Using Data Mining to Automate ADDIE.
Fritz Ray, Keith Brawner, Robby Robson · 2014
The goal of this work is to transform informational and instructional content into adaptive and personalized training experiences. We have developed semi-automated methods to do this that parallel the traditional “ADDIE ” (Analysis, Design, Development, Implementation, and Evaluation) process. The source content can include documents, presentations and manuals and existing courseware. The techniques use artificial intelligence (AI), data mining, and natural language processing and generally belong to the discipline of “educational data mining. ” This poster/demo demonstrates the processes and discusses the algorithms used. 1. PROBLEM STATEMENT Today’s digital environment is rich with learning content, but much of it is purely didactic in nature. This content includes manuals and presentations not intended for instructional purposes and e-learning that consists of presentations and lectures with multiple choice questions. As online learning replaces instructor-led training in corporations, government agencies, and educational institutions [10], its effectiveness can be improved by transforming this wealth of didactic content into more interactive and adaptive learning experiences [5]. Here, we address aspects this transformation problem in the context of multiple research and commercial projects. A large