Student Project Officer

Overview

With funding from the Warwick International Higher Education Academy (WIHEA), we are undertaking a student-led research project to investigate how team composition influences student experiences and outcomes in group-based learning. The project evaluates a novel team allocation approach, where students are allocated to teams based on self-reported skills and working preferences. The allocation aims to create teams that are diverse in skills while maintaining compatibility in working styles.

Responsibilities

• Support the analysis of survey, attendance and assessment data collected from students who participated in the module.
• Help investigate how team diversity and students' perceptions of team balance relate to team effectiveness, learning outcomes, satisfaction and academic performance.
• Work closely with academic staff and, when required, work independently, manage workload, and contribute ideas to the analysis and interpretation of findings.

Essential qualifications

• Postgraduate or final year undergraduate with a background in Statistics, Data Science, Mathematics, Computer Science, Psychology, Education or a related discipline involving statistical skills.
• Excellent analytical skills, with proven experience conducting quantitative analysis and interpreting findings.
• Experience using statistical tools or software such as R, Python, SPSS, or equivalent.
• Understanding of statistical methods including descriptive statistics, correlations, regression and survey analysis.
• Excellent written and verbal communication skills.
• Ability to work independently and manage competing priorities.
• Strong organisational skills and attention to detail.
• Ability to present findings clearly through tables, figures and written reports.
• Proficiency with IT tools including Excel

Desirable qualifications

• Experience conducting a literature review.
• Experience analysing questionnaire or Likert-scale survey data.
• Familiarity with a range of quantitative research methods and approaches to analysing multi-level survey data.
• Experience creating reproducible analyses using code.