Development of a higher-order instruction coding taxonomy for observational data: Initial application to professional...

Area of Science:

• Driver Education
• Instructional Design
• Motor Skill Acquisition

Background:

• Effective driver education requires higher-order instructional strategies.
• Existing frameworks may not fully capture the nuances of effective driving instruction.
• Self-determination theory offers insights into motivating learner drivers.

Purpose of the Study:

• To develop and validate a higher-order instruction taxonomy for driver education.
• To assess the reliability and applicability of the taxonomy in real-world driving lessons.
• To identify opportunities for improving the quality of professional learner driver instruction.

Main Methods:

• A higher-order instruction taxonomy was created, integrating driver education best practices and self-determination theory.
• Inter-coder reliability was established using 93 data elements from driving instructor clips (IOC = 94.6%).
• The taxonomy's validity was tested on 17 professional learner driver lessons.

Main Results:

• High inter-coder reliability (94.6%) was achieved for the developed taxonomy.
• The taxonomy successfully identified missed, taken, and untaken opportunities for higher-order instruction.
• Analysis revealed 9 instruction and 19 teaching-approach categories within the lessons.

Conclusions:

• The developed taxonomy provides a reliable tool for understanding and evaluating driving instruction quality.
• It offers insights into enhancing higher-order instruction for learner drivers.
• The findings have implications for improving training in safety-critical sectors beyond driving.