Science Convergence

Overview

A major goal of LDI conceptualization phase is to develop, implement, test, and refine a framework for data-intensive research in science and engineering enabling science convergence, aligning with the Growing Convergence Research (GCR) “big idea” identified by the National Science Foundation.

According to NSF, “convergence research is a means of solving vexing research problems, in particular, complex problems focusing on societal needs. It entails integrating knowledge, methods, and expertise from different disciplines and forming novel frameworks to catalyze scientific discovery and innovation." Also, “convergence is a deeper, more intentional approach to the integration of knowledge, techniques, and expertise from multiple disciplines in order to address the most compelling scientific and societal challenges” (NSF-GCR, 2020).

NSF identifies Convergence Research as having two primary characteristics:

• “Research driven by a specific and compelling problem. Convergence Research is generally inspired by the need to address a specific challenge or opportunity, whether it arises from deep scientific questions or pressing societal needs.”

• “Deep integration across disciplines. As experts from different disciplines pursue common research challenges, their knowledge, theories, methods, data, research communities and languages become increasingly intermingled or integrated. New frameworks, paradigms or even disciplines can form sustained interactions across multiple communities” NSF-(GCR, 2020).

LDI’s compelling problem is making the learning ecosystem more effective, engaging, equitable, efficient, relevant, and affordable.

To foster deep integration across scientific disciplines, we have put in place a convergence framework, comprising a diverse team, organizational structures, processes, mechanisms, activities, and tools, meant to encourage broad participation, coordination, collaboration, and diffusion and integration of knowledge across disciplines.

LDI has intentionally sought, from its inception, to follow NSF’s characterization of convergence research by “intentionally bring[ing] together [from the inception] intellectually diverse researchers and stakeholders to frame … research questions, develop effective ways of communicating across disciplines and sectors, adopt common frameworks for their solution, and, when appropriate, develop a new scientific vocabulary.” (NSF-GCR, 2020) The LDI team seeks, where possible, to develop “sustainable relationships that may not only create solutions to the problem that engendered the collaboration, but also develop novel ways of framing related research questions and open new research vistas” (NSF-GCR, 2020).

To make these intentions a reality, LDI’s leadership team and participants have designed, prototyped, and tested a process and a corresponding set of tools designed to transform what is currently a loosely coupled group of research centers, AIS commercial providers, and governments research labs engaged in similar but disparate research and development efforts into a set of interacting teams (Berry, 2011; Lilian, 2014), in aggregate constituting a physical and virtual community of practice (Lave & Wenger, 1991). We have not and will not attempt to “tighten” the coupling between participating research centers. As Weik (1991) has argued in respect to educational systems, loosely coupled systems have several advantages over tightly coupled ones—not least flexibility, survivability (with dysfunction in individual nodes tolerable), and increased likelihood of beneficial “mutations.” Rather, LDI’s leadership has intended to design and test a set of processes and tools that will support the independent work of the participating research centers, facilitate the flow of information and ideas within and across these centers, and help to keep participants focused on common problems without the need for direct intervention (e.g., in the form of a top-down, tightly controlled research agenda).

LDI’s team structure and processes enable the harnessing and diffusion of expertise from various areas in an efficient and effective way while fostering individual initiative and interests. For example, LDI team members were encouraged in the conceptualization phase to propose prototyping tasks that they are interested in and which fit the LDI mission statement (see more details later). Organizational structures and processes are intentionally open, flexible, and scalable to enable the LDI to grow and transform based on emerging findings and partnerships with other NSF-supported HDR teams.

The Key Elements of The LDI Convergence Framework

The key elements of the LDI convergence framework are listed below.

· Mission/Common Goal

· An intellectually diverse team with stakeholder representation (researchers, developers, practitioners including school and teachers’ representatives)

· An effective and efficient team structure

· Activities and processes that foster cross-discipline interactions

· Processes, mechanisms, and tools to nurture collaboration, broad participation, diffusion and integration of knowledge across disciplines, and coordination

· Resources, in terms of funding, student support, travel, and access to big edu-data and other cyber-infrastructure resources

· Incentives for team members to proactively and deeply engage in convergent activities and working towards accomplishing the goal/mission of the team which is to solve the compelling problem:

o Resources

o Freedom to propose research tasks that fit their own interests and align with the LDI mission

o Bottom-up and top-down strategies for agenda setting

o Semi-autonomous teams/groups

o Flexible, open structure

· Progress monitoring and refinement of the convergence framework

Additional Information about The LDI Convergence Framework

Our framework will enable team members to develop a shared vision and language, which over time should lead to effective and meaningful cross-discipline, collaborations, i.e., science convergence. Such mutual sense- making, science convergence, and R&D efforts are likely to incubate solutions to complex problems to enable effective, efficient, engaging, equitable, and affordable learning experiences for everyone. We detail next the main components of our science convergence framework.

The Learner Data Institute has ambitious goals for advancing how we approach education. We characterize the manifestation of these changes as Transformative Applications. This is akin to the creation of ImageNet from the existing, but underutilized, repository of millions of image files. Structuring how we approach such a massive undertaking lends itself to the delineation of Concrete Tasks that contribute to the overall goal, but have circumscribed, tangible resources and goals.

The LDI is profoundly interdisciplinary—ranging from data science and psychology to learning science and statistics. To work toward our goals requires creative and flexible methods for science convergence. This involves one grouping of our members within fields of expertise (Expert Panels) to refine and advance proposed Concrete Tasks. Expert Panels also identify key strategic areas of investments related to fundamental issues, both in education and in data science. These may be specific aspects of deep learning, Markov logic, experimental design, assessment, etc.

Another grouping is interdisciplinary, or perhaps transdisciplinary (Mutual Interest Groups), grouped around a theme that requires diverse expertise to encapsulate and grow. These groups are not defined by centralized leadership, but arise epiphenomenally from the gathering of researchers and practitioners with disparate expertise but unified vision.