Writing Your Own Data-Driven Instructional Story

By Cathy Buyrn, M.Ed.

February/March 2012


Everyone loves a good story.  Some of us can’t go to sleep at night because we’re glued to an exciting novel or an episode of our favorite television drama.  We get wrapped up in the characters and events, and in our own predictions about where the stories will go.  Some of us even share our theories and predictions with other faithful readers or viewers, and celebrate or commiserate depending on the outcomes.  Many of the most popular books and television shows are based on medical dramas or crime solving.  Whether the main character is Sherlock Holmes, Dr. Gregory House, or Horatio Caine from C.S.I., he follows the evidence no matter where it takes him.

Classroom teachers are the “first responders” in schools today, and they must take the same dogged approach to sniffing out the evidence when it comes to analyzing student data and using it to write their own instructional stories (Wright, 2010, p. 1).  The trick to sifting through student data is knowing when something is meaningful and when it is not.  Some student data can be misleading.  While individual student data are critical, teachers must be able to step back and consider broader data trends across small groups of students, classrooms, and entire grade levels.  Data in isolation may not point teachers in the right direction.  Teachers must balance data with context and be able to hone in on instructional priorities in order to efficiently and effectively design ongoing instruction (McLeod, 2005, p. 2).

A “teacher-friendly” process for using individual student data to drive instructional decision-making includes collection of baseline data, identification of the concern, development of a measurable objective, design and implementation of an intervention plan, and monitoring of individual student progress (Bright, November/December, 2007).  This same process can be applied to small groups of students, classes, and entire grade levels. 

Teachers must begin with a systematic search for data trends across groups of students.  Such a search for trends is at the core of a practice known as teacher research (Sitler, 2009).   According to Sitler “… teacher research is conducted at the classroom level with the goal of improving student learning by gaining insight into why learning does or does not occur” (p. 29).  Teachers who engage in this type of data-driven practice “… 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).

Focusing on essential skills and knowledge can help teachers prioritize their research and save an enormous amount of time and instructional energy (McLeod, 2005; Popham, 2003).  Once the baseline data for the essential skills and knowledge have been compiled, teachers should look for readily apparent trends. 

In the example below, the teacher has taken the Virginia sixth-grade Standard of Learning organizing topic of word analysis and vocabulary acquisition (Virginia Department of Education, 2004) and unpacked it by identifying strengths, questions, and needs.  The trend data indicated that at least 80% of the students were proficient with some skills, more than 20% of the students were in need of support with some skills, and for other skills no data were available to suggest whether they were either strengths or needs.  When the standard is unpacked and organized around student trend data, teachers can focus on priorities and connect strengths to needs in order to consider instructional implications.  This approach helps teachers make connections among skills within a standard to begin drafting a data-driven instructional story for their students.

o


Teams of educators can join forces to dialogue about their classroom-level research.  While it is important for individual teachers to wrestle with their own data and write their own stories, collaborative discussion can build capacity across grade levels, content specialty areas, schools, and even across divisions.  When teachers dive into their classroom-level data and partner with colleagues to write pedagogically sound instructional stories they will “… regain agency in their classrooms …” (Sitler, 2009, p. 29) and create “… meaningful instructional change” (McLeod, 2005, p. 5).

Additional Resources:

William & Mary T/TAC Library:

t

Test Better, Teach Better:  The Instructional Role of Assessment

Assessment expert W. James Popham explores the links between assessment and instruction and provides a jargon-free look at classroom- and large-scale test construction, interpretation, and application.  Local Call Number TT235

dd

Data Driven Differentiation in the Standards-Based Classroom

Chapter by chapter, this resource covers everything you need to know, including: using data to create positive classroom climate; using data to differentiate instruction; using data for pre-assessment, formative assessment, and final assessment; curriculum approaches for data-driven instruction; adjustable assignments for differentiated learning; and data-driven lesson planning for differentiated instruction.  Local Call Number TT227.1


WORDSALIVE VOCABULARY ACQUISITION MODEL

http://www.doe.virginia.gov/instruction/english/middle/wordsalive/index.shtml

This vocabulary acquisition model combines researched-based strategies into a student-friendly graphic organizer appropriate for vocabulary instruction in any content area for learners in grades 5-9.


A "Word" About Vocabulary
T/TAC Considerations Packet: 

This information packet provides strategies that upper-elementary and secondary school teachers may use to teach vocabulary in a meaningful way. The word-learning instructional strategies are grounded in research and are applicable to all content areas. The packet includes games and other activities that enable students to use newly acquired words as well as review vocabulary words in an engaging manner. 2001)
 
References  
Bright, M. (2007).  Teacher-friendly data collection. Link Lines Newsletter.  Retrieved from http://education.wm.edu/centers/ttac/resources/articles/assessment/teacherfriendly/index.php
 
McLeod, S. (2005, May). Data-driven teachers. Retrieved from www.hved.org/web-content/PDFS/Data%20Driven%20Teachers.pdf.

Popham, W. J. (2003). Test better, teach better:  The instructional role of assessment. Alexandria, VA: Association for Supervision and Curriculum Development.  

Sitler, H. (2009, Spring). Study up on how to teach in a data-driven classroom. Phi Kappa Phi Forum, 89(1), 28-29.  

Virginia Department of Education. (2004). English standards of learning:  Enhanced scope and sequence for grades 6-8. Retrieved from http://www.doe.virginia.gov/testing/sol/scope_sequence/english_scope_sequence/6-8/scopeseq_english6-8.pdf  

Wright, J. (2010). Helping teachers to structure their classroom (Tier 1) data collection. How RtI Works Series.  Retrieved from http://www.interventioncentral.org/response_to_intervention_structuring_teacher_data_collection