Reducing Teachers' Cognitive Overload with a Recommender System in the Workplace

Abstract

This paper presents an assistant, ARIALE (Authoring Resources for Implementing Adaptive Learning Environments), that helps decrease the teacher's cognitive load when he has to deal with traditional help techniques, complex tasks or unknown environments in the workplace. The assistant adapts learning support and problemsolving support according to teachers' characteristics, while they are authoring learning sessions to teach decision-making in Network Design. ARIALE applies on-the-job training when a teacher can not spend time learning how to use a tool or how to solve a problem. Our methods for problem-solving support include Bayesian learning to recommend network topologies and our assistant provides adapted help to use a tool rather than teach how to use it. 2. Research rationale This research addresses two problems that university teachers face when they are authoring teaching materials for their courses: 1. Teachers do not have enough time to create teaching materials. 2.Teachers do not have enough time to learn how to use the tools that could reduce the time required for the creation of materials. In general, users of software and, particularly, authors of educational material such as teachers face other two difficulties: 1.The production paradox that could be experienced by teachers. The teacher is not familiar with the functionalities of the tool he is going to use,