A review of the Course Script — demonstrating how structured QA feedback drives instructional clarity, content integrity, and learner engagement.
This case study presents the QA review process applied to a 155-slide eLearning course Script covering food waste management for everday households. The script spanned 10 content sections — from definitions and taxonomy to practices and habits — and required a comprehensive quality check to ensure pedagogical soundness before production.
The first version of this AV script presented recurring structural, tonal, and alignment issues across multiple content sections. Without systematic QA intervention, these gaps would have reached production, affecting learner comprehension and course credibility.
As the Senior Instructional Design QA Specialist, my role was to ensure the AV script met the learning objectives, maintained pedagogical integrity, and was production-ready. This meant examining every slide for structural soundness, content accuracy, tonal consistency, and alignment with the course framework (AREL: Argument, Reasoning, Evidence, Link-back).
"Hi Samantha, Thank you for creating the Food Waste course! I've reviewed the AV script and have a few concerns. Could you please take a look at my comments? Let me know if you have any questions or need my assistance — I'd be happy to help."
— Siti Nuraeni, opening comment to the instructional designerThis framing — constructive, collaborative, and solution-oriented — reflects the core philosophy of high-quality QA: feedback should empower, not criticize. Every comment in this review was designed to guide the instructional designer toward a stronger output while preserving their creative contribution.
Read all 155 slides in context, cross-referenced against the Handover Document and Master File guidelines to understand the intended learning journey.
Checked each section's content against its stated learning objective to verify that voice-over and slide text actively served learner outcomes.
Evaluated the structural flow of each argument — verifying that thesis, body, evidence, and conclusion slides followed a coherent pedagogical sequence.
Flagged casual phrasing, personal pronouns, and vocabulary that was too technical or too informal for the semi-formal eLearning register.
Identified strong examples of thesis writing and content flow to set a standard for the ID and encourage consistent quality across all sections.
Rather than offering ad hoc observations, the QA review was systematically organized around six recurring issue types. This approach gives instructional designers a clear, learnable framework for improvement.
Comments flagging misplaced information across thesis, body, and evidence slides. Many body slides contained thesis-level content, and vice versa. Correcting this was the single highest-impact improvement needed.
Sentences that were too dense, choppy, or difficult to read as voice-over. Included requests to define key terms, improve sentence flow, and synthesize studies rather than listing them sequentially.
Cases where slide text did not reflect the voice-over content, or where the content diverged from the course objective and handover research. Key for maintaining cohesion between what learners hear and see.
Requests to shift from casual to semi-formal phrasing — including removal of second-person pronouns ("you"), overly colloquial language, and informal transitions unsuitable for eLearning narration.
Proactive recommendations to add missing information — such as proposing an additional taxonomy on food waste management alternatives, requesting definitions for technical terms, or suggesting more detailed explanations of key concepts.
Deliberate positive feedback that acknowledged strong thesis statements and effective body slides — setting a benchmark for the entire document and reinforcing what "good" looks like.
The following examples demonstrate the depth and specificity of the QA feedback — not just what to fix, but why it matters for learner outcomes.
A thorough QA review at the script stage prevents costly revisions during production. The feedback in this case study addressed issues at every layer — from structural pedagogy to individual word choices.
Restructured AREL flow ensures learners receive ideas in a logical sequence — thesis first, evidence second — reducing cognitive load and improving retention.
Catching structural, tonal, and alignment errors before recording eliminates the need for costly re-recording sessions or last-minute script patches.
Consistent terminology, semi-formal tone, and evidence-backed arguments position the course as a trustworthy, professionally developed resource for household management learners.
Deep command of AREL structure — knowing not just that slides are out of place, but precisely how to reorder them for maximum instructional impact.
Cross-referenced the AV script against the Handover Document, Master File guidelines, and academic sources — grounding every comment in evidence.
Framed every comment with rationale, examples, and offers of assistance — modeling the collaborative relationship between QA and ID that high-performing teams require.
Proactively identified content gaps — including a missing food waste management taxonomy — and provided research-backed suggestions for enriching the course.
Applied consistent editorial judgment on learner-appropriate register — distinguishing between formal academic citation language and narrative eLearning voice.
Maintained consistent quality standards across 155 slides and 10 content sections — demonstrating the stamina and precision required for enterprise-level course QA.
Structural / AREL Feedback
Repositioning information for better pedagogical flow
Clarity & Readability Feedback
Making content accessible and voice-over ready
Tone & Register Feedback
Maintaining semi-formal eLearning voice throughout
Positive Recognition
Setting the benchmark through affirmative feedback