Joint CDO & IR/IE Training August 12, 2020 Overview & - - PowerPoint PPT Presentation

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Joint CDO & IR/IE Training August 12, 2020 Overview & - - PowerPoint PPT Presentation

Nov 12, 2023 268 likes •594 views

Office of Equity & Inclusion Equity by Design Joint CDO & IR/IE Training August 12, 2020 Overview & Agenda Lead Facilitators Priyank Shah, PhD, Director of Equity Assessment Nancy D. Floyd, PhD, Senior System Director

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Equity by Design Joint CDO & IR/IE Training

Office of Equity & Inclusion

August 12, 2020

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Lead Facilitators

  • Priyank Shah, PhD, Director of Equity Assessment
  • Nancy D. Floyd, PhD, Senior System Director for Research
  • Josefina Landrieu, PhD, Assistant Chief Diversity Officer
  • Tarrence Robertson, OEI Project Director

Agenda

  • Introductions
  • Equity by Design & Data
  • Campus Experiences - Equity & Data Implementation
  • Data Challenges & Considerations
  • Next Steps & Questions

Overview & Agenda

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What do you think you need, in terms of training and/or support, to effectively implement Equity by Design at your institution?

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Equity by Design Overview & Data

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Equity-minded methodology that equips higher education leaders to address educational disparities.

  • Data-informed
  • Influences organizational change and development
  • Prioritizes equity in academic outcomes (student

success).

  • System-wide implementation (Equity 2030)

Equity by Design - Overview

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Elements of Equity by Design

Student- ready institutions Leadership philosophy Localized context Institutional change Accountability

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Let’s do a quick poll!

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Importance of Data

  • Examining disparity patterns in student outcomes

− Critical for narrowing equity gaps in course level

  • utcomes
  • Relatable & less abstract data

− Course level − Building blocks

  • Facilitate process of seeing & understanding

disparities

Equity by Design & Data

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Aggregate Category

  • Moving beyond binary analysis: “Student of Color” & “Non-

Student of Color”

  • Consideration of tremendously varied experiences & contexts

Imperative for Race/American Indian Disaggregation

  • New approaches are necessary
  • Patterns revealed
  • Disparate experiences, engagement, & outcomes

Data Disaggregation

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  • 1. Ethnicity Individuals are asked to designate ethnicity as:
  • Hispanic or Latino
  • 2. Race Individuals are asked to select 1 or more among following:
  • American Indian or Alaska Native
  • Asian
  • Black or African American
  • Native Hawaiian or Other Pacific Islander
  • White

IPEDS Two Question Format

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Race / Ethnicity Hierarchy: 7 variables  9 reporting possibilities

Nonresident Alien Hispanic/ Latino

Report

Yes No

Report

Yes No American Indian or Alaskan Native Asian Black or African American Native Hawaiian or Other Pac. Islander White

Report

Yes, to one

Report

Two or More Races Race & Ethnicity Unknown

Report

Yes

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Discussion, Q & A

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Equity & Data Campus Experiences

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Wendy Marson

Director of Institutional Research Inver Hills Community College & Dakota County Technical College

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Lessons Learned

  • Figure out the level of understanding across the group about data
  • This is an iterative process – every time you introduce a new data set,

ensure that your group understands what you’re showing them – no one becomes an expert overnight

  • +
  • Anticipate very different interpretations of what you may think is

straightforward information – everyone’s lens on the data is informed by their unique experiences

  • Be prepared to think about the data in different ways – the Diversity

Officers and the IR Directors should be talking early and often about the data and how to best help facilitate the discussions around it

  • Understand the resistance to the data and try to get to the root cause –

it’s often rooted in fear

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Narren J. Brown, PhD

Vice President of Research & Institutional Effectiveness Dean of Faribault Campus South Central College

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Is your goal student success…ours is

Who are the least successful students at your college? National trends suggest that your least successful students are:

Students of Color First Generation Pell Eligible

How do these or any combination of these groups succeed at your institution?

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Data  Information  Knowledge  Wisdom

Data

6.34 6.64 6.45 6.71 6.39 6.82 6.62 7.12 6.57 7.06

Information

SIRIUS SATELLITE RADIO INC. $5.80 $6.00 $6.20 $6.40 $6.60 $6.80 $7.00 $7.20 1 2 3 4 5 6 7 8 9 10 Last 10 Days Stock Price

Knowledge

Sirius stock is on an upward trend

Wisdom

Buy Sirius stock

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Percentage of Entering Fall Students Taking Dev Ed Math Who Complete in Year 1

2009 2010 2011 2012 2013 2014 2015 2016 Completed Dev Ed Math in Year 1 51.55% 48.48% 50.94% 55.82% 56.81% 59.52% 55.59% 56.68% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00%

Information

Data

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Percentage of Entering Fall Students Taking Dev Ed Math Who Complete in Year 1 by Gender and Student of Color

2009 2010 2011 2012 2013 2014 2015 2016 Female - Non SOC 57.38% 48.76% 55.39% 66.47% 61.25% 62.07% 54.55% 58.06% Female - SOC 53.66% 45.83% 47.50% 44.00% 51.79% 49.15% 56.06% 50.79% Male - Non SOC 48.94% 50.29% 48.15% 54.46% 57.63% 63.22% 55.06% 67.82% Male - SOC 22.86% 38.24% 39.53% 34.04% 45.83% 58.33% 54.24% 45.90% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%

Students of Color Complete Dev Ed Math at a lower rate than non SOC Develop an intervention to improve success for Students

  • f Color in Dev Ed Math
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The praxis of creating shared understanding

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Discussion, Q & A

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Data Challenges & Considerations

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Data & Analytics Focus Areas

  • Sharing of student information
  • Interpretation & leveraging data

What & Why Do We Need to Know?

  • Summary level

− Retrospective focus for Equity by Design

  • Student level

Data Challenges & Considerations

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Interpreting Data & Decision Making

  • Small N’s

− Patterns & Repetition

  • Interpretations & Conclusions

− Critical Evaluation & Biases

  • Reactions & Response

− Reflected & Measured

Data Challenges & Considerations

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Ethics & Confidentiality

  • FERPA: Family Educational Rights and Privacy Act 1974

− Protect privacy of student’s academic information − Certain exemptions for sharing data − Legitimate education interest & school officials

  • Story Telling & Sharing Data

− Public sharing of information - Caution − Highlighting themes & notable patterns

Data Challenges & Considerations

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EbD Data

  • Data set development

Upcoming EbD Trainings

  • October 13th (2:00-4:00pm) OR

October 21st (9:30-11:30am)

  • November 18th (2:00-4:00pm) OR

November 23rd (9:30-11:30am)

Next Steps

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Campus Next Steps

  • Review and understand the Equity by Design toolkit
  • Refine campus areas of focus & goals
  • Conduct capacity building activities (review articles, team and

self-reflection, history in context, build equity-minded language)

  • Begin equity-minded data inquiry
  • Attend Fall 2020 training

Next Steps

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Discussion, Q & A

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Thank you for your commitment.

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651-201-1800 888-667-2848

www.MinnState.edu

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