The graduate applies research sources and data types useful in the graduate’s field of specialization to improve learning and development opportunities for all P–12 students.
2046.1.2 : Applied Data Literacy Skills
The graduate applies data literacy and analysis skills appropriate to the graduate’s field of specialization to enhance learning and development opportunities for all P–12 students.
As a leader, you will be asked to use educational data and apply data literacy skills as a part of school improvement efforts. In this task, you will first describe an educational problem from your setting. You will then access existing educational data relevant to the problem and describe the source(s) of data that include information from technologies such as state department of education websites, data warehouses, data dashboards, simple spreadsheets, and apps. They may also include assessment systems, student information systems, instructional management systems, or other relevant technologies. Sources of data could also include district data or classroom data.
Next, you will apply data literacy skills to identify educational problems and data, analyze the data and their types, and then make inferences and draw conclusions. Last, you will summarize three credible research articles and describe how they may inform the next steps of an action plan. You may use the “Data Analysis Template” to help guide your submission.
Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. An originality report is provided when you submit your task that can be used as a guide.
You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.
A. Describe an educational problem relevant to your setting by doing the following:
1. Develop a problem statement that can be investigated by accessing and analyzing data.
Note: You will be accessing existing data. You will not need to collect original data.
2. Describe the setting or context in which the problem exists, including the participant group.
a. Describe the participant subgroups relevant to the problem.
Note: Some examples of participant characteristics include but are not limited to gender; socioeconomic status, homeless status; level of special education; and whether they are English learners, students with a parent in the military, or students in foster care.
3. Explain why each data type (qualitative and quantitative) may or may not be a good fit for the problem.
4. Describe stakeholders who may collaborate to address this problem, explaining why they are relevant.
5. Discuss ethical considerations related to student privacy, including how you will limit potential issues.
B. After selecting data relevant to the problem in part A, describe the source(s) of the data by doing the following:
Note: Data sources could include information from technologies such as state department of education websites, data warehouses, data dashboards, simple spreadsheets, and apps. They also may include assessment systems, student information systems, and instructional management systems. You may consider other relevant technologies that provide access to the analysis and reporting of data. You may access district data or classroom data as well.
1. Describe how you accessed the data, including the technology that you used.
2. Describe why the data you accessed from the source(s) are relevant to the problem and the participants in part A2.
3. Describe how the data source(s) is (are) credible.
C. Analyze each data set using methods aligned to the data type by doing the following:
1. Describe the quantitative, qualitative, or mixed methods used.
Note: Examples of quantitative methods include measures of central tendency and proportions. An example of a qualitative method is coding for themes.
2. Describe how the data were made into a meaningful representation of the information, including any technology that was used to aid this process.
3. Explain what your data say about the measures in the performance of the whole group of participants from part A2.
4. Explain what your data say about the measures in the performance of the subgroups of the participants from part A2a.
D. Discuss your results and inferences by doing the following:
1. Summarize your results.
2. Discuss the inferences you made based on the data by doing the following:
a. Explain how you turned the raw data into actionable knowledge.
b. Discuss how you plan to share the data and the related inferences with each stakeholder group from part A4.
3. Discuss how collaboration with each stakeholder group can help solve the problem from part A.
Note: These stakeholders can include but are not limited to principals, district administrators, parents, taxpayers, and legislators.
E. Summarize three credible articles related to the problem, describing how each may inform the next steps in implementing an action plan related to this problem.
F. Acknowledge sources, using APA-formatted in-text citations and references, for content that is quoted, paraphrased, or summarized.
G. Demonstrate professional communication in the content and presentation of your submission.