Learning Maps as Cognitive Models for Instruction and Assessment

Abstract

This paper describes large, fine-grained, intentionally inclusive, research-based cognitive models called learning maps. Learning maps are the product of an intensive research synthesis process to develop formal learning models that better reflect the diversity of how learners can learn and be assessed on academic content. Students begin at different places and learn at different rates, and they may have cognitive disabilities or may face a variety of barriers that pose challenges when learning content. Learning maps provide numerous starting points and pathways by which students can acquire and demonstrate knowledge, skills, and understandings. Our work in developing learning maps relies on principles of Universal Design for Learning (UDL), which provides a foundation of flexibility and inclusivity to accommodate students with a wide range of cognitive, linguistic, physical, and sensory profiles. In this paper, we describe learning map design, development, and both qualitative and quantitative methods for the evaluation of map structure. In addition, we offer reflections on our experiences with implementing learning maps as the cognitive architecture for assessments and educational interventions through our work on a variety of projects. With examples from these projects, we describe evidence that shows how learning maps can be useful tools for improving instruction and assessment for all learners. We identify areas where further research and inquiry could prove fruitful and conclude with a discussion of potential areas of extension and offer suggestions for the ongoing refinement of learning maps.

Keywords

• cognition
• learning model
• network model
• validation