EpiList: An Intelligent Tutoring System Shell for Implicit Development of Generic Cognitive Skills That Support Bottom-Up Knowledge Construction

摘要

Current research efforts have shifted from the development of intelligent tutoring systems (ITSs) that focus on the teaching of "content" knowledge to those that focus on teaching cognitive skills. This shift is seen as necessary because cognitive skills are increasingly recognized by educational establishments as the foundation for knowledge acquisition, comprehension, and application. Knowledge construction is a cognitive skill and can be broadly divided into "top-down" and "bottom-up" approaches. The former splits a concept to form subordinate concepts, while the latter one groups concepts together to form a superordinate concept. Both approaches require the skill of classification and form different semantic networks or classification schemes with different levels of significance and suitability. List-making games operate within a bottom-up environment, where one has to arrange a list of items according to their respective categories. EpiList is developed along the line of a list-making game. It requires the student to suitably arrange items into categories that they have selected from a given list of categories. EpiList has employed both inductive and deductive teaching strategies to tutor the students and implicitly teach them the skills of generalization and comparison. This is achieved through the use of rules and algorithms. The rules and algorithms focus not only on the incorrect categorization of items but also on the migration of one classification scheme to another scheme that is more significant and suitable under the current teaching context. This paper computationally translates the teaching of cognitive skills into simple sets and represents the instructional process in terms of rules and algorithms operating on these sets. The field evaluations of EpiList demonstrated its capability to develop generic cognitive skills