An Automatic and Innovative Approach for Converting Pedagogical Text Documents to Visual Learning Object

Abstract

In this paper, we present a novel idea of converting pedagogical text documents to visual learning objects by automatically extracting nouns and semantic keywords from the text documents, and representing these keywords as a word cloud. A word cloud contains words that are weighted based on frequency, time, appearance, etc., depending on the concept they are used for. Each word in the word cloud would correspond to a visual representation of that word. A visual representation may contain drawings, figures, images, etc. that explains the given concept. The extracted keywords are used to query the Internet to find the corresponding visual representation of a given word. The idea is to bring text documents to life by creating a visual representation of the important concepts from the text documents. This paper is a work in progress.

Building concept maps by adapting semantic distance metrics to Wikipedia

“…Imran et al [11] observe that visual learners are prevalent among students. This is one of the reasons that explains why images improve learning dramatically.…”

“…The author notes that meaningful learning often implies adding and rejecting some concepts to the concept map. Imran et al [14] convert a text into a word cloud by the following procedure: 1) to extract the terms with NLP techniques; 2) compute the semantic distance of the concepts to avoid natural language ambiguities; and 3) query the Internet to retrieve a semantic visual representation of the word [1].…”

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