Multiple-choice recall is easy to grade but often fails to show whether learners can apply what they’ve learned. Here’s how we think about assessments that measure understanding—and how we support them on the platform.

The limits of recall-only assessment

Recall questions (“What is X?” “Which of the following is true?”) have a place for checking definitions and facts. But they don’t tell you if a learner can:

• Transfer knowledge to a new context.
• Explain why something works or when to use it.
• Synthesize ideas from different parts of the course.
• Create something (e.g. a plan, a solution, a critique) using the material.

Over-relying on recall also encourages surface learning: learners memorize for the test and forget soon after. For credentials and employability, we need evidence of understanding.

Question types that go deeper

We support and encourage a mix of question types:

• Scenario-based multiple choice – “Given [situation], what should you do?” so learners apply principles, not just repeat them.
• Short-answer and open-ended – “Explain in 2–3 sentences why…” so we capture reasoning. These can be graded by rubrics or by AI-assisted review with human oversight.
• Structured tasks – “Submit a one-page plan for…” so learners produce artifacts that instructors (or peers) can evaluate against criteria.
• Peer review – Learners evaluate each other’s work using a rubric, which reinforces standards and reduces grading load.

The goal is to align questions with learning objectives: if the objective is “Learners will be able to analyze…”, the assessment should require analysis, not just recognition.

Rubrics and consistency

For non-multiple-choice items, rubrics keep grading fair and transparent:

• Criteria – What dimensions matter (e.g. accuracy, clarity, use of sources)?
• Levels – What does “meets expectations” vs “exceeds” look like for each criterion?
• Calibration – Instructors or TAs review samples together so scores are consistent.

We support rubric-based grading in our assignment and quiz flows so that instructors can define criteria once and reuse them across cohorts.

Feedback that improves understanding

Assessment is most valuable when it feeds back into learning:

• Explanations – After each question (or at the end of a block), explain the correct answer and why the distractors are wrong.
• Reattempts – Where appropriate, allow a second attempt after feedback so learners can correct misconceptions.
• Aggregate insights – “Many learners missed question 3; consider revisiting topic X” so instructors can adjust teaching.

We treat assessments as part of the learning loop, not only as a gate at the end.

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Well-designed assessments are fairer for learners and more useful for institutions and employers. We’re building the tools to support scenario-based questions, rubrics, and feedback—so that credentials reflect real understanding. Contact us to discuss your assessment strategy or read more on the blog.