Data Analysis in Education

¶ … data collection and analysis legitimize the goals and strategies educators create for change and improvement? Given today's emphasis on standardized testing in the era of No Child Left Behind (NCLB), using data-driven analysis to legitimize various educational strategies is essential. "Daily life in districts and schools requires educators to effectively navigate a sea of data: diagnostic and norm-referenced standardized assessment data, reading assessment data, state and local assessment data, in combination with other data related to instructional programs and demographic, attendance, and dropout trends" (Ronka et al. 2008).

Ideally, educators can use data such as student assessments to tailor the learning experience in a more effective fashion and incorporate formative assessments within the classroom to ensure that lesson plans are responsive and flexible to student needs. On a macro level, districts can use data tracking to see what types of teaching methods are effective or ineffective. Although teachers are always getting feedback in terms of student reactions, often this can be tainted by inevitable personal impressions and biases.

Data, properly collected, allows the teacher, school, or even the state to determine that instinctive impressions about what works are actually yielding dividends. For example, to validate the usefulness of various pedagogical practices, a school district "acquired technology services that used a data-warehousing application to disaggregate vocabulary and reading comprehension results by students' current course sections and to provide information about vocabulary subskills, including basic vocabulary, synonyms, words with multiple meanings, and use of context clues" (Ronka et al. 2008).

The level of refinement of this data allowed teachers to more specifically zone in on what strategies were effective and which were not. "With the assistance of the data coach, school principals developed a dissemination plan that identified what data would be available and when, who would get the data, and how staff members might use it" (Ronka et al. 2008).

Teachers can be naturally resistant to being forced to change teaching strategies without evidence that the changes work and if they feel that the data used to support those changes is not representative or fair, they will be even more reluctant to do so. Data validation procedures must be explained and policies cannot simply be imposed wholesale. However, ideally teachers will eventually see that they can benefit from a more targeted way of focusing upon student deficits.

In one district which implemented data analysis of student performance and survey data, "teachers said that many students were unable to analyze what they read, did not like to write, responded to questions with incomplete answers, and had difficulty learning vocabulary" (Ronka et al. 2008). Based upon this input, learning specialists suggested "several instructional strategies that might address these issues, such as those relating to student choice, student inquiry, the use of technology and varied texts, and student discussion of text materials and what they have learned" (Ronka et al. 2008).

The data was useful to the teacher because it was collected with a purpose to specifically inform classroom strategies, not merely to highlight student deficits (which they teachers were already aware of). This method of data collection was also useful because it incorporated teacher as well as student information. Other effective methods include looking at data to see where gaps occur in terms of student skills rather than solely focusing on.