Activity 10 involved exploring the Library Impact Data Project blog and listing different types of data collected in university libraries. From this exploration, note five ways in which these datasets might be used to support analytics that could lead to the improvement of learning and/or teaching.
Types of datasets found:
- library gate entries
- individual user library behaviour
- library activity data and student attainment
- logins to e-resources
- hours spent on library computers
- information on demographic characteristics
- course and mode of study
- final degree results
- library usage
- ethnicity, country of origin and usage
- age and usage – mature and non-mature
- project reports and outputs
- activity data
Datasets that might be used to support analytics for improved learning and/or teaching
Individual user library behaviour could be used to improve learning and teaching by providing data about length of time spent in the library, what books have been checked out and for how long, internet usage for example what sites are visited and how often and whether they are relevant to course and learning.
The library could use library usage data to analyse individual books that been checked out and returned; if returned late the number of students waiting to borrow the book and whether additional copies need to be purchased.
Number of e-resources accessed which support student courses and if there are e-resources that are not been used or are minimally used to ensure that resources continue to meet the needs of learners. Supporting both learners and teachers/educators.
They could analyse how many students are logged on at peak times or other times throughout the day, how many students log on to library computers and how many use personal mobile devices to connect to the network to ensure that this doesn’t effect quality of access. Planning for peak time usage and whether there is a need for purchase of additional IT resources.
Data could be analysed about subject discipline areas and undergraduate use of the academic library to support learning. This could be broken down further to include demographic characteristics such as ethnicity, gender and age of student and their usage and relate this to their course of study. Library impact project data spreadsheet exemplar could be used to collect this information and relate it to student module and final degree results. Data could be used to inform structure and content of future courses and pedagogical approaches used.
A visit to the Library Analytics and Metric (LAMP) project blog. This includes 49 user stories that identify ways in which the library analytics can be used. This list does include the uses of data I have suggested above. The list is broken down to consider the following types of data :
- Mission – High level ‘mission’ statements that are ‘epic’ user stories
- Data – Stories about the range of data available for analysis
- Collection – Use of analytics for collection management
- Service – Use of analytics for service improvement, including enquiries
- Teaching & Learning – Use of analytics to enhance the learning experience and student success
- Recommendation – Use of analytics to provide recommender services
I believe that each of the analytics highlighted in LAMP are each necessary for ensuring that institutions, academics, educators and learners achieve the highest standard of education to meet the demands of our current and future workforce needs, along with the integration of up-to-date technologies.
Library Data Impact Project [blog] available at https://library3.hud.ac.uk/blogs/lidp/project-outputs/library-analytics-bibliography/ (accessed on 11 July 2016).
Showers, B. (2014) ‘So what do we mean when we say “analytics”?’, LAMP, 9 January [blog], available at http://jisclamp.mimas.ac.uk/ 2014/ 01/ so-what-do-we-mean-when-we-say-analytics/ (accessed 11 July 2016).
http://www.aut.ac.nz/study-at-aut/campuses/south-campus (accessed on 11 July 2016).