Preparing learners with partly incorrect intuitive prior knowledge for learning

Preparing learners with partly incorrect intuitive prior knowledge for learning

Learners sometimes have incoherent and fragmented intuitive prior knowledge that is (partly) "incompatible" with the to-be-learned contents. Such knowledge in pieces can cause conceptual disorientation and cognitive overload while learning. We hypothesized that a pre-training intervention providing a generalized schema as a structuring framework for such knowledge in pieces would support (re)or...

Prior Support Knowledge-Aided Sparse Bayesian Learning with Partly Erroneous Support Information

It has been shown both experimentally and theoretically that sparse signal recovery can be significantly improved given that part of the signal’s support is known a priori. In practice, however, such prior knowledge is usually inaccurate and contains errors. Using such knowledge may result in severe performance degradation or even recovery failure. In this paper, we study the problem of sparse ...

Adapting to Prior Knowledge of Learners

Prior knowledge is an important factor that influences the interaction with a hypertext and the learning gain. Our authoring system NetCoach provides a way to assess the users’ prior knowledge and to adapt the course in different ways. We describe how the adaptation mechanism assesses the user’s knowledge with test items, infers the user’s current learning state from this information, and final...

Online Learning with Prior Knowledge

The standard so-called experts algorithms are methods for utilizing a given set of “experts” to make good choices in a sequential decision-making problem. In the standard setting of experts algorithms, the decision maker chooses repeatedly in the same “state” based on information about how the different experts would have performed if chosen to be followed. In this paper we seek to extend this ...

Combining learning from instruction with recovery from incorrect knowledge