Multi Category Content Selection in Spaced Repetition Based Mobile Learning Games

Abstract

Learning requires repetition. Spaced repetition algorithms are aimed at reducing the number of times a learning item has to be accessed by the learner by scheduling item presentation based on psychological models. These models take into account learner performance on previous interactions with the learning item and the rate at which humans forget what they have learned. In recent years, spaced repetition learning software has become popular for simple learning tasks like flash cards used for learning vocabulary. This paper presents a prototype application that extends the spaced repetition learning approach to more complex content like the kind usually found in learning games. One major difference between this content and flash cards is that learning games usually contain a number of different tasks that convey the same underlying concept categories. To complicate matters, one task might even be classified as belonging to a number of independent or orthogonal categories. This paper explores how these categories can be modeled on the basis of a mobile game designed for training in the field of relational databases. We have chosen a mobile approach to leverage it's anytime/anyplace availability which allows a more precise scheduling by the spaced repetition algorithm.