PROJECT TITLE: Development of Learning Taxonomy
Project completed Dec. 2017
- PI: Dr. Kadie Hayward Mullins, Director of Undergraduate Research, Embry-Riddle Aeronautical University
- PI: Dr. Rachel Kajfez, Assistant Professor, Department of Engineering Education, The Ohio State University
- Co-PI: Dr. Haydee M. Cuevas, Assistant Professor of Doctoral Studies, Embry-Riddle Aeronautical University
- Co-PI: Dr. Krista Kecskemety, Assistant Professor of Practice, Department of Engineering Education, The Ohio State University
- Co-PI: Dr. Matthew Verleger, Associate Professor, Engineering Fundamentals Department, Embry-Riddle Aeronautical University
- Co-PI: Dr. Seth Young, McConnell Chair of Aviation and Director of the Center for Aviation Studies, The Ohio State University
- Students: Pearl Chen (OSU), Clayton Greenbaum (OSU), Carrie Sekeres (ERAU), Brooke Stieber (OSU), Katherine Tanner (OSU), Andy Theiss (OSU)
- FAA Sponsor: Luis Ramirez, Technical Training (AJI-2)
- FAA Technical Monitor: John Stillings
Exploratory and pilot study identifying common language, vocabulary, and understanding of interactions between items related to learning to develop a learning taxonomy and possible strategies for implementation of proposed taxonomy. Different learning taxonomies will be developed that can be evaluated for use in organizing training, evaluating training quality, and developing new training opportunities.
Expected Project Outcomes:
- Report a set of appropriate learning taxonomies aligned with ATO standards, learning models, materials, and desired outcomes as they relate to different learning elements with special consideration to Learning Content Management System utilized by the Agency.
- The initial taxonomy developed based on the content analysis to be used in pilot testing.
- A refined taxonomy following the analysis of findings in pilot testing.
- Report practical options for implementation of developed taxonomies and recommendations for which taxonomy should be implemented.
VALUE OF RESEARCH:
Establishing a common language, vocabulary, and understanding of the interactions between items related to learning is essential in an organization for efficiency and quality across units. The goal of this project is to develop different learning taxonomies that can be evaluated for their use in organizing training, evaluating training quality, and developing new training opportunities.
The final taxonomy was established through Verleger’s development of code to implement a series of algorithms found in the literature for taxonomy generation. These algorithms perform keyword extraction, hypernym identification, and taxonomy generation. The code was applied and evaluated using the curricular materials currently available. This taxonomy was deployed in small-batch mock courses using a similar LCMS to the FAA’s platform Kenexa, and queried utilizing key language to determine if the provided language was likely to result in accurate searches by users.