Measuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learning

Abstract

This paper explores potential ways of evaluating the implicit learning that may be present in autonomous smart learning activities and environments, reflecting on prior phenomenographic research into smart learning activities positioned as local journeys in urban connected public spaces. Implicit learning is considered as intrinsic motivation, value and richer engagement by participants, demonstrating levels of experience complexity, interpreted as levels of implicit learning. The paper reflects on ideas for evaluating implicit smart learning through planning for experience complexity in the context of a pedagogical model, the Pedagogy of Experience Complexity for Smart Learning (PECSL), developed from the research. By supplementing this model with further conceptual mechanisms to describe experience complexity as surface to deep learning alongside cognitive domain taxonomy equivalences, implicit smart learning might be evaluated in broad flexible ways to support the design of more effective and engaging activities. Examples are outlined placing emphasis on learner generated content, learner-directed creative learning and supporting dialogue and reflection, attempting to illustrate how implicit learning might manifest and be evaluated.