Learning & Knowledge Analytics Bibliography

(thanks to Tanya Elias and John Campbell for significant contributions)

Allen, E. & Seaman, J. (2010). Class differences: Online education in the United States. Report from The Sloan Consortium.

Arnold, K. (2010). Signals: Applying academic analytics. EDUCAUSE Quarterly, 33(1).

Astin AW. (1993). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. Phoenix, AZ: Oryx Press.

Astin, A.W. (1975). Financial aid and student persistence. Report from Higher Education Research Institute. Los Angeles, CA.

Astin, A.W. (1996). Degree attainment rates at American colleges and universities: Effects of race, gender, and institutional type. Report from Higher Education Research Institute. Los Angeles, CA.

Baepler, P. (2010). A teaching, technology, and faculty development timeline. The Journal of Faculty Development, 24(2), 40-48.

Baepler, P. M., Cynthia James. (2010). Academic Analytics and Data Mining in Higher Education. International Journal for the Scholarship of Teaching and Learning, 4(2).

Baker, B. (2007). A conceptual framework for making knowledge actionable through capital formation. D.Mgt. dissertation, University of Maryland University College, United States -- Maryland. Retrieved October 19, 2010, from ABI/INFORM Global.(Publication No. AAT 3254328).
Baker, R.S.J.d. (2010) Data Mining for Education. In McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition), vol. 7, pp. 112-118. Oxford, UK: Elsevier. http://users.wpi.edu/~rsbaker/Encyclopedia%20Chapter%20Draft%20v10%20-fw.pdf

Baker, R.S.J.d. (2010) Mining Data for Student Models.
In Nkmabou, R., Mizoguchi, R., & Bourdeau, J. (Eds.) Advances in Intelligent Tutoring Systems, pp. 323-338

Benander, R. & Lightner, R. (2005). Promoting transfer of learning: Connecting general education courses. The Journal of General Education, 54(3), 199-208.

Brallier, S. A., Palm, L. J., & Gilbert, R. M. (2007). Predictors of exam performance in web and lecture courses. [Article]. Journal of Computing in Higher Education, 18(2), 82-98.
Braxton, J.M. (2000). Reworking the student departure puzzle. Nashville, TN: Vanderbilt University Press.

Brownell, J. E. & Swaner, L. E. (2010). Five high-impact practices: Research on learning outcomes, completion, and quality. AAC&U Publications: Washington, D.C.

Brusilovsky, P. (2001). Adaptive hypermedia: From intelligent tutoring systems to web-based education. User Modeling and User-Adapted Interaction, 11(1-2), 87-110.

Brusilovsky, P. & Sosnovsky, S. (2005). Engaging students to work with self-assessment questions: A study of two approaches. Proceedings of 10th Annual Conference on Innovation and Technology in Computer Science Education. Monte de Caparica, Portugal. ACM Press, 251-255.

Brusilovsky, P., Sosnovsky, S., and Yudelson, M. (2009). Addictive links: The motivational value of adaptive link annotation. New Review of Hypermedia and Multimedia, 15(1), 97-118.

Brusilovsky, P., Sosnovsky, S., Lee, D., Yudelson, M., Zadorozhny, V., & Zhou, X. (2010). Learning SQL programming with interactive tools: From integration to personalization. ACM Transactions on Computing Education, 9(4), 1-15.

Campbell, & Oblinger, D. (2007). Academic analytics. Washington, DC: EDUCAUSE Center for Applied Research. View Online

Campbell, J. (2007). Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study. Unpublished 3287222, Purdue University, United States -- Indiana.

Campbell, J. P. (2007) Proceedings from The Higher Learning Commission: Academic success: Using institutional data to predict student achievement. Chicago, IL.

Campbell, J. P. (2007). Seven things you should know about analytics. EDUCAUSE Learning Initiative. Boulder, CO. View PDF

Campbell, J. P. (2007). Utilizing Student Data within the Course Management System to Determine Undergraduate Student Academic Success: An Exploratory Study. Unpublished doctoral dissertation: Purdue University. View Online

Campbell, J. P., Collins, W.B., Finnegan, C., & Gage, K. (2006). "Academic analytics: Using the CMS as an early warning system." WebCT Impact 2006. Chicago, IL. View Online

Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42 (4), 40-42. View PDF

Campbell, J.P., DeBlois, P.B., & Oblinger, D.G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40-57.

Campbell, John P. “Academic Analytics: A New Tool for a New Era” ELI Web Seminar, October 8, 2007. Audio and slides from the presentation. The session will be based on an article [PDF 601 KB] published in the July/August 2007 EDUCAUSE Review by John Campbell, Peter DeBlois, and Diana Oblinger.

Carey, K. (2009). Introducing a remedial program that actually works. The Chronicle of Higher Education, (May, 28th). http://chronicle.com/article/Introducing-a-Remedial-Prog/44409/
Center for Educational Networking's monthly publication "Focus on Results" November 2009 (Volume #8, Issue #1) Packet #16, http://focus.cenmi.org/2009/11/01/flint-community-schools-changing-strikes-into-home-runs-for-all-students/

Chen, H. L. & Light, T. P. (2010). Electronic portfolios and student success: Effectiveness, efficiency, and learning. Association of American Colleges and Universities. https://secure.aacu.org/source/Orders/index.cfm?section=unknown&task=3&CATEGORY=AS&PRODUCT_TYPE=SALES&SKU=VALEPORT&DESCRIPTION=&FindSpec=&continue=1&SEARCH_TYPE=

Cho, Y. H. and Kim, J.K. (2004). Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce, Expert System with Applications 26, pp. 233-246

Cho, Y.H., Kim, J.K. and Kim, S.H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications 23 (3), pp. 329-42.
Corbitt, T. (2003). Business intelligence and data mining. Management Services. Nov 2003, p. 18.

Council of Europe. (1990). Common European framework of reference for languages. Cambridge, UK: Cambridge University Press.

Dawson, S. (2008). A study of the relationship between student social networks and sense of community, Educational Technology and Society 11(3), pp. 224-38.

Dawson, S. (2010). ‘Seeing’ the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736-752. doi:10.1111/j.1467-8535.2009.00970.x.

Dawson, S. M., Erica. (2008). Investigating the application of IT generated data as an indicator of learning and teaching performance. Wollongong: Australian Learning and Teaching Council.

Dawson, S. P., Macfadyen, L. & Lockyer, L. (2009). Learning or performance: Predicting drivers of student motivation. In R. Atkinson & C. McBeath (Eds.), Ascilite 2009: Same places, different spaces (pp. 184-193). Auckland, NZ: Ascilite.

Dawson, S., Bakharia, A., & Heathcote, E. (2010). SNAPP: Realising the affordances of real-time SNA within networked learning environments. Proceedings of the 7th International Conference on Networked Learning, 2010.

Dawson, S., Heathcote, L. and Poole, G. (2010). Harnessing ICT potential: The adoption and analysis of ICT systems for enhancing the student learning experience, International Journal of Educational Management24(2) pp. 116-128.

Dawson, S., McWilliam, E., & Tan, J. P.-L. (2008). Teaching Smarter: How mining ICT data can inform and improve learning and teaching practice. Paper presented at the ASCILITE 2008. Retrieved from <http://www.ascilite.org.au/conferences/melbourne08/procs/dawson.pdf>.

Devany, L. (2010). Purdue's student achievement technology goes national. View Online

Diziol, D., Walker, E., Rummel, N., & Koedinger, K.R. (2010). Using intelligent tutor technology to implement adaptive support for student collaboration. Educational Psychology Review, 22(1), 89-102.

Dron, J. and Anderson, T. (2009). On the design of collective applications, Proceedings of the 2009 International Conference on Computational Science and Engineering , Volume 04, pp. 368-374.

Eckerson, W. W. (2006). Performance dashboards: Measuring, monitoring, and managing your business. Hoboken, New Jersey: John Wiley & Sons.

Educause, 2010. 7 Things you should know about analytics, EDUCAUSE 7 things you should know series. Retrieved October 1, 2010 from http://www.educause.edu/ir/library/pdf/ELI7059.pdf

Ewell, P.T. (2006). Reaching consensus on common indicators: A feasibility analysis. Presentation to the National Center for Higher education Management Systems. http://www.nchems.org/pubs/detail.php?id=82

Eynon, B. (2008). Making connections: Using ePortfolio to transform student learning at LaGuardia Community College. (Keynote speech delivered at Indiana University-Purdue University Indianapolis Assessment Conference.

Few, S. (2007). Dashboard Confusion Revisited. Visual Business Intelligence Newsletter. January 2007. Retrieved October 5, 2010 from http://www.perceptualedge.com/articles/03-22-07.pdf

Finnegan, C., Morris, L.V., & Kangjoo, L. (2008-2009). Differences by course discipline on student behavior, persistence, and achievement in online courses of undergraduate general education. Journal of College Student Retention: Research, Theory and Practice, 10(1), 39-54.

Fritz, J. (2010). "Video demo of UMBC’s 'Check My Activity' tool for students." Educause Quarterly, Vol. 33, Number 4. Retrieved from http://www.educause.edu/library/EQM1049

Fusch, D. and Jones, J. (2012) Vetting Early Alert Technologies, Academic Impressions, January 20

Goldstein, P. J. and Katz, R. N. (2005). Academic Analytics: The Uses of Management Information and Technology in Higher Education, ECAR Research Study Volume 8. Retrieved October 1, 2010 from http://www.educause.edu/ers0508

Goldstein, P.J. & Katz, R.N. (2005). Academic Analytics: The uses of management information and technology in higher education. EDUCAUSE: Key Findings (ID: EKF-508).

Goldstein, Philip J. with Richard N. Katz, “Academic Analytics: The Uses of Management Information and Technology in Higher Education”, ECAR Research Study Volume 8, 2005. The following chapters specifically discuss using learning analytics to increase student retention and monitor student academic success.Chapter 7, Advanced Applications of Academic Analytics (Page 76, Student Services)Chapter 9, Academic Analytics in the Future of Higher Education

Green, K.C. (2010). Campus Computing, 2010. The 21st National Survey of Computing and Information Technology in US Higher Education. http://www.campuscomputing.net/sites/www.campuscomputing.net/files/Green-CampusComputing2010.pdf

Hackman, J.R. and Woolley, A. W. (In press). Creating and leading analytic teams in R. L. Rees & J. W. Harris (Eds.), A handbook of the psychology of intelligence analysis: The human factor. Burlington, MA: CENTRA Technology

Hijon R. and Carlos, R. (2006). E-learning platforms analysis and development of students tracking functionality, in Proceedings of the 18th World Conference on Educational Multimedia,Hypermedia & Telecomunications, pp. 2823-2828.
Hsiao, I.H., Brusilovsky, P., Yudelson, M., & Ortigosa, A. (2010). The value of adaptive link annotation in e-learning: A study of a portal-based approach. Proceedings of the 21st ACM conference on Hypertext and Hypermedia. Toronto, Canada, 223-227.

Karaoglan, B., & Ertaul, L. (2010). A practice in using e-portfolio in a higher education course taught at distance. Electronics and Electrical Engineering, 6(102), 115-118.

Kear, K. (2010). Online and social networking communities: A best practice

Kirkham, T., Winfield, S., Smallwood, A., Coolin, K., Wood, S. & Searchwell, L. (2009). Introducing live ePortfolios to support self-organized learning. Educational Technology & Society, 12(3), 107-114.

Knutov, E., De Bra, P., & Pechenizkiy, M. (2009). AH 12 Years Later: a comprehensive survey of adaptive hypermedia methods and techniques. New Review of Hypermedia and Multimedia 15(1), 5-38.

Koedinger, K.R., Baker, R.S.J.d., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J. (2010) A Data Repository for the EDM community: The PSLC DataShop. In Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.d. (Eds.) Handbook of Educational Data Mining. Boca Raton, FL: CRC Press.

Kuh, G. (2008). High-impact educational practices: What they are, who has access to them, and why they matter. Association of American Colleges and Universities, Washington, DC.

Kunnen, Eric J. and John Fritz, “Using Analytics to Intervene with Underperforming College Students”. EDUCAUSE Learning Initiative, Annual Conference, January 20, 2010. This is a video recording of a presentation.

Lassibille, G. (2011) Student Progress in Higher Education: What We Have Learned from Large-Scale Studies The Open Education Journal, Volume 4 http://www.benthamscience.com/open/toeduj/articles/V004/1TOEDUJ.pdf
Leveraging Academic Analytics http://jmajor.midsolutions.org/?p=1289

Learning Analytic and Knowledge 2011 proceedings (ed Gasevic, Siemens, Long, Conole) http://dl.acm.org/citation.cfm?id=2090116

Levitz, R., Noel, L. & Richter, B. (1999). Strategic moves for retention success. In G.H. Gaither (Ed.), Promising practice in recruitment, remediation, and retention (pp. 31-50). San Francisco: Jossey-Bass.

Macfadyen, L.P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599.

MacFayden, L. P. and Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept, Computers & Education 54(2), pp. 588-599.

Markauskaite, L. (2011). Digital knowledge and digital research: What does eResearch offer education and social policy? In L. Markauskaite, P. Freebody & J. Irwin (Eds.), Methodological choice and design: Scholarship, policy and practice in social and educational research (pp. 235-252). Dordrecht: Springer.

Mazza R., and Dimitrova, V. (2004). Visualising student tracking data to support instructors in web-based distance education, WWW Alt. ‘04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters. New York, NY, USA: ACM Press, pp. 154-161. Retrieved October 7, 2010 from http://www.iw3c2.org/WWW2004/docs/2p154.pdf

Mazza R., and Dimitrova, V. (2007). CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses. International Journal of Human-Computer Studies, 65(2), 125-139.

Mazza, R. & Botturi, L. (2007). Monitoring an online course with the GISMO tool: A case study. Journal of Interactive Learning Research, 18(2). 251-265.

Mazza, R. B., Luca. (2007). Monitoring an Online Course with the GISMO Tool: A Case Study. Journal of Interactive Learning Research, 18(2), 15.

McFadden, C. (2005). Optimizing the Online Business Channel with Web Analytics [blog post]. Retrieved October 5, 2010 from http://www.webanalyticsassociation.org/members/blog_view.asp?id=533997&post=89328&hhSearchTerms=definition+and+of+and+web+and+analytics

McWilliam, E., Dawson, S., & Poole, G. (2008). Monitoring student creative capacity: Using network visualization to evaluate pedagogical practice. ARC Centre of Excellence for Creative Industries and Innovation (CCI).

Measuring and Improving Performance That Matters in Higher Education, EDUCAUSE Review 43(1). Retrieved October 1, 2010 from http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume43/ActionAnalyticsMeasuringandImp/162422

Mobasher, B., Colley, R. and Srivastava, J. (2000). Automatic personalization based on web usage mining, Communications of ACM 43(8), pp.142-151. DOI 345124.345169

NextGeneration: Learning Challenges (n.d.). Learning Analytics [website]. Retrieved October 12, 2010 from http://nextgenlearning.com/the-challenges/learning-analytics

Nistor, N. & Neubauer, K. (2010). From participation to dropout: Quantitative participation patterns in online university courses. Computers & Education, 55(2), 663-672.

Norris, D., Baer, L., Leonard, J., Pugliese, L. and Lefrere, P. (2008). Action Analytics. http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume43/ActionAnalyticsMeasuringandImp/162422

Norris, D., Baer, L., Leonard, J., Pugliese, L. and Lefrere, P. (2008). Framing Action Analytics and Putting Them to Work, EDUCAUSE Review 43(1). Retrieved October 1, 2010 from http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume43/FramingActionAnalyticsandPutti/162423

Norris, Donald, Linda Baer, Joan Leonard, Louis Pugliese, and Paul Lefrere “Action Analytics: Measuring and Improving Performance That Matters in Higher Education”, EDUCAUSE Review, Volume 43, Number 1, January/February 2008.

Oblinger, D. G. and Campbell, J. P. (2007). Academic Analytics, EDUCAUSE White Paper. Retrieved October 1, 2010 from http://www.educause.edu/ir/library/pdf/PUB6101.pdf

Oblinger, D. G. and Campbell, J. P. (2007). Academic Analytics, EDUCAUSE White Paper

Oblinger, Diana G. and John P. Campbell, “Academic Analytics” EDUCAUSE White Paper, October 2007

Papanikolaou, K.A., Grigoriadou, M., Kornilakis, H., & Magoulas, G.D. (2003). Personalizing the interaction in a web-based educational hypermedia system: The case of INSPIRE. Computer Modeling and User-Adapted Interaction, 13(3), 213-267.

Pascarella, E.T. & Terenzini, P.T. (1991). How college affects students: Findings and insights from twenty years of research. San Francisco, CA: Jossey-Bass.

Patterson, B. & McFadden, C. (2009). Attrition in online and campus degree programs. Online Journal of Distance Learning Administration, 12(2).

Patton, L.D., Morelon, C., Whitehead, D.M., & Hossler, D. (2006). Campus-based retention initiatives: Does the emperor have clothes? New Directions for Institutional Research.

Proceedings from three years of the educational data mining conference: http://www.educationaldatamining.org/proceedings.html

Rais-Rohani, M. (2010). The Mississippi institutions of higher learning: Mississippi course redesign initiative final report. http://www.thencat.org/States/MS/Abstracts/MSU%20Statics_Abstract.htm#FinalRpt

Reategui, E., Boff, E., & Campbell, J.A. (2008). Personalization in an interactive learning environment through a virtual character. Computers & Education, 51(2), 530-544.

Romero, C., Ventura, S., & Garcia, E. (2008). Data mining in course management systems: Moodle case study and tutorial. [Article]. Computers & Education, 51(1), 368-384.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. [Article]. Expert Systems with Applications, 33(1), 135-146.

Romero, C., Ventura, S. Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews. (In press)
Romero, C., Ventura, S., & Garcia, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.

Romero, C., Ventura, S., Pchenizky, M., & Baker, S.J.d. (Eds.). (2010). Handbook of educational data mining. Boca Raton, FL: Taylor and Francis.

Sanchez, R.A. & Hueros, A.D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640.

Sharif, N. (1993). Technology management indicators for developing countries. TDRI Quarterly Review. Johnson, A. (ed.). Vol. 8 No. 2 June 1993, pp. 17-24. Retrieved October 10, 2010 from http://www.tdri.or.th/library/quarterly/text/j93_3.htm

Shemwell, S. (2005). Futuristic decision-making. Executive Briefing – Business Value from Technology January 2005. Retrieved October 5, 2010 from http://www.scribd.com/doc/12873609/Dr-Scott-M-Shemwell-Publications-and-Interviews

Snibbe, A.C. (2006). Drowning in data. Stanford Social Innovation Review, Fall 2006, pp. 39–45. Retreived October 10, 2010 from http://www.ssireview.org/pdf/2006FA_feature_snibbe.pdf

Spady, W.G. (1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), 64-85.

Swail, W.S. (1995). The development of a conceptual framework to increase student retention in science, engineering, and mathematics programs at minority institutions of higher education. Doctoral Dissertation.

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition, 2nd edition. Chicago, IL: The University of Chicago Press.

Tinto, V. (1997). Colleges as communities: Taking research on student persistence seriously. The Review of Higher Education, 21(2), 167-177.

Tsandilas, T. & Schraefel, M.C. (2004). Usable adaptive hypermedia systems. New Review of Hypermedia and Multimedia, 10(1), 5-29.

Tsianos, N., Panagiotis, G., Lekkas, Z., Mourlas, C., Samaras, G., & Belk, M. (2009). Working memory differences in e-learning environments: Optimization of learners’ performance through personalization. LNCS, 5535.

Valli, L. & Buese, D. (2007). The changing roles of teachers in an era of high-stakes accountability. American Educational Research Journal, 44(3), 519-558.

Wang, A. Y., & Newlin, M. H. (2002). Predictors of Performance in the Virtual Classroom: Identifying and Helping At-Risk Cyber-Students. [Article]. T H E Journal, 29(10), 21.

Wang, A.Y. and Newlin, M.H. ( 2002). Predictors of Performance in the Virtual Classroom, The Journal. Retrieved October 5, 2010 from http://www.thejournal.com/articles/15973_5

Wang, T. and Ren, Y. (2009). Research on Personalized Recommendation Based on Web Usage Mining Using Collaborative Filtering Technique, WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS 1(6).

Woolley, A.W., Chabris, C., Pentland, A, Hashmi, N. and Malone, T. W. (2010). Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science Magazine, Vol. 330, pp. 686-688. DOI 10.1126/science.1193147

Zhang, H. and Almeroth, K. (2010). Moodog: Tracking Student Activity in Online Course Management Systems. Journal of Interactive Learning Research, 21(3), 407-429. Chesapeake, VA: AACE. Retrieved October 5, 2010 from http://0-www.editlib.org.aupac.lib.athabascau.ca/p/32307.

Zhang, H., Almeroth, K., Knight, A., Bulger, M., and Mayer, R. (2007). Moodog: Tracking Students' Online Learning Activities in C. Montgomerie and J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, Chesapeake, VA: AACE, 2007, pp. 4415–4422. DOI

Zhang, H., Almeroth, K., Knight, A., Bulger, M., and Mayer, R. (2007). Moodog: Tracking students' online learning activities in C. Montgomerie and J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications. Chesapeake, VA: AACE, 2007, pp. 4415-4422.
Zubero, Z., Gil, S. M., Irazusta, A., Hoyos, I., Gil, J. (2008). Is There a Relationship Between the Birth-Date and Entering the University? The Open Education Journal, 2008, 1, 23-28 http://www.benthamscience.com/open/toeduj/articles/V001/23TOEDUJ.pdf

Books, Reports:

Fourth Paradigm:

McKinsey Report http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation

Super Crunchers

Datamining and statistics for decision making: http://www.amazon.com/Mining-Statistics-Decision-Making-Computational/dp/0470688297


Programs of study and research centres:

Northwestern: http://www.analytics.northwestern.edu/ (Master of Science in Analytics)

http://www.predictive-analytics.northwestern.edu/program-information/ (online, master predictive analytics)

NCSU: http://analytics.ncsu.edu/ (Master of Science in Analytics)

Advanced Analytics Institute: http://www.analytics.uts.edu.au/index.html (UTS)