Policy recommendations

Based on the experiences and findings of the projects within four categories data, ethics & privacy, scalability & transferability, and impact, the project formulates the following policy recommendations.

DATA

GDPR and its implications have had the intended effect in European institutes of higher education. There is a broad awareness of responsible collection and use of data within institutes of higher education with policy, transparency, and practices such as informed consent being a high priority in learning analytics projects. This does occasionally pose a challenge at the operational level, as more administrative aspects need to be taken care of each time a learning analytics project is started.

There is a need for clear national and European policies for learning analytics. Commercial educational enterprises that offer online education and make data collection and usage part of their user agreement have a distinct advantage in the field of learning analytics. A level playing field is needed on order to allow traditional institutes of higher education to experiment with learning analytics and to advance the field. National and European policies that provide a clear framework for developing learning analytics initiatives are helpful in this regard.

Alone you go faster, together you go further. Cooperation between institutions of higher education allows for greater strides to be made in the development of the field of learning analytics. Encouraging institutions to work together accelerates innovation in this regard, especially when supported by a policy framework that allows for data interchange or cooperation within each others data environment, in order to facilitate international projects.

ETHICS & PRIVACY

The importance of ethics and privacy has to be balance with the need to experiment.
Learning analytics as a discipline is still in its infancy and it is not yet clear which way of using learning analytics is most valuable. The expectation is that technological possibilities will continue to increase in the coming years. At the same time, the high regulatory requirements for consent and data protection make it complicated to experiment and thus learn. It is therefore important to allow the alignment of any experimental learning analytics projects with existing legal and policy frameworks.

Every institution that wants to get started with learning analytics does this best from a policy text for learning analytics including the ethical aspects and how these frameworks fit into the educational vision and the existing quality assurance system. It is also advisable to provide sufficient space for bottom-up initiatives.

International cooperative projects that work with privacy-sensitive data have to navigate privacy laws from many different countries, which can be very challenging. Privacy laws are necessary and useful, but form administrative hurdles for academic progress in the field of learning analytics. It is highly recommended to create a policy framework that makes international cooperation through differing privacy laws more efficient.

European collaboration is not always easy due to different regulations, but is very stimulating for idea-generation and exchange of knowledge, practices, and experiences. Partners learn from each other and push progress in their own institutions and nations.

SCALABILITY & TRANSFERABILITY

The use of learning analytics can only be successful if the end users have the necessary competencies and the specific application is tailored to its specific context. Therefore, applications of learning analytics should be geared towards the target audience and provide information, training and guidelines tailored to the user group. Only in this way will learning analytics be deployed and used in a correct and desired way. This means that enforcing transnational standards will most likely not be successful, but that creating general framework that can be transposed and adapted to new context will be.

In countries such as the United Kingdom and the Netherlands, national cooperation organizations in the field of educational technology have demonstrated that they are an important support for higher education institutions in the consideration and implementation of learning analytics. In these countries, they have not only given an impulse to the concrete application of learning analytics, but also to the research around it.
Therefore the recommendation is to encourage the creation of national cooperation organizations in the field of Learning Analytics.

IMPACT

Although it is understandable to have high expectations of any new field of science and technology, learning analytics is not the cure for many of the challenges that are facing the current higher education system. Results and findings from online setting are hard to transfer to the campus setting, cultural differences mean the learning analytics applications do not necessarily work in a particular context, and the field as a whole is still in its infancy. 

Importantly, learning analytics should be part of a larger educational vision, culture, and practice.

The importance of ethics and privacy has to be balance with the need to experiment.
Learning analytics as a discipline is still in its infancy and it is not yet clear which way of using learning analytics is most valuable. The expectation is that technological possibilities will continue to increase in the coming years. At the same time, the high regulatory requirements for consent and data protection make it complicated to experiment and thus learn. It is therefore important to allow the alignment of any experimental learning analytics projects with existing legal and policy frameworks.