Respuesta de Conductancia de la Piel (SCR)

Few would deny that information is power, but meaningful information could only have been gained from a proper analysis of data (Streifer, 1999). Data became information when people placed it in context through interpretation to determine patterns, causes, or relationships (Petrides & Nodine, 2003). To be most useful, data had to be transformed into information by means of disaggregated analysis and interpretation, multi-year trends, multiple sources, root cause analysis, and informed discourse among stakeholders (Performance Fact Inc, 2004).

The Center for Collaborative Education (CCE) studied six schools engaged in data-based inquiry and decision making to examine the questions affecting student achievement (Feldman & Tung, 2001a). Data-based inquiry and decision making were processes in which school personnel engaged in ongoing analysis of data from multiple sources to develop a comprehensive picture of the school’s strengths and weaknesses, and developed a plan to prioritize and address the challenges (Feldman & Tung, 2001a). CCE found that the most successful schools had buy- in of a majority of the school staff, had internal capacity in looking at data, and had made a commitment to the process by providing structured time for teachers to engage in the process (Feldman & Tung, 2001a).

In April 2000, Education Commission of the States (ECS) received a grant from the Edna McConnell Clark Foundation to identify the factors, conditions, and policies that support the use of data for decision-making and school improvement. They found that successful districts had a long-term plan for technology hardware and software implementation. Schools had a system to analyze and disaggregate data quickly (Armstrong & Anthes, 2001).

Reporting and retrieval technologies guided the organization and summarization of data into information (Light et al., 2004). Data analysis required a database or data warehouse

(Bernhardt, 2003). Schools had lagged behind industry in adopting technology-based systems. A major obstacle to schools or districts taking advantage of this technology was the cost and complexity of real-time knowledge management systems (Thorn, 2001). Nevertheless, data warehouse systems were an excellent tool for making complex selections of data from multiple sources (Thorn, 2002). New data warehousing systems were emerging that simplified moving student-specific data to teachers (Holcomb, 2004).

Data warehouses supported two categories of data – cross sectional and longitudinal (Rudner & Boston, 2003). Cross-sectional data provided information for different groups of students within one period. Longitudinal data provided information on groups of students as they progressed through the grades. The National Center for Educational Accountability recommended the following steps to generate longitudinal student data systems:

ƒ Create a consistent statewide student identifier to connect records of the same students across multiple databases and years.

ƒ Collect the following information at the individual student level: • Fall enrollment, demographic, and program participation data • Spring test score data

• Information on each student who was absent or exempted from testing • High school course completion data

• High school SAT, ACT and Advanced Placement participation and scores • Graduation and dropout data

ƒ Develop an audit system to monitor the accuracy of information provided by the school district (Dougherty, 2002).

A well-organized easily accessible data warehouse allowed a wide range of analysis using both cross-sectional and longitudinal data (Rudner & Boston, 2003). For improving education, data had to be of high quality, accessible, and in a format that was easy to use. A data warehouse, in addition to simplifying the effort needed to generate required reports, was most beneficial when

educators used the data to make decisions to improve instruction and learning (Rudner & Boston, 2003).

Unfortunately, most educational systems had very little technical capacity (Thorn, 2001). The tools and data necessary for within-classroom analysis were different from those needed for a within-school comparison (Thorn, 2001). Individual teachers needed sufficient computing resources in the classroom with links to district-level data systems to make meaningful queries in real time (Thorn, 2001). Most data available within a district information system was inadequate for making mid-course instructional decisions (Thorn, 2002).

Likewise, there was an increasing need for local analytical capacity (Thorn, 2001). The most important element of an successful data-driven program was not the data, the analytic tools, or even the curriculum framework upon which data analysis was based; rather, it was the school culture in which the data inquiry took place (Salpeter, 2004). The American Association of School Administrators, The National School Boards Association, and the National Center for Research on Evaluation, Standards, and Student Testing formed a research partnership. Based on their research, they highly recommended that districts create a data-informed culture, build school district capacity to use data-informed processes, provide staff development, and implement communication opportunities (Conrad & Eller, 2003).

Professional development for teachers was an essential element of an effective data- driven program (Salpeter, 2004). Most educators did not have the backgrounds that prepared them to engage in systematic inquiry (Herman & Gribbons, 2001). The district needed to provide knowledgeable people to perform the major analyses for the district and each of its schools. Teachers should not have been spending their time performing data analysis but rather studying results (Bernhardt, 2003). Deriving professional development from an analysis of what

was needed to improve student learning assumed that the system had the capacity to acquire useful, accurate information about student learning and that the people in the system had the capacity to apply that information to decisions about instructional content and professional development (Elmore, 2004).

Information gleaned from case studies of nine Michigan public schools conducted by the North Central Regional Educational Laboratory found that schools committed to using data to guide their work allocated time for teachers to meet, discuss, and make instructional decisions (Cromey, 2000). For a data driven decision making to be effective, it was crucial for administrators to make time and resources available to teachers (Salpeter, 2004). Teachers needed time to collaborate in developing curricula and revise instructional strategies by learning from one another (Herman & Gribbons, 2001). Schools attempted to promote a common vision for student achievement and provided teachers and administrators with time and training necessary to properly use data on student progress (Cromey, 2000).