Learning analytics webinars coming soon!

During 2020, the AnalyticsAI project will implement three webinars on learning analytics. The purpose of the webinars is to share the results of the project with a wider audience and to bring out different perspectives related to learning analytics. The webinars will be held in Zoom, and you can find the registration links below. Come along to listen and discuss!

Registration for webinars:

More information about the webinars:

Privacy Policy, Risk Assessment and Impact Assessment

Tommi Haapaniemi (UEF), Meri Sariola (UEF), Jiri Lallimo (Aalto), Viivi Väisänen (UH)

As part of AnalyticAI work on the legal aspects of learning analytics, Viivi Väisänen's presentation deals with data protection principles and impact assessment through Aalto University's case study. The presentation shows the main conclusions of the case study and, as a concrete example, an impact assessment of the Moodle drop-out rate. The case study has also served the work on the AnalyticsAI policy design.

Under the leadership of the University of Eastern Finland, a simplified risk assessment model for the processing of personal data has been developed in the project, which can be applied to different use cases of learning analytics. The presentation introduces the risk assessment model and the key risks and perspectives on the implementation of learning analytics that need to be considered on the basis of the Data Protection Regulation.

AnalyticsAI app, Student Dashboard

Heikki Hyyrö ja Sami-Santeri Svensk (TU)

Under the leadership of the University of Tampere, the project has developed an application that utilizes learning analytics for the use of students, instructors and those responsible for education. The presentation concretely addresses the key elements of the application and in particular how the application can support students with the smooth progression of their studies.

Study path as a service path - Perspectives on analytics

Titta Jylkäs ja Essi Kuure (ULapland)

What development opportunities can we identify when we look at a student’s study path as a service path? The presentation opens up a people-oriented approach to learning analytics through the basics and methods of service design. Through the identification of the student's service path, the benefits of learning analytics can be targetted to studies in a timely manner and thus provide the student with concrete benefits for the promotion of studies. Through service design and learning analytics, we can outline the study path as a whole and offer students the value of a targeted service.

Blog, featured

Learning analytics as an evaluation tool - Privacy, Legal Security and Liability

Utilising artificial intelligence applications to support student learning, student guidance, learning assessment, and knowledge management can change university teaching procedures and practices. Legally, the use of artificial intelligence in student guidance and learning assessment involves a number of problem areas for which there is no legal solution yet.

The Data Protection Regulation sets out the conditions under which personal data may be processed, as well as the restrictions related to data subject profiling and automated decision-making. The processing of personal data must have a legal basis in accordance with the Data Protection Regulation. For example, where processing is necessary to comply with a university statutory obligation, there is an appropriate legal basis for processing personal data. Universities have statutory obligations, such as, in the Universities Act, whereby, for example, they have the task of providing research-based higher education and arranging teaching and study guidance so that students can complete their studies full-time within the stipulated target completion period. The use of learning analytics in universities to meet these obligations is permitted under the legal basis.

When using learning analytics, students are in practice required to be profiled based on their knowledge. According to the Privacy Regulation, "profiling" means any automatic processing of personal data in which the use of personal data is used to assess certain personal characteristics of a natural person. Analysing student learning is profiling within the meaning of the Privacy Regulation, as a student's ability to learn is a personal attribute that is assessed in learning analytics. Profiling is not categorically prohibited by the Data Protection Regulation, but it must have the processing criteria for personal data laid down in the Data Protection Regulation, which must take into account purpose-relatedness, the need and necessity of data minimization, transparency of processing and respect for data subject's rights. In many cases, the data subject has the right to object to profiling, but there is no such right if the processing is based on the fulfillment of the data controller's statutory obligation, such as the provision of instruction and guidance as described under the Universities Act.

Learning analytics can also be used to evaluate students' learning outcomes. Thus, learning analytics may be used for automated decision-making within the scope of the Data Protection Regulation, which is in principle prohibited. In universities, learning analytics can only be used in the assessment of students if it involves effective teacher control over the final outcome of the evaluation or if the automated assessment is specifically provided for by law.

There are issues other than personal data protection in the automated assessment of student learning. For the benefit of the student, the University Act provides legal safeguards related to the assessment of study’s accomplishments. These legal safeguards cannot be compromised when utilizing learning analytics. Assessment of learning is also the exercise of public authority, associated with the effective exercise of official duties. Under the current legislation, official responsibility for automated analysis cannot be outsourced, but it is ultimately the respective teacher the one responsible for the evaluation.

There are technological conditions for the development of learning analytics, but operational and legal conditions are still seeking their place in the development of artificial intelligence. Legal challenges still do not necessarily present a barrier to the use of learning analytics in student guidance and assessment, but where development work is concerned, it requires careful work process development to ensure students’ personal data and legal protection, not forgetting the teacher's own legal protection.

The authors

Tomi Voutilainen and Juuso Ouli

University of Eastern Finland