Principal component analysis and Bloom taxonomy to personalise learning

Abstract

The purpose of spreading, deploying and using information and communication technologies in virtual learning environments and providing digital tools for classrooms is to make learning process easier, more comfortable for students and more personalized, thus helping learners to reach his or her learning objectives. As taxonomies of learning provide incredibly useful tools for defining the types of work that learners should do for reaching their learning purpose, learning environments are designed or constructed on the basis of these taxonomies. The taxonomies function as powerful heuristics, helping analyze learning objectives and to design assignments. Applying taxonomies, the point is to suggest the learner assignments aligned with assessments and current learning objectives, also providing personalized learning environment using advanced information and communication technologies. Here a problem with how to apply taxonomies as the basis for development and/or construction of learning environment arises, bringing up a question how to choose the right and optimal set of taxonomy elements helping the particular group of learners to reach their learning goals and eliminating “noisy”, i.e. non-significant elements. Novel methodology using principal component analysis alongside the techniques for extraction of most important information and communication technology features and elimination the other features is proposed in the paper. Experiment using digital Bloom’s taxonomy activities was conducted to develop the proposed methodology – experimental results are described in the paper.