Development of a higher-order instruction coding taxonomy for observational data

This coding taxonomy was developed from a study looking into when and how often professional driving instructors might provide what we call "higher-order instruction" to young people when they are learning to drive. This relates to instruction about "higher-order skills", those that go beyond basic vehicle handling to thinking and perception skills, and particularly instruction that is not just about the here-and-now but generalises to similar and related circumstances the learner might face in the future. This ability to "transfer learning", particularly higher-order learning, is considered an important skill in preparing learners to be safer drivers once they are licensed to drive unsupervised.

We found an absence in the peer-reviewed published literature of any evidence-based tool to investigate the comprehensiveness of professional driving instruction, including the nature and quality of higher-order instruction, and the teaching strategies and approaches employed. Therefore, we sought to develop a reliable and valid tool for coding higher-order instruction, guided by theory, including the Goads for Driver Education hierarchical model, self-determination theory and constructivist models. We then tested the resulting taxonomy by coding the video recordings of a series of professional driving lessons with a range of instructors and learner drivers.

The resulting taxonomy is believed to be flexible to be applied in a range of settings and circumstances involving instruction and learning, and hope this will be verified by wider applications in the future. A copy of the article, published in Applied Ergonomics, is available free before April 13, 2018 at the following link: