Huang Junsong, David

If school diffusion of innovation is viewed as complex adaptive process, how shall we prepare school leaders to be effective diffusion decision makers? Coming from the epistemological belief that knowledge is subjective and embodied, this paper proposes to use Agent Based Modeling (ABM) for learning by focus on learning to “do” diffusion of innovation rather than learning about diffusion of innovation. We therefore recommend to engage school leaders in iterative agent based model development process and to couple it with their interaction in real world diffusion system. With feedback from real world system used for iterative model calibration and validation, the affordances of the agent based model allow school leaders to participate, experience, appropriate, perform and therefore to learn to make effective diffusion decisions in their schools.

This research seeks contemporary understanding of how we can develop teachers' Technological-Pedagogical-Content Knowledge (TPACK) when scaling pedagogical innovation to different contextual situations. Teaching with technology has long been a wicked problem as the nature of technology is ''protean'' (used in versatile ways), ''unstable'' (rapidly changing) and ''opaque'' (elusive backend mechanisms), resulting in multifarious complexities which are exacerbated when its use is scaled and situated within the broader socio-cultural context of diverse learning ecologies. Scaling innovations to new contexts is rarely a mere supplanting of what works at the seeding school to new pedagogic sites with less hospitable conditions. It entails the perpetual marshalling of resources to mitigate the enfolding tensions that can emanate from many incompatibilities at the new site. Herein lies the tensions of diffusion: the conflation between fidelity adherence and localised accommodation. The purpose of this research then is to study how teachers' three knowledge bases - technology, pedagogy and content - can be holistically developed so that the core ingredients of success at the seeding school can be sustained and not ''amputated'' at new innovation sites. Informed by complexity theory, the qualitative case study will employ the complexity constructs of ''distribution'', ''enaction'' and ''emergence'' to examine how teachers' epistemic resources are distributed during the knowledge creation process and how teachers leverage on TPACK to enact co-designed lessons or improvise their lessons in-situ. More importantly, by studying the diffusion process of Seamless Science Learning project from the seeding FutureSchool (ICT prototype school) to another non-affiliated mainstream primary school, the study aims to articulate how teachers' reified TPACK can emerge through feedback loops between components of TPACK and interaction with other actors in an ecological complex adaptive system. The study will also articulate the implications of such interaction on the translations of teachers' professional learning and the conceptual model related to challenges of nurturing readiness. It has the potential to inform policymakers on the theoretical principles of professional learning support which may culminate into ensuing successful uptake of innovations. By inter-meshing three domains: complexity theory, TPACK and scaling, this project can provide novel methodological perspective to how the inter-locking influences underpinning teacher's TPACK can be studied. Through cross-case analysis, the proposed study aims to reify both ''local divergence'' and the ''noncontextually bounded'' theoretical principles about scaling school-based intervention.

Productive Failure1,2 studies have shown that working on generative and complex activities prepares students for learning from subsequent instruction (i.e., delayed instruction). Under a delayed instruction setting, this study investigated the degree of freedom of generation and the level of task complexity as two key attributes of a preparatory task. The purpose was to make preliminary exploration on whether there is a boundary at which the benefit of a more generative task over a less generative task, such as compare and contrast, may disappear when the task complexity reduces.

This symposium proposes a genre of learning designs called Student-Generated Ideas (SGIs), based on designing learning contexts that promote students as critical producers, distributors, and consumers of knowledge. SGIs place students’ ideas at the center of learning designs, considering the learning process as well as the learning goals/outcomes. By soliciting and foregrounding students’ diversified ideas in the classroom and beyond, the learning environment communicates to students that their ideas matter to others and that they have a position of responsibility to their own and their peers’ learning processes. The notion of SGIs is embodied in a repertoire of studies at the Learning Sciences Lab, National Institute of Education, Singapore, that offer varied yet overlapping interpretations of how student ideas can inform the design of learning contexts. In sharing the core design principles for SGIs approaches, this work contributes important components to the learning sciences discipline and changing educational practice.

This article provides an overview of research in the Learning Sciences from a Design Research perspective, as it has been framed in Singapore by the National Institute of Education (NIE). The initial research agenda is considered in the light of challenges and the subsequent re-casting of objectives, based on the working out of a tripartite relationship between the NIE, the Ministry of Education, and local schools. A conceptual model is proposed as an attempt to provide structure for new research interventions going forward.

Research on Productive Failure and preparatory mechanisms has consistently demonstrated a positive learning effect when students generate problem solutions before receiving formal instruction. However, it has been less examined whether the effect still holds when the generative task does not involve problem solving. Using a 2x2 experimental design, this study investigated the effects of generative tasks that involve analogical problem posing (without solving) on learning and transfer. Pedagogical sequence (i.e., generation-first or instruction-first) and type of analogical reasoning task (i.e., generating one’s own analogical problems or generating analogical mappings between given analogical problems) were the two factors manipulated. Preliminary analysis revealed no multivariate effects of the factors. Thus, we discuss the learning mechanisms enacted by analogical reasoning, reliability of the instruments, and the participants’ prior condition as possible reasons and to inform future studies.

The COVID-19 pandemic is compressing the timeline for Singapore’s digital transformation in education. Reflecting on the implementation of Home-Based Learning (HBL) during the pandemic, we examine three barriers that inhibit digital transformation and technological implementation in education with leadership considerations: the first order barrier is infrastructural and can be mitigated by leadership foresight; the second order barrier concerns design capabilities of teachers which can be mitigated by tight-but-loose calibration; and the third order barrier deals with sustainability which can be mitigated by ecological leadership. The tight-but-loose calibration optimises the ‘tight’ system-led innovations such as Student Learning Space (SLS) for efficient deployment and for equitable access of high quality online resources for students; and ‘loose’ opportunities for teacher-led innovations on learning designs within and beyond system-led innovations to nurture teacher agency and professionalism. We posit that ecological leadership is key to sustaining deep change together with the ‘tight-but-loose’ system calibration.