Ma. Victoria Almeda

Interactive Mathematics Storybooks and Their Friends

Little is yet known about Interactive Mathematics Storybooks (IMS) enveloped in a digital surround of supporting materials—their “Friends”—designed to delight and educate young children as well as those who read with them. Clearly different from paper books and physical manipulatives, interactive books entail a special set of affordances that can promote young children’s mathematics learning, and the surrounding Friends can help the adult understand the mathematics and the child. This chapter relies to the extent possible on existing research and theory, but goes beyond current knowledge to speculate, imagine, and dream about the potential of IMS for helping young children to learn mathematics at home. The chapter uses what is known to imagine what could be.

Using correlational topic modeling for automated topic identification in intelligent tutoring systems

Student knowledge modeling is an important part of modern personalized learning systems, but typically relies upon valid models of the structure of the content and skill in a domain. These models are often developed through expert tagging of skills to items. However, content creators in crowdsourced personalized learning systems often lack the time (and sometimes the domain knowledge) to tag skills themselves. Fully automated approaches that rely on the covariance of correctness on items can lead to effective skill-item mappings, but the resultant mappings are often difficult to interpret. In this paper we propose an alternate approach to automatically labeling skills in a crowdsourced personalized learning system using correlated topic modeling, a natural language processing approach, to analyze the linguistic content of mathematics problems. We find a range of potentially meaningful and useful topics within the context of the ASSISTments system for mathematics problem-solving.