Transforming future trachers' conceptions of Educational Technology: the role of Thinking Routines in the Educational Ecosystem

Educational technologies (Rivoltella & Rossi, 2019) are integrated into contemporary learning contexts with a renewed logic: no longer merely as tools supporting or integrated into the teaching-learning system, but as a structural element of the educational process, within an ecosystemic dimension (Walcutt & Schatz, 2019; Ellis & Goodyear, 2019). This logic must become a posture in the design practices of future teachers, for whom it is necessary to provide training that goes beyond the knowledge or instrumental use of devices and tools, but activates a transformation of ingenuous conceptions related to digital worlds and the idea of the construction of third learning spaces (Wanlin et al., 2019). Transformativity is an essential aspect of learning, particularly in professionalizing training courses (Balas & Touzet, 2023) as this acts on implicit and embodied knowledge patterns and beliefs (Pentucci, 2018). Highlighting change is a crucial aspect of professionalization, which can be made evident through reflective and metacognitive devices. This is the background to a university didactic experience carried out with Primary Education Science students that involved the design of a Digital Learning Ecosystem (Jeladze, Pata & Quaicoe, 2017) in which the actors, tools, resources and interactions between all elements could be captured in a dynamic and transformative logic. Thinking Routines (Ritchhart & Church, 2020) conceived within the "Visible Thinking" framework of Project Zero were used in the course for their potential in promoting critical thinking and active involvement of students in transformative processes. In particular, in order to capture the transformativity of the concepts on educational technology, we chose to analyse the data from students' voices using Braun and Clarke's (2019) model known as reflexive thematic analysis (RTA) in order to develop, analyse and interpret patterns across a qualitative dataset.