Testing the Efficacy of an Automated Decision Model for Selecting Blended Performance Improvement Solutions, 07-R9774Printer Friendly Version
Inclusive Dates: 01/01/08 06/30/09
Background - The primary objective of this research study was to design and test the efficacy of an empirical decision model for selecting the best blend of training delivery methods. Designing a decision model to streamline and automate the process of selecting blended learning solutions increases efficiency considerably and contributes significantly to the development of blended training at the curriculum level in several sectors. Current and potential customers have expressed a need for applying this type of model to several programs that require re-analysis of training or conversion of extensive amounts of training.
Approach - The technical approach for the study includes four phases:
Accomplishments - This project resulted in the development of a prototype web-based decision tool called ABLE (Assisted Blended Learning Environment). The team tested the efficacy of ABLE by comparing its recommendations to those of an expert panel. Results indicated that the ABLE prototype was useful in recommending training delivery methods that were relevant to case study learning objectives, skill types, and audience characteristics. Recommendations included adding modules to assist with broader performance improvement solutions and informal learning in future iterations of the model.