Quantification of Portrayal Concepts using tf-idf Weighting

Abstract

Term frequencies and inverse document frequencies have been successfully applied in determining weighting for document rankings. However these have been more successful in text mining and in extraction techniques used in the web. Concept mining has become increasingly popular in the research and application areas of Computer Science. This paper attempts to demonstrate the limited usage of term frequency and inverse document frequency for the application of weighting calculations for ranking documents that are based on concept quantifications. The case study considered for experiment in this paper, is based on concept terms of David Merrill’s First Principles of Instruction (FPI). Merrill’s FPI applies cognitive structures explicitly for analyzing instructional materials. Therefore it is justified that the terms categorized under each cognitive structure (or portrayal) of FPI can be taken as respective concept of that portrayal. As question papers are representative of cognitive structures in a more clear and logical way, four question papers on ‘C Language’ have been considered for the experimental study, that are detailed in this paper. Manual method has been adopted for the computation of quantities of portrayals in selected documents for the purpose of comparative study. As manual method is accurate, the values (results) are considered as benchmark values. These benchmark values are considered for comparing with normalized term frequencies that are derived (experimented) from automated extractions from the same selected documents. The study is however limited to four documents only. Conclusions are drawn from this experimental study, which will be of immense use to concept mining researchers as well as for instructional designers.