| Cases and Examples of learning Analytics: | Notes |
| SNAPP Overview | A pre, 2002-built system using social network analysis to identify, measure, track, and explain interactionas (IMHO, a proxy for learning) |
| Austin Peay State University: Degree Compass | Recommends courses a student should take based on the predicted grade they will receive in the course using course and course grade data from past students |
| Two Case Studies of Learner Analytics in the University System of Maryland | UMED - Using pre-enrollment (housing paid, tuition paid, enrolled/no data, admissions) data to address retention and graduation rates for at-risk student populations. BSU - fascinating collection of data, but implementation too early to tell how data is connected to intervention yet (lots of ideas, but little tried yet), technology integration is impressive (student services, financial aid, recruitment/admissions, registration/advising, academic services(testing services, learneng communities, tutorng), Curriculum & instruction( course roster, class sched, attendance, assignment scores, official grades) |
| Analytics in Progress: Technology Use, Student Characteristics, and Student Achievement | Using LMS data (use) and student characteristics to correlate with student achievement, student characteristics (gender, race/ethnicity, family income, previous academic achievement) |
Saturday, February 1, 2014
Case Study Reviews (1 of 2)
Recently, I read some examples of cases where Learning Analytics was being applied. Below is a short description of the kinds of data in each of the cases and what was trying to be explained. It is interesting to me that these four do not center on data that a teacher would use to evaluate instruction or learning. Most of this data is centered on institutional statistics rather than more traditional teacher-based data (except for grades), which include evaluating a learner on a particular skill or knowledge component. Another observation is that there is relatively little NEW data being captured. In most cases, there are new connections between existing data that are examined, evaluated, and regressed. But essentially no new data that is being captured here. I suppose one reason for this is it is easier to use available data than to have to create a new data collection process and infrastructure at the institution level. Maybe instructors have the advantage of measuring or observing new data points that could improve the diagnostic/intervening capacity of an instructor or instructional technology.
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John - I think you get at a very important point here: data analytics is limited in providing meaning to learning that happens without computers or the internet. In digital contexts, systems can count and track all kinds of data--some we would consider mundane and useless. These electronic systems do this because it is easy and cheap. It is not easy and cheap for people to manually track all kinds of data. So teachers and researchers limit themselves to attendance records and grades on assignments and reasonably-lengthed surveys (and sometimes unreasonably-lengthed). Anything beyond this type of data just isn't scalable. Which makes me wonder, as we embrace the value of big data, and use it to study and improve e-learning, will traditional F2F learning drop behind and fall off the map? Or will we find scalable ways for machines to capture all kinds of data in F2F contexts?
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