Paul Stephen Prueitt

Deep Learning Manual Paul Stephen Prueitt

This fourth volume in the Deep Learning Manual series builds upon the previous volumes by presenting a well-defined, concrete proposal for addressing the current crisis in mathematics education. The approach presented is based on solid research in cognitive neuroscience, systems theory, and self-directed learning. Most importantly, it provides a roadmap for starting with the individual student and scaling up to the school, district, state, and national levels. Building upon decades of direct experience across a wide diversity of educational settings, including educational institutions serving Native American, minority, socio-economically disadvantaged, and neurologically disadvantaged students, the author addresses head-on the challenges that are often present when attempting to effect positive change within a deeply-entrenched institutional system. The proposed approach is based on empirical evidence presented in the previous volumes, in which dramatic transformations have occurred within these types of institutions. Best of all, the methods presented have been successfully implemented in both online and hybrid learning environments. Given the current turmoil created by a sudden move to virtual classrooms, the timing for the release of this manual could not be better. In terms of the overall impact on future workforce capacity for innovation and discovery in a complex, fast-changing world, the potential return on investment for a relatively small national investment is immeasurable.
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