Bibliography for EDUCAUSE

Learning Analytics – Must Read BibliographyAssembled 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), 318-331.

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%20Navigating%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), 42-57. 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 16-20 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), 42-57.

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_Pistilli-Purdue_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-academic-analytics

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.pdf

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-analytics-university-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, 89-97. 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/wp-content/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-Structured-Analytic-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-and-education

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_LMS_data_to_develop_an_early_warning_system_for_educators_A_proof_of_concept/links/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-S1096751605000412-main.pdf?_tid=24cbb53c-6798-11e5-a4ce-00000aab0f27&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-S0360131511003198-main.pdf?_tid=0d5e95ee-6799-11e5-950a-00000aab0f01&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-guarantee-good-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_analytics_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/wp-content/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-learning-analytics

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/VOL21-2/10_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_E-Learners_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-student-responsibility-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, 89-97. 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-pace-of-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-mediated-advising-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-they-will-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-and-intervention-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), 149-163. 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

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