Modular Annotated Learning for Instructional Authoring (MALINA)

ABSTRACT: Recent efforts have produced effective concepts for reusable infrastructure and standards for intelligent tutoring system (ITS) development, but even with these tools it remains a complex task to encode assessment knowledge for a given domain. Stottler Henke proposes to develop a set of authoring tools called Modular Annotated Learning for Instructional Authoring (MALINA) that combines new machine learning technology for assessment knowledge with standards and components offered by existing current frameworks. The MALINA technology will use scenario-based knowledge representation techniques for cost-effective system development, via innovative machine learning techniques for constructing the knowledge required for automated assessments. These techniques will allow instructors to specify this knowledge by demonstrating and explaining a range of optimal and suboptimal solutions to exercise scenarios using the MALINA authoring tool. Performance will be assessed by comparing a trainee"s performances to the demonstrations to determine the closest match. Assessments of simulation performance will be used to update a long-term student model for each student using Bayesian inference. Phase I will lead to a detailed system design and a Phase II work plan contextualized with a target training domain, as well as a limited, proof-of-concept prototype for the core learning methods in the MALINA design. BENEFIT: The technology and products resulting from this effort have both direct and indirect transition potential. Most specifically, one of the research products will be an exemplar training application developed for a specific domain using our authoring approach. Our partnership with Boeing in Phase I will help in identifying a direct transition path for this training system product in a space related domain such as satellite operations, as well as related systems for related domains. More broadly for authoring tools, there are a variety of commercialization and transition directions for the authoring concept of using annotated machine learning to facilitate the knowledge intensive work of developing ITS performance assessment mechanisms for a given domain. Authoring tools that make ITS development more cost effective ultimately benefit the entire industry, both from an end user perspective and a developer perspective. End users benefit from the prospect of more direct participation in the process of building training systems that meet their needs. Developers also benefit when authoring tools reduce the cost proposition for marketing development services, which may have previously been a barrier to the adoption of ITS technology. Tools to facilitate the representation of domain knowledge through machine learning also have transition opportunities for emerging trends in massively open online courses and even autonomous control technologies for unmanned systems.