Edison Computech 7-8

  1. PLC Story
  2. PLC Practices
  3. Achievement Data
  4. Awards
  5. Resources

Computech was founded in 1983 with exactly the components that would become the foundations of a professional learning community: a collaborative focus on what students should learn, innovation on how to teach those skills, and systematic responses for managing those who have not yet learned and those who need to be challenged even more.  Those founding staff members—some who are still teaching on campus—led the charge in the face of political adversity surrounding the reasons for opening a magnet school in the early 80’s, wondering both if Computech’s doors would remain open as a STEM magnet school as well as what improvements could continuously be made towards enhancing the Computech experience for all students.  More than thirty years later, there is little speculation of if the Computech mission will continue, but a continued need to stay “magnetized” as the fourth largest district in California continues to better itself across its 74,000 student system.  The “perfect storm” of adopting the Common Core State Standards mandated that Computech’s campus make strategic decisions—like adopting a solid PLC state—in order to maintain rigorous standards and exemplary results on the new yearly benchmarks, as well. 

Since inception, Computech has continued to utilize consensus-based management with equitable voice for all staff, and a focus on students and student achievement vigorously.  This form of management has brought Computech (5) National Blue Ribbon Awards and countless technology and innovation awards from Microsoft to Intel.  When the more systematized “PLC movement” began to enter schools and districts years ago, Fresno Unified took an approach to solidifying collaborative work across the large span of schools.  Differently than the rest of the district, Computech’s adoption has been less about a mindset shift toward working in collaborative teams, since it was already a collaborative and consensus-building environment, and more about a shift toward using the same collaborative process as a campus on individual instruction as well as in teams connected by subject and grade level. This meant that professional learning and development was geared towards the macro level of benefitting all students, but carried out from team to team, and led by Lead Teachers with visions that responded to both Computech and individual student needs.  Coupled with increased pressure from families that were—second generation Computech students of first generation Computech alumni—to have classrooms and teachers that were not only equitable in experience, but had assurances of success at high levels, Computech’s last five years have been a journey to successful PLC implementation, which began with a district led commitment to time in collaborative teams and finds itself at present with common prep periods, sharing in the halls at breaks and passing periods, and analyzing student data for information and next steps.  

More specifically, the past three years have been focused on specific steps toward the most impact on student achievement:

-       Three years ago, the movement of school wide expectations in writing and specific study skills became the focus of each collaborative team.  The primary step was to ensure these expectations were agreed upon, taught, and assessed by all collaborative teams.

-       Two years ago, the school wide expectations were more focused into components of the Common Core State Standards (more newly adopted), and more specifically into literacy.  Therefore, collaborative teams were to hone in on argumentative writing and work together towards the creation of a common writing assignment (CWA) that mimicked the power and pointedness of a CFA, but could be both attempted and monitored while learning from one another.  This was another move toward harnessing what the staff founded the school upon. 

-       Last year, the movement took one more, pivotal step toward a focus on common formative assessments that were specific to the high leverage areas in a given subject and grade level.  With two years of practice working toward foci pertaining to all students, this shift was meant to keep those best practices going forward while getting content specific.  This introduced other practices that have led to even more data-rich collaboration.  As a Google Apps for Education school and several Google trained educators, our use of Google Forms and Classroom for assessment tripled, our purchase of a Nearpod account effectively changed the face of “real-time” formative assessment, and even professional learning on using the web-based Kahoot! allowed for formative assessment to be timely, data-rich, meaningful, and even fun.  These choices from the campus PLC at large allowed for discussions to branch off into best practices around grading that are in motion today. 

-       This year, Computech’s focus is on the common formative assessment process in order to make it stronger, tighter, and even more outcome based than it was last year prior. 

 
The common formative assessment initial creation and use was modeled after Ainsworth’s (p. 66, 2006) position to allow staff to experiment, "with the design and administration of thier own common formative assessments" before providing professional development, which allowed for mistakes to be made and garner eagerness to make them better.  Although now we have established essential standards and learning targets, we were not always so attuned.  In fact, when we began this journey a few years ago the goal was just to practice with common formative assessments.  Using our grassroots, Learning By Doing based rubric we can now point to the best practices around CFAs, but it started with getting everyone in the collaborative team to agree upon some common assessment pieces.  Then, we worked together toward making those formative instead of summative.  Then, we worked on making those common, formative, and regarding essential standards and skills.  We continue to hone, focus, and improve in a cycle that is fed into by side-by-side coaching.  Currently, collaborative teams use a plethora of materials to create these CFAs.  Our math department, for example, has grown to use Nearpod as a tool for CFAs.  Nearpod is a web-based product that allows teachers to “push” slides, presentations, videos, and assessment questions onto a students device for interaction.  Our teachers on the math team (both 7th and 8th) define their essential standard(s), and then during the teaching of the concept they have an embedded CFA in their Nearpod lesson.  The teacher can see how students respond and have agreed-upon next steps for any scenario (all students “get” it, some understand, lots of confusion, etc).  This real-time response is determined as a collaborative team regarding the understanding of the concepts they pre-determined, and to what criteria for success.  There are many ways a CFA might come about, but they are all based in the essential standards identified by the team.  For example, if a district benchmark is given, teachers use it as a diagnostic tool about gaps in essential standards that might exist.  Identifying an essential standard or skill, the team uses all available resources (including and starting with one another) to construct the reteaching, and then design an agreed upon, short CFA to show learning growth.  Even without the district benchmark, all subject areas use formative and summative means to diagnose misunderstandings and gaps, bring those to the team meetings and agree upon ways in which they—as a team—can commit to proving whether students have mastered the concept(s), and then what they are going to do about it in class, and—potentially—with the schools support.  
 
In order to both facilitate this planning and provide our students meaningful interaction, we provide quarterly planning days for our teams while our academic counseling team or library team provide instruction on anything from research to career and A-G counseling.  This allows our teachers to plan for an entire day without substitute teachers, and our students to get important information that might otherwise disrupt the learning environment or instructional time.  
 
Our district has purchased curriculum for all ELA and math classrooms, but the process of a guaranteed and viable curriculum really originates in the Mission and Vision of the school towards our STEM magnet orientation, and achievement-based population.  As a professional learning community, we consistently and cyclically revisit our curriculum across the subject areas to ensure that there is continuity with our high school partner, and clarity from class to class regarding what we expect students to learn.  This takes place in bi-weekly staff meetings, as well as through surveys of our students and work with the high school.  The Common Core State Standards (CCSS) are used as a foundation in ELA and math classrooms, and the CCSS literacy standards are woven into each subject area in order to support college and career readiness.  For example, our robotics teacher ensures that CCSS literacy standards regarding reading and writing about informational text(s) are at the forefront of assignments while learning the mechanics and programming languages associated with robotics.  Our ELA classes are combined with social sciences in a CORE block of time, so that ELA and literacy standards are connected to the history content by the same teacher in the same block of time.  This allows essential standards or skills to be practiced, assessment (CFU/CFA/summative) while in the context of history content.  The rest of the campus utilizes the adopted standards consistent with their subject areas.  In the case of science, for example, a curriculum adoption is on the horizon, but that has not stopped our science department from using all available resources in order to identify what concepts apply to the new Next Generation Science Standards (NGSS), what essential standards or skills are present in those standards, and how to best teach those concepts to our students.  Then, those standards are assessed in a CFA, data is analyzed, reteaching occurs, and the cycle continues.  This is true of all subject areas including physical education, arts, career technical education, foreign language, and out mandatory computer classes.  

1. Monitoring student learning on a timely basis.

CFAs, side by side coaching on the process using a grassroots, Learning By Doing based rubric, intervention through Tier 1 in the classroom, Tier 2 in ROAR and after school program and tutorial.  Administration sits side by side when collaborative teams determine it is time to analyze student data as indicated by submission of the date, time, and place of the analysis.  That observation is toward the process of analysis in order to build the team’s abilities and administration’s capacity to learn, as well.  The team them works on the next steps, intervention model, re-teaching strategies and choices, and continues in the continuous cycle of improvement.  

Our district’s student information system houses assessment information, and provides an avenue for administration and counseling to pull reports and data.  However, the information from that data comes to life when monitoring student learning.  We have agreed that counseling of students is everyone’s job, not just an academic counselor.  So, reports like the “One F” list were created to communicate with teachers from the office each week about which students might need direct teacher-to-student counseling because their struggle is in a specific class.  Students with more need are identified differently (ROAR, for instance) in order to get a more global take on that student’s progress.  Finally, reports that showcase more than a minor “will” issue are connected with academic counselors towards a holistic analysis of the student’s progress.  That may mean a student success team meeting, assessment, or even a home visit. 

2. Creating systems of intervention to provide students with additional time and support for learning.

ROAR is the program where we find the most one on one success, and this year have rolled in strategies from best practices working in other places towards truly making our Tiger family accountable for one another.  Our Tigers Helping Tigers class is one where students are trained as mini-AVID tutors, peer counselors, and ambassadors of excellence.  They are deployed to each ROAR session so that they can help academic counselors towards success in areas for growth.  We have found this successful because there must be strategies to combat interventions when there is an issue with “will” as well as “skill”.  

ROAR is our Recovery of Academic Responsibility program, which is one of Computech’s primary school wide interventions.  ROAR occurs during lunch and is not punitive in nature.  Instead, students are invited to ROAR by fellow classmates in order to fulfill needs as identified by CFA results in classes, by parent request, and/or counselor authority.  In previous years, ROAR was run by our single counselor (for all 800+ students) and caps were placed in order to keep it manageable.  However, upon identifying our success, our district offices placed a second academic counselor and we began a class devoted to peer counseling and peer academic tutoring.  That class—Tigers Helping Tigers (THT)—included both AVID training and Fresno State University peer mentoring curriculum toward using students in the ROAR program to assist in organizing, supporting, and modeling student behaviors towards success.  This gives our academic counselors the ability to focus and drive success with students in a strategic way.  Our THT students eat lunch after the lunch period when it is their assigned day to help in ROAR, allowing them to provide one-to-one attention to other students.  This builds leadership, allows for a secondary “way” of teaching essential standards or skills, and provides a less confrontational or punitive setting for which to gain academic responsibility.  Students are invited and escorted to ROAR by our THT students, they are provided lunch, and the atmosphere is one where the counselor(s) and THT students work with ROAR students to get back on track.  This is most successful with our “C” and “D” students, while students maintaining or achieving a number of “F” grades are then moved to the Tier 3 intervention including Student Success Team (SST), conferences, and further after school tutoring in the library (school) or ASES (after school program).  

Furthermore, with the tightening of CFA use and growth across the staff, after school tutoring has begun in a way that engages and supports the work of the collaborative team.  When a student does not meet proficiency in ELA or mathematics after first best teaching and after Tier 1 differentiation and support, they are recommended to after school tutoring by certificated teacher tutors in order to engage in understanding these high leverage skills and standards addressed by the CFA.  Tier 1 intervention takes many forms, but some of the most successful are forms of reorganizing classrooms toward intervening and extending the learning, deploying as necessary, creating student facilitated “stations”, and providing before, during, and after school tutoring.  During school tutoring can only occur because consensus was garnered toward having the longest lunch in the district to allow for eating and pursuing interests or academic help. 

These two areas have both created a stronger sense of urgency and importance of assessing for knowledge instead of just assessing for data. 

When there is further need for intervention, our library has extended hours where a certificated teacher tutors students who are finding essential skills and standards to be difficult as evidenced by CFA results.  Therefore, Mondays, Wednesdays, and Thursdays there is time for one on one tutoring when it is more of a “skill” issue than “will”.

3. Building teacher capacity to work as members of high performing collaborative teams that focus efforts on improved learning for all students.

Collaborative teams at Computech have commonality between subject and grade level, leaving (11) different teams on campus to represent all 35 members of the teaching staff.  Within these collaborative teams, there are many subjects represented, but they are aligned through department, collaborative team, and singleton delineations. 

Our Instructional Leadership Team (ILT) is made up of the administrative staff over instruction, and meet monthly in order to use schoolwide date (such as the Instructional Practice Guide and Tiger Tracks tool) in order to respond to gaps through scheduled professional learning, as well as build knowledge and capacity towards elements of grading, homework, instruction, curriculum, and intervention.  This team also meets with our district lead teachers from our high school feeder system, but Computech is instrumental in leading the region in collaborative work; frequently being paired with other lead teachers/schools toward sharing learning and processes.  

In the past three years, the work of the ILT has evolved to this year specifically finding focus on the work of analyzing student data through common formative assessments.  This has come to fruition though monitoring in more school wide approaches until the last year or so.  Structures were slowly put into place to both support and advance the CFA movement in order to strengthen each collaborative team.  The following are now in place to monitor student achievement:

-       First and foremost, all collaborative teams complete CFA Cycles based on the framework provided by administration and based in Learning By Doing.

-       When a collaborative team is planning/scheduling the CFA analysis portion of the cycle, the Lead Teacher inputs the scheduled date(s) into an online form to alert the administrative staff to calendar a side-by-side observation and coaching meeting.  This can happen at any time.  The commitment from administration is to be a coach during this time, and the commitment from the given collaborative team is to analyze toward understanding what students learned, did not learn, and where misconceptions lie.

-       The side-by-side coaching is a mechanism for monitoring, but is more importantly to build capacity across the campus by using a grassroots “CFA Process” rubric designed using Learning By Doing and with district regional principal partners from two schools in the Edison region.  This tool allows administration, Lead Teachers, and collaborative teams to concentrate on the process of analyzing evidence and data without judgement, enabling trust to be built and next steps to be based solely on the needs of students. 

-       A tiered approach to intervention has been the latest concentration for collaborative teams, and has come full circle back to the consensus-based management style of the staff since the Tier 2 and Tier 3 responses are school wide endeavors. 

 

Attachements to consider:

- CFA expectations is a packet that we started with this school year in order to both highlight where we have been, but showcase where we are headed.  It outlines "tights" and "looses" for the campus, and provides the CFA Process rubric, and out site analysis protocol.

- Side by side observation notes (called "CFA Process Rubric..." are just EXAMPLES for how we observe and provide real time feedback about the process.  These show where that team was at the discussion/analysis time, and notes to propell further coaching/conversation.  These were accompanied by various other evidence pieces.  

- The image is of our hallway (in the office) that has dates teams decided upon for analysis observation.  This keeps us all accountable to the process, allows for teams to encourage other teams, and has a large CFA Process rubric for suggestions to be made towards making us all better.

2015 Computech ELA and Math 2015 Fresno Unified ELA and Math 2015 Californai ELA and Math
2015 7th ELA 8th ELA 7th Math 8th Math   ELA PROF/NOT MATH PROF/NOT ELA PROF/NOT MATH PROF/NOT ELA PROF/NOT MATH PROF/NOT
Exceeded 28% 40% 32% 36%   34% 92% 34% 73% 4.5% 27.0% 4.5% 15.5% 12.0% 44.5% 15.5% 33.5%
Met 63% 53% 46% 32%   58% 39% 22.5% 11.0% 32.5% 18.0%
Nearly Met 8% 6% 19% 26%   7% 8% 23% 27% 28.5% 73.0% 25.5% 84.0% 27.0% 55.5% 27.5% 66.5%
Not Yet 1% 1% 3% 5%   1% 4% 44.5% 58.5% 28.5% 39.0%
            100%   100%   100.0%   99.5%   100.0%   100.0%  
2016 Computech ELA and Math 2016 Fresno Unified ELA and Math 2016 Californai ELA and Math
2016 7th ELA 8th ELA 7th Math 8th Math   ELA PROF/NOT MATH PROF/NOT ELA PROF/NOT MATH PROF/NOT ELA PROF/NOT MATH PROF/NOT
Exceeded 37% 38% 38% 44%   38% 92% 41% 77% 6.0% 30.0% 6.0% 18.0% 14.5% 48.0% 18.0% 36.0%
Met 54% 55% 41% 31%   54% 36% 24.0% 12.0% 33.5% 18.0%
Nearly Met 8% 7% 20% 19%   7% 8% 20% 23% 27.5% 70.0% 26.0% 82.5% 25.5% 52.0% 27.5% 64.0%
Not Yet 1% 1% 1% 5%   1% 3% 42.5% 56.5% 26.5% 36.5%
            100%   100%   100.0%   100.5%   100.0%   100.0%  
2017 Computech ELA and Math 2017 Fresno Unified ELA and Math 2017 Californai ELA and Math
2017 7th ELA 8th ELA 7th Math 8th Math   ELA PROF/NOT MATH PROF/NOT ELA PROF/NOT MATH PROF/NOT ELA PROF/NOT MATH PROF/NOT
Exceeded 44% 32% 46% 45%   38% 92% 45% 79% 6.3% 31.1% 7.1% 18.8% 15.6% 49.0% 19.0% 36.6%
Met 52% 55% 34% 33%   54% 34% 24.8% 11.7% 33.4% 17.6%
Nearly Met 5% 11% 19% 19%   7% 8% 19% 21% 26.7% 68.9% 22.4% 81.2% 24.7% 51.0% 25.2% 63.4%
Not Yet 0% 2% 1% 3%   1% 2% 42.3% 58.8% 26.3% 38.2%
            100%   100%   100.0%   100.0%   100.0%   100.0%  
                            ELA/Low SES PROF/NOT Math/Low SES PROF/NOT
                            7.2% 35.6% 8.8% 22.9%
                            28.4% 14.1%
                            28.6% 64.4% 26.7% 77.1%
                            35.7% 50.4%
                            99.9%   100.0%  
                            ***State achievment by ethnicity attached as pdf.

Breakdown by SES:

CAASPP, 3-Year Percentages by SES
ELA
  Computech FUSD CA
7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 23.00% 32.00% 37.99% 3.00% 4.00% 5.27% 5.00% 7.00% 7.12%
Met 67.00% 58.00% 55.93% 21.00% 20.00% 24.31% 25.00% 27.00% 28.50%
Nearly 9.00% 9.00% 5.78% 26.00% 27.00% 26.24% 29.00% 28.00% 27.26%
Not Met 1.00% 1.00% 0.30% 50.00% 49.00% 44.19% 41.00% 38.00% 37.12%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceded 28.00% 34.00% 24.16% 3.00% 4.00% 3.78% 5.00% 7.00% 7.22%
Met 64.00% 57.00% 62.08% 21.00% 25.00% 22.51% 27.00% 29.00% 28.33%
Nearly 6.00% 9.00% 13.01% 32.00% 29.00% 28.86% 33.00% 31.00% 30.08%
Not Met 2.00% 1.00% 0.74% 44.00% 42.00% 44.85% 34.00% 33.00% 34.37%
Math
  Computech FUSD CA
7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 25.00% 32.00% 37.99% 3.00% 4.00% 5.55% 6.00% 7.00% 7.99%
Met 49.00% 43.00% 38.91% 11.00% 11.00% 11.06% 14.00% 15.00% 14.94%
Nearly 22.00% 24.00% 21.88% 28.00% 28.00% 23.29% 31.00% 32.00% 29.98%
Not Met 4.00% 1.00% 1.22% 58.00% 57.00% 60.10% 49.00% 45.00% 48.09%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 27.00% 36.00% 38.15% 4.00% 4.00% 5.01% 8.00% 9.00% 9.65%
Met 36.00% 35.00% 37.41% 9.00% 10.00% 10.17% 13.00% 14.00% 13.31%
Nearly 30.00% 23.00% 22.59% 23.00% 25.00% 20.68% 27.00% 27.00% 24.33%
Not Met 6.00% 7.00% 1.85% 65.00% 62.00% 64.14% 52.00% 50.00% 52.70%

 Breakdown by Ethnicity:

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- Greatschools.org lists Computech as a 10/10 school (https://www.greatschools.org/california/fresno/816-Edison-Computech-School/#Test_scores)

- National Blue Ribbon School recipeint as recently as 2016, with awards in 1990, 1994, 2001, 2009 (https://www2.ed.gov/programs/nclbbrs/list-2003.pdf)

- Named 2018 #1 Niche Standout Middle School in Calfornia, and #67 of Standout Middle Schools in America (https://www.niche.com/k12/search/niche-standout-middle-schools/s/california/)

- Numorous other awards including California Distinguished School six times, Title 1 Academic Achievement Award four times, an finalist for the Intel School of Distinction Award two times.  

- Chevron and DonorsChoose.org chose Computech as their representative both as one of 17 schools for regional public service announcements, but as the national representative for the Fuel Your School campaign, as well.

- National Science Bowl Champions for the Electric Car in 2013 and 2014.  

Top

CAASPP, 3-Year Percentages by Ethnicity
ELA
  Computech FUSD CA
African American 7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 12.00% * 26.67% 1.00% 2.00% 1.85% 4.00% 6.00% 6.25%
Met 71.00% * 60.00% 13.00% 13.00% 16.20% 23.00% 24.00% 24.61%
Nearly 12.00% * 13.33% 24.00% 25.00% 18.29% 26.00% 26.00% 24.99%
Not Met 6.00% * 0.00% 62.00% 60.00% 63.66% 47.00% 44.00% 44.11%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceded 24 37 * 2 3 2.3 5 6 6.75
Met 71 42 * 18 17 14.52 24 26 24.78
Nearly 5 21 * 24 23 25.35 31 28 27.74
Not Met 0 0 * 56 57 57.83 40 40 40.72
American Indian or Alaska Native 7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded * * n/a 0.00% 0.00% 7.69% 6.00% 8.00% 8.14%
Met * * n/a 36.00% 29.00% 26.92% 26.00% 28.00% 27.12%
Nearly * * n/a 14.00% 32.00% 15.38% 27.00% 26.00% 26.74%
Not Met * * n/a 50.00% 39.00% 50.00% 42.00% 39.00% 38.01%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceded * * * 3.00% 7.00% 3.23% 6.00% 8.00% 8.23%
Met * * * 21.00% 26.00% 29.03% 27.00% 29.00% 26.99%
Nearly * * * 33.00% 41.00% 32.26% 33.00% 29.00% 29.60%
Not Met * * * 42.00% 26.00% 35.48% 34.00% 34.00% 35.18%
Asian 7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 49.00% 37.00% 38.71% 8.00% 9.00% 9.33% 35.00% 40.00% 40.84%
Met 47.00% 53.00% 56.45% 29.00% 30.00% 35.81% 39.00% 36.00% 37.08%
Nearly 4.00% 9.00% 4.84% 29.00% 30.00% 27.05% 15.00% 14.00% 12.62%
Not Met 0.00% 2.00% 0.00% 34.00% 32.00% 27.81% 11.00% 10.00% 9.47%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 51.00% 55.00% 36.36% 8.00% 10.00% 7.34% 33.00% 38.00% 39.75%
Met 37.00% 40.00% 54.55% 29.00% 36.00% 35.97% 41.00% 39.00% 37.29%
Nearly 12.00% 6.00% 9.09% 37.00% 29.00% 30.89% 17.00% 14.00% 14.21%
Not Met 0.00% 0.00% 0.00% 26.00% 25.00% 25.80% 9.00% 8.00% 8.75%
Filipino 7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded * * * 12.00% 5.00% 18.18% 20.00% 26.00% 27.39%
Met * * * 35.00% 40.00% 36.36% 45.00% 43.00% 44.10%
Nearly * * * 29.00% 15.00% 18.18% 21.00% 19.00% 17.74%
Not Met * * * 24.00% 40.00% 27.27% 14.00% 12.00% 10.77%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded * * * 30.00% 18.00% 11.76% 19.00% 25.00% 24.88%
Met * * * 30.00% 29.00% 29.41% 48.00% 45.00% 44.35%
Nearly * * * 25.00% 35.00% 29.41% 23.00% 20.00% 20.20%
Not Met * * * 15.00% 18.00% 29.41% 11.00% 10.00% 10.57%
Hispanic or Latino 7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 22.00% 32.00% 39.08% 2.00% 4.00% 5.54% 5.00% 7.00% 7.52%
Met 70.00% 59.00% 56.32% 21.00% 20.00% 24.30% 26.00% 28.00% 29.77%
Nearly 8.00% 8.00% 4.21% 26.00% 27.00% 26.41% 29.00% 28.00% 27.32%
Not Met 0.00% 0.00% 0.38% 51.00% 48.00% 43.74% 40.00% 36.00% 35.39%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 28.00% 30.00% 25.11% 3.00% 4.00% 4.00% 5.00% 7.00% 7.59%
Met 66.00% 62.00% 59.19% 21.00% 24.00% 21.86% 28.00% 30.00% 29.40%
Nearly 5.00% 7.00% 13.90% 31.00% 29.00% 28.83% 34.00% 31.00% 30.27%
Not Met 2.00% 0.00% 1.79% 45.00% 43.00% 45.31% 33.00% 31.00% 32.74%
Native Hawaiian or Pacific Islander 7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded * * * 5.00% 15.00% 10.00% 7.00% 10.00% 9.65%
Met * * * 50.00% 12.00% 25.00% 31.00% 31.00% 32.03%
Nearly * * * 15.00% 23.00% 35.00% 31.00% 28.00% 27.70%
Not Met * * * 30.00% 50.00% 30.00% 32.00% 30.00% 30.62%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded * * * 5.00% 15.00% 4.17% 7.00% 9.00% 9.62%
Met * * * 29.00% 40.00% 20.83% 31.00% 34.00% 31.23%
Nearly * * * 43.00% 15.00% 25.00% 33.00% 30.00% 30.34%
Not Met * * * 24.00% 30.00% 50.00% 29.00% 27.00% 28.81%
White 7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 37.00% 53.00% 64.56% 8.00% 19.00% 17.42% 19.00% 24.00% 24.45%
Met 51.00% 38.00% 31.65% 33.00% 34.00% 33.46% 41.00% 41.00% 41.28%
Nearly 11.00% 8.00% 3.80% 26.00% 22.00% 21.92% 22.00% 20.00% 19.51%
Not Met 0.00% 2.00% 0.00% 33.00% 26.00% 27.20% 17.00% 15.00% 14.76%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded 65.00% 50.00% 50.57% 17.00% 13.00% 18.78% 19.00% 23.00% 23.72%
Met 30.00% 45.00% 40.23% 34.00% 36.00% 33.12% 42.00% 42.00% 40.06%
Nearly 4.00% 3.00% 5.75% 24.00% 23.00% 20.25% 24.00% 22.00% 21.62%
Not Met 0.00% 2.00% 3.45% 25.00% 28.00% 27.85% 14.00% 14.00% 14.60%
Ethnicity- Two or More Races 7th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded * * * 7.00% 7.00% 5.33% 20.00% 25.00% 26.56%
Met * * * 26.00% 23.00% 18.67% 39.00% 38.00% 39.64%
Nearly * * * 26.00% 21.00% 34.67% 22.00% 19.00% 18.25%
Not Met * * * 40.00% 48.00% 41.33% 19.00% 17.00% 15.55%
8th   2015 2016 2017 2015 2016 2017 2015 2016 2017
Exceeded * * * 5.00% 10.00% 7.02% 20.00% 23.00% 24.42%
Met * * * 28.00% 38.00% 29.82% 40.00% 40.00% 38.68%
Nearly * * * 28.00% 24.00% 26.32% 23.00%