Individual Learning Effectiveness Based on Cognitive Taxonomies and Constructive Alignment

Abstract

Online learning is becoming increasingly popular and used in many academic disciplines due to its advantages, where learners can access courses from anywhere and at any time. Besides benefits, online learning may have limitations, such as slow response times when bandwidth is limited, or inflexible one-size-fits-all content without regard for the learner's background or knowledge state. This paper presents an approach to more flexible online learning, where recommended learning paths are derived from the results of learning activities and assessment tasks. The proposed paths comprise multiple intended learning outcome (ILO) nodes based upon and sequenced according to Bloom's taxonomies and Biggs' principles of constructive alignment (PCA).