Research Problem • UF sciences faculty complain that students lack adequate

computer, math/statistics, and writing skills

• Biology faculty have embraced undergraduate research in the curriculum

• Collaborative projects allow students to learn from one another in ways that model what they may face in future careers

• Arranging collaborative projects between classes in different disciplines can be logistically challenging

• Are biology students engaged in research projects better served by working in interdisciplinary teams with computer students or learning the computer skills themselves?

Research Question• Research Question:• Do the data representation skills of biology students engaged in research

projects improve with targeted consultation from computer science faculty?

• Research Context: • Institution: Master’s L, private not for profit, 3700-student university• BIOL413/L Microbiology II class

• Elective course; Primarily serves Biology, Pre-Vet, Pharmacy, Med Tech, and Forensics Majors

• Typical students are of senior standing• Offered Fall and Spring - 25 students per semester

• My question is interesting because:• Interdisciplinary teams are currently of interest in multiple disciplines (nationwide)

• UF initiatives seeking STEM grants, considering use of resources to accomplish interdisciplinary courses (local)

Research Methodology• Microbiology students in small groups will design and carry out

studies, culminating in poster presentations

• Pretest of conceptual knowledge, with prior experience questions

• Computer science faculty will provide targeted instruction and consultation at strategic points of use

• Post-test with isomorphic conceptual questions

• Rubric for evaluating poster presentations

• Comparison to posters from previous semesters where the interdisciplinary team approach and the unstructured support approach were used

Alignment of Research Question and MethodologyPretest

• Establish baseline with questions on conceptual knowledge of data representation using assigned data sets

• Capture prior knowledge effect

Post test• Isomorphic questions to measure individual knowledge gain as applied to

assigned data sets• Compare to posters for application of concepts to familiar data set

Poster Evaluation• Design or adapt a rubric to assess data presentation concepts• Evaluate past posters with rubric and compare

• lack of pretest and prior knowledge information from past poster groups