Ellen Firth
Fundamentals of Curriculum Development
Dr. Dugan
April 20, 2010
Data Driven Decision Making
According to the RAND corporation, a nonprofit research organization, Data Driven Decision Making (DDDM) is defined as follows: "DDDM in education refers to teachers, principals, and administrators systematically collecting and analyzing various types of data, including input, process, outcome and satisfaction data, to guide a range of decisions to help improve the success of students and schools" (2006). Data Driven Decision is a common buzz phrase in schools across America. As states strive to meet proficiency standards, administrators work toward using data in newer and better ways in order to improve student and teacher performance.
Data can be used in countless ways in order to identify strengths and weaknesses and to move students toward proficiency. For instance, DDDM can identify demographic issues that may affect a student as well as strengths and/or weaknesses that are specific to certain demographic clusters. For example, it is true that across the board, that students that are economically disadvantaged often struggle more than students in a higher economic strata. Although this is not new information, DDDM allows educators to identify specific skills certain students lack or students who are in need of support and decide if these deficits are at least in part related to demographics or some other area.
As a resource for this paper, Akisha Jones, Data Specialist and Statistician for Woodbury Public Schools was interviewed. Akisha was asked to talk about the pros and cons of DDDM. According to Miss Jones, DDDM allows for supporting evidence to be used from data to make sound decisions about a wide range of things. For example, if a cluster of students in an English class struggle with verb tense, then a decision can be made to bring this to the teacher's attention and to ensure that the teacher aligns his or her lesson plans to accommodate this deficit. In addition, teachers can access this data to differentiate lessons that allow for better focused instruction as well as individual student focus and cluster focus on student groups based on need.
Besides student performance, DDDM can measure teacher performance by tracking student progress on certain skills. For example, a student may test in the fall and do poorly in a certain area. The teacher can use this data to focus instruction. If the student continues to do poorly, then the teacher may need to alter instruction. If the student improves, then it will be deduced that the teacher is succeeding in helping the student to make adequate progress and vice-versa. As Akisha notes, "Data doesn't lie." Her point is that facts are facts and if good data is gathered and interpreted, then sound decisions can be made.
However, there are drawbacks to DDDM. First of all, it is expensive to implement computerized testing, train teachers, hire a data expert, etc. Secondly, working with data requires an enormous amount of time that many educators simply do not have. Furthermore, there is a danger of an overreliance on data that would result in the whole child being left out of the picture. For example, if a student has a bad day or week, or his or her parents just divorced, or any number of scenarios, data does not show that critical piece of information. Also, "bad" data could end up in the mix. Since DDDM is relatively new in the broad spectrum of education, bad data could inadvertently end up getting published and used. Educators may lack the necessary training or enough training to clearly interpret data and therefore DDDM may be an ineffective tool. Furthermore, teacher buy-in may be slow as educators resist this daunting technology. With that said, whatever the benefits and drawbacks, it appears that DDDM is here to stay in our globalized and computerized environment.
Resources
Akisha Jones, Data Specialist, Woodbury Public Schools ---Interview
The Rand Corporation www.rand.org/pubs/occasional_papers/2006/RAND_OP170.pdf
U.S. Department of Education
Sunday, April 25, 2010
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