Learning Analytics

Examples of data sources for learning analytics

Study registers

e.g. school registers, class records

Study records, grades, curricula

Learning platforms

e.g. ViLLE, Moodle

Login data, submissions, course enrollments, attendance records

Other material

e.g. national statistics, open data, questionnaires

Background information, research materials

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Big data & analytics

The goal of learning analytics is to offer tools and methods for monitoring studies and predicting difficulties in learning. Guidance counselling and automating teaching and learning aids are of particular interest in the field. Through these means, we seek tested, justified, and ethical ways to enhance learning, teaching, study guidance, and administrative processes.

Example 1: Achieving curriculum objectives Example 2: Analysis of Study Habits Example 3: Predicting Course Grades Example 4: Automating Learning Analytics Ethics and Privacy in Learning Analytics

Example 1:
Achieving curriculum objectives

A typical approach to creating tools in learning analytics is the development of predictive models from existing data. For example, by examining students' yearly accumulation of study points it is possible to predict the accumulation of upcoming years. This enables early intervention when corrective measures are still useful in preventing problems.

Example 2:
Analysis of Study Habits

Questionnaires can be powerful tools in gathering data for learning analytics, especially if it can be combined with other data (such as that gained from learning platforms or sensors). Still, questionnaires alone can also yield important results. The attached picture combines two questions picked from a questionnaire measuring study habits of students.

As we can see, students who report having friends they can complete assignments with also report significantly fewer occasions of feeling mentally or physically tired. Therefore, it is clear that formation of friend groups among students should be facilitated.

Example 3:
Predicting Course Grades

Continuous assessment and comprehensive collection of course achievements enables the development of a model that can be used to predict course outcomes. The picture shows achievements of the first two weeks of an eight-week course. Each dot represents a student enrolled in the course. The dots are color-coded based on the final grade achieved. What is notable here is that the model can predict 80% of students who are going to fail the course during the first two weeks.

Example 4:
Automating Learning Analytics

ViLLE automatically recognizes students' learning misconceptions in mathematics based on the information collected from their submissions.

A study showed that the algorithms of ViLLE predict learning misconceptions as effectively as a widely-used pen and paper test. The difference is that automatic analytics enables real time viewing of information without a separate test.

Ethics and Privacy in Learning Analytics

As data gathered from students is at the center of learning analytics it is imperative to take special care of data privacy. We at the Centre of Learning Analytics have always acted according to law and the following ethical principles:

  1. Data privacy: The Centre for Learning Analytics complies with the General Data Protection Regulation of the EU in all data processing and storage. Accordingly, the data privacy policy of ViLLE is in accordance with GDPR.
  2. Research permits: When conducting research, The Centre for Learning Analytics always collects appropriate research permits if any kind of collected data is used as part of research. ViLLE automatically collects over 2 million submission every month but no submissions are used for research purposes without a separate written permission received from a subject or their guardian.
  3. Transparency: In our view, learning analytics should not be the secret privilege of teachers and administrators. Instead, it is important to maintain transparency throughout the whole process and allow learners to view the analysis of their results whenever possible.