This is a relatively new and emerging research area underpinned by two important research developments in the Learning Sciences area. The first is more theoretical, and tracing back to the seminal work of Herbert Simon,Sciences of the Artificial, in which he put forward a complex system view of cognition. There are debates on how to reconcile Simon’s more rigid hierarchical model of cognition to accommodate more fluid self-organizing changes to build more powerful models of learning across levels. In particular, addressing this problem may shed insight on connecting changes at more macro levels such as whole or segments of entire education systems revealed through large scale international or national assessments and evaluations, and changes in learning taking place at other levels. The second development begins as a methodological one, building on the successes and limitations of design based research (DBR) that started about 25 years ago. DBR emerged as an effort from the cognitive science community to overcome the methodological limitation of traditional “scientific” paradigms of research on learning that fail to take account of and contribute to learning in complex authentic learning contexts such as learners in naturalistic classroom and school settings. However, DBR suffers from the limitations of scale and comparability to build a “big science”. This could be remedied now with properly designed data archives and analytical tools to build more nuanced, hierarchical models of learning similar to what the NSF in US is supporting.

There is great potential for us to build on the leading role we play in conducting evaluation studies of local programs/ initiatives (including contract research for the EDB), as well as international comparative studies of student achievement (the many IEA studies we have conducted and are conducting: TIMSS, PIRLS, ICILS, ECES (Early Childhood Education study)) and of pedagogical innovation (IEA SITES, European Commission funded SCALE CCR study), as well as the insight gained from design-based research to connect research to shed light on system and organizational level policy and strategy with changes in pedagogical practices and learning outcomes. This emerging theme in Learning Sciences research recognizes the nature of education and learning as nested, interdependent and multilevel complex phenomena, and that unless research on learning takes account of and addresses the ecological complexities involved, the impact of our research on learning, and indeed our understanding of learning, would be severely limited.

This is a relatively underdeveloped field and needs more work to develop, but at the same time greater potential impact if we can succeed. Two dimensions of work in this subtheme are relatively promising given our current strengths:

1. Building hierarchical, complex system models of learning in a specific domain of learning. In this regard, language learning, particularly Chinese language learning would probably be the most “ready”, assuming that we will have substantial advances in understanding and connecting levels of language-related learning from neural to cognitive to effective pedagogical interventions in different aspects of language learning in subtheme 1. Other possible domains of learning to afford such theory building would be mathematics learning.
2. Building models of successful strategic and policy interventions for large scale learning improvement. An analogy for this work would be epidemiology, which is “the study of the occurrence and distribution of health-related states or events in specified populations, including the study of the determinants influencing such states, and the application of this knowledge to the control of health problems”^#. One challenge of building causal linkages between policy intervention (which could be a public awareness campaign) and learning outcomes is the long time lag before perceivable change is recorded. Given the wide range of international comparative studies involving Hong Kong at the system level that are ongoing and the massive amounts of data that have accumulated over the past two decades in science, math, language and ICT in education, our colleagues’ deep involvement in design-based and evaluative research in these subject areas, and the Education Policy Unit that is being established in the Faculty of Education, there is a possibility for advances to be made in this subtheme.^# Porta, M. (Ed.). (2008). A dictionary of epidemiology. Oxford University Press.