José Elvano Moraes João Ricardo Vissoci Bruno Melo Ricardo Pietrobon
In the current working environment, Lifelong Learning is no longer merely a desirable skill, but instead a matter of survival. While recent educational movements such as the Massive Open Online Courses (MOOCs) have made a significant contribution in disseminating information to further instruct a massive number of student around the world, some of its promises have been all but delivered. For example, completion rates tend to be low and access is mostly focused on individuals with an extensive previous educational background, thus not really addressing the growing worldwide education gap.
In contrast with massified education, Personalized Lifelong Education proposes a different approach altogether: Learning should focus with content and applications that are specifically tailored to individual students, thus addressing not only the content area that might be of interest to them, but also adapting to their specific expertise levels as well as the specific applications they intend to address. This concept is thus much closer to the idea of personal coaching, although when Personalized Lifelong Education is conducted within an online environment these processes require a much higher degree of scalability.
While the concept of highly scalable personalized education is attractive, its implementation in online environments is still in its infancy, with two complimenting but often isolated concepts: Item Response Theory-based Computerized Adaptive Tests (CAT) and Knowledge Spaces. Item Response Theory is a methodology that allows for individual questions (items) to be associated with a certain knowledge level. This methodology then allows for educational assessment to be delivered through adaptive tests, which will ensure that students are presented with questions that are specific to their knowledge levels for a certain domain. The second methodology, Knowledge Spaces
The objective of this study is therefore to combine Item Response Theory-based Computerized Adaptive Tests and Knowledge Spaces to generate a Personalized Lifelong Learning environment, using the 2008 Adult Literacy and Life Skills Survey to populate and validate our model.
We made use of the 2008 Adult Literacy and Life Skills Survey (ALL) to model