At CRPE, we study how public education can evolve to meet the challenges and opportunities of a rapidly changing world. Our research on innovation and the future of learning examines how schools are rethinking teaching and learning models—from personalized and competency-based approaches to the use of technology and AI—to better prepare students for life beyond school. We investigate how these innovations take shape in real contexts, what barriers and enablers schools encounter, and how systems can support sustainable change. Across this work, we aim to understand how schools and communities can design learning environments that are more equitable, adaptable, and responsive to the diverse needs of students.
The recent New York Times article on New York City’s high school admissions process describes how the incorporation of game theory into an algorithm for matching students with schools has substantially increased the rates at which students are matched to schools of their choosing.
Steven Hodas (@stevenhodas) is a veteran of both the New York City Department of Education and the edtech industry. In this blog series, School District Innovation: When Practice Collides with Policy, he provides insights into the challenges, struggles, and opportunities of large-district attempts to reform longstanding practices and change cultural norms.
This blog was originally published by the National Association of Charter School Authorizers, June 5, 2014 Charter schools are leading the nation in seeking new ways to personalize learning with a blend of teacher-led and technology-based instruction.
A new study released last week provides first glimpses at how blended learning is affecting student performance. The report, published by the Michael & Susan Dell Foundation and SRI International, is rich with information about blended-learning programs and implementation details, but the study’s new contribution to the field is that it presents an impact analysis of K–12 blended-learning programs.
This fiscal analysis finds that early difficulties forecasting enrollment and revenue can undermine implementation of personalized-learning models that blend computer-based and teacher-led instruction.
No results found.