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1. To address one or more SDGs, what content (e.g., formulae, concepts, principles, theories, etc.) is important for students to understand and build upon? 
2. What ideas and/or knowledge should students construct through their learning? 
3. How can students be encouraged to question what counts as knowledge and examine whose perspectives are privileged or marginalized? 
4. What are the opportunities for students to unlearn their biases and challenge assumptions about what is considered normative or universal both in the subject/discipline and in the learning process itself? 

construct, describe, define, discuss, explain, express, identify, illustrate, indicate, list, match, name, outline, paraphrase, recite, recognize, recall, state

1. To address one or more SDGs, what changes would you like to see in students’ attitudes, beliefs, and/or worldviews? 
2. How would you help students:   
   • reflect on their social role, responsibility, and ethics within the subject, discipline, or profession?   
   • develop mutual respect, trust, and reciprocity? 
   • appreciate and value diversity in individuals, perspectives, cultures and worldviews? 

analyze assess, change, choose, create compare, critique, develop, discover, explain, explore, evaluate, examine, identify, illustrate, interpret, justify, modify, negotiate, recognize, reflect, renew, revise

1. To address one or more SDGs, what should students learn about themselves, including:  
   • their positionality in learning ? 
   • their personal bias and privilege? 
2. What should students learn about others, such as instructors, TAs, peers, the university, and local and global communities? 
3. What should students learn about effectively collaborating with others? 
4. What new values, visions, and perspectives would you hope students gain? 

analyze assess, change, choose, create compare, critique, develop, discover, explain, explore, evaluate, examine, identify, illustrate, interpret, justify, modify, negotiate, recognize, reflect, renew, revise

1. To address one or more SDGs, what connections should students make between ideas and knowledge: 
   • within the course? 
   • between this course and others, both within and outside the discipline? 
   • between the course content and their personal, social, professional, and other realms of life? 
2. What diverse knowledges and/or ways of knowing should students engage with? 

analyze, attach, associate, blend, collect, combine, compare, connect, contrast, coordinate, describe, differentiate, distinguish, establish, evaluate, explain, explore, identify, integrate, intermix, link, illustrate, paraphrase, question, relate, select, summarize 

1. To address one or more SDGs, what essential skills and competencies within the subject and/or discipline should students need to develop and practice? 
2. What ways of thinking specific to the subject and/or discipline should students develop and practise? 
   • critical thinking: analyzing and evaluating 
   • creative thinking: imagining and creating 
   • practical thinking: making decisions and solve problems 
   • other ways of thinking 

analyze, argue, assess, communicate, debate, develop (skills, agency, etc.), debate, develop, discuss, document, formulate, identify needs, identify resources, inquire, frame questions, practise, research, set a goal, schedule 

1. How can students meaningfully apply or demonstrate skills, competencies, and ways of thinking developed in the course to equitably tackle one or more SDGs? 
2. What projects or tasks should students manage or undertake to demonstrate their learning? 

address, apply, analyze, assess, calculate, complete, construct, create, communicate, demonstrate, design, develop, employ, estimate, evaluate, examine, generate, implement, interpret, manage, plan (make a plan/ make action plans), propose, produce, promote, propose, resolve, solve 

Builds on BIOL 243 to develop practical skills in the management, visualization, and analysis of biological data in the R coding environment. Covers frequency distributions, central moments and summary statistics, sampling and probability, tests of significance, correlation and regression, linear models and model selection. Emphasis is on hands-on coding, reproducible reporting, and the interpretation of statistical output for biological inference. Examples and assessments throughout the course are drawn from the United Nations Sustainable Development Goals.

LEARNING HOURS 120 (36L;19T;65P). PREREQUISITE BIOL 243.

The course opens in Week One by naming a focal set of six goals that motivate the case examples covered in lectures and assignments:

• SDG 2 Zero Hunger: optimizing crop yields, monitoring pests, understanding ecological interactions in food systems.
• SDG 3 Good Health and Well-being: disease dynamics and pathogen spread, especially zoonotic and vector-borne disease.
• SDG 6 Clean Water and Sanitation: biological monitoring of water quality and microbial contamination.
• SDG 13 Climate Action: species responses to climate shift, carbon cycling.
• SDG 14 Life Below Water: marine biodiversity, population dynamics, pollution and overfishing impacts.
• SDG 15 Life on Land: species distributions, habitat loss, conservation policy.

CLO3: Reflect on how positionality may bias one’s experimental design and data interpretation by exploring historical and contemporary biases on scientific progress.

CLO 5: Simulate data relevant to sustainable development goals to explore assumptions of statistical models.

CLO 8: Apply appropriate statistical models to test biological hypotheses related to sustainable development goals.

BIOL 343 teaches computation and statistical analysis to biology majors. In the offering featured here (Fall 2025), students worked collaboratively through twelve substantial coding assignments:

• Merging messy datasets from different sources 
• Simulating data
• Fitting and selecting linear models
• Producing publication-quality figures 

From Fall 2026 we are reducing the number of graded assignments. Each assignment is built on a real biological problem tied to one of the course's six focal SDGs. The scenarios include:

• Breeding perennial biofuel crops
• Investigating pollinator decline
• Testing invasive-species biocontrol
• Modelling drivers of island biodiversity
• Monitoring contamination effects in the Great Lakes

For scale, the Fall 2026 scheme is:

• Self-reflection assignments 10%
• Weekly quizzes 10%
• Weekly assignments 25%
• Participation and peer review 10%
• Final exam 45%

The two Self-Reflection Assignments are the only individual work in the course. Each worth 5%, for a combined total of 10% of the final grade. These reflections are assessed on:

• Completion
• Clarity of communication

They are not graded on students' opinions or personal viewpoints. Submissions are anonymous to the grading TA.

Students may complete each reflection in one of three formats:

• Half-page written journal entry
• Approximately 3-minute audio recording
• Concept map or graphic representation

Self-Reflection Part 1: Positionality 

Assigned: week 4

Purpose. The task. Students revisit the Sustainable Development Goals introduced in Week 1 and choose one that resonates with them personally—on the basis of life experience, values, or social identity. 

They then answer two questions:

1. Which goal did you choose, and why does it resonate with you?
2. How can your learning in biology contribute to it?

Why It Occurs in Week 4. By week 4 students have completed four coding assignments, each framed around a goal chosen for them—biofuel crops, simulated epidemic and climate scenarios, pollinator decline, and a data-integration problem set in either a hospital ward or a Great Lakes restoration project. 

So they arrive at the reflection having already met four worked examples of "here is a technical skill, here is a global problem it serves." The assignment is early enough in the course that the remaining eight assignments are all read through the lens they choose.

Connection to course design. In the same week there is a coding assignment where students choose between two versions of the same technical problem:

1. one framed as hospital infectious-disease data 
2. the other as freshwater contamination data 

The raw data and code for analysis are identical, but the biological context is completely different. The positionality reflection along with this "choose-your-own-adventure" style of assignment shows how important context and interpretation is to science, independent of the mathematical rigour of the analysis.

Transferability Across Disciplines. This general approach could be transferrable to other courses: 

Have students commit, in writing and early, to a problem they personally care about within the domain of a course. This influences how the rest of the term's assignments be read against that commitment, which gets revisited later in the course through the second self-reflection exercise.

Self-Reflection Part 2: Confirmation Bias 

Assigned: week 11

Purpose. The task. Following a lecture and assigned reading on how documented biases shaped the work of prominent scientists, students address three questions:

1. Where do these biases come from—personal, cultural, institutional, systemic?
2. How might they show up in statistical models—in how data are collected, interpreted, or presented?
3. What can you do to minimize bias in your own scientific practice?

Assignment Focus: Personal interpretation and reflection.

Why It Occurs in Week 11: In week 4 the students are primed to think about positionality, and by week 11 the students have spent weeks making judgement calls that may have felt purely technical at the time. This reflection revisits those decisions, showing how unconscious bias is pervasive even the most prominent figures in the history of biology, such as:

• Gregor Mendel 
• Karl Pearson 
• Francis Galton 
• Ronald Fisher

Some examples from history and the students' own work include:

• How data are collected and processed—which anomalies are typos to clean and which were real observations to keep?
• How data are interpreted—which models to use, and what to do when they disagree?
• How data are presented—every assignment in the course includes publication-quality figures with stand-alone captions. Which comparison goes on the axis, which are relegated to a supplement, what do the caption assert?

The week 11 reflection is asking students to audit two months of their own decisions, which they still have in front of them. In the same week the group assignment has students designing an experiment, prompting them to think about biases at the earliest stages of scientific inquiry.

Transferability Across Disciplines. The more general approach is to place a bias reflection after students have accumulated a body of their own methodological decisions, and ask them to examine those specific decisions rather than the concept in general. 

Assessing potential biases through self-critique is normalized by showing how prominent scientists in history have made mistakes that would be considered obvious, even embarrassing today. For example:  

• Mendel's fudged segregation ratios
• Fisher's published papers questioning the cancer link to cigarette smoking

Why Self-Reflection Assignments Work

Self-reflection assignments that are individual, anonymous, low-stakes, and opinion-based are, in my opinion, a good way to disincentivize the use of generative AI. These are the only individual assignments in a course where everything else is group work. They are anonymous to the grading TA, and they are graded on completion rather than against a correctness standard. 

A reflection on positionality that will be marked for quality, attributed to a named student, and possibly seen by their group becomes an exercise in producing the answer the instructor wants. You get compliance instead of reflection, and the whole thing is worse than not assigning it.

Flexible Formats for Inclusion 

Allowing variable formats feels more inclusive: 

• written entry
• audio recording, or 
• concept map

This started as an accessibility measure and turned out to improve the work generally: some students think more honestly out loud than on the page. 

It doesn't add much time to grading because it's a simple complete/incomplete choice. Submissions that do not meet the standard for 'complete' are returned to the student who has the option to resubmit for a 'complete' grade (e.g., clearly no effort or doesn't address all of the prompting questions). 

I think more assignments along these lines is one way to respond to the problem of generative AI.

Placement Matters

Placement is most of the design. These reflections are placed early and late in the course, to make a personal choice meaningful and spark reflection.

Completion-Based Grading Requires Clear Standards

Ours standard:

1. Does it address all of the questions? 
2. Is the reflection clear? 

Anything short of that gets a revise-and-resubmit option. It's important to stress to the TA that the specific opinion is not being graded. Discuss what happens with a submission that is clear, complete, and expresses a view you disagree with — the answer should be “complete". 

Start Small

Courses are like a house of cards, especially when everything is structured through OnQ. Every change causes knock-on effects. Small things like reframing an assignment or adding one self-reflection exercise are a good starting point with minimal risk of breaking too many things.