2.8 Data Analysis
Candidates model and facilitate the effective use of digital tools and resources to systematically collect and analyze student achievement data, interpret results, communicate findings, and implement appropriate interventions to improve instructional practice and maximize student learning. (PSC 2.8/ISTE 2h)
Candidates model and facilitate the effective use of digital tools and resources to systematically collect and analyze student achievement data, interpret results, communicate findings, and implement appropriate interventions to improve instructional practice and maximize student learning. (PSC 2.8/ISTE 2h)
For the Data Overview a drilled down into state, district, school, and classroom data sources in order to answer questions about the effectiveness of instructional initiatives—specifically student-centered learning and block scheduling—as well as to gain new insight into possible solutions to problem-areas, as indicated by in the data. I created impactful visuals of the data in order to share persuasive solutions to problem areas at my school. To gather data, I used publicly available sources. Additionally, I consulted with my school’s testing coordinator, Assistant Principal, and clerk in order to pull the data I needed to performance this analysis.
This artifact shows mastery of Standard 2.8: Data Analysis. To effectively collect and analyze student achievement data, I used local and publically available state and district data, which I found on the Governor’s Office of Student Achievement Report Card. I used Excel to sort and filter student data in order to compare my school to averages in the state and district, creating colorful and persuasive representations of the data sets I found. For this project, I graphically represent student demographics, teacher demographics, graduation rate, mobility rate, a dropout rate, as important context to understanding more granular problems at my school, such as the 11th grade American Literature scores on the Georgia Milestones. However, this contextual data is important to know in order to avoid making faulty conjectures about test scores on any given year. Next, I interpret Georgia Milestone scores in relation to state and district averages, and separate my observations about the data from my inferences about it. Then, I break down student scores by race, language ability, and disability to analyze the impact of school changes on specific student groups. After weighing the strengths and weaknesses of my observations and inferences, I concluded that block scheduling has improved student retention and dropout rate, but has not improved test scores. I also conclude that students with disabilities need the most resources in order to close the learning gap. I recommend that PLC’s focus on using past data in order to further target weak areas. Implementing professional developments on data training for PLC leaders is the best solution of solve the problem of falling test scores. From this analysis, I created a yearlong action plan to improve problem areas at my school shared it with colleagues.
I learned a lot from creating this artifact. First, I learned that a successful data team is rooted in collaborative inquiry and equity of voices in order to avoid harmful stereotypes. Data teams, and data coaches, don't just look closely at different levels of the data pyramid, they also build a culture of data within the school so that data-driven decision making and data-driven dialogue are sustainable even after the data coach has left the building. Next, I learned that some instructional initiatives have tradeoffs; for example, in the data sets I looked at, block scheduling was a good decision for some student groups but not others. If I were to improve this assignment, I would drill down further into specific teacher data to draw further conclusion about what strategies might help student learning the most.
The work that when into creating this artifact has had an impact on school improvement since I shared results with the principal and other stakeholders who are in positions to act on this information. If my recommended actions are taken, I believe my solution of improving data analysis at the level of PLC’s will significantly impact student learning. The impact can be assessed by clearly stating the goals of the PLC at the beginning of the year—which standards will be targeted? What is the goal? What is the measure for success?—and analyzing student results at the end of the year to see if the goal has been met.