Uncovering themes in personalized learning

Using natural language processing to analyze school interviews

Datos Bibliográficos

ID21743975
AutoresDavid Mchugh (University of Wisconsin–Madison), Sarah Shaw (0000-0002-3517-0881, University of Wisconsin–Madison, autor de correspondencia), Travis R Moore (0000-0002-7874-0813, University of Wisconsin–Madison), L Z Ye (0000-0001-7051-5518, University of Wisconsin–Madison), Philip Romero-Masters (University of Wisconsin Madison, Madison, Wisconsin, USA), Philip Romero‐Masters (University of Wisconsin–Madison), Richard Halverson (0000-0002-5037-7602, University of Wisconsin–Madison)
Año2020
Volumen52
Número3
Páginas391-402
Fecha de publicación2020-07-02
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaJournal of Research on Technology in Education (JOURNAL)
Identificadores de la revistaISSN: 1539-1523 • E-ISSN: 1945-0818
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/15391523.2020.1752337
OpenAlexW3036987833
IdiomaEN
Citas recibidas8
Referencias citadas20

Using a natural language processing tool, this study examined participant discourse in personalized learning schools to better understand what personalized learning looks like in practice. Term frequency-inverse document frequency (tf-idf) was used to identify the significant words and potential emergent themes for 134 interview transcripts. This tool provided a way to swiftly explore the structure of the data, revealing distinctions in the vocabulary students and teachers use as well as a potentially meaningful set of themes. This method provided a valuable lens with which to validate or surface new areas for investigation. By applying this tool to interviews from personalized learning environments, we were able to identify ways educators and students talk differently about project-based learning environments, revealing that tools like tf-idf can be effectively used to quickly provide a preliminary look at large amount of interview data

Linguistics · Mathematics education · Vocabulary · Vocabulary Learning · Computer Science · Educational Technology and Assessment · Educational Tools and Methods · Online Learning and Analytics · Psychology