Data-driven instruction: Difference between revisions

Revision as of 18:10, 30 November 2016

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== Attributes ==

Data is information that is visible during instruction that could be used to inform teaching and learning. Types of data include quantitative and qualitative data, although quantitative data is most often used for data-driven instruction. Examples of quantitative data include test scores, results on a quiz, and levels of performance on a periodic assessment<ref (name=":0">{{Cite book|title=Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning|last=Boudett,|first=K. P.|last2=City|first2=E. & A.|last3=Murname,|first3=R. J.|publisher=Harvard Education Press|year=2013)|isbn=|___location=Cambridge, MA|pages=|quote=|via=}}</ref>. Examples of qualitative data include field notes, student work/artifacts, interviews, focus groups, digital pictures, video, reflective journals<ref>{{Cite (Danabook|title=The &Reflective Educator’s Guide to Classroom Research: Learning to Teach and Teaching to Learn Through Practitioner Inquiry|last=Dana|first=N. F.|last2=Yendol-Hoppey|first2=D.|publisher=Corwin|year=2014|isbn=|edition=3rd Ed.|___location=Thousand Oaks, 2014)CA|pages=|quote=|via=}}</ref>.

Quantitative and qualitative data is generally captured through two forms of assessments: formative and summative. Formative assessment is the information that is revealed and shared during instruction and is actionable by the teacher or student<ref (name=":1">{{Cite journal|last=Black|first=P|last2=Wiliam|first2=D.|year=1998|title=Inside &the Wiliam,Black 1998Box: Raising Standards Through Classroom Assessment|url=|journal=Phi Delta Kappan|volume=80(2)|pages=pp. 139–148|via=}}</ref>. Paul Black and Dylan Wiliam offer examples of classroom assessment that is formative in nature, including student observations and discussions, understand pupils’ needs and challenges, and looking at student work<ref (1998)name=":1" />. Conversely, summative assessments are designed to determine whether or not a student can transfer their learning to new contexts, as well as for accountability purposes<ref (1998)name=":1" />. Formative assessment is the use of information made evident during instruction in order to improve student progress and performance. Summative assessments occur after teaching and learning occurred.

== Examples ==

Understanding the differences between quantitative data vs. qualitative data, as well as formative assessment vs. summative assessment that tease out this data can be defined as assessment literacy<ref (Boudett,name=":0" City & Murname, 2013)/>. Building assessment literacy also includes knowing when to use which type of assessment and the resulting data to use to inform instruction. The purpose of data driven instruction is to use information to guide teaching and learning. Dylan Wiliam offers examples of data driven instruction using formative assessment (2011):

* Clarifying, sharing, and understanding learning intentions and criteria

* Eliciting evidence of learners’ achievement

* Providing feedback that moves learning forward

* Activating students as instructional resources for one another

* Activating students as owners of their own learning<ref>{{Cite book|title=Embedded Formative Assessment|last=Wiliam|first=Dylan|publisher=Solution Tree|year=2011|isbn=|___location=Bloomington, IN|pages=|quote=|via=}}</ref>

Because of the lack of timely feedback regarding the results plus the inability to personalize the approach, summative assessments are not readily used for data driven instruction in the classroom. Instead, a variety of information gleaned from different forms of assessments should be used to make decisions about student progress and performance within data-driven instruction. The use of multiple measures of different forms and at different times to make instructional decisions is referred to as triangulation<ref (Boudett,name=":0" City & Murname, 2013)/>.

== Implications ==

=== For school bistrictsdistricts ===

The primary implication for school districts is in ensuring high quality and relevant data is gathered and available. Beyond creating systems to gather and share the data, the school district must provide the expertise, in the form of data expert personnel and/or the access to professional development resources to ensure school building leaders are able to access and use the data<ref>{{Cite (journal|last=Swan|first=G.|last2=Mazur|first2=J.|year=2011|title=Examining anddata Mazurdriven decision making via formative assessment: A confluence of technology, 2011data interpretation heuristics and curricular policy|url=|journal=Gene|volume=1(1)|pages=1|via=}}</ref>.

Another critical component of the responsibility of the district is to provide the leadership and vision to promulgate the use of information about student performance to improve teaching practice. Zavadsky and Dolejs suggest two areas for school districts to consider:

“The first is data collection and analysis. Districts and schools must carefully consider what data they need to collect, develop instruments with which to collect the data, and make the data available as soon as possible. The second component is data use. Principals and district leaders must give teachers sufficient time and training to understand the data and learn how to respond to what the data reveal”<ref>{{Cite (journal|last=Zavadsky|first=H.|last2=Dolejs|first2=A.|year=2006|title=DATA: Not Just Another Four-Letter Word|url=|journal=Principal Leadership, pMiddle Level Ed.&nbsp;33|volume=7(2)|pages=32–36|via=}}</ref>.

While the literature shows the vital importance of the role of the district in setting the stage for data driven instruction, more of the work of connecting student performance to classroom practices happens at the school and classroom level.

=== For schools ===

Schools have a major role in establishing the conditions for data-driven instruction to flourish. Heppen, et al. indicate a need for a clear and consistent focus on using data and a data-rich environment to support teachers’ efforts to use data to drive instruction. When the leadership creates and maintains an environment which promotes collaboration and clearly communicates the urgency to improve student learning, teachers feel supported to engage in data use. The additional scaffold of modeling the use of data at the school level increases teachers’ expertise in the use of data<ref>{{Cite (2010)book|url=http://eric.ed.gov/?id=ED536737|title=Using Data to Improve Instruction in the Great City Schools: Key Dimensions of Practice. Urban Data Study|last=Heppen|first=Jessica|last2=Faria|first2=Ann-Marie|last3=Thomsen|first3=Kerri|last4=Sawyer|first4=Katherine|last5=Townsend|first5=Monika|last6=Kutner|first6=Melissa|last7=Stachel|first7=Suzanne|last8=Lewis|first8=Sharon|last9=Casserly|first9=Michael|publisher=Council of the Great City Schools|language=en}}</ref>.

=== For teachers ===

Data-driven instruction is created and implemented in the classroom. Teachers have the most direct link between student performance and classroom practices. Through the use of data, teachers can make decisions about what and how to teach including how to use time in class, interventions for students who are not meeting standards, customizing lessons based on real-time information, adapting teaching practice to align to student needs, and making changes to pace, scope and sequence (<ref>Hamilton, et al., - 2009 - Using student achievement data to support instruct.pdf. (n.d.). Retrieved from http://files.eric.ed.gov/fulltext/ED506645.pdf</ref>.

To be able to engage in data-driven instruction, teachers must first develop the knowledge, skills, and dispositions required. Working in a school culture and climate in which data-driven instruction is valued and supported, teachers have the ability to increase student achievement and potentially reduce the achievement gap. Additionally, teachers must have access to learning opportunities or professional development which helps them understand the pedagogical framework and technical skills required to obtain, analyze, and use information about students to make instructional decisions<ref>{{Cite (thesis|last=Furlong-Gordon,|first=Jean Marie|title=Driving classroom instruction with data: From the district to the teachers to the classroom|date=2009-01-01|degree=Ed.D.|publisher=Wilmington University (Delaware)|url=http://search.proquest.com/docview/250914319/|place=United States -- Delaware|language=English}}</ref>.

=== For students ===

A significant new growth in data-driven instruction is in having students shape their lessons using data about their own progress. Younger learners who are able to self-report regarding grades and other assessments can experience high levels of achievement and progress within instruction<ref>{{Cite (Hattie,book|title=Visible Learning for Teachers: Maximizing Impact on Learning|last=Hattie|first=J.|publisher=Routledge|year=2012)|isbn=|___location=New York|pages=|quote=|via=}}</ref>. The strategies that students use to evaluate their own learning vary in effectiveness. In a meta-analysis, Dunlosky, Rawson, Marsh, Nathan & Willingham ranked ten learning strategies based on the projected impact each would have on achievement:

Highly Effective Strategies: