This work-in-progress briefly surveys a selection of open-source Natural Language Processing (NLP) tools and investigates their utility to the qualitative researcher. These NLP tools are widely used in the field of lexical analysis, which is concerned with automating the generation of useful information from human language using a variety of machine processes. Recent research shows that the statistical analysis of software recognized linguistic features can benchmark certain mental processes, such as cognitive load. This investigation generates those linguistic indicators using transcripts from a multi-year, interview based study and compares them to a qualitative analysis of a subject's conceptual understanding of various engineering topics. Our intermediary findings indicate a correlation between changes in the linguistic indicators introduced in this paper and a qualitatively coded analysis of conceptual understanding over time. Future work will involve increasing the breadth of the dataset to further establish the fidelity of this approach and expand on the premise of using automatically generated linguistic indicators to aid the qualitative researcher.
Assessing a student's conceptual understanding of a particular subject is challenging as a result of its abstract complexity and personal nature. A schema-based framework for describing the relationship between grouped knowledge sets can be used to visualize and represent aspects of conceptual understanding. This paper puts forth a set of criteria to indicate when a student's solution generation process fails as a result of inadequately defined schemata. A three-year longitudinal study involving semi-structured interviews of undergraduate engineering students was performed as a means of data collection. An analysis of the data based on the proposed criteria set was performed for the purpose of investigating changes in the student's conceptual understanding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.