LCFF Priority 5

Pupil Engagement: Equity Actions




Equity Actions to Address Attendance, Chronic Absenteeism, and Graduation Rates for African American, Native American, and Latinx students. 


Equity Statement: Using a variety of equity actions, LEAs support students through the creation of dynamic and accessible data systems. These systems include comprehensive and intentional practices to increase student engagement and academic outcomes.


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Explore Equity Actions


District Aligned Resources

Use data systems to accurately assess student progress and develop appropriate interventions and accelerations in academics

Equity Statement

Intentionally selecting resources and metrics based on the needs of African American, Native American, and Latinx students promotes student engagement and success.

Equity Action 1 Corresponding Metrics for the use of data systems to accurately assess student progress and developing appropriate interventions and accelerations in academics.

Overarching LEA Considerations:

  • Are the LEA’s data systems dynamic and easily accessible? 
  • How fluent is your whole organization on both formative and summative assessments?
  • Are goals driven by having fewer behavior referrals or higher levels of joy and belonging?
  • What triggers interventions and accelerations?
  • How is progress communicated?
  • Does the LEA utilize data reflection norms and protocols that understands the complexity of specific student groups (Latinx, African American, American Indian) including their histories, cultures, and linguistic features?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, disparities in student referrals, suspension rates, or student engagement rates are identified at the student group level. The following metrics should be disaggregated to identify gaps in performance.

Metrics

Create a district level team to review LEA student engagement efforts by analyzing graduation rate, chronic absenteeism, attendance rates, and drop-out rates.

  • % increase in graduation rate
  • % decline in chronic absenteeism
  • % decrease in middle school/high school dropout rates

Evaluate the effectiveness of this team regularly by analyzing student outcome data, a locally developed rubric that measures implementation, a locally developed tool that measures progress towards goals, etc.

Example Metrics (Data Sources): CA Dashboard, Student Information System, DataQuest, local measures

Monitor student connectivity by gathering site  data to inform needs. 

  • Collect data on the % of students with technology and/or wifi hotspots.

Example Metrics (Data Sources): Locally created measure

Collect local data about trusting relationships to inform LEA needs.

  • The % of students reporting positive relationships with at least one adult in the school
  • The % of students participating in group relationship building activities led by adults or peers (clubs, enrichment activities, mentoring)
  • The % of families reporting positive relationships with school staff
  • The % of families reporting the opportunity to provide feedback on school decisions

Example Metrics (Data Sources): California Healthy Kids Survey (CHKS), local climate surveys, such as Panorama, student and family empathy interviews, and focus groups.

Actively monitoring discipline referrals by  establishing goals, metrics, and actions.Disaggregate all data by targeted student groups.

  • Create an LEA-wide system for collecting discipline referrals/data. 
    • Collect the number/percent of offenses
  • Collect suspension and expulsion date. 
    • Analyze the number/percent. 
  • Look at trends and patterns across locations, time of day, and re-occurrences.
  • Set short and long term goals for reducing discipline referrals, suspensions, and expulsion rates.  
  • Set short and long term goals for improving school climate for each school and target student group- included in LCAP.

Example Metrics (Data Sources): CA Dashboard, DataQuest, local measures

Review school attendance rates to identify students who have not attended school/class, by school.

  • Collect the number/percent of student attending school
  • Create/monitor early warning systems for chronic absenteeism
    • Track and monitor students that are “at-risk” (7-9% absences) of chronic absenteeism on a weekly basis
    • Track and monitor students that are experiencing moderate levels of chronic absenteeism (10-15% absences)
  • Disaggregate based on African American, Native American, Latinx, and other high needs students.

Example Metrics (Data Sources): CA Dashboard, DataQuest, CALPADS Report 14.2, local measures

Collect and analyze suspension and expulsion data based African American, Native American, Latinx and other high needs students (including low income, English learners, and foster youth).

Equity Statement

Building trusting relationships, addressing implicit biases, and analyzing suspension and expulsion data for African American, Native American, and Latinx students support improved student outcomes.

Equity Action 2 Corresponding Metrics to collect and analyze suspension and expulsion data based on African American, Native American, and Latinx students (including low income, English learners, and foster youth.)

Overarching LEA Considerations:

  • How does the LEA collect data on suspensions and expulsions?
  • Can the data collection be disaggregated?
  • How does the LEA’s professional learning plan align with this work?
    • How does the LEA support assets-based mindsets by intentionally focusing on educators and their mindsets about their students? 
    • How does the LEA address confronting biases and having courageous conversations about race?
  • What work is done to implement The English Learner Roadmap?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, disparities in suspension and expulsion rates are identified at the student group level. The following metrics should be disaggregated to identify gaps in performance.

Metrics


Collect and analyze suspension and expulsion data.

# of African American students suspended organized by type and frequency.
# of Native American students suspended organized by type and frequency.
# of English Learner suspensions organized by type and frequency.
# of Low Income suspensions organized by type, frequency, student group.
# of Foster Youth suspensions organized by type, frequency, student group.
  • Frequency of referrals (not just suspensions/expulsions) by site, course, grade level, and disaggregated by student groups listed.
  • # of suspensions/expulsions by type and disaggregated  by student group
  • Record of interventions offered by site, course, grade, and disaggregated by student groups listed.

Example Metrics (Data Sources): CA Dashboard, DataQuest, Local measures

Create a professional learning plan on collecting and analyzing data.


  • Collect efficacy data on professional learning plan through evaluations
    • % of "satisfied" participants and evidence of impact
    • % of participants meeting or exceeding professional learning success criteria
  • Provide ongoing professional learning that highlights Social Emotional Learning (SEL) within the Multi-Tiered Systems of Support (MTSS) model and incorporate restorative practices
    • # of professional learning sessions that address how to use disaggregated data to target improved student outcomes
    • # of professional learning sessions that highlights Social Emotional Learning (SEL) within the Multi-Tiered Systems of Support (MTSS) model and incorporate restorative practices

Implement The English Learner Roadmap (especially Principles 3 & 4).

Principle #3 = System Conditions that Support Effectiveness (leadership, adequate resources, assessments, and capacity building)
#4 = Alignment and Articulation Within and Across Systems (alignment and articulation, extra resources, and coherency)
  • Self-assessment data using the LCAP English Learner Research-Aligned Rubrics
  • Progress towards implementation

Example Metrics (Data Sources): Local measures

Develop alternatives to suspensions and expulsions at all schools

Equity Statement

Explicitly and intentionally focusing on building staff members assets-based mindsets about their students and confronting implicit biases supports the decrease in suspension and expulsion rates.

Equity Action 3 Corresponding Metrics to develop alternatives to suspensions and expulsions at all schools

Overarching LEA Considerations:

  • How does the district develop staff skills to teach diverse student populations?
    • How does the LEA support assets-based mindsets by intentionally focusing on educators and their mindsets about their students? 
    • How does the LEA address confronting biases and having courageous conversations about race?
  • What is the LEA-wide policy on MTSS and PBIS implementation?
    • How does the LEA monitor site-level MTSS and PBIS data?
  • What short and long-term goals, metrics and actions are created to support school safety?
    • How does the LEA recruit diverse representatives to assist in creating these goals?
  • Is every staff member knowledgeable and using multiple discipline interventions instead of removing students from learning spaces?
  • How are SPSAs aligned to the LCAP and outline alternatives to suspensions and expulsions?
  • How is the LEA collecting implementation data on PBIS?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Create & monitor a district wide plan for alternatives to suspension and expulsion (artifact).

  • Analyze the number of referrals, suspensions, and expulsions 
    • Effectiveness is measured by the decrease of these numbers
  • Record of interventions offered by site, course, grade, and disaggregated by student groups listed.
    • Number of sessions
    • Attendance 
    • Student outcome data
  • Record of counseling sessions/referrals offered
    • Number of counseling sessions
    • Student outcome data based on goals, actions, etc.
  • Accounts of restorative practices; conflict/resolution and peer mediation
    • Number of sites implementing restorative practices
    • Number and type of conflict/resolutions provided by site
      • Outcome data
    • Number of sites using peer mediation
      • Outcomes data
  • Track behavior via PBIS system

Example Metrics (Data Sources): Local measures

Alignment: Collect data on the number of SPSAs that align to Local Control and Accountability Plan in terms of goals/actions for LCFF priority 5.

  • Number of SPSA that align to LCFF Priority 5, specifically, creating aligned goals, metrics, baseline and expected outcomes for 
    • Increasing student attendance
    • Decreasing chronic absenteeism

Example Metrics (Data Sources): Local measures

Measure the effectiveness of the LEA’s PBIS implementation and Measure the effectiveness of collecting data on alternative to suspension and expulsion).

  • Number and % of suspensions/expulsions for each student group by school compared to baseline data
  • Number and % of referrals and interventions 
  • Collect PBIS implementation data
    • Levels of implementation
    • Review goals, actions, and student outcomes

Example Metrics (Data Sources): Local measures

Create a welcome/intake process to co-construct graduation plans, including positive, early monitoring towards graduation

Equity Statement

Families feel welcome when LEAs focus on creating family friendly, respectful, and healthy school environments. Providing opportunities for families, students, and schools to co-construct graduation plans increases student agency and family voice.

Equity Action 4 Corresponding Metrics to create a welcome/intake process to co-construct graduation plans, including positive, early monitoring towards graduation

Overarching LEA Considerations:

  • How does the district/school sites welcome and intentionally engage families in their school systems?
  • How are the school environments inclusive and support all children, including those who have been historically marginalized?
  • Are there staff available to speak to families in their native language?
  • What positive early monitoring systems are in place to support students in graduating?
  • What do the district/school sites do to recognize and respect the diversity of different families and cultures? Is it apparent in the front office?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Collect Graduation Data:

  • Create a plan and collect the number of student/ counseling meetings that allow students to co-construct graduation plans.
    • Plan (artifact)
    • # of meetings
  • Collect  and analyze graduation rates by school and student group
  • Collect and analyze dropout rates by school and student group
  • Track interim measures including GPAs, credit attainment, credit deficiencies, semester transcripts, number of D’s and F’s, suspension data
  • Create early monitoring graduation system
    • Collect the #/% of students that are on track to graduation
      • Identify students who are not on track to graduate
    • Implement interventions/support for student who are not on track
      • Collect the number and type of graduation interventions provided
  • Evaluate effectiveness of graduation supports provided by LEA
    • Graduation rates

Example Metrics (Data Sources): CA Dashboard, DataQuest, local measures

Include comprehensive support services for students (i.e., offer community-informed cultural connections; align “A-G” high school courses)

Equity Statement

Culturally and linguistically responsive instruction provides a space and structure for teachers to (1) engage in dialogue and dynamic learning with students, (2) explore their own identities, mindsets, and skills as they simultaneously seek to understand and affirm their students’ backgrounds, cultures, and languages, and (3) cultivate restorative, student-centered classroom cultures.

Equity Action 5 Corresponding Metrics to include comprehensive support services for students (i.e., offer community-informed cultural connections; align “A-G” high school courses).

Overarching LEA Considerations:

  • How does the district ensure that students have access to courses that are A-G aligned, espHow do the LEAecially for students who are African American, Latinx, or Native American?
  • /school sites acknowledge and learn more about individual and structural racism?
  • How does the LEA provide safe spaces to discuss and honor the contributions of all cultures?
  • What professional learning is being provided that fosters an understanding of culturally responsive instruction?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.


Metrics


Track A-G Completion:

  • #/% of students on track to complete A-G requirements (interim measure)
  • #/% of graduates who have completed A-G requirements

Example Metrics (Data Sources): CA Dashboard, DataQuest, Local measures

Provide teachers with professional learning that addresses how to use data to target academic and social needs of students

Equity Statement

Educators who are trained to use an equity lens while reviewing data, such as including student identities and culture, gain a more comprehensive picture/view of how African-American, Latinx, and Native American students are doing.

Equity Action 6 Corresponding Metrics to provide teachers with professional learning that addresses how to use data to target academic and social needs of students.

Overarching LEA Considerations:

  • How does the LEA determine priorities for professional learning?
  • How does the LEA collect data on professional learning?
  • What systems are in place to support data analysis?
    • What other measures are used to learn about student identities and culture?
  • How are teachers supported in learning about and implementing professional learning communities/data teams?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Create and communicate a LEA-wide professional learning plan that includes culturally responsive teaching, data collection, and improving the outcomes of academic and social needs of African American, Latinx, and Native American students.

  • Plan (artifact)
  • # of professional learning sessions
    • Attendance (educators, classified staff, etc.)
  • # of professional learning sessions that address how to use disaggregated data to target academic and social needs of students
    • Attendance
  • Provide professional learning on PLCs/Data Teams
    • # of sites that are conducting regular PLCs / Data Team meetings
    • Measure the effectiveness by 
      • collecting student outcome baseline data (academic/social-emotional)
      • % of students on track based on local academic and/or social-emotional data
      • Monitoring and regularly collecting data
        • # of meetings
        • Student outcome data

Example Metrics (Data Sources): Local measures

Create multiple opportunities for student leadership development and engagement, including at district stakeholder meetings

Equity Statement

As students go through the grades, there is a predictable, well-documented downward trajectory in student engagement. Research indicates that students who believe they have a voice in school are seven times more likely to be academically motivated than those who do not feel they have a voice. (EdWeek, 2018.)

Equity Action 7 Corresponding Metrics to create multiple opportunities for student leadership development and engagement, including at district stakeholder meetings

Overarching LEA Considerations:

  • How is the LEA amplifying and including student voice?
  • How are students included in governing bodies?
  • How do students take ownership of their learning?
  • How many African American, Latinx, and Native American students serve on or attend stakeholder meetings?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.



Metrics


Create a plan to amplify and include African American, Latinx, and Native American student voice in leadership positions.

  • Plan (artifact)
  • Collect data on the phases of implementation
  • Collect data on the # of students involved in student leadership, engagement, and district stakeholder meetings
    • # of student perspectives on governing bodies
    • #  and types of student governments and councils

Example Metrics (Data Sources): Local measures

Conduct student focus groups with African American, Latinx, and Native American students to learn about their perspective on leadership and engagement opportunities in the district/site.

  • Plan for focus groups (artifact)
    • Include the purpose of the focus groups
    • Identify leadership team involved
  • Collect and analyze data on the focus groups by identifying trends and patterns to assist with next steps
    • # of focus groups conducted
    • % of participants who feel _____
  • Create an action plan based on findings
  • Collect implementation and/or goal completion data
    • Phase of implementation
    • # of goals completed

Example Metrics (Data Sources): Local measures

Cocreate surveys with educators and African American, Latinx, and Native American students to learn about their perspective on leadership and engagement opportunities in the district/site.

  • Create survey (artifact)
  • Analyze results by looking for trends and patterns
    • % of respondents who feel_______

  • Create an action plan based on findings
  • Collect implementation and/or goal completion data
    • Phase of implementation
    • # of goals completed

Example Metrics (Data Sources): Local measures

Conduct Empathy Interviews with African American, Latinx, and Native American students to learn about their perspective on leadership and engagement opportunities in the district/site.

  • Collect and analyze data from empathy interview  by identifying trends and patterns to assist with next steps
    • # of empathy interviews conducted
    • % of participants who feel _____
  • Create an action plan based on findings
  • Collect implementation and/or goal completion data
    • Phase of implementation
    • # of goals completed

Example Metrics (Data Sources): Local measures

Conduct student-led conferences.

  • # of sites in LEA that are conducting student-led conferences
  • # of professional learning sessions on conducting student-led conferences
  • Collect data on the effectiveness of student-led conferences (survey, empathy interviews, etc.)
  • Collect data on the implementation of student-led conferences

Example Metrics (Data Sources): Local measures

Provide opportunities for learning life skills (i.e., study skills, conflict resolution, peer counseling, healthy lifestyles)

Equity Statement

Building relationships that encourage self-advocacy, conflict resolution, and healthy lifestyles helps individuals overcome inequitable circumstances, affirms multifaceted identities, and fosters a relationship where the mentor and mentee teach and learn from one another.

Equity Action 8 Corresponding Metrics to provide opportunities for learning life skills (i.e., study skills, conflict resolution, peer counseling, healthy lifestyles)

Overarching LEA Considerations:

  • How many school sites have peer mentoring/peer counseling programs?
  • How does the district provide resources to support mental health and wellness?
  • Does the district support trauma-sensitive schools?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Create LEA-wide peer mentoring / peer counseling program.

  • # of sites in LEA that have peer mentoring / peer counseling programs
  • List of types of services being provided
  • Number of students participating
  • Survey students and staff on effectiveness of program

Example Metrics (Data Sources): Local measures

Implement LEA-wide bullying prevention and intervention plan.

  • # and % of bullying incidents disaggregated by student groups by site, school, classroom.

Example Metrics (Data Sources): Local measures

Create a LEA-wide mental health and wellness plan that includes community based organizations. Provide site-level support

  • # of referrals for each community based organization or agency each site for specific student groups, such as LGBTQ, African American, NativeAmerican, and high needs students.
  • % distribution of resources and supports provided by student group for each site 

Create a task force/committee to explore creating a plan and Advocating for Trauma-Sensitive Schools.

  • Plan (artifact)
  • # of meetings
  • List of types of services being provided
  • Survey students and staff on effectiveness of plan

Collect data to analyze:

  • Attendance Rates, by site, by student group
  • Suspension Rates, by site, by student group

Example Metrics (Data Sources): CA Dashboard, DataQuest, local measures


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School Aligned Resources

Use data systems to accurately assess student progress and develop appropriate interventions and accelerations in academics


Equity Statement

Intentionally selecting site level resources and metrics based on the needs of African American, Native American, and Latinx students promotes student engagement and success.

Equity Action 1 Corresponding Metrics to use data systems to accurately assess student progress and develop appropriate interventions and acceleration in academics.

Overarching Site Considerations:

  • How does the site monitor attendance and chronic absenteeism, especially for African American, Latinx, and Native American students?
    • Is there a correlation between the academic outcomes and attendance data for African American, Latinx, and Native American students? 
  • How is technology and connectivity monitored at the site? 
  • How does the site gather data on building positive and trusting relationships?
    • What qualitative measures are used, such as empathy interviews, focus groups, and student panels?
  • How are site-level discipline referrals monitored?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Create a site task-force/ PLC/advisory committee (administrator, attendance clerk, family liaison, counselor)to lead work addressing attendance, chronic absenteeism.

Review the Local Attendance Report to identify students who have not attended class.
  • List of students missing 10% of the school year for any reason (excused or unexcused absences and suspensions) 
  • % of students chronically absent by grade and classroom disaggregated by student group (is this an issue schoolwide or specific grades or certain populations?)

Example Metrics (Data Sources): Student Information System, local measures

Create an annual site plan to decrease chronic absenteeism, including 3 Tiers of Intervention (MTSS).

Document absences based on this model
Analyze data regularly
  • List of students missing less than 5% of school (satisfactory)
  • List of students missing 6-10% of school (at-risk)
  • List of students missing 10-19% of school (moderate chronic absence) 
  • List of students missing 20% or more of school (severe chronic absence

Example Metrics (Data Sources): Local measures, Student Information System

Create and Incorporate Student Success Plans.

  • Document the number of students receiving support
  • Disaggregate by student groups
  • Evaluate effectiveness by reviewing periodically and analyzing data
    • # and type of interventions offered to tier 2 (moderate chronic absence)  and tier 3 students (severe chronic absence)
    • Progress monitoring data for tier 2 and tier 3 students- weekly attendance records for targeted students (are the interventions effective?)

Example Metrics (Data Sources): Local measures

Monitor Connectivity.

  • % of students with technology (have computer, software, and internet) on the site and in each classroom disaggregated by  specific student groups that are in need of support
  • % of staff who have the equipment and skills to support digital learning

Example Metrics (Data Sources): Local measures

Relationships: Gather quantitative and qualitative data, such as survey, interview, focus groups, etc.

  • % of students reporting positive relationships with at least one adult in the school
  • % of students participating in group relationship building activities led by adults or peers (clubs, enrichment activities, mentoring)
  • % of families reporting positive relationships with school staff
  • % of families reporting the opportunity to provide feedback on school decisions

Example Metrics (Data Sources): Survey data, focus groups, empathy interviews, local measures

Actively monitor site/classroom discipline referrals and establish goals:

Set short and long term Goals for reducing site/classroom suspensions and expulsion Rates and Improving School Climate for each school and student group - included in LCAP.
  • # and % of discipline referrals by classroom and grade level school wide disaggregated by student group
  • # and % of discipline referrals by type for each grade and classroom disaggregated by student group

Example Metrics (Data Sources): Student Information System, local measures

Create/monitor a site early warning systems for chronic absenteeism.

Disaggregate based on high needs students.
  • Track and monitor students that are “at-risk” (7-9% absences) of chronic absenteeism on a weekly basis
  • Track and monitor students that are experiencing moderate levels of chronic absenteeism (10-15% absences)

Example Metrics (Data Sources): Student Information System

Collect and analyze suspension and expulsion data based on high needs students (including low income, English learners, and foster youth.)

Equity Statement

Building trusting relationships, addressing implicit biases, and analyzing suspension and expulsion data for African American, Native American, and Latinx students support improved student outcomes.

Equity Action 2 Corresponding Metrics to collect and analyze suspension and expulsion data based on African American, Native American, and Latinx students (including low income, English learners, and foster youth.)

Overarching Site Considerations:

  • How does the site collect data on suspensions and expulsions?
    • Can the data collection be disaggregated?
  • How does the site’s and site professional learning plan align with this work?
    • How does the site support assets-based mindsets by intentionally focusing on educators and their mindsets about their students? 
    • How does the site address confronting biases and having courageous conversations about race?
  • What work is done to implement The English Learner Roadmap?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Establish attendance-monitoring systems and disaggregate the data by EL student groups to identify and respond to patterns.

  • Monitor student attendance data for EL student groups with triggers/flags when students achieve a certain number or percentage of absences (e.g. missing more than 10% of school)

Example Metrics (Data Sources): Student Information System

Collect and analyze site suspension and expulsion data:

  • # of English Learner suspensions organized by type and frequency.
  • # of Low Income suspensions organized by type, frequency, student group.
  • # of Foster Youth suspensions organized by type, frequency, student group.

Example Metrics (Data Sources): Student Information System, local measures

Create a site professional learning plan on collecting and analyzing data that aligns with the LEAs vision and PL plan.

  • Plan (artifact)
  • # of professional learning sessions that address how to use disaggregated data to target improved student outcomes
  • Collect efficacy data on professional learning plan through evaluations
    • % of "satisfied" participants and evidence of impact
    • % of participants meeting or exceeding professional learning success criteria

Example Metrics (Data Sources): Survey, self-assessment data, local measures

Develop alternatives to suspensions and expulsions at site

Equity Statement

Explicitly and intentionally focusing on building staff members assets-based mindsets about their students and confronting implicit biases supports the decrease in suspension and expulsion rates.

Equity Action 3 Corresponding Metrics to develop alternatives to suspensions and expulsions LEA-wide.

Overarching Site Considerations

  • How does the site develop staff skills to teach diverse student populations?
    • How does the site support assets-based mindsets by intentionally focusing on educators and their mindsets about their students? 
    • How does the site address confronting biases and having courageous conversations about race?
  • What is the site-wide policy on MTSS and PBIS implementation?
    • How does the site monitor site-level MTSS and PBIS data?
  • What short and long-term goals, metrics and actions are created to support school safety?
    • How does the site recruit diverse representatives to assist in creating these goals?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.


Metrics


Create/update the site’s PBIS implementation or improvement plan.

  • Plan (artifact)
  • Monitor plan regularly
  • Collect PBIS implementation data
    • Levels of implementation
    • Review goals, actions, and student outcomes

Create a school safety committee/task force to review data and to create short and long term goals, metrics, and actions.

  • Recruit diverse stakeholders, such as staff, parents, students; especially targeting stakeholders that represent student groups with the most suspensions / expulsions
  • Regularly evaluate the effectiveness of the plan.
    • Levels of implementation
    • Review goals, actions, and student outcomes

Example Metrics (Data Sources): Local measure

Collect MTSS /PBIS site data on types of suspensions / expulsions. Create baseline data.

  • Disaggregated student outcome data by prioritized student groups (low risk, at risk, high risk)
  • Create baseline and regularly monitor, analyze, and respond
  • # of English Learner suspensions organized by type and frequency.
  • # of Low Income suspensions organized by type, frequency, student group.
  • # of Foster Youth suspensions organized by type, frequency, student group.

Example Metrics (Data Sources): Local measure

Create a communication plan with pertinent stakeholders, such as local governing boards, staff, parents, students, etc.

Offer communication via a number of different platforms to include individual phone calls, home visits, and virtual meetings.

  • Collect data on the #, type, frequency of communication
  • Create a baseline and monitor regularly
    • # of contacts made to families and type (text, phone call, email, home visit)  of contacts made.
    • % of families with working contact information
    • % of students unreachable, disaggregated by student group and zip code

Example Metrics (Data Sources): Local measure

Develop staff skills to teach the diverse student population, promoting engagement of all.

  • Collect data on the # of PL focusing on teaching diverse populations
  • Collect efficacy data on professional learningthrough evaluations
    • % of "satisfied" participants and evidence of impact
    • % of participants meeting or exceeding professional learning success criteria

Provide teachers with professional learning that addresses how to use data to target academic and social needs of students

Equity Statement

Educators who are trained to use an equity lens while reviewing data, such as including student identities and culture, gain a more comprehensive picture/view of how African-American, Latinx, and Native American students are doing.

Equity Action 4 Corresponding Metrics to provide teachers with professional learning that addresses how to use data to target academic and social needs of students.

Overarching Site Considerations

  • How does the site determine priorities for professional learning?
  • How does the site collect data on professional learning?
  • What systems are in place to support data analysis?
    • What other measures are used to learn about student identities and teachers supported culture?
  • How are in learning about and implementing professional learning communities/data teams?

Data Disaggregation

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Create a site professional learning plan on cultural proficiency, cultural identity, implicit bias, and empathy.

  • Plan (artifact)
  • # of professional learning sessions that address how to use disaggregated data to target improved student outcomes
  • Collect efficacy data on professional learning plan through evaluations
    • % of “satisfied” participants and evidence of impact
    • % of participants meeting or exceeding professional learning success criteria

Example Metrics (Data Sources): Survey, self-assessment data, local measures

Provide opportunities for learning life skills (i.e., study skills, conflict resolution, peer counseling, healthy lifestyles)

Equity Statement: 

Building relationships that encourage self-advocacy, conflict resolution, and healthy lifestyles helps individuals overcome inequitable circumstances, affirms multifaceted identities, and fosters a relationship where the mentor and mentee teach and learn from one another.

Equity Action Corresponding Metrics to provide opportunities for learning life skills (i.e., study skills, conflict resolution, peer counseling, healthy lifestyles).

Overarching Site Considerations:

  • Does the site have  peer mentoring/peer counseling programs?
  • How does the site provide resources to support mental health and wellness?
  • Is the site considered a trauma-sensitive school? 

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Create a peer mentoring/counseling program.

  • Collect data on the type of services being provided
  • # of students receiving peer mentoring / peer counseling 
  • List of types of services being provided
  • Survey results of students and staff on effectiveness of program

Example Metrics (Data Sources): Survey, local measures

Implement bullying prevention and intervention plan.

  • Plan (artifact)
  • # and % of bullying incidents disaggregated by student groups by classroom.
  • # and type of interventions provided

Example Metrics (Data Sources): Survey, local measures

Monitor and provide mental health resources.

  • # of resources or referrals provided to students by type and frequency
  • Collect data on historically marginalized students, including LGBTQ student (as applicable) and document the # supports provided to students and parents

Example Metrics (Data Sources): Survey, empathy interviews, focus groups,local measures

Align to LEA work in creating a task force/committee to explore creating a plan and Advocating for Trauma-Sensitive Schools.

  • Plan (artifact)
  • # of meetings
  • List of types of services being provided
  • Survey students and staff on effectiveness of plan

Collect data to analyze:
  • Attendance Rates, by site, by student group
  • Suspension Rates, by site, by student group

Example Metrics (Data Sources): CA Dashboard, DataQuest, local measures


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Classroom Aligned Resources

Use data systems to accurately assess student progress and develop appropriate interventions and accelerations in academics.

Equity Statement

Intentionally selecting classroom resources and metrics based on the needs of African American, Native American, and Latinx students promotes student engagement and success.

Equity Action 1 Corresponding Metrics to use data systems to accurately assess student progress and develop appropriate interventions and acceleration in academics.

Overarching Classroom Considerations:

  • How does the classroom monitor attendance and chronic absenteeism, especially for African American, Latinx, and Native American students?
    • Is there a correlation between the academic outcomes and attendance data for African American, Latinx, and Native American students? 
  • How is technology and connectivity monitored in the classroom? 
  • How does the classroom teacher gather data on building positive and trusting relationships?
    • What qualitative measures are used, such as empathy interviews, focus groups, and student panels?
  • How are classroom discipline referrals monitored?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Work with grade level / grade level span teachers to address attendance and chronic absenteeism.

Review the Local Attendance Report to identify students who have not attended class.
  • List of students missing 10% of the school year for any reason (excused or unexcused absences and suspensions)
  • % of students chronically absent by grade and classroom disaggregated by student group (is this an issue schoolwide or specific grades or certain populations?)

Example Metrics (Data Sources): Student Information System, local measures

Conduct empathy interviews to gain information about attendance and chronic absenteeism.

Create an annual classroom / grade level plan to decrease chronic absenteeism, including 3 Tiers of Intervention (MTSS). Include short and long term goals, actions, and metrics.

  • List of students missing less than 5% of school (satisfactory)
  • List of students missing 6-10% of school (at-risk)
  • List of students missing 10-19% of school (moderate chronic absence)
  • List of students missing 20% or more of school (severe chronic absence

Example Metrics (Data Sources): Local measures, Student Information System

Develop and implement plans to re-engage students who have missed a significant number of school days.

For distance learning: Monitor Connectivity
  • % of students with technology (have computer, software, and internet) on the site and in each classroom disaggregated by specific student groups that are in need of support
  • % of staff who have the equipment and skills to support digital learning

Example Metrics (Data Sources): Local measures

Collect and analyze suspension and expulsion data based on high needs students (including low income, English learners, and foster youth.)

Equity Statement

Building trusting relationships, addressing implicit biases, and analyzing suspension and expulsion data for African American, Native American, and Latinx students support improved student outcomes.

Equity Action Corresponding Metrics to collect and analyze suspension and expulsion data based on African American, Native American, and Latinx students (including low income, English learners, and foster youth.)

Overarching Classroom Considerations:

  • How does the classroom/grade level/grade level span collect data on suspensions and expulsions?
  • Can the data collection be disaggregated?
  • How does the classroom teacher  support assets-based mindsets by intentionally focusing on educators and their mindsets about their students?
  • How does the teacher address confronting biases and having courageous conversations about race?
  • What work is done to implement The English Learner Roadmap?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, disparities in suspension and expulsion rates are identified at the student group level. The following metrics should be disaggregated to identify gaps in performance.

Metrics


Conduct a cumulative file check on students that have high numbers of suspensions and expulsions.

  • # of English Learner suspensions organized by type and frequency.
  • # of Low Income suspensions organized by type, frequency, student group.
  • # of Foster Youth suspensions organized by type, frequency, student group.

Example Metrics (Data Sources): Student Information System, local measures

Conduct Transdisciplinary Observations by systematically and directly observing students. This method of data collection enables the observer to focus on actual and relevant behaviors (in context), and it provides insightful and reflective data.


Example Metrics (Data Sources): Local measures

Develop alternatives to suspensions and expulsions in classroom

Equity Statement

Explicitly and intentionally focusing on building teachers assets-based mindsets about their students and confronting implicit biases supports the decrease in suspension and expulsion rates.

Equity Action Corresponding Metrics to develop alternatives to suspensions and expulsions at all schools

Overarching Classroom Considerations:

  • How does the teacher develop skills to teach diverse student populations?
    • How does the teacher support assets-based mindsets by intentionally focusing on their mindsets about their students? 
    • How does the teacher address confronting biases and having courageous conversations about race?
  • What is the classroom policy on MTSS and PBIS implementation?
    • How does the teacher monitor classroom MTSS and PBIS data?
  • What short and long-term goals, metrics and actions are created to support school safety?
    • How does the teacher work with diverse representatives to assist in creating these goals?
  • Is the teacher knowledgeable and using multiple discipline interventions instead of removing students from learning spaces?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Create/update classroom PBIS implementation or improvement plan.

  • Plan (artifact)
  • Monitor plan regularly (monthly, bi-weekly, etc.)
  • Collect PBIS implementation data
    • Levels of implementation
    • Review goals, actions, and student outcomes

Collect MTSS /PBIS classroom data on types of suspensions / expulsions. Create baseline data.

  • Disaggregated student outcome data by prioritized student groups (low risk, at risk, high risk)
  • Create baseline and regularly monitor, analyze, and respond
  • # of English Learner suspensions organized by type and frequency.
  • # of Low Income suspensions organized by type, frequency, student group.
  • # of Foster Youth suspensions organized by type, frequency, student group.

Example Metrics (Data Sources): Local measure

Create a communication plan with parents and students.

Offer communication via a number of different platforms to include individual phone calls, home visits, and virtual meetings.
  • Collect data on the #, type, frequency of communication
  • Create a baseline and monitor regularly
    • # of contacts made to families and type (text, phone call, email, home visit) of contacts made.
    • % of families with working contact information
    • % of students unreachable, disaggregated by student group and zip code

Example Metrics (Data Sources): Local measure

Provide opportunities for learning life skills (i.e., study skills, conflict resolution, peer counseling, healthy lifestyles)

Equity Statement:  

Building relationships that encourage self-advocacy, conflict resolution, and healthy lifestyles helps individuals overcome inequitable circumstances, affirms multifaceted identities, and fosters a relationship where the mentor and mentee teach and learn from one another.

Equity Action Corresponding Metrics to provide opportunities for learning life skills (i.e., study skills, conflict resolution, peer counseling, healthy lifestyles)

Overarching Classroom Considerations:

  • Does the classroom teacher support and participate in peer mentoring/peer counseling programs?
  • How does the teacher provide resources to support mental health and wellness?
  • Does the teacher support and participate in the essential elements of a trauma-informed school system?

Data Disaggregation:

Use disaggregated data to reveal underlying trends, patterns, or insights that would not be observable at the aggregated data level. For example, measuring the effectiveness of actions associated with alternatives to suspension and expulsion should be disaggregated to identify gaps in outcomes. The following metrics should be disaggregated to assist with next steps that are specific to each student group.

Metrics


Teach restorative practices
Teach Social-Emotional Learning


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