Tagged: W22A10

Week 22 – Activity 10: Learning analytics in the library

Timing: 2 hours

  • Explore the Library Impact Data Project blog.
  • Make a list of the different types of data collected in university libraries. These range from swipe card data about student visitors to usage figures for specific websites.

1. number of items borrowed from library (excluding renewals)

2. number of visits to the library

3. number of logins to e-resources

4. which sites were accessed

5. most popular resources

6. time spent browsing (from entry to checkout)

  • Note in your learning journal or blog five ways in which these datasets might be used to support analytics that could lead to the improvement of learning and/or teaching.

(refers to both physical library, or university online library)

 1. Student’s grades, participation vs. books checked out

2. Students’s grades, participation vs. books checked out

3. Time spent in the library vs. contributions to online learning spaces (i.e. is the library an indicator of effort)

4. Resources accessed vs. those chosen as references (student ability to find relavent information). Can assist with embodied learning.

5. Time spent on downloaded resources – i.e. did students download them, read them off screen, where there distractions (were uses using social networks etc.) whilst browsing resources.

  • Visit the Library Analytics and Metrics (LAMP) project blog and read the guest post, So what do we mean when we say ‘analytics’?(Showers, 2014).This includes 49 user stories (presented in images of two tables) that identify ways in which library analytics can be used. Does the list include the five uses of data that you noted in your blog/learning journal at the start of this activity?

Not really, as there is little discussion about cross analytics comparison to really gather valuable usage data (i.e. triangulation). Besides some points such as:

– Merge data from the student registry

– Map e-resource usage events to actual users

  • Refer back to the definition of learning analytics you developed last week. Could all these analytics be classified as learning analytics, or just the ones that the table in the blog categorises as ‘T&L’? Or do they belong to some other subset?

More than just T&L. As for example, data analysis could transform into learning analytics if delivered (in a meaningful way) to students or educators. Recommendation analytics could also be seen to form part of adaptive teaching at a lower level. Overall, it is difficult to say they do or don’t, as on their own, many of the analytics might not be useful (partly due to a lack of explanation in the table). However, if the data is triangulated (those labeled as ‘data’ talk to this point) then they might well have characterises of LA.