Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment

Abstract

Self-regulated individual learning is widely used in academia. Besides the model's advantages, such as flexible learning in time and space, some implementations have limitations, for example fixed learning paths, and unclear relationships between learning activities and intended learning outcomes. This paper introduces an individualized learning model based on Bloom's cognitive taxonomy and Biggs' Principle of Constructive Alignment (PCA). The model provides individual tailored learning paths, adjusted for different background knowledge and ability to learn, based on regularly measured achievement of the intended learning outcomes.