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)
ITEC 7305
Data Overview
The Data Overview provided me an opportunity to grow in my skills of data collection, data reporting, data analysis and data interpretation. For the Data Overview, I collected the Milestones data for our school for the last five years. Once I obtained the Milestones data, I analyzed it to describe the patterns I saw emerging from the data. Once I drilled down into the assessment data, I precisely described a student learning problem in our context: the ELL population who attempted Milestones - and failed to pass it -struggled with the rigors of the language of the questions themselves. In order to address this student learning problem, I made some specific recommendations about how teaching the rigor of the language, including academic vocabulary and syntax, may improve our Milestones scores for this specific population. Finally, I made a presentation which included the graphic display of my findings and the narrative that explained the student learning problem.
Because our school is part of a large district, it has access to rich sources of data to compare our school’s performance to performance throughout the district. However, that data isn’t accessible to every educator; administrators have to access the aggregate data for us. We have a team in place to analyze and interpret that data for the whole school and to enact decisions based on that analysis. However, I wanted to model how a teacher leader can use “data to help educators become accountable to one another and their students” when assessing learning (Boudett, City, and Murname, 2017, p. 14). Choosing this path will “increase the chances that [my] school will use data to inspire teachers rather than burden them and to illuminate deep issues rather than amplify superficial ones” (p. 14). In order to collect data, I used Excel spreadsheet skills to collect and filter aggregate data from our state’s website and from our school’s own dataset. Then, I built a pivot table to facilitate the analysis of item-level questions missed per ethnicity from the Milestones exam. I saw a pattern emerge from manipulating the data according to our ELL (Hispanic) population: their language skills were likely preventing them from achieving high scores on items where the questions involved both a critical thinking skill and specialized academic vocabulary.
Then, I chose appropriate graphing and display methods for the data I’d gathered. When part-to-whole analysis was appropriate, I generated a pie graph from the table of data I’d collected. When the pattern I wanted to display was a trend like graduation rate of ELLs which is dependent on Milestones success, I generated a line chart compared to all other population’s success rates. I created a presentation with a voice over that communicated the graphics and the findings so that interventions could be justified to various audiences, especially our teachers, whose business it is to know what student learning problems need to be addressed. Finally, in this presentation, I gave examples of what an appropriate language intervention in Social Studies would look like. I suggested that teachers implement vocabulary remediation into the eCLASS platform so that students could practice academic vocabulary using a digital tool for instant feedback and anywhere learning. From creating this presentation, I hoped to inspire teachers to look more individually as teams at student performance data so that they could own the data for themselves. Teachers themselves should be able to collect, analyze, and interpret data and then make recommendations from their own analysis.
Gaining access to the state’s data for Milestones testing proved to be absolutely overwhelming. The size of the dataset was too big to manipulate on my own with an application such as Excel. I ended up frustrated at the amount of time it was taking me to access data for my school. Our county does not grant open access to raw data for district assessments; they do process the data for our principals and deliver it to them for use in their schools. I learned two important things: the democratization of data for practical teacher use is not yet a widespread concept, and it is better to partner with a principal or administrator in my efforts to mine data, since the administrator can be a support as well as a source of information. Next time I want to encourage a team of teachers to analyze data, I’ll start by coaching the team to ask their administrator for usable raw data that is mined for us. This will not alter the quality of the results but rather increase the likelihood that teachers will continue to process mined data on their own, which is the ultimate goal.
Performing a Data Overview as a teacher leader allows teachers to see a model of how to own the “culture of inquiry” or to build a “data culture” at our school without having to feel like the data is a top-down initiative. Instead, teams should always be in control of the learning that they are attempting to inspire in their students. My project impacts my teams by empowering them to collect, analyze, and interpret their own formative and diagnostic data in ways that reveal useful patterns and potential student learning problems. Knowledge of a student learning problem is more than half the battle for teachers whose job is it to improve instructional practice and aid student learning.
References
Boudett, K., City, E., & Murname, R. (2017). Data wise: a step-by-step guide to using assessment results to improve teaching and learning. Harvard Education Press: Cambridge, MA.
Data Overview
The Data Overview provided me an opportunity to grow in my skills of data collection, data reporting, data analysis and data interpretation. For the Data Overview, I collected the Milestones data for our school for the last five years. Once I obtained the Milestones data, I analyzed it to describe the patterns I saw emerging from the data. Once I drilled down into the assessment data, I precisely described a student learning problem in our context: the ELL population who attempted Milestones - and failed to pass it -struggled with the rigors of the language of the questions themselves. In order to address this student learning problem, I made some specific recommendations about how teaching the rigor of the language, including academic vocabulary and syntax, may improve our Milestones scores for this specific population. Finally, I made a presentation which included the graphic display of my findings and the narrative that explained the student learning problem.
Because our school is part of a large district, it has access to rich sources of data to compare our school’s performance to performance throughout the district. However, that data isn’t accessible to every educator; administrators have to access the aggregate data for us. We have a team in place to analyze and interpret that data for the whole school and to enact decisions based on that analysis. However, I wanted to model how a teacher leader can use “data to help educators become accountable to one another and their students” when assessing learning (Boudett, City, and Murname, 2017, p. 14). Choosing this path will “increase the chances that [my] school will use data to inspire teachers rather than burden them and to illuminate deep issues rather than amplify superficial ones” (p. 14). In order to collect data, I used Excel spreadsheet skills to collect and filter aggregate data from our state’s website and from our school’s own dataset. Then, I built a pivot table to facilitate the analysis of item-level questions missed per ethnicity from the Milestones exam. I saw a pattern emerge from manipulating the data according to our ELL (Hispanic) population: their language skills were likely preventing them from achieving high scores on items where the questions involved both a critical thinking skill and specialized academic vocabulary.
Then, I chose appropriate graphing and display methods for the data I’d gathered. When part-to-whole analysis was appropriate, I generated a pie graph from the table of data I’d collected. When the pattern I wanted to display was a trend like graduation rate of ELLs which is dependent on Milestones success, I generated a line chart compared to all other population’s success rates. I created a presentation with a voice over that communicated the graphics and the findings so that interventions could be justified to various audiences, especially our teachers, whose business it is to know what student learning problems need to be addressed. Finally, in this presentation, I gave examples of what an appropriate language intervention in Social Studies would look like. I suggested that teachers implement vocabulary remediation into the eCLASS platform so that students could practice academic vocabulary using a digital tool for instant feedback and anywhere learning. From creating this presentation, I hoped to inspire teachers to look more individually as teams at student performance data so that they could own the data for themselves. Teachers themselves should be able to collect, analyze, and interpret data and then make recommendations from their own analysis.
Gaining access to the state’s data for Milestones testing proved to be absolutely overwhelming. The size of the dataset was too big to manipulate on my own with an application such as Excel. I ended up frustrated at the amount of time it was taking me to access data for my school. Our county does not grant open access to raw data for district assessments; they do process the data for our principals and deliver it to them for use in their schools. I learned two important things: the democratization of data for practical teacher use is not yet a widespread concept, and it is better to partner with a principal or administrator in my efforts to mine data, since the administrator can be a support as well as a source of information. Next time I want to encourage a team of teachers to analyze data, I’ll start by coaching the team to ask their administrator for usable raw data that is mined for us. This will not alter the quality of the results but rather increase the likelihood that teachers will continue to process mined data on their own, which is the ultimate goal.
Performing a Data Overview as a teacher leader allows teachers to see a model of how to own the “culture of inquiry” or to build a “data culture” at our school without having to feel like the data is a top-down initiative. Instead, teams should always be in control of the learning that they are attempting to inspire in their students. My project impacts my teams by empowering them to collect, analyze, and interpret their own formative and diagnostic data in ways that reveal useful patterns and potential student learning problems. Knowledge of a student learning problem is more than half the battle for teachers whose job is it to improve instructional practice and aid student learning.
References
Boudett, K., City, E., & Murname, R. (2017). Data wise: a step-by-step guide to using assessment results to improve teaching and learning. Harvard Education Press: Cambridge, MA.