Every engineering program in Canada is built on twelve Graduate Attributes. Every accreditation self-study revolves around them. And every Accreditation Coordinator has spent at least one late night wondering whether they've actually captured what the visiting team is looking for.
This is a practical guide to what Graduate Attributes actually are, why mapping them is more complex than it seems, and how to manage that complexity without losing your mind.
What Graduate Attributes Actually Are
The Canadian Engineering Accreditation Board (CEAB) defines twelve Graduate Attributes — the knowledge, skills, and abilities that every engineering graduate in Canada is expected to demonstrate. They appear in the Accreditation Criteria as GA 1 through GA 12, and they're the foundation of every engineering program's curriculum design.
Each Graduate Attribute has associated Indicators — measurable sub-items that describe what it looks like in practice. Your program's evidence map is built on these Indicators: courses map to them, exemplars support them, and the visiting team evaluates how well your program demonstrates them.
If you've been doing this for more than a year, you know the structure by heart. But knowing the structure and managing the complexity of that structure across dozens of courses, multiple programs, and a full curriculum are two different things.
The Hidden Complexity of GA Mapping
Here's what most programs don't realise until they try to map it out: Graduate Attributes don't exist in isolation. A single course often contributes to three, four, or more different Indicators across multiple Graduate Attributes. A capstone project might demonstrate GA 4 (technical knowledge), GA 8 (professionalism), and GA 11 (societal and environmental context) simultaneously.
This cross-cutting nature is by design — engineering education is integrated, not siloed. But it's a nightmare for spreadsheet-based systems. When Indicator A lives in column D and Indicator B lives in column H, and both are demonstrated by the same course, you're looking at a data model that treats connected things as disconnected.
The problem gets worse over time. As courses change, new ones are added, and old ones are retired, the mapping becomes stale. A course that used to contribute to GA 7 might have been redesigned two years ago to focus on a different outcome, but the mapping still shows the old contribution. The spreadsheet can't flag that inconsistency because it doesn't understand relationships — only cells.
Why Spreadsheets Fail at GA Mapping
It's not that spreadsheets are bad tools. They're excellent for linear, static data. The problem is that GA mapping is neither linear nor static.
Consider a program with 40 courses, 12 Graduate Attributes, roughly 60-80 Indicators across all attributes, and an average of 3-5 exemplars per Indicator. That's a dataset with thousands of relationships. A spreadsheet forces you to flatten those relationships into rows and columns, losing the connections in the process.
A proper evidence map preserves the relationships. You can navigate from a course to the Indicators it contributes to, from an Indicator to the courses that support it, and from a specific exemplar to the Attribute it demonstrates. This multi-axis navigation is what transforms GA mapping from a data-entry exercise into an actual management tool.
What a Living Evidence Map Looks Like
In a living evidence map, the structure doesn't get rebuilt each cycle. It accumulates. Here's what that looks like in practice:
- Year 1: You map your 40 courses to the Indicators. Half the Indicators have solid evidence. The other half are thin — you have course mappings but no exemplars, or you have one old exemplar per Indicator.
- Year 2: The mappings haven't changed because the curriculum hasn't changed. But now you've added five new exemplars from courses that were just evaluated, and you've identified three Indicators that need immediate attention because the visiting team is coming in Year 3.
- Year 3: Your evidence map shows a complete picture — which Indicators are strong, which are at risk, and which exemplars are outdated. The visiting team sees a program that knows its own strengths and weaknesses.
This isn't a fantasy. It's what happens when you maintain a connected data model year-round instead of treating accreditation as a cyclical event.
Five Steps to Better GA Mapping
Whether you're starting fresh or looking to improve an existing process, these five steps will strengthen your GA evidence mapping:
1. Audit your current mappings before adding new evidence.
Before you start collecting new exemplars, check what's already mapped. Courses change, curricula evolve, but legacy mappings often persist long after they've become inaccurate. Cleaning up outdated mappings before adding new data is like decluttering before you move — the new stuff has somewhere to go.
2. Prioritise Indicators by risk, not by number.
Not all twelve Graduate Attributes carry the same risk profile. Some indicators may be well-covered across your curriculum. Others — especially GA 11 (impact of engineering solutions on society and the environment) or GA 12 (individual and team skills) — are consistently harder to evidence with concrete exemplars. Focus your effort where the gaps are.
3. Collect exemplars at the point of assessment, not after the fact.
When a faculty member grades a student assignment, that's the moment to flag it as a potential exemplar. Waiting until "accreditation season" means you're asking people to remember what they graded two years ago. Collect at the point of assessment, tag it with the relevant Indicator, and file it.
4. Review mappings during curriculum review cycles, not during self-study.
Most programs have an annual or biennial curriculum review process. That's the natural time to check whether course-to-Indicator mappings are still accurate. If a course was redesigned, update its mappings. If a course was retired, archive its mappings. Do this as part of regular business, not as a self-study task.
5. Use multi-axis navigation to find evidence, not search.
If you can only find your evidence by searching for specific keywords or browsing through folders, you're not navigating — you're hunting. A proper evidence map lets you start from any point (a course, an Indicator, an Attribute) and see everything connected to it. That's the difference between finding evidence and managing it.
The Real Value of Good GA Mapping
Here's what happens when your Graduate Attribute mapping is strong and current: the visiting team doesn't just see evidence — they see a program that understands itself.
They can ask a question like "Show me evidence for GA 7" and within minutes, your program produces a coherent response: the courses that cover it, the exemplars that demonstrate it, the trends over time. That response is more powerful than any self-study report because it's not constructed for the visit — it's the normal state of your program.
That's what good GA mapping does. It turns accreditation from something you prepare for into something your program already is.
MapOutcomes was built to make GA mapping the foundation of your accreditation process — not an afterthought. We'd be glad to show you how it works.
This post was written for Accreditation Coordinators at CEAB-accredited engineering programs in Canada. If you're managing accreditation for ABET programs, the same principles apply — reach out and we'll adapt our thinking to your framework.