Additional Data-based Resources
From “Using High-Stakes Assessments for Grade Retention and Graduation Decisions”
To address concerns about the use of high-stakes assessments for making grade retention and
graduation decisions, IRA recommends the following actions:
1. Grade retention and graduation decisions should be based on multiple assessments, including
teacher professional judgment, results of formative assessments, and student and family input, as
well as results from standardized literacy assessments.
2. Schools, school districts, and policymakers should be guided by the expertise of professional
associations and literacy professionals when making decisions about how to best utilize results
obtained from high-stakes literacy assessments.
3. Professional development should be available for teachers on assessment strategies for
obtaining a complete picture of a student’s literacy performance.
Source: http://www.reading.org/Libraries/position-statements-andresolutions/ps1081_high_stakes.pdf
From: Navigating a data-driven education
Most states also gather student data in what are known as state longitudinal data systems, with
the underlying theory that "better decisions require better information.”
There are no laws requiring that states publish a list of the types of data they collect on students,
and it can be hard information to track down. Marketplace has put together a list of data nearly
every state collects.
One of the main reasons for this student data grab is the belief that the more educators know
about how a child learns, the better learner he can be. The more you know who is struggling
with which part of the lesson, the better you can tailor an education to that child.
If you've been around a school in the last few years, it's likely you've heard the buzzwords
associated with the data-driven classroom:individualized learning, personalized learning,
differentiated learning.
They’re all based on data. And in some schools, millions of pieces of data are being collected on
individual children every week.
Source:
http://www.marketplace.org/topics/education/learningcurve/navigating-data-driven-education
From: Gamed by the System: Adequate Yearly Progress as an Indicator of Persistently LowAchieving School Performance
The recent policy focus on the turnaround of persistently low-achieving schools has generated
considerable debate about the reforms needed to dramatically and quickly increase school
performance. The purpose of this article is not to focus on specific turnaround interventions, but
rather on the identification of schools slated to receive these interventions. Results from the
study of 1,059 public schools suggested that operational definitions that relied on No Child Left
Behind Act of 2001 metrics overidentified high schools educating diverse populations.
Source: http://bul.sagepub.com/content/97/3/270.abstract
Connection Between Instruction and Data
AdvancEd (2013) explains that, “the institution has resources and provides services that support
its purpose and direction to ensure success for all students” (p.5) under its resource and support
systems. This step provides for instructional materials and technologies, the health of students,
and fiscal support to meet the changing initial needs.
By addressing how teaching and learning will be measured, I have considered how, “curriculum,
instructional design and assessment practices guide [that will] ensure teacher effectiveness and
student learning” (AdvancEd, 2013, p.4). The curriculum factors in, “equitable and challenging
academic content and authentic learning experiences that ensure all students have sufficient
opportunities to develop learning, thinking and life skills” (AdvancEd, 2013, p.4) into its design,
but also consider that curriculum must be refined and adapted to the needs of the student
population.
For achieving each desired result of increased data used to differentiate instruction, multiple
specific and quantifiable “measureable indicators for the outcome goal.” (United Way, n.d., p. 5).
These data sets will include both qualitative and quantitative assessments such as OCPAdesigned benchmarks, state standardized assessments, educator observations, satisfaction
surveys, portfolio assessments, teacher evaluations, and census demographic data. This targeted
data collection documents methods for each outcome.
References
AdvancEd. (2013). Standards for quality: Special purpose institutions [PDF]. Retrieved from
http://www.advanc-ed.org/webfm_send/487
United Way. (n.d.). A Guide to Developing an Outcome Logic Model and Measurement
Plan.Retrieved from https://cupo.blackboard.com/bbcswebdav/pid-28451-dt-content-rid
429529_1/courses/20152021570/resources/week7/w7%20Guide_for_Logic_Models_and_
Measurements-United_Way.pdf