Tuesday, March 31, 2015

A Closer Look at Your School Data Team Data

With a wealth of data now available to districts, schools, and classrooms, a common question we hear is- What data should our school's data team look at?  If we consider the purpose of a data team as informing classroom instruction and improving student performance, then the information data teams review should fit within this context.

  • Primarily, data teams should focus on formative assessments which provide real-time, mid-year or mid-unit input on student learning.  These may be in the form of exit tickets, quizzes or interim assessments, or performance and writing tasks with a rubric.  This type of data is most valuable to data teams as it provides a midway check-in on student progress with time to modify instruction to support student mastery.

  • After formative assessment, data teams should review summative assessment.  State tests, end of unit assessments, and other standardized summative assessments provide a fairly reliable measure of student mastery of content standards.  Summative assessment provides a view of how well students learned content within a given unit or academic course.  Summative assessments also provide comparative data, as they can be used to compare the performance of cohorts of students year-to-year, and in some cases, measure student growth.  Summative assessments are also helpful in identifying bigger picture trends in student learning, such as proficiency gaps between subgroups, or gaps in student learning over time.

  • However, as data teams dive into formative and summative assessments, additional questions may arise that can not be answered from student assessment data or student work.  Here, data teams should look to the additional data available to them, particularly when conducting a deeper analysis of longitudinal trends.  This data may include demographic and enrollment information, school climate or student surveys, or teacher preparation or evaluation data.

Steve Ventura from Advanced Collaborative Solutions reminds us that, "data teams are not about student test scores.  Data teams are about cause and effect."  In this vein the work of data teams is not a focus on analyzing data, but about using the data to impact a positive change in student learning and success through informed classroom instruction.

Sunday, March 22, 2015

Coding for Kids

Had you asked me when I began my career in education 12 years ago, if coding would be the new learning trend- I'm sure I would have laughed.  Coding the new cool?  But with learning moving to an online forum, and gaming a popular pastime, it makes sense that kids want to know how it all works.  And coding not only teaches students computer programming, a currently on-demand job, but also builds skills in logic, problem solving, persistence, and communication.

Scratch is not a new resource for supporting children in coding (launched in 2007 by MIT), but it is quickly gaining popularity with millions of users in 150 countries.  And now there's a new coding community for Scratch users, ScratchEd, and monthly free Scratch meet-ups in Cambridge, to share resources and "talk code."

Additional coding resources:
7 Apps for Teaching Students Coding Skills
Hour of Code
Coding for Kids Revisited
Cool Tools to Help Kids Learn to Code

Tuesday, March 17, 2015

Creating Your Classroom Data Wall

Why Create a Data Wall
That phrase, "making student learning visible," is quickly becoming a cliche as it is so often used, and overused.  But this phrase certainly applies to data walls.  Data walls can make growth and learning visible to students, and particularly our underperforming students, and support students in setting goals and monitoring their own individual progress.

Selecting your Data Wall Subject
If you are new to data walls, take the advice of Scholastic writer Rhonda Stewart and "keep it simple."  Pick an area that you want your students to grow in and see their growth.   Reading levels, homework completion, and math fact memorization are areas that many teachers begin with.  But make sure that you select a topic that is relevant to your students, relevant to what they are currently learning, and in which you can track growth.

The Do's and Don't's of Data Walls

Be able to show student growth over time. In the picture below, the teacher has created a very visible student data wall using state assessment data in math and ELA.  While this summative assessment data is readily available, it isn't meaningful for students, nor does it provide opportunities for reflecting student growth.  Student take the assessment at the end of the year, and do not have an opportunity to see their results until months later.

Student learning levels should be identifiable to students. In the data wall pictured below, the teacher plotted student reading levels, with a clearly identified class goal.  However, student initials, numbers or other identifiers are not shown.  A data wall should have a means for students to identify and track their own individual progress.  To ensure some anonymity and protect underperforming students from feel uncomfortable with their learning level, many teachers randomly assign students numbers (and communicate those numbers to students).  Others have students decorate a symbol to use for themselves, such as a rocket ship or animal, that they can then move up the chart as their learning progresses.

Make the learning goal/objective visible.  Clearly label the data wall with the data it represents.  What is the learning goal or objective?  What are the expectations for student learning?  In the two pictures below, "how was it" does not communicate student learning to anyone coming into the classroom.  However, "I can make a letter to go with a sound" shows a clearly defined learning goal for students, and in student-friendly terms that make the objective accessible to both students and classroom visitors.

Keep it positive.  Don't focus on how far away your class is from reaching a standard- focus on the positive.  How much growth have students made?  How many students have met or are approaching the goal?

Additional Resources on Data Walls


Thursday, March 12, 2015

Student Data Principles: Guiding Student Data Quality and Data Privacy

On Tueday, Student Data Principles were released to guide schools and districts in how to effectively use student data and protect student privacy.  A collaboration of more than 30 groups from across the country through the Data Quality Campaign and Consortium for School Networking, the Principles address the growing concerns from parents, teachers, school administrators and state education chiefs regarding the need for both data quality and data privacy.  Their 10 Principles are intended to be used as a guide for anyone, from researcher to educator, who uses student data.  The Principles include:

1. Student data should be used to further and support student learning and success.

2. Student data are most powerful when used for continuous improvement and personalizing student learning.

3. Student data should be used as a tool for informing, engaging,  and empowering students, families, teachers and school systems.

To see all 10 Principles, visit their website: http://studentdataprinciples.org/the-principles/

Upcoming free EdWeek Webinars:
Evolving Role of the School Leader, March 19, 2-3pm ET
Google Aps for Education, March 16, 2-3pm ET
Closing the Achievement Gap for ELLs through Technology and Proven Language Pedagogy, March 17, 2-3pm ET

Tuesday, March 3, 2015

Teaching Data Analysis to Support College and Career Readiness

Last week I attended the High School Math Network meeting at Malden High School.  Panelists from Bunker Hill Community College, UMass Boston and Freedom House discussed with district leaders and high school math educators from the Greater Boston area the skill gaps they see in students as they transition from high school to college level math, and how high schools can better support students in preparing for college and careers in mathematics.

Representatives from both Bunker Hill and UMass Boston highlighted the need to support students in data analysis and statistics, emphasizing this area of advanced math over Calculus in preparing students for math careers.  Panelists shared that students need increased access to statistics, to analyzing large sets of data, and to understand how to present data to an audience in a coherent way.

Additional Resources:
Quantway and Statway programs from the Carnegie Foundation