We develop theoretical models, learning methods, and analytical technology to guide lifelong learning processes in the face of digitalization of society.

Digital technologies have a major impact on our private and working lives which requires continuous learning. How lifelong learning happens in such settings is also undergoing major change.

The research center considers challenges that arise through the digitalization of working life, examines how this will impact our need to continuously learn, and studies the opportunities and risks that digitalization has for lifelong learning. Data-driven technologies and AI, in particular, are changing the landscape of working and learning as they threaten established roles and practices. However, the center also looks at the opportunities that arise with these technologies. A focus on “learning analytics” for workplace and professional learning attempts to make use of the collected data for understanding and improving learning processes for adult learners (Ruiz-Calleja et al., 2021).

We consider lifelong learners as active agents in the process of digitalization. We seek opportunities for learners to develop agency through collective knowledge creation, in which they are empowered to actively shape the change as it happens. Such collective knowledge development can be embedded into continuing education programs or into processes of non-formal or informal learning.

The center is developing theoretical models of these learning processes, such as the knowledge appropriation model (Ley et al. 2020). We create research instruments to analyze processes of knowledge creation and lifelong learning, such as through qualitative inquiry (Leoste et al. 2019), through quantitative research (Ley et al. 2021), or through the modeling of digital traces (“learning analytics”) (Ley, 2020; Rodriguez-Triana et al. 2020). The Center also develops new forms of continuing education and training methods that combine collective and individual learning and connect learning with practice. An example is the Innovation Laboratory for teacher education that has been shown to increase meaningful adoption of digital technologies in classroom teaching (Ley et al. 2021).

Example: Digitalization of schools and the role of the teacher

School teachers are under pressure to employ digital technologies in their teaching. Many feel unsure about the efficacy of these technologies and their own future role. Use of Artificial Intelligence (AI) in classroom teaching may question teachers’ roles as the main facilitators of learning. At the same time, digital technologies also enable new learning processes for teachers themselves, as they are able to connect to peers and experts online, share materials and collect data about learning processes in their own classrooms. All this helps them gain a better understanding of the use of digital technologies in school. The Teacher Innovation Laboratory is a continuing education format that introduces some of the important elements of this learning in teacher education. Together, teachers design, experiment with and reflect on the use of digital tools in their classroom, thereby getting a better grasp on what works and what doesn’t. At the same time, they increase their skills and gain confidence in the use of technologies in a safe environment.

Team
  • Leoste, J., Tammets, K., & Ley, T. (2019). Co-Creating Learning Designs in Professional Teacher Education: Knowledge Appropriation in the Teacher’s Innovation Laboratory. Interaction Design and Architecture(s) Journal, 42, 131–163. http://www.mifav.uniroma2.it/inevent/events/idea2010/doc/42_7.pdf
  • Ley, T., Tammets, K., Sarmiento-Márquez, E. M., Leoste, J., Hallik, M., & Poom-Valickis, K. (2021). Adopting technology in schools: modelling, measuring and supporting knowledge appropriation. European Journal of Teacher Education, 1–24. https://doi.org/10.1080/02619768.2021.1937113
  • Ley, T. (2020). Knowledge structures for integrating working and learning: A reflection on a decade of learning technology research for workplace learning. British Journal of Educational Technology, 51(2), 331–346. https://doi.org/10.1111/bjet.12835
  • Ley, T., Maier, R., Thalmann, S., Waizenegger, L., Pata, K., & Ruiz-Calleja, A. (2020). A Knowledge Appropriation Model to Connect Scaffolded Learning and Knowledge Maturation in Workplace Learning Settings. Vocations and Learning, 13(1), 91–112. https://doi.org/10.1007/s12186-019-09231-2
  • Rodríguez-Triana, M. J., Prieto, L. P., Ley, T., de Jong, T., & Gillet, D. (2020). Social practices in teacher knowledge creation and innovation adoption: a large-scale study in an online instructional design community for inquiry learning. International Journal of Computer-Supported Collaborative Learning, 15(4), 445–467. https://doi.org/10.1007/s11412-020-09331-5
  • Ruiz-Calleja, A., Prieto, L. P., Ley, T., Rodriguez-Triana, M. J., & Dennerlein, S. (2021). Learning Analytics for Professional and Workplace Learning: A Literature Review. IEEE Transactions on Learning Technologies, 14(3), 353–366. https://doi.org/10.1109/TLT.2021.3092219