The Sciences of Learning, Instruction, and Assessment as Underpinnings of the Morningside Model of Generative Instruction
Department or Administrative Unit
This paper focuses on a subset of the practices that have created the powerful learning technology developed and disseminated by Morningside Academy in Seattle, Washington, U.S.A. We briefly describe this technology, known as the Morningside Model of Generative Instruction, and tell how it builds on the selectionist approach of B. F. Skinner and the pragmatic approach of John Dewey. We also describe the critical role Precision Teaching plays at Morningside Academy and its dependence on findings from the science of learning and the science of instruction, including placement of learners, task analysis, content analysis, instructional protocols, and principles of instructional design. Last, we acknowledge the symbiotic relation between effective Direct Instruction programs that teach skills to accuracy levels and Precision Teaching, which takes these accurate repertoires and systematically turns them into high frequency performances that take on the character of fluent repertoires. Over time, using Precision Teaching across multiple and successive repertoires also creates more agile learners.
Street, E. M., & Johnson, K. (2014). The Sciences of Learning, Instruction, and Assessment as Underpinnings of the Morningside Model of Generative Instruction. Acta de Investigación Psicológica, 4(3), 1773–1793. https://doi.org/10.1016/s2007-4719(14)70979-2
Acta de Investigación Psicológica
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This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
© UNAM Facultad de Psicología, 2014
Educational Assessment, Evaluation, and Research Commons, Educational Methods Commons, Educational Psychology Commons
This article was originally published Open Access in Acta de Investigación Psicológica. The full-text article from the publisher can be found here.
Spanish Title: Las Ciencias de Aprendizaje, Instrucción y Evaluación como Cimientos de la Instrucción Generativa del Modelo Morningside