Identifying Profiles of Motivation-Signaling Language Use and Its Association With Social Background, Interest, and Persistence

• In Event: Measurement of Motivation and Cognitive Constructs

Abstract

Text analysis programs are promising research avenues for gaining insights into whether students’ motivation impacts learning behavior, in particular content writing. Analyzing writing assignments of socio-demographically diverse undergraduates (N=1,230) exploring learned content in a physics course and its usefulness, the current study used the Linguistic Inquiry and Word Count program to code for motivation-signaling language use. Using latent profile analyses, five unique profiles of motivation-signaling language use were identified. To validate found profiles, membership in profiles was predicted by students’ social background and associated with surveyed interest and persistence intention. Students in profiles defined by higher levels of affective word use reported higher interest than students in profiles defined with lower levels of affective word use. Implications will be discussed.

Authors

• Nayssan Safavian, University of California - Irvine
  Presenting Author

• Anna-Lena Dicke, University of California - Irvine
  Non-Presenting Author

• Yannan Gao, NYU Shanghai
  Non-Presenting Author

• Jacquelynne S. Eccles, University of California - Irvine
  Non-Presenting Author

• Glona Lee, University of California - Irvine
  Non-Presenting Author