Culturally Relevant Data Science Analytics, Student Motivation, and Learning at an HBCU

• In Event: AERA Poster Session Wednesday 1:45pm
  In Poster Session: Black Post-Secondary Student Experience, Data, and Belonging Across Educational Spaces

Abstract

Big data and data science have arrived. This quasi-experimental study investigated the extent to which culturally relevant materials in DSA enhanced motivation and learning for HBCU undergraduates. Culturally responsive problem-based learning (CRPBL) and social learning theory informed the study. Results revealed significant increases in student motivation and learning linked to modules on introductory DSA topics. Gains in machine learning were significant, but DSA motivational shifts in machine learning were not. Cognitive dissonance and unevenness in CRPBL content coverage may have mitigated student motivation in machine learning. Culturally relevant DSA curriculum modules have real potential for fostering greater motivation and learning for HBCU students. Future research is needed to examine CRPBL content that supports student motivation in advanced DSA topics.

Authors

• Monica B. Mitchell, MERAssociates

• LaTanya Brown-Robertson, Howard University

• Amy Quarkume, Howard University

• Andrea Calloway, Howard University