Inferring learning and attitudes from a Bayesian Network of log file data
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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The presence of transparency in an online learning environment may potentially enhance the educational value of the system, yet there are many aspects to transparency, and the configuration of these features can prove both difficult and daunting for educators. In this paper we propose a framework for controlling transparency in an online learning environment taking into account the task, learner model and learner preferences. These characteristics will be dynamically mapped onto a transparency configuration, allowing the system to adjust its presentation on-the-fly via a form of end-user programming. We describe the kinds of transparency our framework addresses, how users control transparency, and how the settings of multiple users are resolved into a single transparency configuration.