Learning analytics in the library

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Gord Webster Flickr Capture creative commons

H817 Week 22 Activity 10

This week I’m looking at learning analytics in the library and have identified the following data collected by University libraries taking part in the Library Impact Data Project.

Data collected:

  • usage by discipline
  • number of items borrowed
  • items checked out from the library
  • number of library visits, measured via gate entries
  • number of hours in a year in which a student was logged into a library PC
  • hours logged into e-resources
  • total number of e-resources accessed
  • identification of most visited resources
  • pages visited and how long a student stays
  • pathways students take through the resources
  • student attainment
  • demographics of users
  • course discipline and method of study
  • final degree results
  • social networks and activity

Five ways in which these data sets might support analytics that could lead to the improvement of learning and or teaching.

  1. Identify the relationship between student attainment and library usage and potentially identify at risk students by analysing library usage. The teacher can then adjust their approach to at risk individuals.
  2. To improve the quality and usefulness of eLearning resources through analysing the most visited resources and the length of time students spent on these pages.
  3. Create more meaningful links between resources to promote the most useful and to lead students to other relevant resources –  through analysing path ways students take.
  4. Track the learner journey to design user friendly and engaging content.
  5. Identify the knowledge gaps in individual students to highlight the need for academic intervention.

MAODE , H817, Open University, Block 4, Activity 10

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