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Reflection Paper on Data Driven Assessment

Reflection Paper

A teacher’s central focus should be evaluating what their students know, what they need to know, and how to provide instruction effectively for each individual student. As I reflect on my teaching and move forward in my path to becoming an effective leader, I realize that a major problem I face is how to evaluate my students and make data driven decisions on my planning and implementation of the standards. Overall, data driven assessment is a necessity in making informed decisions when developing curriculum and informed decisions on instruction. This is an area that I lack in because other than during my evaluation, I never take the time to truly sit down and look at my students from a pre, mid, and post assessment standpoint to drive my instruction. While I do use a multitude of formative and summative assessments I really feel I lack at making quality decisions based on this data.

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One of the first key benefits of data driven assessment is that it is needed to build curriculum. A teacher’s use of data provides opportunities in building quality units for individualized instruction. In the past, I have built my lessons based on what my idea is on what quality instruction looks like. I truly do try to make my lessons differentiated as I have 30 students with IEP’s; however, as the resource teacher has pointed out to me, I lack in taking these students data and working with them individually on improving. Instead, I just try to make sure they are passing in my class and move onto the next unit. In doing this, these students fall further behind the academic curve. Therefore, data driven instruction is greatly needed to create high quality assessments that will show a true account of the student’s ability and knowledge in my classroom. In the article, “Data Driven Decision Making In The Social Studies,” Dr. Marlow Ediger explains that teachers who develop high quality test in turn receive more relevant data for curricular decision making. For example, on true/false questions, teachers need to require students to not only give an answer, but to also change what is false to make it true. Thus reducing the occurrence of merely guessing and getting incorrect data (Ediger, 360). I realize now that in my first year of teaching, many of my tests were not of a quality that I needed them to be, based on how the students are performing. For example, many of my assessments asked students basic comprehension questions that were multiple choices where students could simply guess the answer. Thus, I received inaccurate data with these assessments, which in turn did not show me if they really understood or met their learner outcome of the lesson. I then changed my curriculum assessments this year by including multi-leveled questions, such as a question requiring the students to answer but also they must provide evidence of how they came up with the answer with evidence from the text.

Not only does data driven assessments aid in the development of quality test, it eliminates or lowers the percentage of bias in professional judgment decisions. Sometimes when teachers are grading an assessment they will give a student a grade bump based on their behavior. Therefore, the student’s real skill level is never truly shown because of the teacher’s bias. When you are dealing with straight data all of that is removed and the data is black and white and will show the true illustration of a student’s skill set. Currently, I have a student in class who has been passed on from the elementary level to the middle school without the skills needed to be successful in the middle school environment. When I looked back at his grades, he received high marks even though he can barely read through 4th grade material and needs text read aloud to him. My question is how did he receive these high academic marks when the student’s independent reading level is so low and he cannot comprehend the information on his own? Unbiased assessment and data driven decision making for this student would have shown his true ability so that instruction could have been assisted him in meeting his own individual learning needs. Instead of trying to fix these lower level skills in 6th grade, I could be focusing on the actual subject matter that he is supposed to be learning with his peers.

According to M. Cay Holbrook of “Supporting Students Literacy Through Data-Driven Decision-Making and Ongoing Assessment of Achievement” the process by which educators gather data and documentation provides the basis for their best professional judgment on decision making. The goal in literacy instruction is to include success in the skills but also enjoyment and active participation in reading. With this in mind, teachers need to make the most informed decisions on a student’s academic performance and reading achievement. Collecting a wide range of data and not focusing on one specific skill set or class should be what teachers use to make decisions. In agreement with M. Cay Holbrook, Dominic Gullow author of “Improving Instructional Practices, Policies, and Student Outcomes for Early Childhood Language and Literacy Through Data-Driven Decision Making” states early language and literacy proficiency data should be used to make informed decisions on a student’s reading achievement. This data can provide the evidence needed to plan for intervention and instructional reform.

In my school, we have a specific timeframe each day dedicated to response to intervention (RTI) and enrichment. This year, we switched our benchmark testing over to an online program called Reading Plus. With this system the student complete an entrance exam where the data is collected and students are given a specific reading score and hand selected specific reading passages and skill activities targeted to them individually. The challenge for us as educators is taking that data and being able to appropriately interpret and use it for the purpose of improving the students reading ability and placing them in an appropriate RTI or enrichment group. I personally am our grade level Tier 3 teacher where I take the lower students and have them on Reading Plus daily along with progress monitoring them using AIMS maze passages. My goal is to start having the students’ track their data on a sheet and be able to share these sheets with the student’s core teachers. In my school, we use a specific committees of teachers to examine data and construct meaningful plans for staff and student decisions. I currently serve on the schools RTI committee where we meet every month to look at interventions being used and how students are responding. We then look at their scores on Reading plus and their ELA grades to determine and action plan. One key factor to we should be taking into consideration when looking at this data is prioritizing usable data and making instructional decisions with feedback from all stakeholders instead of just simply looking at a student’s performance on a specific concept or classroom level misconception. I also feel I should be doing a better job at sharing the data from the Tier 3 students with their core teachers instead of just within the committee.

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In a classroom with only one teacher and roughly 25 students, it can be difficult to gather concrete data and from a wide variety of sources on a specific student. For example, in the middle school environment I see roughly 120 students on any given day. Believing I am getting meaningful data on each student and then transforming the data into individualized instruction every day is hard to imagine. One goal I have for the year is to work on collecting data all the time based on observations, questions, and assessments. Luckily I have five other educators on my team that can work together to collect and analyze data in order to provide more accurate decision making for the student. I want to steer away from the “data rich but information poor” model and instead use data as a resource to support and implement educational practices to enhance the learning environment (Mandinach, 82).

In conclusion, when reflecting on my classroom and instruction, Data-driven decision-making is my area of weakness and should be my area of concentration as I move forward on my path to become a school leader. Data driven decision making is a key factor in the educational process and curriculum development. By analyzing data and making informed decisions I can better serve the needs of my students. Data-driven decision-making also decreases teacher bias, which truly shows the level of a student’s performance. This in turn will allow me to create more meaningful instruction geared towards the students’ academic success. All in all, data-driven assessment provides the means for teachers like myself, to evaluate their classroom practices and curriculum design to enhance student learning.

References

  • Ball, C. R., & Christ, T. J. (2012). Supporting valid decision making: Uses and misuses of assessment data within the context of RTI. Psychology In The Schools49(3), 231-244.
  • Eckelmann, S., Jorgensen, S. C., & Robison, K. (2016). From Data Beast to Beast of Burden: A               Case Study of Learning Outcomes in Faculty-Led Assessment as a Tool for Undergraduate History Curriculum Design. History Teacher, 49(4), 587-606.
  • Ediger, M. (2010). DATA DRIVEN DECISION MAKING IN THE SOCIAL STUDIES. Education131(2), 359-362.
  • Gullo, D. (2013). Improving Instructional Practices, Policies, and Student Outcomes for Early Childhood Language and Literacy Through Data-Driven Decision Making. Early Childhood Education Journal41(6), 413-421.
  • Holbrook, M. C., & Spungin, S. J. (2009). Supporting Students’ Literacy Through Data-Driven Decision-Making and Ongoing Assessment of Achievement. Journal Of Visual Impairment & Blindness103(3), 133-136.
  • Mandinach, E. B. (2012). A Perfect Time for Data Use: Using Data-Driven Decision Making to Inform Practice. Educational Psychologist47(2), 71-85.
  • O’Neill, A. (2015). The New Zealand experiment: assessment-driven curriculum – managing standards, competition and performance to strengthen governmentality. Journal Of Education Policy, 30(6), 831-854. doi:10.1080/02680939.2015.1033766
  • Patterson, G. (2014). Data-driven student achievement. Phi Delta Kappan, 95(7), 79.
  • Trudel, H. (2007). Making Data-Driven Decisions: Silent Reading. Reading Teacher61(4), 308-315.
  • Wolf, ,. P. (2007). A model for facilitating curriculum development in higher education: A faculty-driven, data-informed, and educational developer–supported approach. New Directions For Teaching & Learning, 2007(112), 15-20.

 



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