D179 builds the data literacy that K-12 teachers need before they can research their own practice. It opens the research sequence of WGU's MS Curriculum and Instruction.
WGU's program guide describes the course as teaching the types of data, their benefits and limitations, and how to use data to identify and solve problems. Students learn to locate, collect and analyze data from credible sources and draw conclusions that drive continuous improvement.
Teachers handle data daily, from grades to attendance. The course asks for something harder: choosing the right data for a defined problem and justifying how you will analyze it.
Course at a Glance
| Item | Details |
|---|---|
| University | Western Governors University (WGU) |
| Course code | D179 (codes can change between catalog versions) |
| Program | MS Curriculum and Instruction (MSCIN) |
| Competency units | 3 (per the program guide) |
| Prerequisites | None |
| Typical work | Written tasks on identifying data sources and on collecting and analyzing data |
What D179 Covers
The program guide lists two competencies:
- Data types and sources: identify relevant types and credible sources of data needed to solve an identified problem.
- Methods and conclusions: select appropriate methods to collect and analyze data, present conclusions and drive continuous improvement.
Behind these sit ideas such as qualitative versus quantitative data, validity and reliability, and ethical handling of student information.
Key Concepts Explained
Qualitative and Quantitative Data
Quantitative data are numbers: test scores, attendance rates, reading levels. Qualitative data are words and observations: interviews, open survey answers, classroom notes. Each answers different questions, and many school problems need both.
Credibility of Sources
A credible source is accurate, relevant and current. State assessment results and validated screeners are strong; a single informal quiz is weaker. Always say what a data source cannot tell you.
Example: A fourth-grade teacher notices weak reading fluency. Quantitative data: words-correct-per-minute scores from three benchmark checks. Qualitative data: short student interviews about reading at home. Analysis: compare average scores across checks, then group interview themes. Conclusion: students reading at home less than three times a week show the slowest growth, which suggests a home reading routine as an improvement step.
Continuous Improvement
Data-informed practice is a cycle: identify a problem, gather data, analyze, act, then check again. Your conclusions should lead to a specific action and a plan to measure its effect.
Typical Assignments and How to Approach Them
| Task type | What it tests | How to approach it |
|---|---|---|
| Problem statement | Focus | Describe one specific, observable classroom or school issue |
| Data source evaluation | Types and credibility | Name each source, its type, strengths and limitations |
| Collection and analysis plan | Methods | Match each method to the question it answers |
| Conclusions | Interpretation | State what the data show and the improvement step that follows |
Handling Student Data Responsibly
Classroom data are about real children, so responsible handling is part of data literacy. In the United States, FERPA protects the privacy of student education records, and districts add their own policies on sharing data.
- Remove names and replace them with codes such as Student A or S1.
- Avoid reporting tiny groups where a student could be identified.
- Store files securely and share only what the task requires.
- Check your own assumptions; data can reflect bias in how it was collected.
Triangulation
Triangulation means checking a conclusion against more than one data source. If test scores, work samples and observations all point the same way, your conclusion is stronger. When they disagree, that difference is worth investigating and discussing in your task.
Where Students Get Stuck
- A problem that is too broad. "Low achievement" cannot be analyzed; "low fractions accuracy in grade 5" can.
- Listing data without evaluating it. Say why each source is credible and what it misses.
- Overstating conclusions. Small samples suggest, they rarely prove.
- Forgetting privacy. Remove student names and follow school data policies.
Where D179 Fits in the MSCIN Program
WGU's standard path places Data-Informed Practices in the third term, alongside The Collaborative Leader and Educational Research. It is the first of the two research courses and the prerequisite for Educational Research.
That order matters. The problem you define and the data sources you evaluate here can become the basis of your D180 research plan and, eventually, your capstone study. Choosing a problem you care about now saves you from starting over later.
Study Tips for D179
- Choose a real problem from your classroom; D180 and the capstone can build on it.
- Keep one table of every data source with type, origin and limitations.
- Use simple descriptive statistics (mean, range, percentage) unless the task asks for more.
- Read the rubric before collecting anything.
How We Help with D179
Send the task, rubric and de-identified data or a description of your problem. An education writer prepares a model task, explains data concepts, or edits your draft. Our survey data analysis guide may help if you use questionnaires.
GradeEssays is independent of Western Governors University. Your data and conclusions must come from your own setting; use our work as a reference and submit your own tasks under WGU's academic integrity rules.
Use Classroom Data with Confidence in D179
Share your problem of practice, data and rubric. We prepare a custom analysis or review your draft.
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Frequently Asked Questions
Data literacy for teachers: choosing credible data for a problem, collecting and analyzing it, and drawing conclusions for improvement.
WGU's program guide states there are no prerequisites.
The course focuses on data literacy; most classroom analyses use descriptive methods. Follow your task instructions.
Usually yes, if you follow school policy and remove identifying information. Check your task instructions.
Data-Informed Practices is the prerequisite for Educational Research, which leads into the capstone.
No. We never invent data; we help you analyze and write about data you collected.