The Komperda group in the Chemistry & Biochemistry Department at San Diego State University is hiring two postdoctoral researchers. Both positions are for projects where a strong quantitative background will be beneficial (details below). These positions are open to candidates both trained in CER and candidates in other fields with relevant training, so please share widely within your networks. Start dates can be as soon as this July and review of applications begins immediately. Please email Regis Komperda ( firstname.lastname@example.org) with any questions.
Position 1: https://bit.ly/SDSU-CHIRAL-postdoc
The postdoctoral researcher will advance the goals of the NSF-funded CHemistry Instrument Review and Assessment Library (CHIRAL) project (#1914996). These goals include identifying and reading relevant chemistry education publications and extracting metadata about measurement instruments used to generate quantitative data (i.e., tests, surveys, observation protocols, etc.). This metadata includes basic information such as instrument topic and target as well as more complex information related to the evaluation of validity and reliability evidence for data collected with the instrument.
Responsibilities include: 1) refinement and implementation of the publication and instrument coding protocol used to enter information in the CHIRAL database (Microsoft Access skills or a willingness to learn desired); 2) training the next generation of undergraduate and graduate researchers on using the protocol to ensure sustainability of the project; and 3) analyzing the data within the database (R skills or a willingness to learn desired) to develop and lead publications and presentations for the chemistry education community showing trends and patterns in the topics and methods used for assessment.
Position 2: https://bit.ly/SDSU-SEM-postdoc
The postdoctoral researcher will join an ongoing project analyzing data collected using a modified version of the Community of Inquiry survey instrument. This instrument was designed to identify constructivist aspects of online learning environments and has been adapted for use in face to face courses. The current dataset consists of over 3,000 responses from students in chemistry courses at a large R1 university in the Midwestern US, a large R1 Hispanic Serving Institution (HSI) in the Southeastern US, and a large R2 HSI in the Western US. Some data were collected both pre-COVID and during COVID, which provides a unique opportunity to test the viability of the statistical model across different institutions and instructional contexts.
Responsibilities include: 1) analyzing existing data to replicate a prior (unpublished) initial single-institution study; 2) applying best practices for statistical modeling of survey data from multiple groups (i.e., invariance testing) to examine how the relation between constructivist learning environment variables and academic achievement may differ by course type and student characteristics; and 3) expanding on the current project and/or developing their own independent project. Experience analyzing student data from minority serving institutions (MSIs) would be beneficial for this role as would interest in QuantCrit frameworks.
Regis Komperda, Ph.D. (hear it