Data Analysis

In today’s current state of education, directives are implementing new expectations of learning and a commitment to closing the achievement gaps. The theory now is that schools should allow and provide for every student to achieve a high level of learning and achievement, in order to make students become independent learners. Accountability and assessment are at the heart of today’s educational reform efforts. As is indicated in numerous scholarly articles, strong accountability mandates, with heavy stakes attached, are sweeping the country at every level (Herman & Gribbons, 2001). As Ornstein & Hunkins (2013) state, learning is ongoing, never ceasing to enrich understanding. With this idea in mind, and the pressure put on schools to constantly improve, effective measures are needed to ensure schools are adhering to such standards. Recently, “high-stake” statewide tests have been at the forefront as an evaluation method of student performance. These tests, such as the NJASK, are aligned with state common core standards and measure the degree to which standards are being implemented at each level. However, as Bernhardt (2004), indicates Analyzing state assessment results is only the beginning of effective data-driven decision-making. The issue is that statewide assessments are one-dimensional; they only measure the scores of a group of students over a period of time. There are many factors that are not considered in these scores.  

            Bernhardt (2004), states NCLB (2001), has made the use of data to improve student achievement imperative. The use of data analysis provides an objective prospective as to what actually is happening to children in their school experience. It looks at the whole picture of a student. (Crew, 2013). I recently spoke with a special education monitor for the NJ Department of Education, who will remain anonymous. To summarize what she stated, the use of data analysis in schools is a multidimensional tool. It helps to enhance the curriculum, measure teacher effectiveness, and assess and improve overall school & district performance. The impression that was developed during this Q&A was that the use of data analysis in the school system is a never-ending cycle. To clarify this more, data analysis is a Multiple Measure System, meaning demographics, perceptions, student learning, and school processes are categories used to assess the productivity of the school. The question that needs to be answered is, how can we as educators serve our main cliental, the students? By analyzing a combination of areas such as ethnicity, socioeconomic status, dropout rate, observations, values/beliefs, standardized tests, teacher assessments, etc., data will then help indicate where the school needs to make adjustment to fit the needs of their population.

            Where the criticism or cons of data analysis lies is that educators have to know how to properly interpret the data. Often, teachers are analyzing and comprehending data. This means that schools need to put the resources in to educate their teachers on proper processes. Also, even though the teacher’s may understand what data is telling them, the process to reevaluate their lessons and change their lessons in a very short period of time is time consuming. Many teachers have expressed that it takes time to self-critique and make pedagogical adjustments (Ornstein & Hunkins, 2013). On the other end of the spectrum, districts need to be careful to not put all their eggs in one basket. In other words, administration needs to make it understood amongst their educators not to get over-eager about the use of data. Finally, when the data is looking at the teacher’s performance, it can lead to a sense of threat for that teacher. It can lead one to feel like they are under a microscope.

Although the ceiling seems high for the use of data analysis, it’s important to understand that; there is no substitute for good leadership in the schools. Data analysis is simply the face of what many consider a cultural change in education. It is a way to learn to improve from our mistakes and create the most positive learning environment. Data analysis is merely a set of tools for educators. By giving this data information to educators, and helping them to understand how to use it [the data] to approve student achievement, you will get good results (Klein, 2013).

References:

Bernhardt, V. (2004). Continuous Improvement: It takes more than test scores. ACSA Leadership , 16-19

Herman, J. & Gribbons, B. (2001). Lessons learned in using data to support school inquiry and continuous improvement: Final report to the Stuart Foundation. CSE Technical Reprt 535 .

Ornsteing, A.C., & Hunkins. (2013). Curriculum: Foundations, Principals, and Issues Sixth Edition. Pearson Education Inc.

New Jersey Department of Education. www.state.nj.us/education/data