We live in a period of customization, with laser-fitted body-tight jeans, personally configured computers and less welcomed highly targeted online advertizing. We can customize our personal technology eco-system, from com-puters to music, entertainment and TV. Our fitness program is tailored to our individual needs and goals.
But what about education? The dominant form of education today is “one size fits all”. All students proceed through a sequence of learning exercises, at the same speed and with the same pedagogical model. This industrial style paradigm has been the leading form of teaching for more than a century. Think of Taylorism, made famous with Henry Ford’s 19-teens mass production of Model T’s. Students in K-12 can be visualized as in-process widgets, proceeding down the production line having 13 manufacturing steps, each corresponding to a school grade level. Concerning any customization of the output, Henry Ford said, “Yes, they can have any color, as long as it is black”. Perhaps the greatest disruptive force in education today is due to Moore’s Law that has made feasible customized learning environments, “laser fitted” for each student. Which is more important: Customized jeans or customized learning environments? Yet, we have the first and not yet the second. In this article we are trying to define a framework for design of a computer-based system that could provide the second – a rich learning environment made by contributors from around the world, an environment that learns a student’s strengths, weakness and learning style preferences, one that allows each student to move at her preferred pace and through different learning paths. We call it “Guided Learning Pathways”.
GLP is envisioned to be a modular platform, and as new learning technologies and improvements to existing technologies emerge, new modules could integrate into the platform. Different learners or educators could select which type of module they wish to utilize—one may wish to use a crowd-sourced and “approved” Content Map, while another may wish to use a custom version created by a high-school teacher in different city.
GLP terms and definitions
In today’s educational context, subjects (such as calculus) are divided into classes that are taught over a semester or year, and classes sequentially go through a list of topics in a syllabus or textbook. GLP eliminates the idea of a “class” and instead focuses on the topics—furthermore, it arranges the topics as they are conceptually related to each other instead of using a purely linear sequence. One can imagine that Content Maps look like directed, acyclic graphs, such as the Khan Academy Knowledge Map for practicing math topics. The content topics that are not directly related to each other can be learned in parallel sequences, per the learner’s interests. Other topics require prerequisite knowledge that must be learned first. Example topics in Math might be Addition, Random Numbers, or Derivatives.
Pathways are flexible groups of content topics that move a learner towards his or her learning goal. They include all of the prerequisite topics that need to be mastered before the learner can master her goal topic/s. Pathways are flexible because the learner can select to learn about topics outside of her pathway, or educators and friends can add additional topics to the ones recommended by GLP. Educators can also customize pathways—for example, a biology teacher in Maine may wish to address certain, locally-relevant topics that a biology teacher in Arizona may not, so they could both modify a standard “biology” pathway to fit their local needs.
GLP emphasizes content-based mastery and provides learners with recommended learning materials (Nuggets) that help them achieve this mastery.
Next time we will describe a general framework for GLP and provide details on five modules: User Interface, Content Map, Content Topic Recommendation Algorithm, Learning Nuggets, and Learning Nugget Recommendation Algorithm.
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