Exploring the Affective Domain of Learning in Technology Education, utilising a Qualitative Modified Delphi Study.

This research investigates the affective domain in Technology education, examining how the representation of emotions, values, and motivation within the curriculum shapes students’ learning, alongside teachers’ professional learning, pedagogical judgement, and perceptions of technological capability. Drawing on the perspectives of experienced teachers, pre-service teachers, and experts in the field, the study advocates for balanced recognition of the cognitive, psychomotor, and affective domains of learning, fostering social and emotional development while supporting the development of technological capability (Buckley et al., 2018). Drawing on Krathwohl et al.’s (1964) taxonomy of the affective domain, alongside Biesta’s (2010, 2017) theory of subjectification and the philosophical accounts of technology as a value-laden human activity advanced by Mitcham (1994) and Gibson (2008), the wider research conceptualises affective learning as integral to authentic technological practice. This framing is further supported by Immordino-Yang’s (2016) neuroscientific account of the inseparability of emotion and cognition in meaningful learning. Earlier phases employed Constructivist Grounded Theory (Charmaz, 2006, 2014) to analyse curriculum documents, questionnaires, and focus groups, generating themes including affective practice, constraint, emotional safety, engagement, resilience, well-being, teacher development, and life beyond school.
To refine and extend these themes, a qualitative modified Delphi design is employed. Unlike a classical Delphi, this study presents panel members with structured statements derived from prior qualitative findings. In Round 1, participants review statements, indicate their level of agreement using a Likert scale, and provide comments, amendments, and concise professional rationales based on their experience and context. Responses are analysed using constant comparison (Charmaz, 2014) to identify convergence, divergence, and conceptual refinement. In Round 2, revised statements and anonymised panel feedback are returned for further endorsement and clarification. Findings will identify areas of convergence and divergence, contributing to a theoretically robust and practice-informed framework for understanding affective learning in technology education.