Refining impact assessment in undergraduate STEM education: Differential item functioning analysis of field-based learning interventions

This dataset contains self-efficacy survey responses from undergraduate
biology students enrolled in three different course formats (lecture,
introductory field, and intensive field) at the University of California,
Santa Cruz from 2016-2019. The data structure includes pre/post survey
responses measuring students' self-efficacy across four skill areas
(species identification, experimental design, oral presentation, and field
research), demographic information (URM status, first-generation status,
gender, and Educational Opportunity Program status), and calculated change
scores for 564 students. The dataset demonstrates how Differential Item
Functioning (DIF) analysis can quantify both the magnitude and demographic
patterns of educational interventions with greater precision than
traditional assessment methods. Analysis revealed field course students
were significantly more likely to report higher self-efficacy ratings
compared to lecture course students (odds ratios ranging from 2-167 times
higher), with historically minoritized students showing greater gains in
field settings. This dataset has significant reuse potential for
researchers studying educational interventions, assessment methodology,
field-based learning, and equity in STEM education. All data were
collected under IRB approval (UCSC #HS3230) with student identifiers
anonymized to ensure ethical compliance and privacy protection.RetryClaude
can make mistakes. Please double-check responses.