Drexel Co-Op: Measurement Science Assistant

Drexel Co-Op: Measurement Science Assistant

NBME offers a versatile selection of high-quality assessments and educational services for students, professionals, educators, regulators and institutions dedicated to the evolving needs of medical education and health care. To ensure our assessments meet the highest standards of quality, stay relevant and align to the current curriculum in medical schools and training programs, we rely on a wide network of collaborators. These include the volunteers who help develop our exam questions, the committees and panels who represent various groups within the medical education community, external researchers and health profession organizations. NBME views diversity, equity and inclusion (DEI) as foundational and enduring to our strategy and vision. We continue to focus on ensuring that our DEI work is impactful and ingrained in everything we do, including with our staff, culture, products and services, the Philadelphia community and the broader medical education landscape. Our commitment manifests in our hiring and staff development, recruitment for committees, grants programs, design and review of our assessments, and involvement in our local and national communities. Learn more about NBME at NBME.org. Co-ops must be within a commutable distance of university city during the duration of their co-op. Please note, this co-op assignment will begin on September 28, 2026.

The Office of Research Strategy (ORS) at NBME is seeking a motivated Drexel University Co op student to support the Measurement Science team. This role provides hands on experience in educational and psychological measurement, applied data analysis, and research supporting automated scoring of constructed responses-particularly related to the Communication Learning Assessment (CLA).

The Co op will work closely with measurement scientists and collaborators across data science and research teams, contributing to ongoing research, documentation, and reporting activities.

Programs and Initiatives Supported:

Communication Learning Assessment (CLA)

• Research related to automated scoring of open ended, constructed response data
• Studies examining scoring quality, accuracy, and fairness

Automated Scoring and Annotation Research

• Support for analyses involving human annotations, model outputs, and response level data

The Co-op student will support research and analytic activities, including:

Data Preparation and Cleaning

• Clean and organize research datasets (e.g., constructed responses, annotations, scoring outputs)
• Identify and resolve data quality issues such as missing values, duplicates, or formatting inconsistencies
• Prepare analytic datasets following established project protocols

Descriptive and Preliminary Analyses

• Conduct basic descriptive statistics (e.g., frequencies, distributions, summary statistics)
• Create tables and figures to support internal research discussions
• Perform preliminary comparisons and exploratory analyses under guidance from measurement scientists
• Documentation and Research Support
• Draft and maintain documentation such as data dictionaries, README files, and analysis notes
• Contribute to project documentation
• Assist in organizing materials for reviews

Reporting and Communication

• Develop brief written summaries of analytic findings for internal audiences
• Support preparation of internal research reports, presentations, or technical appendices
• Clearly document analytic decisions and assumptions in a reproducible manner

Recommended Qualifications:

• Completion of at least one course involving statistical analysis or programming in R and/or Python
• Ability to perform basic data cleaning, data validation, and descriptive statistical analyses
• Familiarity with working with structured datasets (e.g., CSV, Excel)
• Ability to document analytic workflows clearly and accurately, including data dictionaries and analysis notes
• Strong attention to detail and commitment to data quality and reproducibility
• Ability to communicate findings clearly in written summaries and tables
• Interest in measurement, assessment, data science, or applied research
• Coursework or experience in statistics, data science, psychology, education, public health, or a related quantitative field

Experience working with:

• R and/or Python for data analysis (coursework, projects, or prior work experience)
• Pandas, tidyverse, or similar data analysis libraries
• Reproducible analysis practices (e.g., well commented code, versioned files)

Exposure to:

• Open ended or text based data (e.g., survey responses, written responses, annotations)
• Research or academic project environments
• Ability to work independently while also collaborating with a research team