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.
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