Practices in Action
How to Use This Resource
How do strong learning outcomes look in different fields?
Each example below pairs a weaker outcome with a stronger revision in the same course context. The annotation column explains what changed and why, so you can apply the same thinking to your own outcomes.
These examples are illustrations of the principles covered in the main Teaching Tip: using observable action verbs, calibrating to the right level of Bloom’s Taxonomy, specifying meaningful context, and writing outcomes that are genuinely assessable.
A note on “weaker” and “stronger”: these terms describe outcome-writing quality, not the instructor’s intent. The weaker versions reflect phrasing that commonly appears in syllabi, often because the underlying goal is sound, but the wording doesn’t yet make it observable or assessable.
Examples by Discipline
ARTS, MEDIA & DESIGN
Graphic Design — Visual Communication
Students in an intermediate design course are learning to produce work for real audiences. The course covers typography, layout, hierarchy, and critique. The instructor wants students to move beyond technical execution toward purposeful, audience-aware design decisions.
| Weaker | Students will understand the principles of visual hierarchy. | What changed: “Understand” is unobservable. The stronger outcome names a concrete task (poster), a real audience (campus community), and asks students to articulate their reasoning, making both the design process and the thinking behind it assessable.Bloom’s level: Applying + Evaluating |
|---|---|---|
| Stronger | Students will apply principles of visual hierarchy to design a poster for a specific campus audience, and justify their design choices in a written rationale. |
Context note: Adding a rationale component shifts the assessment from product alone to process + product. This also makes the outcome more meaningful in a portfolio context, where employers want to see not just what students made but how they think.
BUSINESS
Marketing — Consumer Behavior
An undergraduate marketing course asks students to analyze how consumers make purchasing decisions. The course draws on psychology, data literacy, and case analysis. Ultimately, the instructor wants students to apply frameworks.
| Weaker | Students will learn about consumer decision-making models. | What changed: “Learn about” describes exposure, not learning. The stronger outcome specifies the intellectual task (analyze), the material (a real product failure), the tools to use (two frameworks), and the focus of inquiry (psychological or social factors), all of which can be directly assessed.Bloom’s level: Analyzing |
|---|---|---|
| Stronger | Students will analyze a real product failure using at least two consumer behavior frameworks, identifying which psychological or social factors contributed to the outcome. |
Context note: Using a real product failure grounds the outcome in the kind of messy, ambiguous evidence students will encounter in professional contexts. It also makes the outcome specific enough that instructors can build a focused rubric around it.
STEM / ENGINEERING
Civil Engineering — Structural Analysis
A junior-level structural engineering course introduces load analysis, material properties, and failure modes. Students will go on to apply these concepts in design courses and co-op placements. The instructor wants outcomes that reflect professional-level reasoning, not just technical recall.
| Weaker | Students will know how to calculate structural loads. | What changed: “Know how to calculate” describes a procedure students can look up or reproduce mechanically. The stronger outcome asks students to make a judgment under competing constraints which is closer to what engineers actually do. It also introduces the ethical and environmental dimensions of engineering decisions.Bloom’s level: Evaluating |
|---|---|---|
| Stronger | Students will evaluate competing structural design options for a bridge scenario, selecting a preferred solution and defending the trade-offs made between cost, safety, and environmental impact. |
Context note: Outcome verbs like “evaluate” and “defend” signal to students that they are expected to reason, not just compute. This also opens the door to richer assessment formats: design briefs, oral defenses, or client-facing presentations rather than problem sets alone.
SOCIAL SCIENCES & HUMANITIES
Sociology — Race, Power, and Inequality
An upper-division sociology course examines structural racism, institutional power, and the experiences of marginalized communities. The instructor wants students to connect sociological theory to contemporary social issues and to reflect critically on their own positionality.
| Weaker | Students will understand systemic racism and its effects on society. | What changed: “Understand systemic racism” is broadly stated and difficult to assess directly. The stronger outcome asks students to construct an argument (a generative, high-order task) and specifies that it must draw on course frameworks and address structural (not individual) explanations. This resists surface-level responses and pushes students toward analytical precision.Bloom’s level: Evaluating + Creating |
|---|---|---|
| Stronger | Students will construct a sociological argument about a current policy issue, drawing on course frameworks to explain how structural factors shape disparate outcomes across racial groups. |
Context note: In courses that deal with identity, power, and lived experience, outcome language matters. Framing outcomes around argument construction and structural analysis signals that the course values rigorous engagement over mere exposure. Consider pairing this outcome with a reflection component that explicitly invites students to examine their own frameworks and assumptions.
COMPUTER SCIENCE
Computer Science — Software Engineering
An upper-division software engineering course covers system design, testing practices, and collaborative development. Students work in teams and are expected to produce professional-quality code. The instructor wants outcomes that reflect both technical competency and collaborative practice.
| Weaker | Students will be able to write well-tested code. | What changed: “Write well-tested code” describes a product without specifying the thinking behind it. The stronger outcome asks students to design a strategy, implement it, and document their reasoning, distinguishing between students who test thoroughly by habit and those who do so deliberately. The rationale component also models professional communication practices.Bloom’s level: Creating + Evaluating |
|---|---|---|
| Stronger | Students will design and implement a testing strategy for a team software project, documenting decisions about coverage, edge cases, and trade-offs in a written technical rationale. |
Context note: Software engineering courses are among those most affected by generative AI, since AI tools can produce syntactically correct, well-structured code on demand. Outcomes that focus on decision-making, trade-off analysis, and documentation of reasoning are harder to fulfill through AI alone and better reflect what employers expect from junior engineers.
A Few Patterns Worth Noticing
Across all five examples, a few moves consistently strengthen an outcome:
- Replace state verbs with action verbs. “Understand,” “know,” and “learn” name internal states. Verbs like analyze, evaluate, construct, and defend name observable intellectual actions.
- Add meaningful specificity. A real audience, a specific scenario, a named framework, a professional context—these details make an outcome assessable and signal to students what doing the work well actually looks like.
- Name the reasoning, not just the product. Outcomes that ask students to justify, defend, or document their thinking assess deeper learning than outcomes focused only on what students produce.
- Match the level to the course. Introductory courses appropriately target remembering, understanding, and applying. Upper-division courses should push toward analyzing, evaluating, and creating—and the outcome language should reflect that.