Emerging DIgital Ecosystem for Competency Education (slide from iNACOL Symposium session)

Data Standards for Competency Education

This is a video capture of the slide deck that Liz Glowa, Maria Worthen, and I used in our session "Data Standards for Competency Education" at the STATS-DC conference on July 31, 2014.  (Slides from the one hour presentation compressed into two minutes of video...use pause as needed.)

Open Education Resources (OER) Digital Ecosystem

I recently participated in the "OER Annotation Summit" in Berkley, California.  The event was  supported in large part by an OER technology grant from the William and Flora Hewlett Foundation.  The Summit explored opportunities and barriers to fostering greater collaboration in solving shared technology challenges for open education resources initiatives.

At the event I worked with a break-out group to map the OER Digital Ecosystem. The following infographic is the QIP visualization of the "ecosystem" derived from a picture of the whiteboard and Felix Tscheulin's gliffy diagram of the same...

EdFacts Community Video

Here is a video that I produced with QIP and AEM teams...

Technology-Enabled Personalized Learning

Last week I had the privilege of representing QIP at the Technology-Enabled Personalized Learning Summit. The invitation-only event was hosted by the Friday Institute for Educational Innovation at NC State University and included the right mix of education practitioners, service providers, nonprofit leaders, researchers, and education technology thought leaders to tackle some of the big questions around the future of personalized learning.

Here are some take-aways:

• Personalized learning is more than differentiated instruction -- it is learner-centered vs. teacher/group-centered mass-customization.
• Personalized learning requires a different set of practices, tools, roles, and resources.
• Research-based evidence about effective personalized learning has not made it into mainstream practice.
• Noncognitive factors, such as grit, tenacity, and perseverance, become important as learners develop habits of success and take on greater autonomy in personalized models.
• Personalized learning uses different data -- metrics for "growth" and "grit" continuously measured for feedback to the learner rather than less frequent measurement of achievement for benchmarking and accountability.
• Technical standards for interoperability of personalized learning data exist, but there are issues preventing scaled adoption/implementation...data integration barriers are slowing down progress toward personalized learning.
• Personalized learning at scale can only work by leveraging technology and "big data" -- privacy concerns must be addressed.
• We can compel students to attend school but we can't compel them to learn...or can we -- the human-centered design of personalized learning must include motivational design.
• "Engagement isn't necessarily enjoyment. If you're drowning, you're engaged in the experienced, but it's not enjoyable" - Chris Dede
• Human to human relationships matter (student-to-student, educator-to-student) 
• Emerging models leverage non-instructional roles and technology to free up teacher time to work more with individuals and smaller groups.
• Personalized learning opens new career pathways for education professionals and opportunities for both traditional schools of education and other organizations to develop/support new professions.  A new set of professional competencies are needed and those competencies need to be defined as new professional roles and delivery models emerge. 
• Personalized competency-based professional learning is important to optimize professional development and as a model for personalized student learning. 
• Personalized learning at scale can benefit by new kinds of collaboration between learning science research and practice. 

Technology-enabled personalized learning calls for human-focused designs and delivery systems that are fundamentally different from traditional classroom models.  Summit participants identified some potential solutions to current barriers and important next steps to make scaled personalized learning a reality.  Stay tuned...

"2-Sigma" Learning at Scale (Part 2): What research tells us.

Under what conditions can MOST STUDENTS learn as effectively as the top 20% of students under the conventional classroom condition?  This 11 minute video explores what research tells us.