HLS D3 2019

The spaces we teach and learn in are changing. 

Technology is permeating physical spaces, augmenting and enhancing learning experiences. At the same time, mobile and pervasive internet-connected technology (IoT) creates interfaces between virtual spaces and real-world phenomena in which big data is collected. These dynamics give rise to a growing presence of hybridity: the blurring of boundaries between distinct contexts of learning and activity, and the unexpected interleaved experiences they engender (Ellis & Goodyear, 2016; Trentin, 2016). Hybridity is not a technical issue. As Stommel (2012) notes: “The word ‘hybrid’ has deeper resonances, suggesting not just that the place of learning is changed but that a hybrid pedagogy fundamentally rethinks our conception of place”. Cook et al. (2015) identify two dimensions of hybridity: the interleaving of formal and informal social structures in an activity system, and the combination of physical and digital tools mediating individual’s interaction with the world and society. They argue: “people connect and interact through a hybrid network of physical and technology-mediated encounters to co-construct knowledge and effectively engage in positioning practices necessary for their work” (Cook et al., 2015, pp. 125). Higher education (but not only) recognises the potential of hybrid learning spaces in promoting significant changes in learning, and increasingly use pedagogical hybrid learning model is its instruction (do Mejía Gallegos et al., 2017). Recent work has begun exploring the nature of hybridity from an educational design perspective (Köppe, Nørgård, & Pedersen, 2017).

Hybrid learning spaces open opportunities and pose challenges to designers of learning experiences. Apart from the complexity of combining multiple modalities to achieve effective synergies, these spaces have a novel quality: activities within them generate data, which can be used to monitor individual and social learning processes, and potentially feed back into them, to enable “double loop learning”: awareness and control of the process of learning and teaching itself (Blaschke, 2012). Recent years have witnessed a growing interest in the promise of educational data science (EDS), a term coalescing learning analytics (Ferguson, 2012), artificial intelligence and educational data mining (Cohen, 2017; du Boulay et al., 2018; Levi Gamlieli, Cohen, & Nachmias, 2015; Lim, 2016). In particular, there is an emerging recognition of the valuable intersection between data and educational design (Hernández-Leo et al., 2017; Mor et al., 2015; Toetenel, & Rienties, 2016). While the tradition of EDS originated in the study of virtual learning environments, recently we see first advances into its use in physical environments (Cukurova et al., 2017; Prieto et al., 2018). However, although the correlation between physical space design and educational effect is well established (Tanner, 2000), Learning space research is a relatively new field of study that seeks to inform the design, evaluation and management of learning spaces (Ellis & Goodyear, 2016) and EDS has not yet ventured into this domain. 

Along with the opportunities that arise from these hybrid learning spaces, there are issues that require an in-depth discussion among the community of researchers, developers, and practitioners in the field. While some of these issues are well understood, others are only beginning to be explored. For example:

  • Personalisation and collaboration: hybrid learning spaces bring together learners with different constraints, agendas, assumptions, and expectations. Some might be co-located, some might be remote in time and place. How do we cater to such diverse and ill-defined cohorts? How do we leverage this diversity to create effective and powerful learning experiences?
  • Ownership and empowerment: when we mix learning contexts, e.g. a curricular course and a MOOC, who sets the learning objectives? Who is responsible for monitoring achievement? Who “owns” the space, the curriculum, the content and the data? 
  • Representation and interpretation: How do we map the data we collect to complex learning dynamics? How do we avoid the “streetlight effect”, of valuing what we can measure rather than measuring what we value? How do we derive insights from data, and present them in such a way that will inform and assist learners, teachers, and administrators? 
  • Ethics: what are the risks and consequences of collecting and manipulating data about learners and learning environments? How do we draw the line between assessment, evaluation and surveillance? What are the appropriate modes of behaviour in hybrid learning spaces?  Moreover, what is the purpose of education in hybrid learning spaces, where learners come from divergent backgrounds and with different aims?

This unique trans-disciplinary workshop will bring together leading researchers and practitioners in this emerging field, to explore the promises and dilemmas it raises from ethical, methodological, ontological, epistemic, pedagogic, and technological perspectives. We are seeking contributions that contribute to a design discourse: design as a practical approach to shaping the future and design as a scientific paradigm, drawing on the traditions of educational design research (Mor & Winters, 2007) and utilising canonical design representations such as design principles and design patterns (Dimitriadis et al., 2009; Falconer et al., 2011; Mor, 2013; Retalis et al., 2006; Warburton & Mor, 2015).