Teacher-Friendly Data Collection

By Melinda Bright, M.Ed., VDOE T/TAC at James Madison University

from T/TAC Link Lines
November/December 2007

Why did you become a teacher? Whatever the reason was, the undeniable reward that keeps many of us motivated once we enter the profession is the heartfelt fulfillment we experience when a child’s face displays understanding. But, what about the struggling student whose face displays frustration? Once upon a time, well-meaning teachers immediately referred struggling students for an evaluation to determine eligibility for special education services. Feelings of inadequacy to meet each and every student’s needs in the general education classroom were abated by choosing this course of action. If a student was indeed found eligible for services, the general educator was often not involved to a great degree in providing for individual needs in the general education classroom. Self-contained classrooms and resource rooms were the places to go for adaptations to the curriculum.

Reform initiatives have demanded a paradigm shift in how schools operate, with emphasis on the achievement of results rather than process and delivery in the classroom. While accountability has always been around, educators are truly being held accountable for student progress at individual and aggregate levels. Many schools across the country are realizing improvement in student learning by using data-driven practices. “Teachers in these schools are finding that intelligent and pervasive uses of data can improve their instructional interventions for students, re-energize their enthusiasm for teaching, and increase their feelings of professional fulfillment and job satisfaction” (McLeod, 2005, p. 1).

Now when a student struggles, teachers are expected to: 1) gather data about skills and levels of performance, 2) study the data to identify the concern, 3) develop a measurable objective, 4) develop and implement an intervention plan, and 5) monitor progress. One of the resulting positive implications of this shift is the necessity to collaborate with others on how to best meet individual student needs. “No educator, no matter how experienced or skilled, is able to meet all the unique instructional needs of every child without the assistance of colleagues” (Ralabate, 2003, p. 14). To enrich collaborative efforts, many schools have teams in place to assist the teacher with this process. These teams are known by different names (e.g., teacher assistance teams, instructional consultation teams, instructional support teams, early intervention teams, etc.), but the purpose is the same – to suggest alternative general education strategies and to help analyze and record observations and assessment data. Realistically this data will be useful should a referral for special education services be deemed necessary. But, more importantly, this process will often afford the struggling student optimal learning conditions with understanding more likely to occur.

Collect Baseline Data
Excellent teachers find it very motivating to have enough information to make the best decisions for the students in their charge. Data from summative yearly assessments inform teachers about areas of need for improved instructional practice, but teachers must also consider formative assessments and their value in driving instruction. Collection of data should begin with baseline to indicate what the student is able to do without the intervention. Baseline data might be collected through classroom-based assessments (e.g., quizzes, tests, rubrics, checklists, portfolios) and observations regarding student learning (Ralabate, 2003). Collecting and analyzing data, seeking input and assistance from other professionals, and collaboratively making decisions about instruction are evidence of a professional learning community.

Identify the Concern
Baseline data will either confirm or deny initial concerns about the struggling student and direct goal-setting. Guidelines for analyzing data to create a specific picture of the concern and state goals in observable terms include:
• visually represent your data, • look for gains, and
• look for obvious gaps, • recognize competence.
• look for patterns,

Develop a Measurable Objective
Once the baseline data has been used to more specifically identify the concern, goals can be established for the student, which are relevant to the concern, baseline data, and academic achievement. The acronym SMART reminds us of the essential components of well-defined goals.
S = specific
M = measurable
A = attainable
R = results-oriented
T = time-bound

Develop & Implement an Intervention Plan
This information is used to determine an intervention that addresses what appears to be the main concern. (See the Intervention Strategies Menu insert.) If you are working with a team, decide who will be responsible for implementation of the intervention and consistently document your student’s performance. Be patient. It normally takes at least two weeks to see if an intervention is effective.

Monitor Progress
The data will inform you as to whether or not the objective has been achieved. If so, decide whether to maintain the intervention, stop it, or move to another concern. If the objective is not met, at least you know more about the needs of your student and can choose a different intervention or refer the student for an evaluation for special education services.

McLeod’s diagram below (2005) delineates these essential elements of effective data-driven education.

The current demands on educators are unavoidable, but the attitude with which they are met is a choice. We can complain about legislation that constitutes accountability, the students we work with, the parents and home environment, or we can professionally collaborate to make informed decisions about instruction to positively impact student learning. Educators who choose the latter are experiencing success in closing achievement gaps.

References
Gravois, T., Rosenfield, S., & Gickling, E. (2003). Instructional consultation teams: Training manual. Unpublished manual, University of Maryland
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McLeod, S. (2005). Data-driven Teachers. Retrieved January 4, 2007, from Microsoft™ Innovative Teachers Thought Leaders Web site: www.microsoft.com/Educators/ThoughtLeaders.aspx.

Ralabate, P. (2003). Meeting the challenge: Special education tools that work for all kids. National Education Association.

This article is reprinted with permission.