Week 1

Introduction to Learning and Knowledge Analytics

Have you ever stopped to consider just how much data we produce and consume on a daily basis? Consider the data trails you leave in your daily routine:

• A quick login to Facebook/Twitter to see what’s happened in your social network since you went to bed.
• A few moments spent reading your email (gmail/yahoo/hotmail) with your morning coffee . . . followed by signing in to a few accounts (with Facebook Connect) to read/interact with your network.
• As you leave for work, you stop and fill your car with gas, paying with a credit card and supplying some type of frequent flyer or airmiles card. Perhaps, to hit your peak morning caffeine intake, you stop by Starbucks and pay with your preloaded Starbucks card.
• You check in to Foursquare while at Starbucks. You need to defend your Mayor status.
• You swipe your parking card as you enter the parkade at work.
• You log on to your computer at work and start leaving work-related data trails: emails, webinar activity, corporate database searches, Skype conversations, activity in Sharepoint, are all recorded—data waiting to be analyzed to determine your productivity in relation to others in the organization.

. . . and so on. (Even the vegetables you pick up on the way home from work are tracked by the small discount your grocery store offers when you swipe your customer loyalty card).

Bohn and Short estimate that an average American consumes an astonishing 34 gigabytes of information daily.

Xavier Ochoa: Learnometrics: Metrics for learning (objects)

We live in digital times. The conversations that used to evaporate around the water cooler are now digitized, waiting for a clever algorithm for analysis. The potential of analytics to increase employee efficiency, match the right people to the right tasks, and to improve access to help resources is tremendous. But significant privacy and ethics concerns exist. Data silos protect individuals from inappropriate use of *our* data. We don’t necessarily want our doctor, insurance provider, or banker to know us fully. Cross-data silo access products are far more accurate representations of who we are (and what our interests are) than we might feel comfortable sharing.

When applied to learning—corporate, higher education, K–12—analytics raise similar concerns about the interplay between the value between transparent data silos and privacy and ethics. This course will explore learning and knowledge analytics, including analytics methods and models, systemic application, potential data sources, the “soft/human/non-quantifiable” aspect of learning, as well as privacy and ethical considerations in deploying analytics.

In Week 1 and Week 2 of this course, we focus mainly on building some familiarity with the concepts (and language) of learning and knowledge analytics. We define learning analytics as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Learning Analytics 2011 Conference site: https://tekri.athabascau.ca/analytics/).

Week 1 Readings

Baker, S.J.D., Yacef, K. (2009) The state of educational data mining in 2009: A review and future visions. Journal of Educational data Mining, 1(1), 3–7. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.295.3687&rep=rep1&type=pdf

Ochoa, X. (2011, March). Learnometrics: Metrics for learning (objects). Keynote presented at the first International Conference on Learning Analytics and Knowledge, Banff, Alberta. Retrieved from https://dl.acm.org/doi/10.1145/2090116.2090117

Siemens, G. (2011). Learning analytics: Foundation for informed change in higher education [Recording from EDUCAUSE presentation, January 10, 2011]. Retrieved from https://www.slideshare.net/gsiemens/learning-analytics-educause

Untangling the social Web. (2010, September 2). The Economist. Retrieved from https://www.economist.com/technology-quarterly/2010/09/04/untangling-the-social-web

Activities

The following activities are required of course participants this week:

1. Introduce yourself in the Landing Group: what do you hope to gain from this course? Why are you taking it? What has been your experience with analytics or data mining/analysis?
2. After reviewing the readings and presentations for Week 1, detail, in the discussion forum, how data mining differs from learning analytics (or, if you don't think there is a distinction, please detail why this is the case). You may have to spend a bit of time on Google Scholar or other search tools to address distinctions. Learning analytics is a "younger" term, but still generates numerous search results.
3. Optional: Attend the weekly live session for this week.

References

Bohn, R.E., & Short, J.E. (2010, January) How much information? 2009 report on American consumers. Retrieved from the UCSD Global Information Industry Center website: http://ijoc.org/index.php/ijoc/article/download/1566/743