QLS common mistakes
QLS · Understand the traps and how to avoid them.
Choosing an instructional strategy by naming a learning theory (e.g., "use constructivism") instead of matching the strategy to the stated task type, learner prior knowledge, and context constraints in the scenario.
Candidates memorize theory labels and treat them as answers, so they pick the most sophisticated-sounding theory rather than the one the evidence in the scenario supports.
What to do instead
For each scenario, write down the task type (recall, procedure, problem-solving, attitude), learner prior knowledge, and context constraints before selecting a strategy; then justify the choice against those three factors, not against the theory's prestige.
Treating Bloom's taxonomy levels as a strict hierarchy and assigning "evaluate/create" objectives to learners who lack the prerequisite knowledge, or writing objectives that name an activity ("discuss X") rather than an observable outcome.
The pyramid graphic is widely misremembered as a rigid ladder, and activity-based verbs feel concrete and easy to write.
What to do instead
Write objectives as condition + observable behavior + criterion, and check the verb against the assessment you plan; verify prerequisites are met before assigning higher-order levels.
Designing content by dumping all available material into the course (subject-matter completeness) instead of working backward from terminal objectives and assessment evidence.
SMEs and new authors equate thoroughness with volume and fear omitting something, so scope creeps and the course loses alignment.
What to do instead
Build a backward-design map: terminal objective, then assessment evidence, then enabling objectives, then content; cut any content that no objective or assessment item requires.
Writing assessment items that cue the answer through grammatical mismatch, absolute terms, or a longest-option pattern, then reporting the item as valid because it "looks fine."
Authors write items from their own expert framing and do not review them from a naive learner's perspective or run item analysis.
What to do instead
Apply a checklist (parallel options, one defensible key, no cueing, no negatives unless flagged) and review distractor performance data; revise items with low discrimination or nonfunctioning distractors.
Running a session as a lecture with a token "any questions?" and no planned interaction, checks for understanding, or pacing changes.
Content anxiety drives facilitators to cover material rather than verify learning, and interaction is treated as optional garnish.
What to do instead
Plan interaction at fixed intervals (e.g., every 10-12 minutes) with a specific technique matched to the objective, and script check-for-understanding questions with expected responses and a fallback explanation.
Choosing an LMS feature or authoring tool first and shaping the learning design around its limitations, or assuming a tool's default settings meet accessibility and data-privacy requirements.
Procurement and platform familiarity drive tool choice, and defaults are trusted without inspection.
What to do instead
Define functional and non-functional requirements (including accessibility conformance and data handling) before tool selection, and test defaults against those requirements in a pilot.
Treating accessibility as a final remediation pass (adding alt text at the end) rather than a design constraint applied to content, interaction, and assessment from the start.
Accessibility is often owned by a separate team or checklist, and retrofitting feels cheaper during development.
What to do instead
Embed accessibility criteria in design reviews and acceptance criteria for every deliverable (documents, media, activities, assessments), and test with assistive technology before sign-off.
Confusing evaluation levels: reporting satisfaction survey scores as evidence of learning, or reporting completion rates as evidence of impact.
Kirkpatrick-style levels are memorized as a list, and easy-to-collect data is substituted for the data the question actually asks about.
What to do instead
Before interpreting any data, label which question it answers (reaction, learning, behavior, results) and state explicitly what it cannot tell you; match the measurement method to the level being claimed.
Ignoring consent, data minimization, and record-retention rules when piloting a new tool or collecting learner analytics, on the assumption that internal use is exempt.
Pilots feel informal and low-risk, and analytics collection is treated as a technical rather than a compliance decision.
What to do instead
Run every pilot and analytics request through a short data-protection screen (purpose, minimum data, retention, access, consent basis) and document the decision before collecting data.
Reporting project status by describing activity ("modules drafted") rather than progress against outcomes, risks, and decisions needed from stakeholders.
Activity metrics are easy to produce and feel like evidence of progress; raising risks feels like admitting failure.
What to do instead
Use a status format that states milestone status against plan, top risks with owners, and explicit decisions or escalations required, and send it on a fixed cadence to named stakeholders.