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Data Driven Decision Making in Education

Data driven decision making in education refers to the procedures used by educators in examining assessment data with an aim of identifying student strengths and related deficiencies and thereafter apply the resultant findings to their actual practice. Teachers use information about their students when informing their instructional decision making processes as a procedure towards formalizing education within the United States. The process establishes a critical examination of curriculum and instructional practices with regards to the actual performance exhibited by students on standardized tests, as well as other assessments that yield important information for helping administrators settle for accurately informed instructional decisions. Local assessments, particularly the summative assessments (performance based assessments, classroom tests and quizzes, portfolios) as well as formative assessments (reflections on coursework, student responses, homework and teacher observations) are the most legitimate and viable sources of student data for facilitating such processes. This essay seeks to focus on the degree to which data-informed decision making impacts professional practice in education.

Adopting data-driven approach towards instructional decision making necessitates education professionals to consider all alternative instructional and assessment strategies in a systematically appropriate manner. In the event of teaching students regarding scientific methods in studies, they learn how to generate better ideas, design scientific research investigations, develop meaningful hypotheses, data collection methodologies, data analysis, drawing meaningful conclusions and providing an evidence based decision making (Mandinach & Jackson, 2012). Additionally, educational practitioners employ such scientific procedures in exploring and weighing better options with regards to student teaching and learning.

Employing the use of data in driving meaningful instructional decisions within learning institutions is essential. The principles allow teachers to successfully understand the procedural sue of disparate information for better understanding of the misunderstandings and misinterpretations exhibited by their students, as evidenced through experiential evidence and tests scores. Mandinach (2012) defines the capacity to use data in making informed learning or pedagogical decisions as “pedagogical data literacy” (p. 76). Greater emphasis has been put on the ideology that effective use of data necessitates teachers to look beyond using statistical properties and numbers in establishing a meaning and instead translating the data into better knowledge to inform instructions.

According to Swan (2009), the process encompasses multiple steps between different data forms and the ones generated by teachers in bringing classroom knowledge to the process. Teachers contain a wealth of knowledge of their students through tutor-made test projects, observation data, project outcomes as well as relevant learning products for better informing their practice. The teachers, therefore, evaluate their students’ ability by understanding how to inform practice and make the information more relevant to different disciplines (Mandinach & Jackson, 2012). Through collaborative team effort, teachers can possibly design lesson plans towards providing differential study instructions to their students, hence fully addressing their misunderstandings as identified through data analysis procedures.

By establishing the most appropriate technological infrastructure for storing and organizing information, for example, the dash broad, teachers are provided with the appropriate resources and support systems for achieving data driven discussions (Mandinach & Jackson, 2012). Teachers will be entitled to better chances of extracting meaningful information regarding the performance regimes of their students, and make note on how much students struggle with underlying scientific concepts and particular enquiry skills (Mandinach & Jackson, 2012). Such collaborative networks result into generation of a knowledge base of lesson plans and important steps for addressing diverse changes in instructions basing on student learning needs. They can also make instructional decisions for implementing such changes even at classroom levels. Using such a project components, teachers can possibly learn how to employ their students’ tacit knowledge in conjunction with performance data generated from the dashboard in making differentiated instructional decisions. Such decisions are important in supporting student learning on scientific enquiries and content (Mandinach & Jackson, 2012).

 Best data decisions require policies, effective infrastructure and appropriate practices for supporting them. Data infrastructure refers to the creation and improvement of information systems for facilitating effective collection, transfer and manipulation of information (Mandinach & Jackson, 2012). Creation of links between distinct databases enhances different analysis that need connections of varying nature, data quality which can as well be supported by generating low-burden data collection procedures, procedural certification and monitoring or data collectors. Adjusting to effective practices for data access and management ensures timely delivery and the probability that data will be utilized and evenly verified towards enhancing data integrity. Under such data-rich environments, the decision makers in the field of education have direct access to a wealthy source of information about the operational activities, students, staff as well as the larger communities they serve (Mandinach & Jackson, 2012). Decision makers need to fully understand the diverse advantages and limitations of the data, outline the particular ones that are relevant to making decisions, and how the information can be better incorporated towards boosting academic performance regimes of a given institution.