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Vision for a New Approach to Educator Learning 

access the right materials, to support the right competency development, at the right time, through a shared system for competency-based educator development. 

 

Educators and talent developers face major data interoperability and content portability challenges. There is a lack of common language for what educators need to learn and to execute upon in new school models. Resources for learning are fragmented onto multiple, often closed, platforms. Data about when and how learning happens is scarce, and when it does exist it is shared and analyzed in ways that paint a bigger picture of skill and competency.

These challenges are purely technical and entirely solvable. 

This project aims to create an open, adaptive starting point connecting our Initiative's individual programs and resources through an accessible and open standard for teacher development content and experiences. Together, we are developing a system, an "Educator Learning Model", in which teachers and leaders have an actionable, data-based, real-time map for modeling educator competency-based learning that gives them the ability to access the right materials, to support the right competency development, at the right time, through a shared system for competency-based educator development. 

There are three major components of this Model work:

  1. Develop a shared language for educator competencies. Too many of our existing educator supports lack direct alignment to a shared, clear picture of what educators need to learn. This results in fragmented credentials, degrees, and continuous education hours that don’t really add up in an integrated way. The TLA cohort grounded our conversations and mapped content to a common set of educator competencies that we felt were a good start for blended learning environments—the iNACOL Blended Learning Educator Competency Framework. To make these competencies more actionable, we further needed each educator learning standard from the iNACOL Framework to be parsed into highly specific, action-based learning Elements, to allow for more granularity and measurability. 

  2. Create a shared tagging mechanism for connecting learning resources across platforms. Each of the cohort organizations own and locally host their content independently in the platform and format of their choice, which allows for continued future growth and iteration. Educators need ways to find content addressing the same learning topics across these platforms easily, so we needed a common tagging model for all of the resources based on all of the learning Elements identified in the competency set. This allows for content to be searchable by topic as well as by more specific learning objectives.

  3. Connect the back-end learning data. Once a shared language for competency and content tagging is created, we needed a common format for data coming out of activities and assessments. Yet developed a model for solving the interoperability and portability challenge through a system of data exchange using the Experience API (xAPI), a data specification that makes it possible to collect, standardize, and analyze, experiential learning data from any source, across systems.