Intelligent tutoring system based on pedagogy and andragogy orientation

Abstract

One of the promises of Intelligent Tutoring System (ITS) is that it will teach and assist learning in an intelligent manner. It tracks student's performance and uses the information to provide suitable instruction and teaching material to each student dynamically to best help them learn that domain. ITS critiques are focused on queries whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers against students' preferences based on their learning orientation, experience, motivational state, and etc. This paper proposed a conceptual model that integrates four different stages of student learning orientation in an ITS for tutoring students according to their needs, viz, Stage 1 : High Pedagogy / Low Andragogy, Stage 2 : High Pedagogy / High Andragogy, Stage 3 : Low Pedagogy / High Andragogy and Stage 4 : Low Pedagogy / Low Andragogy. Particularly, student at stage 1 will be taken to a guided learning environment while students at stage 3 preference will be more self-directed with the machine tutor acting as a facilitator. Hopefully, if this model were considered when developing an ITS, the machine teachers can be as effective as the human teachers.