The Science Education Department (SED) at the Center for Astrophysics l Harvard & Smithsonian is a national leader in the study of science learning and in the research and development of evidence-based STEM learning experiences for adults and youth in both formal and informal settings. The SED has the explicit goal of conducting rigorous educational research on STEM learning and interest in STEM careers (through psychometric construction of assessments), developing cutting-edge teaching materials (including a network of student-controlled telescopes), and creating traveling museum exhibits based on breakthroughs in astronomy.
The Post-Doctoral Fellow will work on data collection and analysis in large-scale NSF-funded empirical research projects on science education and will co-author publications about the research. The first project titled Preservice Teachers’ Assessment of Science Knowledge (PT-ASK) assesses preservice teachers’ level of knowledge in various scientific disciplines and investigates its determinants. The second project titled Crowd-sourced Online Nexus for Science Teachers Researching and Upgrading Classroom Tests (CONSTRUCT) explores how crowdsourcing might be used for producing high-quality science assessments.
The fellow’s responsibilities include collecting, cleaning, and processing data, conducting literature searches and reviews, developing testable hypotheses, conducting statistical data analyses to test these hypotheses, and writing up the results in publishable form, under the guidance of, and in co-authorship with, senior members of the research team. The fellow will use statistical techniques such as multivariate regression and logistic regression, hierarchical linear modeling, propensity weighting, and factor analysis, as well as psychometric methods based on classical test theory and item response theory. The fellow will also serve as a mentor to junior members of our research group and will co-chair the monthly departmental seminar series.
Doctoral degree in science education, social science, science/math education, or engineering education. Evidence of publication productivity in peer-reviewed journals. Strong literature review and statistical skills. Proficiency in R.
Start date: September 2022 for one year, full-time; with the possibility of renewal for another year.
Please email cover letter and updated CV for consideration to:
Application closure date
Friday June 10, 2022
Harvard University is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law