D019: Data Literacy and Evidence-Based Practices

Data Literacy and Evidence-Based Practices (D019) is a graduate core course at Western Governors University. It builds the data skills educators need to improve learning for K-12 students.

WGU's program guide says candidates practice identifying educational problems and data types, generating and analyzing data, making inferences and drawing conclusions, and creating action plans in their own settings. The course also covers continuous improvement planning, professional learning communities and instructional decision-making.

The guide states that D019 has no prerequisites, and it is itself the prerequisite for Educational Inquiry (D035). Students find the shift from collecting data to drawing defensible conclusions the hardest step.

What D019 Covers

Key Concepts Explained

Types of School Data

Education researchers often group data into four kinds: achievement (test scores), demographic (who students are), process (programs and practices) and perception (surveys of students, staff, families). Combining them tells a fuller story than test scores alone.

Inference, Not Just Description

Description says what the data shows. Inference asks why, and how confident you can be. Small groups, missing data and a single snapshot all limit what you can conclude.

Action Plans That Can Be Monitored

A good plan names the goal, the strategy, who is responsible, the timeline and how progress will be measured. Without a measure, nobody can tell whether it worked.

Data in Professional Learning Communities

The course links data literacy to continuous improvement and professional learning communities (PLCs). In a PLC, teams of teachers meet regularly to look at student work and results, agree what to change, and check whether the change helped.

A widely used set of PLC questions, associated with Richard DuFour, keeps those meetings focused:

1. What do we want students to learn?
2. How will we know they have learned it?
3. How will we respond when some students do not learn?
4. How will we extend learning for students who already know it?

Questions two to four are data questions. If a task asks how you would use data with colleagues, describing a short, regular cycle like this (look at data, plan, teach, check again) shows you understand continuous improvement in practice rather than as a slogan.

Typical Work and How to Approach It

Follow your course's own assessment instructions. Tasks in a data literacy course typically ask for:

Where Students Get Stuck

• Jumping to causes. Correlation in school data rarely proves cause. Phrase conclusions with care.
• Only test scores. Bring in perception or process data to explain the numbers.
• Unmeasurable plans. "Improve engagement" needs a measure, such as attendance or survey results.
• Privacy slips. Never include identifiable student data; aggregate and anonymize.

Study Tips for D019

• Practice disaggregating one data set by grade, subgroup and time.
• Write every conclusion as "the data suggests" and add one limitation.
• Keep your analysis; Educational Inquiry and the capstone build on it.
• Use simple charts with labeled axes rather than complex visuals.

How We Help with D019

Send the task, rubric, anonymized data and any feedback. A writer with education research skills can explain analysis methods, check your interpretation and action plan, or prepare an explained model. Our survey data analysis guide is useful if your task uses perception data.

GradeEssays is independent of Western Governors University. We never invent or alter data; your analysis must use your real or assigned data set. Our work is a study aid, and you submit your own work under WGU's academic integrity policy, with free revisions within the scope of your original request on every order.