El día 11 de febrero comencé un curso en Canvas: Learning Analytics and knowledge (LAK13).
El curso es un MOOC (Masive Open Online Course) de 8 semanas de duración.
Cada semana incluye nuevas lecturas, actividades de aprendizaje, debates, y conferencias en vivo.
Un aspecto fundamental de este curso es la participación y contribución en el foro del curso, grupos de Facebook, Twitter o Google. El curso hace énfasis en la participación a través de la creación de artefactos que reflejen el proceso de aprendizaje.
Los profesores:
George Siemens, Simon Buckingham Shum, Erik Duval, Shane Dawson, Dragan Gasevic y Fridolin Wild.
Las tareas a realizar:
Analytics: Logic and Structure
Analytics are often seen as a technical activity. While this is obviously true at some levels, the most critical aspect of any analytics project is the logic, structure, and intent. Before tools are considered, questions need to first be answered about what an analytics project is intended to address, the problems to be solved, the data sources to be accessed. Only after this work has been done do tools become important.
For this assignment, develop an analytics model to gain insight into a complex topic using both qualitative and quantitative methods. Select a particular topic or subject area that interests you (current events, historical activities, a learning challenge) and detail how you will "interrogate" this subject using various analytics tools or techniques. Your project can be in the form of a presentation, a blog post, a video, a simulation, or other digital artifact. The important aspect of this assignment is to walk through the processes and considerations that pre-date tool selection.
Basically, you are asked to look at something (a problem, a challenge, an opportunity, whatever) that you would like to understand better. Instead of confining your exploration to what your favorite tools do, play around. Be clear about what you want to achieve. You are creating an analytic model that will later guide tool selection.
There is no set or prescribed way to address that "something". One approach is somewhat linear:
1. What do you want to do/understand better/solve?
2. Defining the context: what is it that you want to solve or do? Who are the people that are involved? What are social implications? Cultural?
3. Brainstorm ideas/challenges around your problem/opportunity. How could you solve it? What are the most important variables?
4. Explore potential data sources. Will you have problems accessing the data? What is the shape of the data (reasonably clean? or a mess of log files that span different systems and will require time and effort to clean/integrate?) Will the data be sufficient in scope to address the problem/opportunity that you are investigating?
5. Consider the aspects of the problem/opportunity that are beyond the scope of analytics. How will your analytics model respond to these analytics blind spots?
MY JOB:
Measurement of the effectiveness of actives learning methods like flipping.
Do students
that use this method get better results?
What do I
want to do? As a teacher I am interested in improve the learning of my
students, so use some innovative method like flipping classroom and collaborative
activities. But it really supposes the improvement on their learning?
I want to
develop some software devices to capture, to measure and to visualize some dates
from the students and from the activities.
VARIABLES:
- Time that
students are involved in this process visualizing the videos.
- Time
doing exercises related to the materials provided, before, during or after the
face class. These activities must measure the understanding and advantages of
using the material.
- Student's
surveys
- Assignment
results
METHOD
-Use of a platform
that supports the activities, and the videos. (LMS, blog, wiki, google doc...) And
exercises.
Requirements
Should let
to measure the number of connections (logins), time spent, also the student can
watch a comparative to motivate them that show their time relate with their
classmates and the number of their exercises (solves and corrects).
PROBLEMS TO SOLVE
- To choose
the platform that centralizes the
activities and let to capture the data. (Logins and time)
- To choose
tools to capture data.
-Time (logins, visualizing,
solving exercises)
- Exercises and results
-Student survey (about student's
satisfaction)
-To choose
visualization method.
- For students (times and
results comparative)
- For teacher (times spent,
comparative and finally the success or not of this method compared with the
traditional classroom)
As the course progresses, a weekly activity of reflecting on discussions and topics provides learners an opportunity to explicitly map connections between concepts. Once several weeks have been reviewed, connections between weekly topics can be formed so that the concept map becomes an integrated reflection of the relationships that you see between different course concepts.
LAK12: http://www.solaresearch.org/events/lak/2012videos/
Structure and Logic of Analytics: (Columbia University, 2013) https://vialogues.com/vialogues/play/7045
SBS?
Journal of Asynchronous Learning Networks (Vol 16, issue 3) http://sloanconsortium.org/publications/jaln_main
Journal of Educational Data Mining: http://www.educationaldatamining.org/JEDM/
IEDMS: http://www.educationaldatamining.org/IEDMS/events
Learning Analytic and Knowledge 2011 proceedings (ed. Gasevic, Siemens, Long, Conole) http://dl.acm.org/citation.cfm?id=2090116
Learning Analytics and Knowledge 2012 proceedings (ed. Dawson, Haythornthwaite, Gasevic, Buckingham Shum, Ferguson) http://dl.acm.org/citation.cfm?id=2330601&coll=DL&dl=ACM&CFID=99982000&CFTOKEN=43560004
Fourth Paradigm: http://research.microsoft.com/en-us/collaboration/fourthparadigm/default.aspx
Romero, C., Ventura, S., Pchenizky, M., & Baker, S.J.d. (Eds.). (2010). Handbook of educational data mining. Boca Raton, FL: Taylor and Francis.
McKinsey Report http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation
Super Crunchers: http://www.amazon.com/Super-Crunchers-Thinking---Numbers-Smart/dp/0553384732/
Datamining and statistics for decision making: http://www.amazon.com/Mining-Statistics-Decision-Making-Computational/dp/0470688297
Numerati: http://www.amazon.com/The-Numerati-Stephen-Baker/dp/0547247931
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. 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
http://users.wpi.edu/~rsbaker/Baker-for-AITS-v8.pdf
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.
Brusilovsky, P. (2001). Adaptive hypermedia: From intelligent tutoring systems to web-based education. User Modeling and User-Adapted Interaction, 11(1-2), 87-110.
Campbell, & Oblinger, D. (2007). Academic analytics. Washington, DC: EDUCAUSE Center for Applied Research. 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
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.
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. 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., 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>.
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.
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
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
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
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.
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.
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.
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 fromhttp://www.iw3c2.org/WWW2004/docs/2p154.pdf
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.
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
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
Patterson, B. & McFadden, C. (2009). Attrition in online and campus degree programs. Online Journal of Distance Learning Administration, 12(2).
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.
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.
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, 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).
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.
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
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)
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Concept Map
Knowledge is a connective process and product. In the learning process, connections are formed between different concepts. The resulting knowledge web, how individual learners have connected the course concepts, can be expressed in different artifacts: graphics, videos, or an article. For the purposes of LAK13, course participants are encouraged to download free concept mapping software (such as VUE, CMAP, Compendium or on the web, Cohere).As the course progresses, a weekly activity of reflecting on discussions and topics provides learners an opportunity to explicitly map connections between concepts. Once several weeks have been reviewed, connections between weekly topics can be formed so that the concept map becomes an integrated reflection of the relationships that you see between different course concepts.
Select Learning Analytics Readings and Resources
(From LAK13)
Videos
LAK11: http://blip.tv/solaresearchLAK12: http://www.solaresearch.org/events/lak/2012videos/
Structure and Logic of Analytics: (Columbia University, 2013) https://vialogues.com/vialogues/play/7045
SBS?
Journal Issues & Conference Proceedings
Journal of Educational Technology & Society (guest eds, Siemens & Gasevic, 2012) : http://www.ifets.info/journals/15_3/ets_15_3.pdf (p. 1- 163)Journal of Asynchronous Learning Networks (Vol 16, issue 3) http://sloanconsortium.org/publications/jaln_main
Journal of Educational Data Mining: http://www.educationaldatamining.org/JEDM/
IEDMS: http://www.educationaldatamining.org/IEDMS/events
Learning Analytic and Knowledge 2011 proceedings (ed. Gasevic, Siemens, Long, Conole) http://dl.acm.org/citation.cfm?id=2090116
Learning Analytics and Knowledge 2012 proceedings (ed. Dawson, Haythornthwaite, Gasevic, Buckingham Shum, Ferguson) http://dl.acm.org/citation.cfm?id=2330601&coll=DL&dl=ACM&CFID=99982000&CFTOKEN=43560004
Books & Reports
JISC Learning Analytics Series: http://publications.cetis.ac.uk/c/analyticsFourth Paradigm: http://research.microsoft.com/en-us/collaboration/fourthparadigm/default.aspx
Romero, C., Ventura, S., Pchenizky, M., & Baker, S.J.d. (Eds.). (2010). Handbook of educational data mining. Boca Raton, FL: Taylor and Francis.
McKinsey Report http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation
Super Crunchers: http://www.amazon.com/Super-Crunchers-Thinking---Numbers-Smart/dp/0553384732/
Datamining and statistics for decision making: http://www.amazon.com/Mining-Statistics-Decision-Making-Computational/dp/0470688297
Numerati: http://www.amazon.com/The-Numerati-Stephen-Baker/dp/0547247931
Articles
Arnold, K. (2010). Signals: Applying academic analytics. EDUCAUSE Quarterly, 33(1).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. 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
http://users.wpi.edu/~rsbaker/Baker-for-AITS-v8.pdf
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.
Brusilovsky, P. (2001). Adaptive hypermedia: From intelligent tutoring systems to web-based education. User Modeling and User-Adapted Interaction, 11(1-2), 87-110.
Campbell, & Oblinger, D. (2007). Academic analytics. Washington, DC: EDUCAUSE Center for Applied Research. 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
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.
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. 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., 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>.
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.
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
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
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
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.
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.
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.
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 fromhttp://www.iw3c2.org/WWW2004/docs/2p154.pdf
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.
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
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
Patterson, B. & McFadden, C. (2009). Attrition in online and campus degree programs. Online Journal of Distance Learning Administration, 12(2).
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.
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.
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, 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).
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.
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
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)
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