Adaptive Versus Learner Control in a Multiple Intelligence Learning Environment

摘要

Within the field of technology enhanced learning, adaptive educational systems offer an advanced form of learning environment that attempts to meet the needs of different students. Such systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model. Subsequently, using the resulting model it dynamically adapts the learning environment for each student in a manner that attempts to best support learning. However, there are some unresolved issues in building adaptive educational systems that adapt to individual traits. For example in what way should the learning environment support users with different learning characteristic and what advantage does adaptive control have over learner control. This article describes an experiment using the Multiple Intelligence based adaptive intelligent educational system, EDUCE, that explores how the learning environment should change for users with different trait characteristics. In particular it explores the effect of using different adaptive presentation strategies in contrast to giving the learner complete control over the learning environment. Results suggest that students who do not explore alternative resources beyond the first presented resource have the most to benefit from adaptive presentation strategies. The results surprisingly suggest that learning gain increases for these students when they are provided with resources not normally preferred.