During week 7, we discussed concepts in measurement. This includes how we use statistics in the classroom, as well as display and analyze data. We also talked about criterion vs. norm- referenced assessments and what the differences are between the two. We discussed population sampling, validity and reliability, as well as average, mean, median, mode, and range. These terms are important because they contribute to calculating and analyzing data. Therefore, it is important to be familiar with these terms and understand what they mean in students' data.
During week 8, we discussed how we use data to drive instruction. Data can tell us a lot about our students: including their earning needs, how well an assignment was understood, which students may need more interventions, and which students are high flyers. Data can even serve as a reference for the teacher as to how well students are being taught. The excel practice allowed us to graph fake students' scores and analyze what their scores mean. By analyzing, even though these were fake students, I was able to come up with ways to help these students make the appropriate academic gains. In areas where students collectively seemed to struggle, the data served as an indicator that maybe these are areas that need to be re- taught or revisited. Data can show patterns in learning as well as provide feedback as to what improvements you would like to make as the teacher. It is important to always reflect on the data in a holistic way. Use the data to make decisions but also recognize that sometimes, data isn't always accurate and can be biased. When we look at data with a holistic view, we are looking at it both quantitatively and qualitatively. Quantitatively means that we look at the numbers, and qualitatively means that background information is considered.
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