AUGMENTING LEARNING MATERIAL WITH EXPLANATORY ANALOGIES
Tata Consultancy Services Limited (INDIA)
About this paper:
Appears in: ICERI2014 Proceedings
Publication year: 2014
Conference name: 7th International Conference of Education, Research and Innovation
Dates: 17-19 November, 2014
Location: Seville, Spain
Abstract:Explanatory analogies help learners in learning complex ‘target’ concepts by mapping them onto more familiar ‘source’ concept(s). Such analogies can be found on the internet for a number of target concepts, but are scattered as webpages on different websites. In this paper, we propose an approach to augment electronic learning material with explanatory analogies retrieved automatically from the internet. We automatically find the target concepts being taught in the given learning material and identify webpages containing explanatory analogies for these concepts. We also present a method to automatically identify source concept(s) from the webpages containing an analogy.
Typically, multiple analogies are available on the internet for a given target concept. The analogies vary in parameters such as familiarity and interestingness of the source concept(s) used, readability of the analogy, and size of the analogy. We envisage two approaches to present analogies to learners while learning. One approach is a focussed approach that recommends only one or two analogies while learning a concept in order to reduce the effort of going through many analogies. For this, we propose an approach to rank analogies based on the aforementioned parameters. We support this proposition with a learner survey that observes the role of familiarity and interest in choosing source concepts from multiple analogies. The other approach is an exploratory approach that allows the learners to choose from a number of available analogies while learning a concept. For this, we propose a design for an augmented learning interface that enables the learners to easily chose from multiple analogies by quickly visualizing the aforementioned parameters for the available analogies.
Keywords: Analogies, Concept Learning, Augmentation, Personalization, Evaluation.