D293 Assessment and Learning Analytics, catalog number LXD 5070, is the three-CU course that teaches how e-learning products know whether they worked. It covers assessment design for digital products, competency-based and skills-based models, universal design for learning, and the analytics that digital delivery generates whether you plan for them or not. The idea that organises the course is inference. An assessment is not a set of questions; it is an argument that performance on those questions licenses a claim about what somebody can do. An analytics dashboard is not knowledge; it is a set of measurable proxies that may or may not stand for learning. Submissions that treat either one as self-evident lose aspects.
Assessment is a claim, and the claim has to be earned
Write the claim first. Not the objective, not the question bank, the claim: after this module, a learner can perform this task, in these conditions, to this standard. Everything downstream is evidence for that sentence, and the quality of the sentence determines whether the evidence can ever be sufficient.
Two failure modes follow from a weak claim. The first is measuring recall and asserting capability. A multiple-choice item on the steps of a procedure supports a claim about recognising the steps, not about performing them under pressure, and a design document that slides between the two is making an unearned inference. The second is measuring completion and asserting learning. Time in module, pages viewed and clicks are behavioural traces, and a learner who left the tab open during lunch produces the same trace as one who read carefully.
Competency-based and skills-based models exist to close that gap by anchoring the claim to a performance rather than to a score, which is why they feature in this course and why WGU is a useful case to reason about. In a competency model the question is binary in a specific sense: the evidence either supports the claim or it does not, and partial credit has nowhere to live. Designing that way forces precision about what counts as sufficient evidence, and rubric aspects covering assessment design are usually scored on exactly that precision.
Universal design for learning enters at the same point rather than as a separate topic. If your assessment can only be completed by a learner who reads fluently in English and uses a mouse, then reading fluency and motor control are inside your measurement whether you intended them or not. That is a validity problem before it is an access problem, and saying so is what separates a strong access section from a compliance paragraph.
Turning the rubric aspects into a section plan
Scoring detail for D293 lives in your Course of Study rather than in the public catalog. Open it first. Each aspect is scored on its own against a three-point scale and needs a 2, so a sophisticated analytics plan cannot offset an assessment blueprint with no alignment shown.
Head each section with the rubric's own noun. Assessment writing invites clever thematic headings, and every one of them costs an evaluator time they will spend deciding your score.
The word budget, worked. Assume eight scored aspects and directions asking for roughly 2,800 words. Reserve 200 words for the product and audience context and 150 for limitations and next steps. That leaves about 2,450 for scored content, or 305 an aspect. Then weight it. The alignment aspect and the ethics or data-use aspect are the two that most often need extra room, the first because it is a table plus an argument and the second because a short answer always reads as an afterthought. Take 60 words from each of the four descriptive aspects and add 120 to each of those two.
A practical note specific to this course. Where an aspect asks you to interpret data, supply the data. An interpretation section with no table, chart or figure to interpret is scored as assertion, and candidates lose it while believing they answered fully.
A structure that fits an assessment and analytics deliverable
Where the task directions supply a template, follow it. Otherwise this arrangement matches the way assessment and analytics aspects are usually written.
| Section | What belongs in it | What earns the aspect |
|---|---|---|
| Product and learner context | What the e-learning product is, who uses it, in what conditions | Usually unscored, but every validity claim is judged against it |
| Performance claims | What a learner will be able to do, stated as observable performance with conditions and standard | Scored for specificity; understand and be aware of cannot be evidenced |
| Assessment blueprint | A map from each claim to the evidence that would support it and the item or task that produces it | Scored for coverage and for absence of unmeasured claims |
| Item and task design | The actual assessment pieces, with the reasoning behind format choices | Scored on format fit; recognition tasks for performance claims are the classic miss |
| Scoring approach | The rubric or key, what sufficient evidence looks like, how consistency is maintained | Scored for whether two scorers would reach the same decision |
| Accessibility and variability | Where construct-irrelevant demands were removed and what remains deliberate | Scored as validity work, not as a compliance list |
| Analytics plan | The measures collected, what each is a proxy for, and the decision each one informs | Scored for purpose; metrics with no attached decision are noise |
| Ethics and data use | Consent, retention, who sees what, and what the data will never be used for | Scored for concreteness; a privacy sentence is not a data policy |
Keep one product across the document. Assessment work goes wrong quickly when the blueprint describes a compliance module and the analytics section describes an academic course, because alignment can no longer be checked.
Evidence craft when you are arguing about measurement
Measurement writing has a higher standard of proof than most design writing, because the claims are checkable.
- Cite assessment literature for assessment claims. Statements about validity, reliability and alignment have a research base, and unsourced assertions about them read as opinion.
- Show the alignment rather than asserting it. A table mapping claim to evidence to item is worth more than three paragraphs saying the assessment is aligned.
- Name what each metric is a proxy for. Completion is a proxy for exposure. Time on task is a proxy for effort and also for distraction. Saying which is which is the analytics equivalent of showing your working.
- Use realistic sample data if the task allows it, and label it clearly as illustrative. Interpretation aspects need something to interpret.
- Handle learner data carefully in the document itself. No real identifiers, no screenshots showing names, no exported records attached.
- Use APA throughout, and cite any framework or standard you rely on rather than treating it as common knowledge.
The sentence that marks out a strong submission is the one admitting what the data cannot settle. Engagement metrics cannot demonstrate transfer. Naming that, then stating what a follow-up study would need to measure, is stronger than an optimistic dashboard.
What separates Competent from a submission sent back
Aspects are scored independently, so returns tend to hit one section. The most frequent is an analytics plan that lists metrics without attaching a decision to any of them.
- Every aspect has its own heading using the rubric's noun.
- Every performance claim has at least one evidence source, and every assessment piece serves a claim.
- Item formats match the kind of claim being made.
- The scoring approach is specific enough that two people would agree.
- Access decisions are argued as validity, with the construct-irrelevant demand named.
- Every metric names its proxy relationship and the decision it informs.
Performance assessment work at WGU can be revised and resubmitted with no grade penalty, so a return is a delay rather than a mark. In a six-month flat-rate term the delay is the cost that matters, since effective cost per course falls only as courses close inside the term.
Where D293 sits alongside a proctored objective assessment, we prepare only. We never sit an assessment and never ask for portal credentials.
Six mistakes that cost time in D293
- Writing objectives instead of claims. An objective describes instruction. A claim describes what the learner can then do, and only the second can be assessed.
- Assessing what is easy to score. Multiple choice is cheap and defensible for recognition claims. Used for performance claims it produces an assessment that measures the wrong thing precisely.
- Treating a dashboard as findings. A screenshot with no interpretation, no proxy statement and no decision attached is a picture, and evaluators score it as one.
- Adding universal design at the end. Retrofitted access reads as compliance. Designed access shows up in the item formats themselves and in the reasoning behind them.
- Ignoring the base rate. A completion figure with no comparison point supports nothing. Say what normal looks like in this context or say that you do not know.
- Leaving data ethics as one sentence. Retention period, access rights, learner visibility and prohibited uses are concrete decisions, and rubrics that name ethics are scored on the specifics.
How support works on this course
Send the rubric from your Course of Study, the task directions and a description of the product you are assessing. What comes back is aspect-mapped: performance claims written so they can be evidenced, a blueprint table an evaluator can check in a minute, item formats matched to claim types, an analytics plan where every measure carries a decision, and a data-use section with real specifics.
The alignment discipline is what carries forward. The capstone sequence asks you to justify measurement choices again at greater length, and students who learned to write claims properly here spend far less time defending them later.
Questions students ask about D293
Is D293 the same course as LXD 5070?
Do I need statistics for the analytics half of this course?
Can I use assessment data from my workplace?
Blueprint not lining up with your claims?
Send your Course of Study rubric, the task directions and your product description. You get an aspect-mapped draft with an alignment table an evaluator can check at a glance.
Where D293 sits in WGU's programs
The July 2026 catalog places this code in 3 current WGU programs. Open a program page for the complete standard path and term positions. The live Degree Plan remains authoritative after transfer credit, substitutions, and mentor planning.
The assessments, one by one
The public catalog does not publish this course's PA/OA identity or task count. WGU Tutors publishes at most one PA manual per course and only from a WGU-controlled public rubric. Until that source exists, PA help begins from the student's real Course of Study and OA support remains preparation only.