Of course researchers and practitioners in vocational education and training (VET) and in Adult Education are long used to their secondary status compared to School and Higher Education. And although Learning Analytics has been around for quite some years now, there has been little consideration of its use in VET and in workplace learning.
Is it is good to report that the German Federal Ministry of Education and Research together with the German Federal Institute for Vocational Education and Training have funded a study on artificial intelligence offers benefits for implementing personalised and adaptive learning environments (PALE; Schumacher, 2018). PALE are digital learning systems that continuously analyse and leverage education-related data to adapt the learning environment to individual needs and constantly changing requirements
As the authors of the study, Four Perspectives on Personalised and Adaptive Learning Environments for Workplace Learning, Yvonne M. Hemmler and Dirk Ifenthaler, say”
A major challenge in designing trusted PALE for workplace learning remains the identification of reliable indicators. Indicators are variables (e.g., interests, demographics, location) that reveal useful information about learning behavior and that are processed by specific algorithms to personalize and adapt the learning environment. Reliable indicators are crucial for PALE as accurate and comprehensive information about learners and their contexts is needed to design effective interventions to support learning processes and outcomes.
The research identified three profiles as being central to the collection of data for developing and implementing personalised and adaptive learning environments. These profiles were examined against different perspectives: Pedagogical perspective, ethical perspective , data analysis perspectives and Information perspective.
The results are cautious. “Despite rich datasets and advanced analytics methodologies, not all approaches utilising artificial intelligence in education seem to be effective for workplace learning.” They conclude that so far “no wide-scale organisational implementation of artificial intelligence for workplace learning exists and no empirical evidence is available for supporting the assumption that PALE improve the performance of involved stakeholders and organisations.”
However, they suggest “Interdisciplinary perspectives on adoption models as well as on pol icy recommendations may help to move the pioneering efforts on artificial intelligence for workplace learning forward”
Schumacher, C. (2018). Supporting informal workplace learning through analytics. In D. Ifenthaler
(Ed.), Digital workplace learning: Bridging formal and informal learning with digital tech-
nologies (pp. 43–61). Springer. https://doi.org/10.1007/978- 3- 319- 46215- 8