Regis Komperda

Assistant Professor, Chemical Education

office: (arriving Jan 2019)
phone:
email: rkomperda@sdsu.edu
Komperda photo

Curriculum Vitae

Professional Experience
  • Assistant Professor, San Diego State University, 2019—
  • Postdoctoral Fellow, Barbera Group, Portland State University, 2016—2018
  • Lecturer in Chemistry, Wright State University, 2010—2012
  • High School Chemistry & Physics Teacher, Minooka Community High School, 2008—2010
Education
  • PhD in Chemical Education, Bunce Group, Catholic University of America, 2016
  • MS in the Teaching of Chemistry, University of Illinois at Urbana-Champaign, 2008
  • BS in Chemistry, Minor in Secondary Education, University of Illinois at Urbana-Champaign, 2007

Research Interests

Chemistry education research (CER) investigates how students learn chemistry and works to develop evidence-based teaching practices. CER is grounded in knowledge of chemistry content and skills and integrates theoretical frameworks and research methodologies from fields including psychology and education. The field of CER is a specific area within the broader discipline-based education research (DBER) community and working in CER offers many opportunities for collaboration with other DBER community members. At SDSU this work is supported by the Center for Research in Mathematics and Science Education (CRMSE), where I am a member.

Discipline-Based Education Research diagram

My research is primarily quantitative and has two main areas of emphasis. The first focus is investigating issues of measurement and psychometric instrumentation in chemistry education. Having tools available to make high quality measurements is crucial to generating valid and reliable data. These data support chemistry and other discipline-based education researchers when measuring variables in various classroom and research contexts. Current projects include psychometric evaluation of instruments measuring student motivation, student study approaches, instructor teaching approaches, and learning environment characteristics. These instruments support my second focus on building larger statistical models using latent variable techniques, such as structural equation modeling, to better understand how the adoption of evidence-based teaching practices influences student outcomes in chemistry courses. A common theme throughout all areas of my research is use of the open-source R statistical software to encourage reproducible research through code sharing.


Selected Publications

  1. Komperda, R., Pentecost, T. C., & Barbera, J. (2018). Moving beyond alpha: A primer on alternative sources of single-administration reliability evidence for quantitative chemistry education research. Journal of Chemical Education. doi: 10.1021/acs.jchemed.8b00220.
  2. Komperda, R., Hosbein, K. N., & Barbera, J. (2018). Evaluation of the influence of wording changes and course type on motivation instrument functioning in chemistry. Chemistry Education Research and Practice, 19(1), 184—198. doi: 10.1039/C7RP00181A.
  3. Komperda, R. (2017). Likert-type survey data analysis with R and RStudio. In T. Gupta (Ed.), Computer-Aided Data Analysis in Chemical Education Research (CADACER): Advances and avenues (pp. 91—116). Washington, DC: American Chemical Society. doi: 10.1021/bk-2017-1260.ch007.
  4. Bunce, D. M., Komperda, R., Lin, S., Schroeder, M., Dillner, D., Teichert, M. A., & Hartman, J. R. (2017). Differential use of study approaches by students of different achievement levels. Journal of Chemical Education, 94(10), 1415—1424. doi: 10.1021/acs.jchemed.7b00202".
  5. Bunce, D. M., Komperda, R., Dillner, D., Lin, S., Schroeder, M., & Hartman, J. R. (2017). Choice of study resources in general chemistry by students who have little time to study. Journal of Chemical Education, 94(1), 11—18. doi: 10.1021/acs.jchemed.6b00285.