Learning maps (LMaps) are hierarchical, graphical organizers of course topics and learning outcomes. Learning maps are a tool developed through the instructional design field of Analysis, Design, Development, Implementation, Evaluation (ADDIE) [4] and used to support Backward Design [1]. The ADDIE model supports knowledge and skills transfer across learning contexts or modules, but sees limited application in academia. Applied to STEM course sequences, LMaps can be used to visualize course- and unit-level learning outcomes that build upon one another to progress knowledge to more advanced levels and applications within the curriculum. In this project, we refer to these learning outcomes as interdependent learning outcomes.
To build a learning map, instructors begin by choosing a course topic in an downstream course (e.g., statics comes after the prerequisite physics course) and then lists all associated learning outcomes for that topic. The learning outcomes are then organized hierarchically within the map (below). Course- and unit-level learning outcomes can be further organized by sub-topic as needed. Subordinate skills – i.e., knowledge, skills, or procedural learning in a prior course – can also be documented within the LMap (e.g., mathematical functions, procedures, or problem-solving approaches needed to achieve a given learning outcome).
After each instructor has done this for their course, the learning maps are shared with the upstream instructor (e.g., physics is prerequisite to statics), who consults on areas of overlap. The process of comparison allows instructors to identify the interdependent learning outcomes, which can then be used for backward design and course planning steps. An example learning map for the topic of Vectors in a Statics course is shown below.
