUCAUSE Review. Retrieved
from http://er.educause.edu/~/media/files/article-downloads/erm0813.pdf
Phillips, E. D. (2013). Improving advising using technology and data analytics. Change: The
Magazine of Higher Learning, 45(1), 48–55. Retrieved from
http://www.changemag.org/Archives/Back Issues/2013/January-February
2013/improving-advising-full.html
Pirani, J. A., & Albrecht, B. (2005). University of Phoenix: Driving decisions through academic
analytics. ECAR Case Study 9. Retrieved from
https://net.educause.edu/ir/library/pdf/ers0508/cs/ECS0509.pdf
Ravishanker, G. (2011). Doing academic analytics right: Intelligence answers to simple
questions. ECAR Research Bulletin, 2. Retrieved from
http://net.educause.edu/ir/library/pdf/ERB1102.pdf
Romero-Zaldivar, V.A., Pardo, A., Burgos, D., & Kloos, C. D. (2012). Monitoring student
progress using virtual appliances: A case study. Computers & Education, 58, 1058-1067.
Retrieved from http://ac.els-cdn.com/S0360131511003198/1-s2.0-S0360131511003198main.pdf?_tid=0d5e95ee-6799-11e5-950a00000aab0f01&acdnat=1443634455_b3753e99ab7b0947ba35b38ef40efdab
Salaway, G. Caruso, J. B., Nelson, M. R. & Ellison, N. B. (2008). The ECAR student of
undergraduate students and information technology, 2008. ECAR, 8. Retrieved from
https://net.educause.edu/ir/library/pdf/ERS0808/RS/ERS0808w.pdf
Shah, S., Horne, A., & Capella, J. (2012). Good data won’t guarantee good decisions. Harvard
Business Review. Retrieved from https://hbr.org/2012/04/good-data-wont-guaranteegood-decisions
Sharkey, M. (2013). The ins and outs of analytics. Educause Review. Sept/Oct 2013. 110-111
Retrieved from http://er.educause.edu/~/media/files/article-downloads/erm1359.pdf.
Buckingham Shum, S., & Ferguson, R. (2012). Social Learning Analytics. Educational
Technology & Society, 15 (3), 3–26. Retrieved from
http://www.ifets.info/journals/15_3/2.pdf
Siemens, G, Gasevic, D., Haythornthwaite, C., Dawson, S. Shum, S.B., Ferguson, R. Duval, E.,
Verbert, K, & Ryan B. (2011). Open Learning Analytics: An Integrated & Modularized
Platform. Retrieved from: http://solaresearch.org/OpenLearningAnalytics.pdf
Smith, V. C., Lange, A. & Huston, D. R. (2012). Predictive Modeling to Forecast Student
Outcomes and Drive Effective Interventions in Online Community College Courses.
Journal of Asynchronous Learning Networks, 16(3). Retrieved from
http://files.eric.ed.gov/fulltext/EJ982673.pdf
The learning analytics challenge: Culture of data or culture of evidence? (Report). Contact
North– Ontario’s Distance Education & Training Network. Retrieved from
http://teachonline.ca/sites/default/files/contactNorth/files/pdf/publications/learning_analy
tics_challenge.pdf
U.S. Department of Education, Office of Educational Technology. (2012). Enhancing Teaching
and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief.
Washington, DC. Retrieved June 10, 2013, from https://tech.ed.gov/wpcontent/uploads/2014/03/edm-la-brief.pdf
Van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012). Analytics in higher education:
Establishing a common language. EDUCAUSE Learning Initiative. Retrieved from
http://net.educause.edu/ir/library/pdf/ELI3026.pdf
Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to
support learning and knowledge analytics. Educational Technology & Society, 15(3),
133–148.
Willis, III, J. E. & Pistilli, M. D. (2014, April 7). Ethical discourse: Guiding the future of
learning analytics. EDUCAUSE Review Online. Retrieved from
http://er.educause.edu/articles/2014/4/ethical-discourse-guiding-the-future-of-learninganalytics
Wise, A. F. (2014, March). Designing pedagogical interventions to support student use of
learning analytics. In Proceedings of the Fourth International Conference on Learning
Analytics and Knowledge (pp. 203-211). ACM.
Early Alert Bibliography
Agudo-Peregina, A. F., Iglesias-Pradas, S., Conde-Gonzalez, M. A., & Hernandez-Garcia, A.
(2014). Can we predict success from log data in VLEs? Classification of interactions for
learning analytics and their relation with performance in VLE-supported FSF and online
learning. Computers in Human Behavior, 31, 542-550. Retrieved from
http://oa.upm.es/19925/1/INVE_MEM_2012_130976.pdf
Bainbridge, J., Melitski, J., Zahradnik, A., Lauría, E. J. M., Jayaprakash, S., & Baron, J. (2015).
Using Learning Analytics to Predict At-Risk Students in Online Graduate Public Affairs
and Administration Education. Journal of Public Affairs Education, 21(2), 247–262.
Retrieved from Retrieved from http://www.naspaa.org/jpaemessenger/Article/VOL212/10_Bainbridge.pdf
Beer, C., Clark, K., & Jones, D. (2010). Indicators of engagement. Proceedings Ascilite 2010,
Sydney. Retrieved from http://link.springer.com/chapter/10.1007/978-1-4615-0885-4_3
Biktimirov, E. N., & Klassen, K. J. (2010). Relationship between use of online support materials
and student performance in an introductory finance course. Journal of Education for
Business, 83(3), 153-158. Retrieved from
http://www.tandfonline.com/doi/abs/10.3200/JOEB.83.3.153-158
Chanchary, F. H., & Haque, I. (2007). Preparedness of E-Learners: A study on first year
university students. In Proceedings of the 5th International Conference on ICT and
Higher Education, Knowledge Management 2007 (pp. 1-8). Retrieved from
http://www.researchgate.net/publication/265673430_Preparedness_of_ELearners_A_Study_on_First_Year_University_Students
Coldwell, J., Craig, A., Paterson, T., & Mustard, J. (2008). Online students: Relationships
between participation, demographics, and academic performance. The Electronic Journal
of e-Learning, 6(1), 19-30. Retrieved from
www.ejel.org/issue/download.html?idArticle=57
Dawson, S., Heathcote, L., & Poople, G. (2010). Harnessing ICT potential. International Journal
of Education Managemenr, 24(2), 115-128. Retrieved from
www.nasscom.in/download/summary_file/fid/74744
Dawson, S., McWilliam, E., & Tan, J. P. (2008). Teaching smarter: How mining ICT data can
inform and improve learning and teaching practice. Proceedings Ascilite 2008, Retrieved
from Melbourne. http://ro.uow.edu.au/medpapers/141/
Fritz, J. (2013, April 30). Using analytics at UMBC: Encouraging student responsibility and
identifying effective course designs. Educause Research Bulletin. Retrieved from
http://www.educause.edu/library/resources/using-analytics-umbc-encouraging-studentresponsibility-and-identifying-effective-course-designs
Fritz, J. (2011). Classroom walls that talk: Using online course activity data of successful student
to raise self-awareness of underperforming peers. Internet and Higher Education, 14, 8997. Retrieved from http://www.sciencedirect.com/science/journal/10967516/14/2
Greenland, S., (2011). Using log data to investigate the impact of (a)synchronous learning tools
on LMS interaction. Proceedings Ascilite 2011, Wrest Point, Tasmania. Retrieved from
http://researchbank.swinburne.edu.au/vital/access/manager/Repository/swin:25239
Grush, M. (2011). Monitoring the PACE of Student Learning: Analytics at Rio Salado College.
Retrieved from http://campustechnology.com/articles/2011/12/14/monitoring-the-paceof-student-learning-analytics-at-rio-salado-college.aspx
Huberth M, Chen P, Tritz J, McKay TA (2015) Computer-Tailored Student Support in
Introductory Physics. PLoS ONE 10(9): e0137001. doi:10.1371/journal.pone.0137001
Retrieved from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137001
Jayaparkash, S. M., Moody, E. M., Lauria, E. J. M., Regan, J. R., & Baron, J. D. (2014). Early
alert of academically at-risk students: An open source analytics initiative. Journal of
Learning Analytics, 1(1), 6-47. Retrieved from
https://epress.lib.uts.edu.au/journals/index.php/JLA/article/view/3249
Kalamkarian, H. S., Karp, M. M. (2015). Student attitudes toward technology-mediating
advising systems. Community College, Research Center, Working Paper 82. Retrieved
from http://ccrc.tc.columbia.edu/publications/student-attitudes-technology-mediatedadvising-systems.html
Lauria, E. J.M., Baron, J. D., Devireddy, M., Sundararju, V., & Jayaprakash, S. M. (2012).
Mining academic data to improve college student retention: An open source perspective.
LAK, Vancouver, BC. Retrieved from http://dl.acm.org/citation.cfm?id=2330637
Little, R., & Burns, M. F. (August 6, 2013) If you MAP the way, they will follow. EDUCAUSE
Review. Retrieved from http://er.educause.edu/articles/2013/8/if-you-map-the-way-theywill-follow
Little, R. (Decenber 15, 2011). The student success plan: Case management and intervention
software. EDUCAUSE Review. Retrieved from
http://er.educause.edu/articles/2011/12/the-student-success-plan-case-management-andintervention-software
Lovett, M. (2012, November 9). Next-generation analytics with the learning dashboard.
Retrieved from http://www.crlt.umich.edu/node/591
Macfadyen, L., & Dawson, S. (2010). Mining LMS data to develop an “early warning system”
for educators: A proof of concept. Computers & Education, 54, 588-599. Retrieved from
http://www.sciencedirect.com/science/article/pii/S0360131509002486
Macfadyen, L., & Dawson, S. (2012). Numbers are not enough. Why e-Learning analytics failed
to inform an institutional strategic plan. Educational Technology & Society, 15(3), 149163. Retrieved from http://ro.uow.edu.au/edupapers/1129/
Mcay, T. (2013, September 13). Learning analytics at U-M: An update and agenda for 2013-15.
Retrieved from http://www.crlt.umich.edu/node/57770
.
Milne, J., Jeffery, L. M., Suddaby, G., & Hiigins, A., (2012). Early identification of students at
risk of failing. Proceedings Ascilite 2012, Wellington, New Zealand. Retrieved from
http://www.editlib.org/p/42660/
.
Nandi, D., Hamilton, M., Harland, J., & Warburton, G. (2011). How active are students in online
discussion forums? Research and Practice in Information Technology, 114. Retrieved
from http://dl.acm.org/citation.cfm?id=2459952
.
Rankine, L., Stevenson, L., Malfory, J. & Ashford-Rowe, K. (2009). Benchmarking across
universities: A framework for LMS analysis. Proceedings Ascilite 2009, Auckland.
Retrieved from http://www.ascilite.org.au/conferences/auckland09/procs/rankine.pdf
Sclater, N. & Bailey, P. (2015). Code of practice for learning analytics. JISC. Retrieved from
https://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics
SEI Case Study: Increasing student success. Evaluating the effectiveness of U-Pace instruction at
UWM. Retrieved from http://www.educause.edu/library/resources/increasing-studentsuccess-evaluating-effectiveness-u-pace-instruction-uwm
SEI Case Study: Scaling learning analytics across institutions of higher education. Retrieved
from http://www.educause.edu/library/resources/scaling-learning-analytics-acrossinstitutions-higher-education
Shamah, D., & Ohlsen, S. (2013). Student success plan: Constructing an evidence-based student
support system that promotes college completion. Gateway to College. Retrieved from
http://www.studentsuccessplan.org/research.pdf
Sin, K. & Muthu, L. (2015). Application of big data in education data mining and learning
analytics – A literature review. Journal of Soft Computing: Speical Issue, 5(4), 10351049. Retrieved from http://ictactjournals.in/paper/IJSC_Paper_6_pp_1035_1049.pdf
Smith, V. C., Lange, A., & Huston, D. R. (2010). Predictive modeling to forecast student
outcomes and drive effective interventions in online community college courses. Journal
of Asynchronous Learning Networks, 16(3), 51-61. Retrieved from
http://files.eric.ed.gov/fulltext/EJ982673.pdf
Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled
blended learning courses. Internet and Higher Education, 27, 44-53. Retrieved from
http://www.sciencedirect.com/science/article/pii/S1096751615000391