By Drew Schrader
In his excellent piece untangling the myth, hype, and hope of personalized learning, Phil McCrae notes:
Personalized learning, as promoted under a new canopy of blended learning, is neither a pedagogic theory nor a coherent set of learning approaches, regardless of the proposed models. In fact, personalized learning is an idea struggling for an identity (McRae 2014, 2010).
This struggle for identity and the confusion around differences in terminology like individualized, blended, and personalized learning underscore the lack of coherence and the prevalence of confusion (Curtis, 2014). What is really critical is noting that personalization and blended learning are not pedagogies. The temptation might be to lump that insight into the case for the general disorder around these approaches. But that fact is actually more problematic for those of us interested in outcomes that prepare students for a lifetime of learning opportunities and needs.
In the absence of a corresponding pedagogy, blended and personalized approaches becomes, to steal a phrase from Stephen Downs, “redressing old models in new clothes.” And, further borrowing, new versions of old models don’t produce new results.
Stephen Downs points to the key insight missing from delineations of personalized learning: Personalized learning is something that is done to you, while personal learning is something you do for yourself. The aim of most schools and educators as evidenced in countless mission and vision statements, is to produce passionate, self-directed, lifelong learners. Part of the answer to “Where are the expert learners” is to ask “Where are the models that design with personal learning as an end rather than personalized learning as a means?”
Back to McCrae, he goes on in the quote above to note:
A description of personalization that’s tightly linked to technology-mediated individualization “anywhere, anytime” is premised on archaic ideas of teaching machines imagined early in the 20th century (McRae 2013).
The modern version of the “teaching machine” are self-paced computer-based programs. Unfortunately, this is often what comes to mind when we think about “realizing the potential of digital learning.” In essence, this approach takes a “one-size fits all” model of education and assuming the solution is to make it come in more sizes; to “standardize personalization” as McCrae puts it. But again, we are dressing an old model in new clothes. While no doubt gains can be made by zeroing in on a student’s zone of proximal development and tailoring content to their current skill level, what we haven’t shifted is the outcomes we are after.
Rather than pursuing the perfect playlist generating algorithms to expertly shepherd students through an old, outdated, one-sized model, we need to clarify the knowledge, skills and attributes of personal, self-organizing learning and engineer our models of learning to reach those results. Rather than merely making the path through existing curricular knowledge tighter and more adaptive to individual needs, we need models that moves students through increasing levels of independence and self-organization, culminating in the expert, lifelong learners we all profess to desire.
If we are genuinely after new results, technology can and will clearly play a part in those new outcomes. For educators interested in ways to make the inclusion of rich, student-centered technology into their practice, the place to start is selecting a model that supports the results you are after, and maximizing the tools and concepts related to blending and personalizing within that model. Many of the purported benefits of blending/personalizing are features of transformative school models like the one NTN helps communities implement.
To that end, those of us in the Deeper Learning community need to take responsibility for connecting the waves of tools being developed to transform how students learn to our long commitment to what kinds of learning truly matter. We must continue to promote value of deeper learning outcomes as well as what it looks like for students to produce evidence of achieving those outcomes. Attention to outcomes is the foundation for the models we have designed. Continued refinement of those outcomes and ongoing model redesign that thoughtfully incorporates new tools to accelerates how our models achieve those outcomes more consistently with more students is key to realizing the best of the hopes of the blended and personalized movement. Then, teacher personalized can be rightly seen as part of progressions that lead to students becoming experts of their own learning.