Presenter Information

Kevin Michael Berkopes

Start Date

6-6-2017 12:00 AM

Description

Short Abstract:

Current research restores our awareness that students’ mathematical proficiency impacts their future academic success, choices of majors at the university level, career choice, and early career earnings potential (Boaler, 2015; Geiser & Santelices, 2007; Rose & Betts, 2004). In this discussion, Dr. Berkopes presents the Learning Commons as a methodology for scaling competency based learning and peer-to-peer academic centered interactions.

Full Abstract:

The teaching and learning of mathematics in the US has been a highly publicized political and economic issue for more than four decades. Despite decades of reform efforts, the US education system still predominantly provides access to mathematical content through lecture or other formal classroom activities. This first-access point for content necessitates that content be filtered through the active educator and her\his beliefs about mathematics, how students learn, and the goals of teaching the content (Schoenfeld, 1999). Often, this setting results in students rotely learning the product of mathematical thought, rather than the desired learning of mathematical thought (Skemp, 1987, Boaler, 2015). In this discussion, the presenter posits that in fact reforming first-access techniques is not a scalable option, but rather efforts and resources should be allocated towards a pragmatic and scalable alternative approach to mathematics (or any content) education. This alternative, define as the Learning Commons Model (Berkopes & Abshire, 2016) has the flexibility to capitalize on interventions proven to lessen mathematics anxiety and enable a much higher level of student fluency with core curriculum. These techniques can include individualized instruction, self-efficacy development, systematic desensitization, customized curriculum, competency based-learning, and working memory enhancements (Hembree, 1990; Passolunghi, Caviola, De Agostini, Perin, & Mammarella, 2016).

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Jun 6th, 12:00 AM

The Learning Commons: Building Collaboration, Assessment, and User Experience into the Education System

Short Abstract:

Current research restores our awareness that students’ mathematical proficiency impacts their future academic success, choices of majors at the university level, career choice, and early career earnings potential (Boaler, 2015; Geiser & Santelices, 2007; Rose & Betts, 2004). In this discussion, Dr. Berkopes presents the Learning Commons as a methodology for scaling competency based learning and peer-to-peer academic centered interactions.

Full Abstract:

The teaching and learning of mathematics in the US has been a highly publicized political and economic issue for more than four decades. Despite decades of reform efforts, the US education system still predominantly provides access to mathematical content through lecture or other formal classroom activities. This first-access point for content necessitates that content be filtered through the active educator and her\his beliefs about mathematics, how students learn, and the goals of teaching the content (Schoenfeld, 1999). Often, this setting results in students rotely learning the product of mathematical thought, rather than the desired learning of mathematical thought (Skemp, 1987, Boaler, 2015). In this discussion, the presenter posits that in fact reforming first-access techniques is not a scalable option, but rather efforts and resources should be allocated towards a pragmatic and scalable alternative approach to mathematics (or any content) education. This alternative, define as the Learning Commons Model (Berkopes & Abshire, 2016) has the flexibility to capitalize on interventions proven to lessen mathematics anxiety and enable a much higher level of student fluency with core curriculum. These techniques can include individualized instruction, self-efficacy development, systematic desensitization, customized curriculum, competency based-learning, and working memory enhancements (Hembree, 1990; Passolunghi, Caviola, De Agostini, Perin, & Mammarella, 2016).