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AVT – exit report

About the project

One of the values for Norway’s primary and secondary education is that it should “open doors to the world and the future”. In order to achieve this, it is essential that our children receive a good education. We have a little over half a million primary and lower secondary school pupils in Norway. All of these children are unique individuals, with unique prerequisites for learning. In order to ensure that the children benefit from education, school owners (primarily municipalities) have an obligation to assess the performance and progression of each pupil and adapt the education to each individual pupil’s needs, as far as it can be done  (see Section 1-3 of the Education Act).

In practice, teachers are responsible for adapting the teaching. Today, the information base that teachers need in their work on assessment and adaptation is scattered across paper sources and various digital tools, as well as, in part, the teacher’s own memory. What if a system could gather this information in one place and systemize it to support teachers in their work? A system that analyses the progress made by each pupil, presents an overview of this progress and makes recommendations for what the pupil could focus on next. This is the goal for the so-called AVT project (Aktivitetsdata for vurdering og tilpassing – Activity data for assessment and adaptation).

The AVT project is a research and development project focused on digital learning analytics in schools. The project explores opportunities and challenges associated with the use of learning analytics and artificial intelligence (AI) to analyse pupil activity data from various digital teaching aids. Activity data is the term used for the data that is generated when a pupil completes activities in a teaching tool. The activity could be the answer the student gave to a question, information about which activity the pupil completed, how long the pupil spent working on it, and whether the pupil gave the correct answer or an incorrect one. Similar to how schools purchase textbooks from publishers, they also purchase digital teaching aids from different providers. The AVT project focuses on digital teaching aids, but we simply refer to them as “teaching aids” in this report.

The context for the project is the research field artificial intelligence (AI) in education, with a primary focus on intelligent tutoring systems and learning analytics. Specifically, the AVT project uses an open learner model, and analytics and recommendation algorithms for analysis of learning progress and recommendations for pupils. Results of the analysis are presented in an online portal (dashboard) customised for each user group – such as teachers, pupils and parents. Users will use Feide to log on to the dashboard.

The objective of the project is to explore whether it will be appropriate to develop a publicly managed solution that could support teachers in their work on adaptations for each individual pupil, give pupils greater insight into their own learning, and support teachers in their work on pupil assessment. The goal of adaptations is to ensure that the pupils achieve the best possible learning outcome from their education. On a more general level, the AVT project aims to drive the development of national solutions, guidelines, norms and infrastructure.

The first part of the project (AVT1) was initiated in 2017. During this phase, a prototype was developed, as well as a solution for collecting activity data from different providers. And a preliminary data protection impact assessment (DPIA) was also conducted. This assessment maps the potential impact of the system on the protection of personal data and develops measures to mitigate the risks. The second part of the project (AVT2) was initiated in 2020. During this phase, activity data on pupils at participating schools in Oslo was collected from various teaching aids, before the results were analysed and presented to teachers and pupils.

The project owner for the AVT2 project is the Norwegian Association of Local and Regional Authorities (KS). The project has been led by the University of Bergen (UiB) and its Centre for the Science of Learning & Technology (SLATE), and the City of Oslo’s Education Agency has been the main partner and driving force since the project’s initiation in 2017. Recently, the Municipality of Bærum and the regional intermunicipal collaboration Inn-Trøndelag have also joined, in minor roles. All project partners have been involved in the sandbox project.

So far, the system itself has only been tested with constructed test data and pseudonymised activity data related to mathematics from a group of pupils in Oslo schools. This data was collected from transition tests in mathematics after 4th and 7th grade, as well as from a couple of the participating suppliers, who have been able to share data in a suitable format.

Objective for the sandbox process

The objective of this sandbox project must be seen in relation to the Data Protection Authority’s overall objective for the sandbox, which is to promote the development of artificial intelligence solutions that, from a data protection perspective, are both ethical and responsible. The Data Protection Authority plays a supervisory role in the sandbox, where we work with the AVT project to arrive at good data protection solutions for learning analytics.

In previous phases of the AVT project, project participants got the impression that the education sector is not quite prepared for the use of artificial intelligence – and that this applies both to the regulation of this technology and to its use. In addition, it is not quite clear what the boundaries for “responsible artificial intelligence” actually are. Through participation in the sandbox, the AVT project seeks to explore the legal framework, in addition to frameworks for responsibility and ethics. The overall goal is to pave the way for adapted education for each individual pupil, while also protecting the pupil’s privacy in the best way possible.

The sandbox project has had three sub-objectives, where we have explored:

  1. Legal basis. Whether the legal basis for the processing of pupil activity data meets the requirements of the General Data Protection Regulation (GDPR). If not: what will it take to ensure compliance?
  2. Pupil privacy. What are the consequences of the learning analytics system on the protection of the pupils’ personal data? What do the parties responsible have to keep in mind to ensure that the pupils’ privacy is protected?
  3. How can information about the learning analytics system be given to its users (teachers, pupils and parents)? What do they have to be informed about, and what should they be informed about?