麻豆视频

The 麻豆视频 Institute for the Study of Teaching and Learning in the Disciplines and the Faculty of Science invites applications for a short-term research assistant position. 

Teaching Assistants (TAs) lead the tutorial sections in our introductory biology courses. This responsibility has been identified as a point of anxiety, isolation, and inexperience for these TAs, and so we sought to support them by building community together through peer tutorial visits. Over multiple semesters, we have integrated a peer observation and feedback process within the role. We have collected data on this process for ~15 TAs using pre- and post- surveys (numerical and short-answer questions), as well as some classroom observation data (modified from COPUS, Smith et al, CBE-LSE 2017). This project seeks to answer the questions: How does the process of formal peer observation & feedback impact 1) TA perspectives about their confidence in teaching, 2) perspectives on classroom observation, and possibly 3) TA teaching practices?  With this size of dataset, a mixed methods (qualitative/quantitative) approach may be most feasible.  

Timeline and expectations of applicant experience:

We are intending this to be a short-term contract, with reasonably intense data analysis/writing work focussed towards submitting your first-author manuscripts by end-of-December 2020 (ideally). All data has been collected, and we are excited to build a team this semester to analyze, interpret, and publish the findings. Thus, we are looking for self-directed applicants experienced with data science / statistical analysis and academic writing in the Discipline-Based Education Research (DBER) or Learning Analytics (LA) fields. This may be through a formal program (e.g., graduate training, postdoctoral fellowship, etc.) or it may be through other equivalent experiences. 

Required qualifications and skills:

• Data science: Ability to select and implement meaningful analyses, including appropriate statistical treatment, and effectively communicate the work to a DBER or LA audience (who are not necessarily data scientists themselves).
• Writing & publishing: Academic research and manuscript- writing skills, as demonstrated by prior publication(s). (In other words, have published at least one science education paper in DBER, learning analytics, or similar field.)
• Communication: Excellent interpersonal skills, including reliable and effective communication with the team and project lead.
• Initiative and experience: Self-directed, independent project completion skills, in consultation with the project lead and research team.  Note that the project lead has reasonable DBER, education, and academic writing experience, but is not a data scientist.
• An interest in evidence-based teaching and learning, and science education.  Personal experience with undergraduate science courses is an asset, but not required.

Research assistant responsibilities:

• Analysis of large- or moderate-scale datasets (selecting & implementing analysis approach; documenting workflow, drawing conclusions, data visualization);
• Writing a full draft of a (first-author) paper for publication in the DBER or LA field.  including short literature review for the introduction and context. 
• Meeting remotely each week with project team, during PDT daytime hours, from contract start until papers are submitted (ideally end of December 2020). 
• Available intermittently in 2021 to discuss and handle journal revision requests.
• Optional: Presentation at a conference.  (e.g. at SABER or LAK or similar); conference registration costs may be eligible to be covered, depending on cost.

Rate of pay: $25-30/hr (Canadian dollar; incl. vacation & statutory holiday pay, no medical or dental benefits). Based on experience & qualifications. Minimum of 120 hours with the possibility of additional hours.

To Apply: Applicants must be eligible to work in Canada (with a valid Canadian SIN and bank account). Applicants should submit: (1) a 1-page cover letter structured to address the interest, skills, and role responsibilities as listed above; and (2) a 1-2 page resum茅 that highlights relevant qualifications, experience, and dissemination of prior projects. These two items should be organized as one PDF attachment addressed to Megan Barker, and emailed to megan.barker@sfu.ca, with the subject line: 鈥�RA position: Peer Impact鈥�. Short-listed applicants will be provided with a mock, small-scale dataset, and then invited to interview (via Zoom).

Application deadline: Friday, October 23, 2020, 4:30 p.m. We appreciate all replies to this posting, however, we will only contact short-listed applicants.

Direct all inquiries to Dr. Megan Barker (megan.barker@sfu.ca).