The document provides an overview of the Kindergarten Observation Form (KOF) and its use in assessing kindergarten readiness. It discusses how the KOF measures readiness across four domains using teacher observations and a parent survey. Results from multiple studies show the KOF is a valid predictor of later school performance. Data from the KOF have been used to identify factors linked to readiness, evaluate programs, and inform policies and investments to improve outcomes for children.
Organisational Structure of Secondary Education in PakistanR.A Duhdra
Objective
To Differentiate educational scenario before and after 18th amendment.
To differentiate role of Director Public Instruction schools and Colleges.
To know the curriculum development process and textbook development.
Types of Education Schools in Pakistan
Comparative perspective on teacher education Pakistan and UKseharalam
subject: Teacher Education
topic: Comparative perspective on teacher education Pakistan and UK
similarities and differences
which is best.
you read the suggested articles and thesis .........
Topic: Teacher Made Test vs Standardized Test
Student Name: Kanwal Naz
Class: B.Ed 1.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Organisational Structure of Secondary Education in PakistanR.A Duhdra
Objective
To Differentiate educational scenario before and after 18th amendment.
To differentiate role of Director Public Instruction schools and Colleges.
To know the curriculum development process and textbook development.
Types of Education Schools in Pakistan
Comparative perspective on teacher education Pakistan and UKseharalam
subject: Teacher Education
topic: Comparative perspective on teacher education Pakistan and UK
similarities and differences
which is best.
you read the suggested articles and thesis .........
Topic: Teacher Made Test vs Standardized Test
Student Name: Kanwal Naz
Class: B.Ed 1.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Euclid City Schools DLT Presentation Feb 9 2009tlysiak
This presentation was conducted by the Euclid City Schools' District Leadership team. Speakers were: Superintendent, Dr. Joffrey Jones, Principal, Dr. Charlie Smialek, Teachers Airel Townes and Margo Smolic, and State Support Team members Paula Woods and Ross May. The purpose of this presentation is to provide an overview of the actions the District Leadership Team has taken while examining data and forming goals.
March 2019 Directors Meeting featuring:
- Jennifer Keup, National Resource Center for The First-Year Experience and Students in Transition
- Althea Counts, Ashley Bailey-Taylor, Gamecock Guarantee
- Elizabeth White-Hurst, Blueprints
- Dennis Pruitt, vice president for student affairs
The Difference You Make: Using Data to Highlight Equity for Allappliedsurveyresearch
Breakout workshop presented at the 11th Annual Santa Clara County Children's Summit on March 9th, 2018. Part one of a series of two workshops designed to organize data collected using RBA and Collective Impact.
Accountability and equity are key components in achieving the Children's Agenda goals. Collecting the right data and communicating it effectively are essential to achieving results at scale. Applied Survey Research (ASR) will share its Results Based Accountability (RBA) tools and practices to enable partners to tell their stories of contribution to community-wide increases in equity and improved results. This session uses school-readiness as a case study for ways of implementing performance data to define contribution, highlight disparities, and identify opportunities.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
An Introduction to the Kindergarten Observation Form
1. AN INTRODUCTION TO THE
KINDERGARTEN OBSERVATION FORM
& THE ASR SCHOOL READINESS ASSESSMENT MODEL
Susan Brutschy, ASR President
Lisa Colvig-Niclai, MA
Kim Carpenter, PhD
Penny Huang, PhD
Christina Branom, PhD
Yoonyoung Kwak, PhD
Casey Coneway, MPP
2. Overview
Overview of the KOF and assessment methodology
- Why assess kindergarten readiness?
- Where have we assessed readiness?
- How do we assess readiness? The Kindergarten Observation Form
How do we tell the readiness story?
- How do we use these readiness data to spark action?
2
3. Purposes for readiness assessment
Create a portrait of readiness for a population of children
- Which children are more ready…and less ready?
- Which child and family factors are linked to greater readiness?
Set baseline as springboard for coordinated action…and track trends or
“sea changes” over time
“Look backward” to evaluate interventions for program participants
“Look forward” to provide formative data to guide K-3 interventions
To build bridges between ECE and K-12 with common framework and
indicators for readiness…a platform for coordinated intervention
We use these data at variety of levels or units of analyses: county-wide,
program or initiative, district, school, class, and recently, between teachers,
parents and individual children
3
4. Where has the KOF been used, and why?
4
5 longitudinal studies:
San Francisco, San Mateo,
Santa Clara, Marin, LA
Preschool
Assess at entry
• Formative
assessment
• Tailor instruction
• Engage parents
as partners
Assess at exit
• Profile of readiness
• Engage parents as
partners for
summer learning
support
• Collaborate with
kindergartens
Summer
Evaluate the readiness
‘boosters’
• Pre and post of summer
bridge programs
• Engage parents as
partners; help them be
school ready
Kindergarten
Assess at entry
• Create portrait of readiness
• Evaluate what works to boost
readiness
• Inform class instruction
• Engage parents as partners
• Use findings to inform readiness
investments, align systems
Third grade
Assess test scores
• Assess whether kinder
readiness predicts
third grade success
• Assess influence of
certain factors or
interventions
F5 Alameda County, 2008,09, 10, 11,13,14,15, 16 (Hayward PN)
F5 Contra Costa, 2016, 2017
F5 Del Norte, 2011,12,13,14,15,16, 17
F5 Sacramento, 2012,13,14,15,16, 17
F5 Marin/Marin Community Foundation, 2010, 11,12,13,14
F5 Napa (NVELI eval), 2013,14,15,16, 17
F5 San Francisco, 2007, 2009, 2013, 2014, 2015, 2017 (KRI)
F5 Santa Clara County 2004, 05, 06, 08, 11,12,13 (Quality Matters
study), 2016 (Alum Rock universal Pre-K), 2017
F5 San Mateo, 2001, 2002, 2003, 2005, 2008
F5 San Mateo- Preschool for All Evaluation 2005, 2008
F5 San Mateo Kickoff to Kindergarten Eval, 2008, 2009
F5 Santa Cruz, 2008
F5 Siskiyou, 2017
United Way, Lake County, Illinois, 2005, 2006
F5 San Mateo Kickoff to Kindergarten 2001, 02, 03, 04, 06, 07, 09, 13
Palo Alto USD Springboard to Kindergarten 2010, 11,12
Santa Clara County Migrant Ed, 2010
Duke University, 2014, 15,16
F5 Contra Costa
Literacy preschool
2010, 11, 12, 13, 14
F5 San Benito, 2012-14
Santa Clara County Head
Start, 2010, 11
San Jose Smart Start, 2010
Gilroy Unified, 2009, 2010
F5 Santa Cruz “Snapshot,”
2010, 11, 12, 13, 14, 15
5. How do we measure school readiness?
Holistic view of
readiness
Teacher-generated,
researcher-refined
Used with over
55,000
children
Predicts 3rd grade
test scores
5
K
Academics
Recog. shapes
Recog. colors
Counts 20 objects
Recog. letters
Recognizes rhyming words
Engages with books
Writes own first name
Answers questions about literature
Self-
Regulation
Stays focused
Follows class rules
Follows directions
Plays cooperatively
Participates in circle time
Handles frustration well
Social
Expression
Expresses empathy
Tells about story or experience
Curious & eager to learn
Expresses needs & wants
Motor Skills Items
Use of pencil (fine motor)
General coordination
6. How do we measure school readiness?
The Kindergarten Observation Form
- 10 minute teacher-administered assessment (average)
- 20 items = 14 observational, 6 interactive
- Common core aligned
- Four readiness dimensions or “Basic Building Blocks of Readiness”
• Self- Care & Motor Skills
• Self-Regulation
• Social Expression
• Kindergarten Academics
Parent Information Form
- Self-administered parent survey
- Research-based predictors of readiness, such as:
• Early education experience of child
• Transition activities
• Family activities like reading-aloud, arts/crafts, exercise
• Protective factors
• Background and demographic information
Secondary program/service data (program or school records)
6
8. How is the KOF implemented?
8
April ASR or a school district rep contacts each of the schools’ principals to
schedule teacher trainings for the Fall assessment
August ASR conducts 90 minute in-person teacher trainings (on school campuses)
Sept Teachers hand out Parent Packet: consent form, incentive book, and
Parent Information Form (PIF) survey
Teachers complete Kindergarten Observation Forms (KOF)
(paper, IPAD or computer!) for each student in their class.
October Teachers return the KOF, and PIFs materials to ASR by pre-paid Fed Ex
envelope
ASR sends each teacher their stipend (usually $150 to $250)
November ASR begins data entry and analysis
Teachers receive a data profile for their classroom
Jan-Mar ASR finalizes reports (comprehensive, school report, district dashboard)
9. The KOF is a valid measure of readiness
9
Content
validity
YES: Domains and items premised upon National
Education Goals Panel framework, Common Core
standards, and extensive, ongoing literature
reviews
Do experts view KOF
items as essential to
readiness?
Construct
validity
YES: Correlated with other validated assessments:
• Woodcock Johnson III,
• Brigance K-1 Screens,
• Expressive One Word Picture Vocabulary Test
(EOWPVT),
• Work Sampling System (WSS)
• Preschool/Kindergarten Behavior Scales (PKBS),
• Head Toes Knees Shoulder test (HTKS),
• Ages and Stages (ASQ)
Does the KOF measure
what we think it is
measuring?
Predictive
validity
YES: Five large longitudinal studies have shown that
KOF scores independently predict 3rd grade English and
Math scores, and 3rd grade DIBELS scores
Is the KOF a good
predictor of later
school performance?
11. Percent at each level of proficiency
11
Self-Care &
Motor Skills
Self-
Regulation
Social
Expression
Kindergarten
Academics
7%
6%
23%
6%
5%
7%
5%
6%
5%
7%
5%
6%
19%
12%
18%
11%
11%
19%
13%
18%
12%
13%
13%
14%
10%
11%
15%
17%
7%
10%
23%
9%
32%
34%
21%
24%
28%
36%
30%
33%
29%
26%
30%
31%
28%
27%
30%
32%
30%
30%
67%
87%
42%
49%
39%
59%
56%
37%
53%
43%
54%
58%
50%
51%
58%
58%
53%
47%
62%
56%
Recognizes primary shapes
Recognizes basic colors
Recognizes letters of the alphabet
Counts up to 20 objects
Recognizes rhyming words
Writes own first name
Understands structure, basic features of books
Answers questions about details in literature
Demonstrates curiosity, eagerness for learning
Tells about a story or experience
Expresses empathy or caring for others
Appropriately expresses needs and wants
Handles frustration well
Participates successfully in large group activities
Works and plays cooperatively with peers
Follows two-step directions
Follows class rules and routines
Stays focused in individual/small group activities
Has general coordination
Uses a pencil with proper grip
Not Yet Beginning In Progress Proficient
Source: Applied Survey Research, Kindergarten Observation Form (2015), First 5 Alameda, N=1,495-1,514
12. Readiness levels by KOF Building Block
12
3.29
3.52
3.20
3.32 3.25
1.00
2.00
3.00
4.00
Overall Readiness Self-Care & Motor
Skills
Self-Regulation Social Expression Kindergarten
Academics
Average readiness scores across all students
Proficient
In Progress
Beginning
Not Yet
13. Readiness levels across student groups
13
2.85
3.19
2.80 2.82 2.81
3.32
3.58
3.23 3.33 3.27
1.00
2.00
3.00
4.00
Overall Self-Care & Motor
Skills
Self-Regulation Social Expression Kindergarten
Academics
Cesar Chavez, 2012 SCC-Low income, 2008 SCC, 2008
Comparing readiness levels:
Cesar Chavez Elementary Students, County-wide, and County-wide Low Income
Proficient
In Progress
Beginning
Not Yet
14. Percentage of students ready for school
Not Ready
(0 areas),
20%
Partially Ready (1-
2 areas), 36%
Fully Ready
(all areas),
44%
14
Source: KOF, First 5 Alameda, 2015. N=1,460. Note: Data were weighted to approximate district and EL representation.
Fully Ready: Mean score of 3.25 or higher in all three domains: Self-Regulation, Social Expression and K. Academics.
Partially Ready: Mean score of 3.25 or higher in one or two domains.
Not Ready: Mean score below 3.25 in all three domains.
Percent Ready for Kindergarten, Alameda County, 2015
15. Readiness portraits across regions
15
Prevalence of students in each “readiness portrait,” 2009
City and County of San Francisco
16. Readiness levels across regions
16
94607
94606
94601
94621
94603
94579
94578
94556
94546
94541
Ave Readiness Score
Dark Red: 1-2.49
Light Red: 2.5-2.99
Light Green: 3-3.49
Dark Green: 3.5-4
Alameda County,
2017
17. What factors predict readiness?
17
School
Readiness
Quality ECE
(Pre-school, TK,
family care)
Gender
Age
Family
SES
English
Learner
Special
Needs
Health & Well-
Being
Single
Parent
Screen
Time
Race/
Ethnicity
Source: Kindergarten Observation Form (2015), Parent Information Form (2015), First 5 Alameda.
Note: All variables in the chart are statistically significant (p<.05). The overall regression model was significant (p<.001),
explaining 33% of the variance in kindergarten readiness (R2 = .33).
18. Percent fully ready, by key predictor (adjusted)
27%
52%
27%
50%
42%
51%
38%
48%
35%
51%
Overall
Sample:
44% Ready
Health & Well-
Being
Quality ECE
(Pre-school, TK,
family care)
Age
Special
Needs
English
Learner
Source: Kindergarten Observation Form (2015), Parent Information Form (2015), First 5
Alameda. Note: N=1,201. **All differences are statistically significant (p<.01).
19. Family Engagement matters!
The children of more highly engaged families had significantly higher
readiness scores, controlling for child and family demographics
3.00
3.09
2.86
2.97
3.16 3.15 3.07
3.17
Overall* Self-Regulation Social Expression* K Academics*
Less engaged
More engaged
Source: Kindergarten Observation Form, Parent Information Form 2016. Note: N=275-294. Analyses adjusted for gender, special
needs, English Learner, age, family SES, race/ethnicity, child well-being. *Difference statistically significant, p<.05.
21. Cumulative effect of predictors
37% 38%
45%
38% 39%
26%
23%
18%
32% 34%
43%
52%
70%
77%
45%
31% 21% 19%
10%
4%
0%
0-5 6 7 8 9 10 11
Percent of Children Ready for Kindergarten, by Number of Predictors
Partially Ready Fully Ready Not Ready
Source: Kindergarten Observation Form, Parent Information Form, First 5 Alameda, 2015. Note:
N=1,461. ***Statistically significant, p<.001.
22. Readiness is linked to certain interventions
22
57%
34%
F5 Preschool No preschool
Children in preschools supported by
First 5 Sacramento are almost
twice as likely to be fully ready for
kindergarten than children with no
preschool (adjusted)
English Learner students who participate in
Napa Valley Early Learning Services are
almost three times as likely to be fully
ready for kindergarten as EL students
who did not have such services
33%
13%
ELLs who
participated in NVELI
ELLs who did not
participate in NVELI
Source: Applied Survey Research, KOF,
Napa Valley Early Learning Initiative,
2015.
NVELI n=76, Non-NVELI n=45
Source: Applied Survey Research, KOF, PIF, First 5
Sacramento, 2015.
N= 610 ***Significant at p<.001.
23. Readiness is linked to certain interventions
23
2.65
2.81
2.51 2.48
2.86
3.07
3.29
3.05
2.92
3.09
Overall
Readiness***
Self-Care &
Motor Skills***
Self
Regulation***
Social
Expression**
Kindergarten
Academics*
No quality
factors
Two or
three
quality
factors
Source: Applied Survey Research, F5 Santa Clara Quality Matters study, 2013. Kindergarten Observation Form, 2013. N=106 (45 with no quality indicators,
61 with 2 or 3 quality indicators). ANCOVA- means adjusted for well-being variable. ***p<0.001; **p<0.01;*p<0.05.
Quality Indicators: PD >24 hours/year, Average years of teaching experience >10, Parent activity almost every day.
Readiness Levels of Santa Clara County Low Income Students,
by Former Preschools’ Number of Quality Factors, 2013
24. Longer-term impact of school readiness
24
25%
50%
68%
Not ready Partially ready Ready
Source: Kindergarten Observation Form, Ns = 3rd grade: 882. SFUSD
Percentage of students proficient in third grade ELA and Math, by kindergarten readiness level
25. Factors that predict third grade achievement
25
-0.60 -0.40 -0.20 0.00 0.20 0.40 0.60
Child age
Preschool
API of kindergarten school
Use of community resources
Risk factors
English learner at kindergarten
Child is a girl
Kindergarten well-being
Was read to at home as a kindergartner
Special ed/IEP in kindergarten
Kindergarten Academics
Predictive Weight (Unstandardized)
CSTMath
CSTELA
Negative effect Positive effect
Source: Kindergarten Observation Form, Parent Information Form; First 5 administrative data, SFUSD administrative data. N=576
CSTELA; 577 CST Math Notes: The overall regression model explains 34% of the variance in 3rd grade CSTELA and 33% of the
variance in 3rd grade CST Math scores.
26. Academics and Self Reg both matter for later success
25% 29%
55%
70%
Low on both K
Academics and Self-
Regulation
Low K Academics, High
Self-Regulation
High K Academics,
Low Self-Regulation
High on both K
Academics and Self-
Regulation
26
Source: Kindergarten Observation Form and individual school district data, 2010, Santa Clara and San Mateo Counties.
Note: Sample sizes = 367, 211, 235, 515, respectively. Students were divided into high and low levels of Kindergarten Academics and Self-Regulation based on
whether they were above or below the mean score on each.
Percent of Students Scoring at “Proficient” or “Advanced” on Third Grade ELA Tests, by
Kindergarten Readiness Patterns
27. Examples of how readiness data sparks action
Readiness guides for parents in several First 5 counties (Santa Clara, San
Francisco, Sacramento)
Calls to Action and Policy Briefs: Alameda, Siskiyou county
Stronger Pre-K and Kinder articulation, alignment and collaboration (Marin
County, Santa Cruz “Snapshots”)
Deeper views of who’s “not ready” in our programs, so we can better serve
children like them in the future (Napa, Sacramento,Alameda)
Direct feedback about the ECE practices that work (Santa Clara Quality
Matters)
Continued funding for interventions that show consistent, repeated linkage
with stronger readiness scores (San Francisco, Sacramento, Napa)
Baseline and trend data for community initiatives (Hayward Promise
Neighborhoods, Mission Promise Neighborhood, My Brother’s Keeper San
Jose, Grade Level Reading Campaign San Jose)
27
28. Summary
The KOF and the companion tools offer a 360 degree view of readiness
Readiness data reveals important differences in proficiencies…patterns of
skills…patterns of children…patterns of influences --- enabling partners
to better target their next efforts
Readiness data predicts 3rd grade outcomes…but early absenteeism
erases the benefits of readiness by third grade
Readiness is a movable needle, if groups can create the right recipe
28