Learning analytics at BI
To improve teaching, learning and student well-being, we need to understand learner behavior. Learning analytics means using data about learners to improve teaching, learning, and the learning environment.
We use data to support a knowledge-driven discussion about effective teaching, and to gather and distribute insight to help faculty and students alike spend more of their precious time on pedagogically meaningful tasks. In other words, learning analytics is a tool for making teaching and learning more data-driven.
The Learning Center is the hub of learning analytics at BI.
How may learning analytics be beneficial to you?
The Learning Center supports many different types of learning analytics initiatives from faculty. Here are some examples relevant to individual faculty:
- How students interacted with course material - and how it affected final grades. This includes interactions with course material, video viewership, activity during synchronous teaching (as a proxy for attendance).
- How to revise course material more efficiently.
- Create student groups.
- Time announcements, uploads and releases.
- Help collecting and analyzing data useful for research on your own teaching or to document systematic efforts towards e.g., ETP status or promotion.
As of fall 2024, help is delivered as reports but efforts are on-going to enable continuous delivery through interactive dashboards.
Examples relevant to groups of faculty and management:
- Evaluate aspects of course delivery
- Identify groups of students based on online behaviour and evaluate the pedagogical potential in tailoring teaching towards specific student groups
- Understand how Itslearning elements are utilized
- Advice on how to collect data for and measure effect in Learning analytics endeavours
Principles of learning analytics
BI has adopted a privacy policy for learning analytics to ensure the quality of the analyses carried out, and the products and services offered thereof. The policy ensures that learning analytics activity complies with the GDPR and is in line with our students’ expectations of BI as a responsible data manager. You can read more about how personal information is used for analytical purposes in BI’s privacy statement.
The policy can be summarized in eight principles:
- Pedagogical value: Learning analytics-based products and services will be developed only insofar as there are pre-existing reasons to believe that the product or service will offer pedagogical value to our students.
- Analytical accountability: Learning analytics is carried out by qualified personnel following specified routines to ensure that insight results in valid action.
- Ethical accountability: Learning analytics is carried out in accordance with current laws and regulations, and the risk associated with Learning analytics should never outweigh the projected benefits. Long-term consequences for the data subjects should be considered.
- Support: Learning analytics products and services are created and designed to support, not Learning analytics, human decision-making and interaction.
- Transparency: Information stating what data are used and how, shall be made available to all stakeholders, either automatically or upon request.
- Participation: Stakeholders, including representatives of the data subject, should always be consulted as part of the development of Learning analytics-based products and services.
- Inclusion and equity: Services and products are designed with particular emphasis on universal design; groups of students with special needs should be considered.
- Feedback: Feedback, including criticism, on all levels, from anyone, is welcomed and considered for future development of policy and projects alike.