Distinguishing MOOCs from OER

Stanford mathematics professor Keith Devlin suggests that we should drop MOOCs and focus on MOORs (massively open online resources) or OERs (open educational resources):

no single MOOC should see itself as the primary educational resource for a particular learning topic. Rather, those of us currently engaged in developing and offering MOOCs are, surely, creating resources that will be part of a vast smorgasbord from which people will pick and choose what they want or need at any particular time.

Yet even if current MOOCs follow a mediocre model for structuring learning experiences, they do still attempt to meet a need for learners who seek guidance, structure, and social cohorting for the way they access educational resources. I would be interested in decoupling OERs from MOOCs and similar pathways, in order to broaden the scope of available OERs from which anyone can choose. That opens up possibilities for more innovative approaches to enabling diverse learning paths and cohorting models.


Repositioning personalized and adaptive learning

Amidst all the excitement about personalized and adaptive learning is a lot of confusion about what the terms mean. In this post, I will examine some of the commonly-used definitions around personalized learning and explore the relationships between the ideas to clarify how I intend to use the terms here. My next post will explore the differences between personalized and adaptive assessment.

Education Growth Advisors describes personalization as “moving beyond a one-size-fits-all approach to instruction,” in which students might receive additional work as challenge or remediation, depending on their current performance. In their view, adaptivity is a more specialized form of personalization which “takes a more sophisticated, data-driven, and, in some cases, non-linear approach to remediation.” Some examples might use past performance to adjust the focus, timing, or path of content delivered to a learner.

In contrast, EdSurge places personalized learning at the top of its hierarchy with this definition: “When instruction is truly geared to the student: learning objectives, content, presentation methods and pace may all vary depending on the learner.” By their definitions, adaptive learning varies the content, individualized learning is self-paced, and differentiated learning varies the content and presentation methods, all three being lesser versions of personalization.

While both groups classify adaptive learning as a subcategory of personalized learning, they differ in where they place that subcategory along the personalized-learning spectrum, with Education Growth Advisors deeming it more sophisticated and EdSurge considering it less sophisticated compared to other forms of personalized learning. My definition will likewise situate adaptive learning as being narrower than personalized learning, but without making any claims as to which is superior in either sophistication or effectiveness. In being broader than adaptive learning, personalized learning can be both better and worse. The difference between them depends on what is being tailored to the individual, not how well it is executed.

EdSurge restricts adaptivity to selection of content; my definition will also encompass adaptivity of learning objectives, presentation methods, focus, timing, and path through content. (This is akin to Education Growth Advisors’ definition of adaptivity and closer to EdSurge’s definition of personalization.) The reasoning is that the learning environment may be tailored along all of these dimensions according to individual needs.

In my definition, personalized learning includes other features beyond adaptive learning to make the learning experience more personal—quite simply, involving the person. These may include individual preferences, chosen by the user; instructor actions, informed by a faculty dashboard offering analytics and recommendations but with decisions left up to the teacher; and social interaction, facilitated by collaborative tools and engineered to encourage productive learning, but again leaving room for human input. While adaptive learning relies on data-driven decisions from machine intelligence to tailor the experience to the learner, personalized learning also adds an explicit role for judgments made by human intelligence.

A fully personalized learning ecosystem may allocate some decisions to be determined by machine intelligence and delivered through its automated adaptive learning system, designating others to be handled by human intelligence and provided by various actors in the ecosystem. The adaptive component optimizes the environment, to the extent that it can control it, along parameters which its data and algorithms predict will be beneficial. The personalized components make allowances where human intervention is deemed to be more valuable (such as user choice, yet-to-be-modeled human expertise, and social interaction). Still, the unpredictability of human behavior also means that those components cannot claim to be truly adaptive, only personalized.

Thus, personalized learning has the potential to be much more sophisticated and powerful than adaptive learning, if realized effectively. Early iterations of personalization may not take full advantage of these possibilities, and in some cases, human decisionmaking may end up being maladaptive. Our goal is to help clarify those dimensions and parameters to enable better design and use of personalized learning.