Reading: Advice for interpretation of learning analytics

Institutional activities

It is necessary to develop and implement a data-driven culture and to provide regular training in data analytics so that artificial intelligence (AI) and learning analytics in particular are used in a responsible and trustworthy manner while avoiding bias. To support the development of such a culture, it is beneficial to have appropriate knowledge in the area of strategic planning, which can be acquired in the course Strategic Planning for Digital Technology Implementation. In addition, reports and materials created within learning analytics can be transformed into open educational resources, which is discussed in the course Open Education and Open Science.

For university instructors, education on the meaningful use of data analytics is essential. MOOCs and online tutorials are available as a starting point, but institutional training is needed to open up discussion.

Learning analytics should be grounded in ethical principles and in principles of equity and fairness. An institutional code of practice, or at least a set of recommendations, can be developed.

Data literacy, as well as assessment literacy, should be an integral part of the set of instructors' competencies. This can be included as part of doctoral programs for early-career academics and/or as part of continuing professional development for university teachers and staff.

Institutions should provide support to instructors in the use of learning analytics and artificial intelligence in terms of training, resources, and equipment, as well as staff support.

The most effective approach is to implement a strategic planning cycle.

Questions to be addressed at the institutional level:

• What competencies in the area of data literacy are required by stakeholders (students, instructors, developers, researchers, educational decision-makers, and employers) for the effective use of AI-enhanced learning analytics tools? Do these competencies differ among students from different disciplines and among different stakeholder groups?
• How are end users’ data literacy competencies related to their interpretation of the opportunities offered by learning analytics, their reasoning, and their decision-making related to the learning process when interacting with learning analytics tools?
• How can a data-driven culture, digital literacy, and ethical data use be fostered in the development of responsible applications of artificial intelligence in education?
• How can learning analytics and related resources be strategically planned and co-created to achieve strategic goals?