Registrants will be asked to provide the following information:
length of time involved in NPDL
Supervisor Name (a letter will be sent to the supervisor as well)
Supervisor email (supervisors will be asked to sign off on all evidence/artifacts)
Upon registration participants will be provided a link to the relevant Distinction Pathway template.
See Below for Distinction Pathway Templates.
Please save the template to your device. We recommend filling in the templates using Adobe Acrobat Reader DC (free) which is available to download here https://get.adobe.com/reader/
Timeline for submission of relevant artifacts:
This process is self driven and monitored. Individuals/organizations will be responsible for collecting and saving artifacts and evidence that indicate that they have successfully met the criteria for their chosen pathway. Please save artifacts in the cloud (Google drive, OneDrive etc.) and link to them from the document template (see below for Distinction Pathway Templates). Please then email your competed template to Max Drummy or Mag Gardner
*NOTE: Recipients of the distinction pathway are normally recognized at the Global Deep Learning Lab. Therefore, the deadline for submission changes annually. Contact Max Drummy or Mag Gardner to learn about specific submission deadlines.
Letters of Recognition:
Participants will receive letters of recognition, a certificate of distinction, and digital badging.
Distinction Pathway Requirements:
Distinction is determined by completing all elements of the chosen pathway.
Teachers, Schools, Districts and Clusters must show evidence of the following activities/contributions:
Global Deep Learning Teachers:
Contribute exemplars locally
Participate in exemplar moderation process locally
Participate in a Collaborative Inquiry Practice based on one or more of the NPDL tools and/or processes
Submit a 2-5 minute video of Deep Learning in their classroom
Contribute to the global network (e.g. host webinar, podcast, present at regional or global DLL, write a blog)
Global Deep Learning Schools:
Conduct school-wide exemplar moderation
Contribute exemplars to cluster/district process
Participate in inter-school moderation
Participate in a cross school Virtual Collaborative Inquiry
Contribute to the global network (e.g. host visits, mentor a school, present, host
or webinars, write blogs…)
Global Deep Learning Districts or Clusters:
Conduct inter-school moderation
Contribute exemplars to the global moderation
Participate in global exemplar moderation
Participate in a Virtual Collaborative Inquiry: cluster/district conditions
Contribute to the global network (e.g. presentations, blogs, hosting visitors)
Notes regarding Collaborative Inquiries:
Collaborative Inquiries should be based on the NPDL Collaborative Inquiry Cycle. They necessarily involve at least two individuals or organizations investigating and reporting back on impact or outcomes resulting from explicit use of NPDL Tools and Processes.
Teacher Collaborative Inquiry:
This level of inquiry should focus on how the use of one (or more) of the NPDL Tools or Processes has deepened learning; either for students or teachers. Applicants may choose to collaborate with another teacher in their own school, or might work with a colleague from outside their own direct context.
School Collaborative Inquiry:
This level of inquiry should focus on how the use of one (or more) of the NPDL Tools or Processes has deepened learning across one or more schools at student, teacher, leadership or community level. This Inquiry may require connecting to other contexts virtually. The scope of the connections ranges from local to global.
District or Cluster Collaborative Inquiry:
This level of inquiry should focus on how the use of the School and/or District Conditions Rubrics has deepened learning across the District/Cluster at student, teacher, leadership or community level.
Guidelines for Supervisors:
We are deeply grateful to those of you who are willing to act as supervisors in the Distinction Pathways process. Supervisors are key partners. They are responsible for quality assurance and verifying the authenticity of the applicant’s body of work. As a supervisor, please review the artifacts for submission. When you are satisfied that they provide robust and authentic evidence of an applicant’s powerful engagement in deep learning, and contributions back to the Global Network, please sign off on their submission.