1. Inquiry, Pedagogy, & Technology: Automated Textual Analysis of 30 Refereed Journal Articles
David A. Thomas Mathematics Center, University of Great Falls, Great Falls, MT
Introduction
The storehouse of human knowledge and experience is vast, complex, messy, and growing exponentially. To cope with the information explosion, scholars in many knowledge domains rely on sophisticated information technologies to search for and retrieve records and publications pertinent to their research interests. But what is a scholar to do when a search identifies hundreds of documents, any of which might be vital or irrelevant to his/her work? More and more, scholars are turning to automated content analysis technologies to achieve what they do not have time to do themselves; characterize the global features of a large corpus of work and identify relationships between significant concepts and themes. This study used automated textual analysis and visualization technologies to investigate a collection of 30 articles. Specifically, the study asks
Which concepts occur most frequently? Co-occur most frequently?
How do concepts vary between articles? Between disciplines (i.e., mathematics vs. science)?
Which themes (i.e., co-occurring sets of concepts) emerge?
Methods
This study is an informal analysis of 30 refereed journal articles identified using Google Scholar and the keyword set {inquiry, pedagogy, technology, and mathematics or science}. Mathematics (15) and science articles (15) published between 2014 and 2016 were selected, downloaded, and analyzed using Leximancer ( a textual analytics tool that extracts an unbiased dictionary of terms from source documents, discovers concepts, and constructs a thesaurus of terms associated with each concept.
Results
Leximancer depicts concept relationships in graphical representations called network spanning trees. In such trees, concepts appear as circular nodes and most frequent co-occurrences appear as segments. Concept proximity is strongly related to concept co-occurrence, that is, concept nodes positioned near to one another co-occur more frequently than more widely separated concepts. Themes are collections of concepts.
THEME: students
(students, learning, inquiry, classroom, activities, experiences, process, school, question, curriculum, data, focus, collaborative, project, ideas, groups, skills, materials, evidence, thinking)
THEME: teachers
(teachers, technology, knowledge, education, content, practice, development, pedagogical, course, approach, context, professional)
THEME: science
(science, research, design, support, understanding, tools, instruction, model, beliefs, information, example, concepts, environmental, change, nature, issues, role, potential, social, online)
THEME: mathematics
(mathematics, different, problem, level, strategies, analysis, number)
THEME: Tpack
(Tpack, framework, chemistry)
FILE FOLDERS: Mathematics vs. Science
Separating the files folders and treating the folder contents as a single file provides an alternative perspective.
MOST FREQUENT CONCEPTS: Science vs. Mathematics
Separating the files into math and science folders yielded very different sets of concepts.
Science Mathematics
Conclusion
This approach provides an unbiased and potentially useful basis for identifying conceptual relationships and hypothesis formulation in large, unstructured data sets.