Digital footprint and learning analytics

Whenever you use digital services, you leave behind a large amount of data. This amount of data is called a digital footprint. You leave traces daily without necessarily noticing it at all: when you use your bonus card in the grocery store, borrow books from the library or use electronic services, you will leave a trace. (Hannula 2017, 8)

When you log in to Moodle or Optima, for example, you will leave a trace. The same happens when you return assignments. This data is called learning data. (Hannula 2017, 8)

During their studies, data is collected about the students in a variety of systems:

• basic information after the acceptance of the study place (student number, address, etc.)
• log entries on the learning platform (e.g. the return date and time of an assignment, a visit to a discussion forum)
• course markings in the transcript of records (e.g. the grade and its entry date)

A student cannot influence the accumulation of data much, because the collection of this data is necessary for organising the studies. (Suhonen, 2018)

“Learning analytics makes use of this data generated in connection with the learning process to develop teaching and guidance and to support learning.” eAMK project

Learning analytics is divided into the analytics of learning, analytics of studying, analytics of space, biometrics and analytics of teaching.

The analytics of learning allows you to monitor your progress. The teacher will see where you potentially need guidance if, for example, you are not making any progress with your assignments.

In other words, learning analytics allows you to monitor your own performance and learning activities and get immediate feedback on your studies.  At its best, learning analytics helps you to get an overview of your abilities and development.