The pedagogy of experience complexity for smart learning: considerations for designing urban digital citizen learning activities

Abstract

The Pedagogy of Experience Complexity for Smart Learning (PECSL) is a four-tier model of considerations for the design and development of learning activities situated in real world hyperlocal locations, mediated by smart enough technologies. Learner experience is placed at the centre of learning design, focusing on the complex interrelated experiences that may be possible. A wider awareness of types of learning may enhance potential for gaining value for learners and offer more flexibility for instructors or others. Learning is considered as any potential object of vital interest for the learner, and may include making connections with others, dialogic space expansion between learners and wider relevance of topic or location as much as any intended learning outcome.Taking inspiration from digital artefact user centred design, the PECSL adopts a position of flexible layers of considerations that impact stages of design for complex smart learning activities. Each tier being interrelated to the others, these iteratively adapting as a result of decisions being made throughout the design and development process. Categories of learner experience variation derived from a phenomenographic study of smart learning journeys inform the foundation of the PECSL, providing concepts of experience relevance structures leading to related pedagogies, further pedagogical relevance considerations and deeper epistemological reflections. Acknowledging significance of the context, process and content of learning in these activities, considerations expand to enable pragmatic support for much of value towards effective learning. This paper seeks to provide a means for learners to learn from each other as much as any specified learning goals or assessment.

Applying the PECSL: using case studies to demonstrate the Pedagogy of Experience Complexity for Smart Learning

“…Analysis was then reviewed by a co-judge (Booth, 1992, p. 68) to further review the analysis perspective and establish its communicability and interpretive awareness (Cope, 2004;Sandberg, 1997). Reproduced here from Lister (2021d) for sake of convenience, Table 1 shows the relational categories of experience complexity of a smart learning journey that formed the foundation of the Pedagogy Of Experience Complexity For Smart Learning (PECSL), further outlined in this paper and elsewhere (Lister, 2021a(Lister, , 2021b(Lister, , 2021d.…”

“…Table 1 The experience complexity of a smart learning journey (Lister, 2021d) The categories and levels of experience complexity provided glimpses of possible ways to anticipate areas of potential experience variation that participants may have, depending on types of activity and emphasis on focus of learning. These potentially indicated possible interpretations of intrinsic or extrinsic motivation and relevance, dependent on the nature and location of a smart learning activity.…”

“…The 'Pedagogy of Experience Complexity for Smart Learning' comprises a four tier model of considerations, as outlined in previous sections concerning experience relevance structures, their related pedagogies, pedagogical relevance structures and epistemological context (Lister, 2021b(Lister, , 2021d. This model seeks to offer an iterative cycle of broad considerations to support smart learning activities, particularly situated in hyperlocal 'in the wild' urban locations (e.g.…”

“…These activities are reimagined for learning, using the PECSL model to plan and scope experience variation and subsequent pedagogical approach. Beginning by scoping core practical and pedagogical considerations (Lister, 2021d) and planning for experience variation by scoping activity experience relevance provides the initial strategy for the activity. Related pedagogies are then selected depending on nature of activity and desired experience variation.…”

“…The PECSL model, together with the research it is based upon are summarised in this paper in subsequent sections. An additional paper by the author (Lister, 2021d) outlines in more detail what the PECSL is, how it came about and why it might be useful and relevant to smart learning and smart learning environments. The further challenges of how to measure 'implicit' learning in these contexts are discussed in a forthcoming paper (Lister: Measuring learning that is hard to measure: using the PECSL model to assess implicit learning, in preparation), outlining potential mechanisms for measuring aspects of learning using the PECSL model alongside cognitive domain equivalences.…”