Concept Mapping

Tags: Concept maps

Created and embedded using the Rhumbl Mapping Engine.

Concept Mapping

The traditional arrangement of topics in a class is a linear progression, usually laid out according to the term schedule. But this paradigm is being challenged. There is a growing push to create more flexible, more valuable and more targeted learning experiences — all enabled by digital educational technologies. Competency-based programs, personalized learning and adaptive learning are examples of new educational models that seek to break down the traditional linear progression through content.

So why are current practices of topics listings insufficient to support these new educational models?

  1. A list implies a step-by-step progression, starting from one to the next, which is counter to the desire to be flexible, personalized and/or adaptive.
  2. A list fails to capture the relationships among topics. What if Topic 5 can only be consumed after Topic 3 but does not require Topic 2? What about topics that are related but reside in different domain areas and thus in different lists?
  3. It's difficult to analyze relationships on a list.

To support adaptive learning not only is it critical to understand how topics relate to each other, but also we need to model how topics relate to student learning outcomes. This is where the concept mapping graph model comes in.

Model ontology of the concept graph model

We create a graph-based model, which comprises two types of entities: topics and learning outcomes. Each topic and learning outcome is modeled as a vertex. Dependencies among topics and outcomes are modeled as edges.