Data-driven decision making: position and implications

¶ … driven Decision Making

Proponents of data-driven decision making for school improvement emphasize the fact that information and knowledge lie at the root of effective action. Without such information, it is impossible to make targeted decisions that will lead to standardization and improvement measurements. Others, on the other hand, believe that the uniqueness of the class situation and indeed of each student make accurate, precise data invalid for effective decision making. Furthermore, such state-mandated data also entail state-mandated aims and goals for school improvement. This however means that there is a state-wide mandate for such improvements, meaning state-wide standardization. Schools therefore have no control over how specific, in-class action research is used to set goals for the improvement and goal-reaching of individual students. Opponents of the data-driven decision making process therefore believe that there is not sufficient integration of school data with state-mandated goals to make decision-making truly effective. Below is a consideration of both sides of the issue by means of three articles by experts in the field.

Pamela Wheaton Shorr (2003), for example, provides an article containing ten characteristics of data-driven decision making. As mentioned above, she emphasizes the need for sufficient and accurate data for effective decision making in schools. However, Shorr warns that data technology should fulfill a role as the means to an end, i.e. student improvement, rather than be the end in itself. A further advantage of data-driven decision making, voiced both by Shorr and other critics is the fact that it entails more accountability.

The only possible drawbacks that Shorr mentions is that this system, as a result of accountability, could place emphasis on punishment for handling the decision-making process "incorrectly." This might stimulate negative feelings regarding data-driven decision making. Another problematic issue the author raises is cost. The implementation of a data-driven decision-making system is an expensive and time-consuming undertaking. According to the author, it takes two to three years for such a system to begin displaying results. Such a time investment can be expensive.

According to the author, data-driven decision-making system is an important tool to help students improve their school performance, because it helps teachers adjust their methods to accomplish goals. This is also the premise of James H. Johnson's (2000) article. According to the author, timely and accurate data on student learning are essential in terms of determining the effectiveness of current teaching methods. He also mentions that the focus of data-driven systems for schools are changing from their traditional use in assessing existing student improvement towards an improvement approach focusing on better serving students and the community. In other words, the focus is on teachers and curriculum improvement rather than the effect of the existing system without considering change.

Like Shorr, Johnson places emphasis upon accuracy and consistency in data. Without effective communication between teachers and state administrators, data becomes fragmented. This results in a variety of different datasets, which makes it impossible to effectively implement improvement techniques in the curriculum. With accurate data, students in need of particular help can be identified much earlier and helped more effectively.

To do this, educators themselves need to assume the role of researcher. Because students and classrooms have unique characteristics, teachers working with them can best assess their needs at any given time. The root of making this approach a success, however, is effective communication not only between teachers, but also with state administrators.

Marlow Ediger (2003) uses this element to point out the reasons for the relative ineffectiveness of data-driven decision making. The difference between this author's and the others' opinion lies in the definition and implementation of data-driven systems. According to Ediger, data is in itself a very specific science. The application of this approach to the teaching profession is to assume that teaching is itself an accurate science. There can be little dispute that it is not. Each teacher, being human, is unique in his or her approach to students. Each student is furthermore unique. According to Ediger, this is precisely why data-driven decision making cannot apply to the teaching profession in terms of assessing student needs and/or improvement. Instead, Ediger suggests a less rigid approach to assessment, such as the portfolio philosophy. By using portfolios rather than rigid data, teachers can recognize the flexible nature of teaching and learning, and make improvements accordingly.

I believe that each author mentioned above makes valid points both for and against the data-driven approach. While I tend to agree with the fact that information provides the teacher with the power to make effective teaching decisions, it is also important to consider points such as those made be Ediger. However, Ediger's view appears to suggest that the gathered data are to be taken at face value without acknowledging each student's unique learning ability and style.