POSTDOCTORAL FELLOWSHIP IN EDUCATIONAL RESEARCH

Postdoctoral Fellowship in Educational Research

Start Date: July 1, 2024

Duration of Appointment: 12 months

Schedule: Monday-Friday, 35 hours/week

Bargaining Unit: CUPE Local 3906, Unit 3

Remuneration: $45,000/year + benefits

Supervisors: Dr. Sharonna Greenberg (Assistant Professor, Chemistry & Chemical Biology) and Dr. Caroline Junkins (Assistant Professor, Mathematics & Statistics)

Academic Unit: The Department of Chemistry & Chemical Biology

Position Overview:

We are inviting applicants with a passion for educational research and a commitment to advancing teaching and learning practices to apply for a new postdoctoral fellowship focusing on student evaluations of teaching. The fellowship will be offered through McMaster University’s Department of Chemistry and Chemical Biology, in collaboration with Paul R. MacPherson Institute for Leadership, Innovation and Excellent in Teaching.

Purpose and Key Functions:

This post-doctoral fellow (PDF) will conduct research on how McMaster University instructors gain insights from and respond to mid-semester qualitative feedback from students, particularly in large-enrollment courses. The PDF’s research contributions may inform pedagogical practices, shape university policies, and advance understanding of the challenges around student evaluations of teaching (SET).

Supervised by an interdisciplinary advisory committee across the University, the PDF will be responsible for the following: (1) conducting qualitative research to evaluate how instructors interpret student comments in SET; and (2) contributing to programming for educators that encourages best practices in converting SET data from insights to actions.

The successful candidate may explore:

• Faculty perceptions of their experiences in analyzing qualitative SET data
• Models of training and support for faculty to engage with qualitative SET data
• EDI principles associated with SET

Background:

Student evaluation of teaching (SET) lies at the heart of teaching and learning. At McMaster, the recent MUFA report (Grignon et al., 2019) found that current SET methods have a low participation rate, lack validity, and are biased, and that there is a general feeling among students and instructors that feedback is not being used. Accordingly, the report recommends formative student feedback, accountability, and collecting qualitative information.

Collecting and analyzing mid-semester qualitative comments from students can address recommendations from the MUFA report and close the feedback loop with students. Instructors would gain insights into their teaching methods and their students’ learning preferences, and students would feel their voices are being heard. However, analyzing mid-semester qualitative feedback is a labour-intensive process, for which many instructors are not trained and have little capacity. Moreover, instructors may be reluctant to read or use student feedback due to a perception that the feedback will be negative and/or biased, and they will not be able to change anything.

The Machine Learning in SET team has been working together to facilitate the use of qualitative SET data in a scalable, efficient way. We are using techniques in data science to build a toolkit that makes use of natural language processing (NLP) and machine learning (ML) to provide automated or semi-automated analysis of qualitative SET data. The next step of our work is extending the toolkit to other courses across the university, evaluating its impact on instructors at McMaster, and understanding how to convert student insights into actions.

Requirements:

• Ph.D. completed within the last 5 years
• Educational Background: A completed doctoral degree in a relevant field, with a focus on educational research, qualitative methods, or a related discipline.
• Research Expertise: Proven experience in conducting qualitative research, with a strong foundation in study design.
• Pedagogical Knowledge: A background in post-secondary pedagogy, demonstrating a understanding of evidence-based teaching and learning strategies such as active learning, student-centered learning, and universal design for learning is preferred.

Additional Information:

The start date for this position is targeted at July 1, 2024, however, depending on when the toolkit mentioned above is available, start date may be delayed.

A hybrid work arrangement may be possible for this position. Work location will be determined in collaboration with the supervisor and will be determined on operational needs.

How to Apply:

Please submit your cover letter, curriculum vitae (CV), and 3 professional references (name and contact information) online.

The review of applicants will begin in April, and will continue until a suitable candidate is found. We appreciate all replies to this advertisement, but only applicants selected for an interview will be contacted.  

If you have any questions, please contact Sharonna Greenberg at greenbsh@mcmaster.ca.