Monday, April 27, 2015

Post-secondary Attendance and Persistence

As seniors receive acceptance letters from colleges and universities, high schools across the state are also gaining valuable data, including: rates of students applying to college, and rates of students gaining acceptance and enrolling in two and four year postsecondary institutions.


Many Greater Boston DSAC partnering districts and schools are also using this time to analyze high school data, evaluating how well their programs are preparing students for entrance into postsecondary education.  In June 2014 MA DESE partnered with the Center for Education Policy Research at Harvard University to better understand academic preparation, postsecondary educational attainment, and college-going outcomes of students.  The Strategic Data Project's College Going Diagnostic of MA DESE shared the following key findings:


  • Low income students are less likely to persist in college than their non-low income peers.  Low income students are also more likely to enroll in two year colleges.
  • Less than half of ninth grade MA students completed high school within five years, entered college and persisted through their second year of college
  • More than 20% of high school graduates planning to attend college fail to enroll at any post-secondary institution.
  • Students attending four year colleges are more likely to persist to the second year that students attending two year institutions.
  • Students who enroll in college immediately following high school graduation are more likely to persist than those who delay enrollment.



Wednesday, April 22, 2015

What is a Student Learning Challenge?

A student learning challenge is a statement of student needs, identified through data analysis, that the group is committed to addressing instructionally.

When developing a student learning challenge, review the patterns and trends in student assessment data, identifying the areas of strength and need for groups of students.


Examples of student learning challenge statements:

  • As evidenced by the first grade early literacy assessments, students with disabilities are scoring 20% below their peers in phonological awareness.
  • The 8th grade state assessment and end of unit math assessments show that approximately 32% of ELLs are scoring proficient in Measurement & Data standards, or approximately 67% of ELLs are below proficient.
  • Graduation rates from 2011-2014 show that on average 20% fewer Hispanic/Latino students graduate from high school in four years, compared with their white peers.


Sources: REL & NE Islands and MA DESE Workshop on Practitioner Data Use for Special Educators

Tuesday, April 21, 2015

How are we looking at the same data differently?



Research by Nelson, Slavit, Deuel, Kennedy and Mason reviewed how teacher teams used student assessment data to improve classroom instruction.  Their findings highlight a conceptual difference in the way individual teachers use and interpret data, based upon the approach taken when looking at student work.


  • The Proving Stance: Who "got it" and who didn't?  Teachers taking a "proving" approach to analyzing assessments look more quantitatively at the data. They want to know percentages, facts and figures, and overall scores.  Their takeaways from the data are more general and often summative- did students master this concept or not?  The researchers suggest that often with this approach there is little influence on classroom instruction.  Rather, teachers identify class proficiency, then move on to the next target to be taught and assessed.
  • The Improving Stance: How are students thinking about the assessed concept?  Teachers taking an "improving" approach to analyzing assessments may look more qualitatively at the data.  Teachers identify students conceptions and misconceptions about a topic, and may use multiple forms of data to better understand students' thinking. Teachers have conversations about expectations for student learning and what that translates to in student work.

In observing teams of teachers analyzing student data, the researchers found that those taking an "improving stance" were more likely to have conversations about how the data would inform their practice.  These teams discussed instructional practices, expectations, and continued to ask questions of each other throughout the data cycle.



Source: Nelson, Tamara Holmlund, et al. "A Three-Dimensional Theoretical Framework for Understanding Teachers’ Use of Classroom-Based Data in Collaborative Inquiry Groups."

Friday, April 17, 2015

More Tips for Creating Data Displays

Clearly label the chart

  • Clearly title the data display with the data it represents (e.g. assessment, standard, skill, etc.)
  • Clearly label the axes and key
  • Provide the dates of assessment, content areas, grades tested, number of students
Make the chart easy to read
  • Make the chart SIMPLE!
  • Minimize distracting elements
  • Consider simple fonts and colors
  • Provide data point values where helpful
  • Consistent scales and formats when comparing data


Sources:
From the REL Northeast & Islands and MA DESE Practitioner Use of Special Education Data Workshop;
Boudett, City & Murnane (2013) Data Wise: A Step-by-Step Guide ot Using Assessment Data to Improve Teaching and Learning

Thursday, April 16, 2015

Data Use for Special Education Teachers, Adminstrators

This week I attended the Practitioner Data Use Workshop for Special Educators, presented by REL Northeast & Islands and MA DESE.  The workshop led educators through the cycle of inquiry, developing a focus question, analyzing data, developing actions from the data, and progress monitoring, as well as a root cause analysis for special education data.  As this was a workshop specifically for special education data, school-based teams explored data in the following areas:

Identification and Placement Decision Data:

  • Psycho-educational assessments
  • Initial screening assessments
  • Readiness and transition planning
Instructional Data:
  • Diagnostic-prescriptive teaching
  • Ongoing progress monitoring
  • Modifications, accommodations and differentiation
School and District Improvement Data:
  • Multi-tiered systems of support implementation data (e.g. Response to intervention, behavioral interventions and supports)
Several school teams shared a common struggle with collecting special education data- as most students remain in the general education classroom, and are primarily assessed and monitored by the classroom teacher, special education teachers often don't have access to formative and summative assessments.  We know that classroom teachers regularly assess students in a variety of ways, through exit tickets, observations, writing samples, quizzes and worksheets, but too frequently these assessments are graded and sent home before the special education teacher can view it.  Special educators must then rely on developing and administering their own assessments to build student portfolios and track student progress toward goals.

Thursday, April 9, 2015

Literacy and Math Coaching Courses to Support SEI Instruction

The Office of English Language Acquisition and Academic Achievement (OELAAA) is recommending the following courses to support literacy, math and ELL coaches.  The courses target how to support PK-12 teachers in sheltered English immersion strategies in literacy and math content.

http://www.matsol.org/coaching-courses

Literacy Coaching
Integrating SEI into Literacy Coaching

Course Description
Integrating SEI into Literacy Coaching explores the teaching-learning process for students, teachers, and instructional coaches from critical sociocultural perspectives. The course prepares literacy instructional coaches, including ELL coaches, to use principles of learning to support teachers in sheltering instruction for English Language Learners (ELLs) in Pre-K-12 classrooms. Coaches learn to recognize, use, and promote the type of sheltering of instruction that maximizes opportunities for engagement, differentiation, and achievement for culturally, linguistically, economically, and learning diverse students.

Mathematics Coaching
Supporting Teachers of English Language Learners in the Math Classroom

Announcing an opportunity to nominate a team of two to three Math Coaches from your district to participate in this Massachusetts Department of Elementary and Secondary Education pilot course. This coaching course, to eventually be rolled out statewide, is based on the Six Standards of Effective Pedagogy Coaching Framework, and is part of the state’s Extending the Learning phase of RETELL. Designed specifically for Mathematics coaches, it focuses on the roles of the coach in supporting, expanding, and sustaining effective mathematical practices in diverse Math classrooms with ELLs, and supporting long-term integration of sheltered mathematics into core instruction. 
This course, along with the abbreviated Short Bridge course jointly offer a customized route to SEI Endorsement for instructional coaches, and 3 Graduate Credits/Professional Development Points.

Thursday, April 2, 2015

Putting the "Team" in Your Data Team


When building data teams the focus is primary on the "what"- What data will we look at?  What day and time will we meet?  What findings will we share with staff?  But it's equally important to focus on the "how"- How will we go about doing this work together?  How will we build a collaborative team?

Harvard Graduate School of Education Senior Lecturer Katherine Boles, and Director of The Power of Teacher Learning, Vivian Troen have focused much of their writing and research on building teacher leadership teams.  They have found that while the roles exist for school-based teacher teams (whether grade level, content area, ILT, data, or other teams), the teaching profession has for so long been a culture of autonomy and collaboration is not intuitive.

A culture and structure need to be in place to support collegiality and collaboration.  In building school-based teams, Boles and Troen recommend specific training in working as a team, setting norms and agendas for teamwork, and setting goals for improvements in instruction to support student learning.  They also recommend establishing a climate that supports collegiality, and building personal accountability for student success.

Additional Resources:
7 Norms for Collaboration Toolkit
District Data Team Toolkit Module 1
8 Ways to Build Collaborative Teams, Harvard Business Review
Rating Your Teacher Team, Boles & Troen
The Power of Teacher Teams, Boles & Troen