The New York State Board of Regents has adopted emergency regulations aimed at ensuring the recent
wave of undocumented minors that arrived in the state can enroll in school.
The state has received several complaints of undocumented students facing
barriers to school enrollment and has set up training sessions and issued
guidance to help correct the issue. The new regulations were passed to give
schools clarity on how to comply with federal law, the Board of Regents said in
a release. The regulations specify that schools can't ask about the citizenship
or immigration status of students or their parents, and it outlines acceptable
documents schools can use to determine childrens' ages when enrolling them in
school.
"The Board of Regents has enacted these regulations to protect the
right of each and every child to a free public education, no matter where they
come from or what they look like," Board of Regents Chancellor Merryl Tisch
said “We are resolute in the belief that enrollment obstacles cannot hold back
the hopes and aspirations of our children.”
New York state isn't alone in its concerns about barriers for undocumented
minors enrolling in school: The federal Education Department has also issued
multiple rounds of guidance in recent years reminding states of their
obligations when enrolling undocumented students.
HIDOE will include each school’s Index points and composite scores on the school report card. The Index scores will be used to customize the supports and interventions to meet the school’s needs. Index scores will be provided for the following categories in the Strive Hi Performance System:
• Achievement: The Achievement indicators measure whether a school is providing students with the math, reading, and science skills for a solid academic foundation. Math, reading, and science proficiency will be measured by the statewide assessments in grades 3 - 8 and 10. New assessments will be aligned to Common Core Standards. SY 2013-14 will be a “bridge” assessment from the Hawaii State Assessment to Common Core Standards. The Smarter Balance assessment will be administered in SY 2014-15.
• Growth: The Growth indicators measure whether a school is improving students’ reading and math scores over time in grades 4 – 8 and 10.
RFP D15-041
• Readiness: The Readiness indicators measure whether a school is doing its part in ensuring students are ready to move through the K-12 pipeline prepared to graduate for college and careers.
o For elementary schools, the chronic absenteeism rate is defined as the percentage of students absent for 15 or more days a year (excluding medical emergencies).
o For middle schools, the readiness indicators will be 8th grade ACT scores, which include English, reading, math and science.
o For high schools, the Index will use 11th grade ACT scores (including English, reading, math and science) and graduation and college going rates.
• Achievement Gap: The Achievement Gap indicators measure the achievement gap between student subgroups and how well a school is narrowing gaps over time.
o The current year indicator will measure the current year gap, while the multi-year indicator will measure how the school has narrowed the gap over time.
o The Achievement Gap indicators will compare reading and math proficiency between two subgroups: “High-Needs” students and “Non-High Needs” students. The High-Needs category includes students in any one of the federally defined subgroups: disability, language or family income.
To illuminate the
possibilities for next-generation assessments in K–12 schools, our latest case
study profiles the Cisco Networking Academy, which creates comprehensive online
training curriculum to teach networking skills. Since 1997, the Cisco Networking
Academy has served more than five million high school and college students and
now delivers approximately one million online assessments per month in a
variety of formats. Its advanced and highly integrated assessment system offers
lessons for K–12 technology and assessment.
In an article published
this week in Education Week, Michael and Heather discuss how blended
learning is a scalable strategy that
can break the trade-offs inherent in the traditional school design to allow
teachers to reach students in ways never before possible. But for it to work,
school leaders must not start with blended learning or technology for its own
sake, but instead undertake a careful design process to unlock its potential.
Clashes over testing in
K–12 schools have grown in intensity in recent years. In some quarters, parents
decry the over-testing of their children, for example, whereas others point out
the need for testing for accountability over the use of public funds. Fewer
talk about how important assessment is for learning—for students and teachers—because
our education… Read More
This week marks National
Computer Science Education Week. Not only are K–12 schools, parents, and
leaders around the country engaged in activities like the Hour of Code, but the
week is also a chance for advocacy groups like code.org to highlight the
beleaguered state of computer science education in America. For example,
currently only around… Read More
This blog was first
published on CompetencyWorks. The running joke about higher education is that
change doesn’t come eventually, but glacially. Much of academic inertia stems
from the complicated business model of delivering higher education, not to
mention the orchestration of multiple stakeholders on campuses: the
administration, faculty members, trustees, senate committees, unions, and
other… Read More
IN CASE YOU MISSED IT
The BLU is back
The newly expanded Blended Learning Universe—or BLU—is now live! The BLU
is a comprehensive online hub packed with blended-learning resources. Whether
you’re looking for a primer on the basics or want to dive deep into the supporting
research, the BLU has you covered. The site provides helpful tools for
practitioners, policymakers, parents, and innovators working to improve
education through personalized, student-centered learning. Check it out: www.blendedlearning.org
Co-authored by Michael Horn
and Heather Staker,Blended: Using Disruptive Innovation to Improve Schoolsserves as a design guide for K–12 stakeholders looking
to effectively embrace the rise of blended learning. This book is a must-have
resource for educators, parents, and innovators navigating the future of
learning.
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How Game Theory Helped Improve New York City’s High School Application Process
Tuesday was the deadline for eighth graders in New York City to submit applications to secure a spot at one of 426 public high schools. After months of school tours and tests, auditions and interviews, 75,000 students have entrusted their choices to a computer program that will arrange their school assignments for the coming year. The weeks of research and deliberation will be reduced to a fraction of a second of mathematical calculation: In just a couple of hours, all the sorting for the Class of 2019 will be finished.
To middle-school students and their parents, the high-school admissions process is a grueling and universally loathed rite of passage. But as awful as it can be, it used to be much worse. In the late 1990s, for instance, tens of thousands of children were shunted off to schools that had nothing going for them, it seemed, beyond empty desks. The process was so byzantine it appeared nothing short of a Nobel Prize-worthy algorithm could fix it.
Which is essentially what happened.
Photo
Alvin E. Roth at Stanford University in 2012.Credit Norbert Von Der Groeben/Reuters
About a decade ago, three economists — Atila Abdulkadiroglu (Duke), Parag Pathak (M.I.T.) and Alvin E. Roth (Stanford), all experts in game theory and market design — were invited to attack the sorting problem together. Their solution was a model of mathematical efficiency and elegance, and it helped earn Professor Roth a Nobel Memorial Prize in Economic Science in 2012.
Before the redesign, the application process was a mess. Or, as an economist might say, it was an example of a congested market. Each student submitted a wish list of five schools. Some of them would be matched with one of their choices, and thousands — usually the higher-performing ones — would be matched with more than one school, giving them the luxury of choosing. Nearly half of the city’s eighth graders — many of them lower-performing students from poor families — got no match at all. That some received surplus offers while others got none illustrated the market’s fundamental inefficiency.
Thousands of unlucky teenagers wound up waiting through the summer to get placed, only to be sent to schools they had not listed at all. And those schools, Professor Pathak discovered in a recent analysis, were “worse in all dimensions” — including student achievement, graduation rate and college admissions — than the schools the students had asked to attend.
Even more bizarre, the system encouraged safe, rather than ambitious, choices. Some sought-after schools accepted only the applicants who had made them their first choice. So students who aimed high and listed several such schools but were rejected by the first could blow their chances all the way down the list.
To address this flaw, the Education Department’s high school directory advised students to “determine what your competition is for a seat in this program"— a vexing task for even the best-informed among them.
“It was an allocation problem,” explained Neil Dorosin, the director of high-school admissions at the time of the redesign. The city had a scarce resource — in this case, good schools — and had to work out an equitable way to distribute it. “But unlike a scarce resource like Rolling Stones tickets, where whoever’s willing to pay the most gets the tickets, here we can’t use price,” Mr. Dorosin said.
Professors Roth, Abdulkadiroglu and Pathak modeled their solution to this conundrum on a famous puzzle in economics: the stable marriage problem. In the early 1960s, the economists David Gale and Lloyd Shapley proved that it was theoretically possible to pair an unlimited number of men and women in stable marriages according to their preferences.
Parag Pathak of M.I.T.Credit Gretchen Ertl for The New York Times
Here is how it works: Each suitor proposes to his first-choice mate; each woman has her own list of favorites. (The economists worked from the now-quaint premise that men only married women, and did the proposing.) She rejects all proposals except her favorite — but does not give him a firm answer. Each suitor rejected by his most beloved then proposes to his second choice, and each woman being wooed in this round again rejects all but her favorite.
The courting continues until everyone is betrothed. But because each woman has waited to give her final answer (the “deferred acceptance”), she has the opportunity to accept a proposal later from a suitor whom she prefers to someone she had tentatively considered earlier. The later match is preferable for her, and therefore more stable.
The deferred acceptance algorithm, Professor Pathak said, is “one of the great ideas in economics.” It quickly became the basis for a standard lesson in graduate-level economics courses.
Of course, there seldom is much need for mass betrothals. It was Professor Roth who developed the first practical application for this idea. In 1995 he configured a deferred acceptance algorithm to connect graduating medical students with hospital residencies. Professor Shapley shared the Nobel for economics with Professor Roth for his pioneering work on the subject. When officials at the city’s Education Department learned about the residency formula, they realized that something similar might tame the chaotic school-choice system in New York.
Photo
Atila Abdulkadiroglu of Duke UniversityCredit Les Todd/Duke University
Playing matchmaker to doctors or students is a little more complex than pairing off couples to be married, since hospitals and schools are, in effect, polygamous — they accept many proposals. But the principle is the same: Students list their favorite schools, in order of preference (they can now list up to 12). The algorithm allows students to “propose” to their favorite school, which accepts or rejects the proposal. In the case of rejection, the algorithm looks to make a match with a student’s second-choice school, and so on. Like the brides and grooms of Professors Gale and Shapley, students and schools connect only tentatively until the very end of the process.
In 2004, the first year that students were sorted in this way, the number who went unmatched plummeted, from 31,000 in 2003 to about 3,000 — still a lot of disappointed teenagers. That year, and every year since, the algorithm has assigned roughly half of all students to their first–choice schools; another third or so have been assigned to their second or third choices. (The city’s nine specialized high schools have their own separate admissions process.)
While those represent pretty good odds, parent chat groups roil with dark speculation about some mercurial trick through which a child may be deprived of her dream school. Parents worry that their children could “waste” the crucial first-place spot if they choose wrong. And they fret that a popular school will fill up with children who ranked it first, before the algorithm has a chance to consider their own, equally qualified, child.
Professor Abdulkadiroglu said he had fielded calls from anguished parents seeking advice on how their children could snare the best match. His advice: “Rank them in true preference order.”
The allocation problem has not disappeared. Good schools remain a scarce resource, especially in poor neighborhoods, and low-income and low-performing children are still more likely to end up in underfunded schools. Sean Corcoran, associate professor of educational economics at New York University’s Steinhardt School of Culture, Education and Human Development, has studied the choices made by low-achieving students, who are disproportionately poor. He found that the algorithm matches low- and high-achieving applicants with their first-choice schools at roughly the same rate. But Professor Corcoran said, “Lower-achieving kids are applying to lower-achieving schools and ranking them as their top choices.”
It seems that most students prefer to go to school close to home, and if nearby schools are underperforming, students will choose them nevertheless. Researching other options is labor intensive, and poor and immigrant children in particular may not get the help they need to do it.
But that is a political problem, and so far, there is no algorithm that can fix it.
Substantially more teachers in New York state are coming to the profession
with top academic qualifications, according to a new study published in the journal Educational Researcher.
The authors found that the number of new teachers with SAT scores in the top
third of all test takers jumped by 13 percentage points from 1999 through 2010.
In 2010, a full 42 percent of newly hired teachers came from that elite group.
The study attributed the surge to tougher teacher training and licensure
policies implemented in New York starting in 1999. The researchers postulate
that the more rigorous standards raised the stature of teaching as a profession
and drew higher-performing students to the field.
“These findings signal a resurgence of interest in teaching in public schools
as a respected and worthy career,” said Luke Miller, a research professor at
the University of Virginia’s Curry School of Education.
Other states have adopted elements of New York state’s reforms to licensure
and training. The Education Department has proposed regulations
aimed at boosting the quality of teacher training programs nationwide.
