On Improving the Effectiveness of Open Learning Environments through Tailored Support for Exploration
چکیده
Open learning environments can be beneficial for learning in ways not available in more tutor-controlled systems, because of the active role the learner plays in knowledge acquisition. However, it has been shown that not all learners are proficient in unconstrained exploration, restricting their ability to learn effectively in these environments. In this paper we present the Adaptive Coach for Exploration (ACE), a prototype computational framework that supports active exploration in an open learning environment by providing tailored support to overcome specific student difficulties. ACE provides students with a highly-graphical, exploratory learning environment in the domain of mathematical functions. To generate a more immersive experience, ACE’s interface allows continuous manipulation of graphical objects specifically designed to encourage exploration. The Student Model assesses student knowledge and exploratory behaviour using a Bayesian network. The Coach uses the Student Model’s assessment to generate tailored hints, which are intended to guide and improve the exploratory behaviour of those students who would otherwise experience difficulties in learning from an unsupervised environment. After describing ACE’s components, we present the results of a preliminary user study which gauges the system’s effectiveness. The results provide evidence that ACE positively affects students learning, that it does support exploration, and that more thorough exploration leads to better learning. We also discuss our results in terms of limitations detected and possible improvements.