Português - The Carnegie Foundation has engaged expert community college faculty, academic researchers, curriculum designers and community college students in creating networked communities focused on the high leverage problem of improving the success rates of developmental math students. These Carnegie Networked Improvement Communities (NICs) have developed and launched two new mathematics pathways that provide an alternative to the current community college developmental mathematics sequence. The Statway™ is designed to take developmental math students to and through transferable college statistics in one year. The Quantway™ provides an alternate and accelerated pathway with an innovative quantitative literacy focus in which students use mathematical skills and quantitative and algebraic reasoning to make sense of the world around them.

In addition, both Pathways include an intensive student engagement component called Productive Persistence. Productive Persistence is the tenacity and good strategies students need to be academically successful. To help more students successfully complete the math pathways, we want them to both persist in their studying and attendance (tenacity) and to do so efficiently and effectively (good strategies). These Productive Persistence supports include fostering productive mindsets, along with developing the skills needed for academic success.

Lastly, in an effort to learn by doing and continuously improve outcomes, the Carnegie Foundation and The Statway™ and Quantway™ Networked Improvement Communities are engaged in mapping and implementing the tenets, tools, and methods of improvement research found in healthcare and industry to develop a science of performance improvement in education. In an effort to establish a “third way” to think of education research, beyond translational academic research and the practitioner knowledge found in communities of practice. The proposed “third way” partakes of each of these traditions and brings to bear an emerging science of improvement research to work on high leverage problems of practice in ways that produces knowledge that drives improvement -- ultimately at system-wide scale (Reich, 2012).

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Português: desenvolvimento de matemática

-- GregorioIvanoff - 13 Sep 2023
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