Rethinking Educational Assessment from the Perspective of Design Thinking

Abstract

Because human processes are subtle, complex, and contextualized, computational representations of those processes face highly significant unmet design challenges. Design Thinking (DT) offers a potential new paradigm of creativity and innovation in education capable of effecting meaningful culture change. DT is nonlinear but encompasses elements of empathy, problem definition, ideation, prototyping, and tests that may freely move as needed from and to each other. DT's empathic focus on end users' needs suggests educational measurement's information infrastructures will have to coherently integrate assessment and instruction across multiple levels of complexity in communication. Applying DT reveals the need to attend to previously undeveloped technical issues in communication. Especially important are developmental, horizontal, and vertical forms of coherence, and denotative, metalinguistic, and metacommunicative levels of complexity. New solutions emerge when classrooms are reconceived as meta-design ecosystem niches of creativity and innovation structured from the bottom up by flows of self-organizing information. Recently identified correspondences between educational measurement and metrology support efforts aimed at developing multilevel common languages for the communication of learning outcomes. Prototype reports illustrate how emergent measured constructs can be brought into language in ways that integrate developmental, horizontal, and vertical coherence across levels of complexity. Coherent information infrastructures of these kinds are capable of adapting to new circumstances as populations of persons and items change, doing so without compromising the continuity of comparisons or the uniqueness of locally situated knowledge and practices.

Bateson and Wright on Number and Quantity: How to Not Separate Thinking from Its Relational Context

“…In education, therefore, individualized instruction should not be based only on quantitative measures and associated uncertainties indicating where the student is positioned along the learning progression. It should also take into account evidence of special weaknesses and strengths, as shown in the numeric pattern of responses scored correct and incorrect [147][148][149][150]. Notable directions for ongoing research in this area break out separate but interrelated investigations taking place at these levels:…”

Digitizing Production Systems

“…4. IMEKO 2016 TC 1-7-13 Joint Symposium, University of California, Berkeley [39,40] instructional or clinical feedback with standardized quantitative expressions [47][48][49]; these tools will be essential to the management and growth of personally owned shares of human, social and natural capital stocks. • Thousands of publications in peer-reviewed scientific journals document the development and use of probabilistic models for measurement in research and practice.…”