WGU D179: Data-Informed Practices

Where D179 Fits in Your Master's Program

D179, Data-Informed Practices, is a graduate course in WGU's Master of Science, Curriculum and Instruction program, where the institutional catalog lists it at three competency units in the third term of the standard sequence. If you are enrolled, you probably arrived here already carrying a full teaching load, and you want to know what the course actually demands before you open the first module. The short version: this is a course about data literacy for educators, and it is assessed entirely through written work you submit, not through a proctored exam.

Direct answer: D179 has no objective assessment. You pass by submitting performance assessment work in which you identify a real problem in your own practice, locate credible qualitative and quantitative data about it, evaluate that data's trustworthiness, and draw defensible conclusions. Work the rubric line by line and choose a narrow problem you can actually gather data on, and the rest of the course becomes straightforward.

WGU's catalog describes the course as building data literacy: understanding the different types of data and the benefits and limitations of each, using data to identify and solve problems and inform decisions, locating and collecting data from relevant and credible sources, analyzing it, and drawing conclusions that drive continuous improvement. That framing matters more than it looks. The course is not asking you to become a statistician. It is asking you to stop treating classroom decisions as intuition and start treating them as claims you can support with evidence.

D179 also carries weight beyond its own term. The catalog states there are no prerequisites for D179 itself, but lists it as a prerequisite for D180, Educational Research, which in turn prepares you for the program capstone in the ten-course, thirty-two-competency-unit sequence. The habits you build here, especially framing a problem narrowly and defending your sources, are the same habits the capstone will grade you on months later. Students who rush D179 tend to pay for it twice.

What the Written Work Actually Asks For

D179 is assessed through performance tasks rather than a test. Students who have taken the course consistently describe two submissions, and the work in each one breaks down along these lines:

• Identifying a problem of practice. You name a specific, observable issue in your setting, such as a gap in reading comprehension, weak mastery of a foundational skill, or low engagement during a particular block of instruction. Vague problems fail; narrow ones pass.
• Distinguishing qualitative from quantitative data. You must show you understand what each type can and cannot tell you, and why a well-informed decision usually needs both. The catalog names benefits and limitations explicitly, so this is not a section to skim.
• Locating relevant data sources. Assessment results, attendance and behavior records, observation notes, student work samples, surveys, and interviews all count, provided you can explain why each one speaks to your stated problem.
• Evaluating credibility. Graduate coursework in data literacy typically teaches a structured way to judge a source, and many students lean on a framework such as the CRAAP test (currency, relevance, authority, accuracy, purpose) when they write this section up.
• Collecting and analyzing data. The later half of the course moves from finding data to working with it: organizing what you gathered, describing patterns honestly, and stating what the numbers and observations do and do not support.
• Drawing conclusions for continuous improvement. You close by explaining what you would change in practice and how you would know whether the change worked.

Because rubric wording is revised regularly, treat the version inside your own course of study as the authority. Anything you read online, this page included, is orientation, not the requirement itself.

How Hard It Is and How Much Time to Budget

Students who discuss D179 publicly generally describe it as one of the more approachable courses in the sequence, mainly because there is no exam to sit and no advanced statistics to learn. The intellectual content tends to feel familiar to practicing teachers, since looking at student data is already part of the job. The difficulty is procedural rather than conceptual: writing to a rubric at graduate level, citing correctly, and being precise about what your evidence supports.

A realistic budget for a working teacher is two to four weeks of steady evening work, and students who already have a clear problem and easy access to their own classroom data often move faster. Students who take longer usually get stuck in one of two places: choosing a problem too broad to support with available data, or waiting on evaluator feedback after a revision request. Build a buffer for at least one revision cycle. Assuming a clean first pass is the single most common scheduling mistake in this course.

If your data-analysis muscles are rusty, the fix is small. You need to be comfortable reading a score report, calculating basic descriptive figures, and building a simple table or chart. You do not need specialized software. A spreadsheet is enough.

A Study Plan Built Around the Tasks

Because everything you submit flows from one problem statement, sequence your work rather than reading the whole course first.

1. Days 1–2: choose your problem, then stress-test it. Write it in one sentence and ask what data already exists that speaks to it. If you cannot name three sources you can genuinely reach, narrow the problem until you can. This decision determines how hard the rest of the course will be.
2. Days 3–5: read the course material against the rubric, not linearly. Open the rubric beside the reading and take notes organized by rubric row. Every note should answer, "which requirement does this help me satisfy?" This is active recall applied to written assessment: you are retrieving requirements, not just absorbing content.
3. Days 6–9: draft the data-source section. For each source, write one sentence naming it, one explaining its relevance, and one evaluating its credibility. That three-sentence pattern keeps you from drifting into narrative and makes rubric alignment visible at a glance.
4. Days 10–14: collect, organize, and analyze. Put your data in a spreadsheet even if it is small. Seeing it laid out prevents the classic error of describing a pattern the data does not actually show.
5. Days 15–18: write conclusions, then reread cold. Leave the draft alone for a full day, come back, and read it once purely as an evaluator checking rubric rows. Gaps you were blind to on the day of writing become obvious after a break.

One more tactic that transfers well from exam prep: self-test before you submit. Cover your draft and try to state, from memory, what each rubric row requires and where your paper satisfies it. Anything you cannot locate quickly is probably not stated explicitly enough for an evaluator either. The same rubric-first discipline pays off in D184 Standards-Based Assessment and in D630 Designing Curriculum and Instruction I, which sit in earlier terms of the same program.

Mistakes That Send D179 Submissions Back

• A problem statement that is really a topic. "Student engagement" is a topic. "Sixth-grade students disengage during independent reading blocks" is a problem you can gather data on.
• Treating credibility as a formality. Naming a source is not evaluating it. Say who produced the data, when, for what purpose, and what that implies about its limits.
• Overclaiming. Small classroom data sets show patterns, not proof. Writing "this proves" where you should write "this suggests" invites a revision request.
• Ignoring one data type. The course is explicitly about the benefits and limitations of different data types. A submission built only on test scores usually misses the qualitative side.
• Reaching for someone else's paper. Submissions are screened for originality, and borrowed structure rarely fits your specific problem anyway. The academic integrity consequences are far worse than a revision request, and your own classroom already gives you better material than any sample could.
• Skipping the course instructor. Course instructors are there to talk through your problem choice before you invest a week writing about it. That conversation is free and prevents the most expensive kind of rework.

D179 Readiness Checklist

• Can you state your problem of practice in one specific sentence that names the learners, the setting, and the observable issue?
• Can you name the data sources you will use and confirm you can actually access each one?
• Can you explain the difference between qualitative and quantitative data in your own words, with an example of each from your own setting?
• Can you judge a source's credibility using a consistent framework rather than a general impression?
• Can you describe a limitation of every data source you plan to cite?
• Can you organize your data into a simple table or chart and describe the pattern without overstating it?
• Can you connect each conclusion you draw back to a specific piece of evidence?
• Can you point to the exact place in your draft that satisfies each rubric row?
• Can you explain how you would measure whether your recommended change worked?

D179 FAQ

Is D179 an objective assessment or a performance assessment?

It is a performance assessment. There is no proctored multiple-choice exam, so all of your preparation should go toward written work that satisfies a rubric.

How many competency units is D179 worth?

WGU's institutional catalog lists Data-Informed Practices at three competency units, in term three of the Master of Science, Curriculum and Instruction program.

Are there prerequisites for D179?

The catalog states there are no prerequisites for this course. D179 is itself listed as a prerequisite for D180, Educational Research.

How long does D179 usually take?

Many students working full time report finishing in roughly two to four weeks. Your pace depends heavily on how quickly you settle on a workable problem and whether your first submission needs revision.

Do I need statistics or special software?

No. The course focuses on data literacy rather than advanced analysis. A spreadsheet and comfort with basic descriptive figures cover what the tasks ask.

What if my submission comes back for revision?

Revision requests are routine in competency-based programs and are part of how evaluation works. Evaluators mark the specific rubric rows that fell short. Address exactly those rows, resist rewriting the whole paper, and resubmit. For more course-by-course guidance, browse the School of Education hub, the related D293 Assessment and Learning Analytics guide, or the full library of WGU study guides.