Using Text Data Mining to Enhance the Literature Search Process for Novice STEM Researchers

Abstract

A literature search can be an arduous process, especially for novice researchers. We have developed a tool that allows a researcher to rank order a list of references that are returned by a keyword-based search engine, based on similarity to known exemplars. This significantly accelerates literature searches by novices. Our research question was: can we produce a text-analytic tool that, when used by an inexperienced scholar, rank-orders a list of references against an exemplar, so that the time needed to find relevant literature is reduced, and the literature survey section of their paper will be superior. An experiment was set up where one course section used the tool to produce the literature review section of a thesis proposal, and the other class used traditional literature research tools. We surveyed both sections to self-report the time used for the literature search. We found some time savings by some of the students using the tool. We also provided blind, randomly selected pairs of completed proposals to SME faculty who teach that same class to assess the quality of the literature sections of the samples. We found that the tool-using section of students reported significantly less time to do the literature search, and the quality of their literature review produced had a significantly higher quality.