A literary thinker, I've never been much for Excel and numbers and graphs. Not that I had difficulty in math courses, but none of it excited me, so I came to know word processor and presentation software intimately but largely ignored numerical data analysis until I had to learn how to collect and use data for my capstone project. Reading through the incredibly dull research manual and collecting my own data helped me understand the basics of Excel enough to prepare me for this course. Encouraged to use data meaningfully at this late date in my academic career, I finally understand that intrigue is hidden in those eye-numbing spreadsheets. I can maybe get into this data stuff now that I know there's intrigue.
The Data Analysis & School Improvement course led me through how to develop a culture of inquiry and how to systematically analyze student performance data to determine student learning problems and establish student learning goals. At each step of data drilling, new revelations become clear. At the aggregate level, schools can determine how their students' performance compares to national, state, and district averages on standardized assessments. At the disaggregated level, schools can determine whether inequities exist in certain curricula and use those findings to drill deeper into strand-level data, item-level data, and student work samples. These three levels can inform collaboration team and individual teacher unit and lesson design, offering students the interventions for which data supports the need.
Before taking this class, I had significant experience collaborating with teams exploring standardized test data but had never actually created my own data chart presentations and had never enjoyed the data drilling. The depth of consideration and time involved in doing so surprised me and helped me learn to see that number-disguised intrigue. I will take this knowledge back to my own collaboration team; in fact, many of the assignments for this course will directly influence the functioning of my collaboration team, if I can convince the team to see what I've seen: equity issues persist in our course, and we can remedy them. That, or we can persist with the tried and untrue, an easy but tragic "solution" to demonstrable problems. Nah - the literary empath in me would never stand for that.
Before taking this class, I had significant experience collaborating with teams exploring standardized test data but had never actually created my own data chart presentations and had never enjoyed the data drilling. The depth of consideration and time involved in doing so surprised me and helped me learn to see that number-disguised intrigue. I will take this knowledge back to my own collaboration team; in fact, many of the assignments for this course will directly influence the functioning of my collaboration team, if I can convince the team to see what I've seen: equity issues persist in our course, and we can remedy them. That, or we can persist with the tried and untrue, an easy but tragic "solution" to demonstrable problems. Nah - the literary empath in me would never stand for that.