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ASFM Grade 9 ELA Labsite - November 2023

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Email: jenny@erinkentconsulting.com

Linked In: Jenny Killion

Twitter/X: @profesorajennyk

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Resources to support our January 2023 onsite work together!

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ASFM MS Science of Reading Overview - November 2023

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ASFM MS Science of Reading Overview - November 2023 (2)

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This book offers teachers a complete framework for organizing instruction, aligned with the language comprehension strands of Scarborough's Reading Rope. In-depth chapters are devoted to each facet of reading comprehension, including vocabulary, syntax and sentence comprehension, text structures, background knowledge, and levels of understanding and inference.

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Winner of a silver medal from the Nautilus Book Awards in the category of Social Sciences and Education

"Is it possible to create a more inclusive and fairer education system? The Knowledge Gap shows the way." Daisy Christodoulou, tes.

"The Knowledge Gap is not just a scholarly work. It pulsates with vivid detail as it winds its way through the strange terrain of the Educational Upside Down. Anyone who seeks to understand the quixotic state of educational ideas in American schools and beyond should place it on the top of their summer reading list." Greg Ashman, Quillette

"The best book I have seen on improving elementary schools." Jay Mathews, Washington Post

"The Knowledge Gap makes a persuasive case for attending to the importance of rich content and high-quality curricula in elementary classrooms." Carnegie Corporation of New York

"This book serves as a reminder of our own potential knowledge gap and provides an opportunity for educators to be wiser consumers of information with the ability to 'distinguish between approaches that are likely to produce the outcomes we want and those that will only lead to a heartbreaking waste of precious time.'" Sheryl Ferlito, Perspectives on Language and Literacy

"Wexler ... has made a first-rate case for content. ... If you are among the uninitiated on the value of content knowledge for kids, buy a copy. If you're already among the true believers, buy two copies and put them in the hands of persuadable educators." Robert Pondiscio, Education Next

"In this compelling work, journalist Wexler pinpoints the underlying issues that plague the American public education system, provides context to recent educational reforms, and offers possible solutions." Library Journal (starred review)

"A compelling critique of American elementary schools" that "highlights pervasive problems." Kirkus Reviews

"Illuminating and thought-provoking." Publishers Weekly

THE KNOWLEDGE GAP focuses on a long-overlooked issue lying at the heart of what is known as the achievement gap: the failure of most elementary schools, and especially those serving low-income children, to systematically build knowledge of the world.

Every school day, elementary teachers spend hours drilling students on comprehension skills and strategies like "finding the main idea" or "making inferences," in an effort to boost their reading ability-and their scores on standardized reading tests. Subjects like history, science, and art have virtually disappeared from the curriculum in many schools.

In fact, these schools are doing the exact opposite of what can actually turn students into good readers. Comprehension strategies can be useful-when delivered in limited doses and connected to specific content. But cognitive science has shown that the primary factor in whether you're able to understand what you read is whether you possess relevant background knowledge and vocabulary.

The prevailing narrow, skills-focused approach to literacy instruction can hold any child back, but its effects fall hardest on those at the bottom of the socioeconomic spectrum. Children from more educated families often absorb knowledge about the world at home. But those from less educated families have to rely on schools for the kind of knowledge that will equip them to do well on tests-not to mention in high school, college, and life. Every year they spend in school without getting that knowledge puts them farther behind their more affluent peers. By the time they get to middle or high school, it's often too late for them to catch up.

Through the stories of educators, students, and parents, The Knowledge Gap examines what elementary literacy instruction currently looks like, how it evolved to this point - and how it can be changed so that all students receive a meaningful, rigorous education.

Excerpts from the book have appeared in The Atlantic, Chalkbeat, MindShift, American Educator, and the blog of the Albert Shanker Institute.

Read or listen to interviews with Natalie and presentations about The Knowledge Gap:

TV:

Morning Joe, MSNBC

After Words With Natalie Wexler, C-SPAN

Good Morning Washington, WJLA

VIDEO:

Why Prior Knowledge Wins the Game (two-minute video from Edutopia)

Western NY Science of Reading Conference, November 2022

Iniciativa Educacao, Lisbon (in English)

Why Teaching Social Studies Is Teaching Reading, The OC for Social Studies Conference

RADIO:
Total Information AM South Dakota Public Broadcasting
The Pulse (WHYY/NPR) (episode on comprehension)

On Point (WBUR/NPR)

Leonard Lopate at Large, WBAI

All of It, WNYC

The Record, KUOW

St. Louis in Tune, KWRH

Nightside with Dan Rea, WBZ

PODCASTS:

Through the Educational Looking Glass with Don Sturm and Courtney Eddleson

Lars og pal with Lars Sandaker

Education Uncharted with Amanda Bratten (Propello)

Raising Healthy Kid Brains podcast with Amy Nielson

TeachThought Podcast panel discussion with Emily Hanford and others

The Literacy View with Faith Borkowsky, Judy Boksner, and Mary McCool Berry

Building Knowledge with Kristen Rodriguez

Mr. Nico Summer Schoolin' with Nico Eaglin

Melissa and Lori Love Literacy (with Judith Hochman)

On the Scene with SST 13 with Mary Williams

The Inquiring Mind with Stanley Goldberg

After the Bell with Cassie Tabrizi
Talking to Teens
Alabama Literacy Network's Podcast with Shelley Vail-Smith

The Charles Love Show (second hour)

Catalysts for Change with Jill Shah

Rethinking Intervention with Emily Freitag

Tests and the Rest with Mike Bergin and Amy Seeley

Filling the Pail with Greg Ashman

Rethinking Intervention with Emily Freitag and Christina Gonzales

PodcastED with Doug Tuthill
EDVIEW 360 Podcast, Voyager Sopris: Righting the Wrongs of the Elementary Education System EWA Radio: Can Schools Close "The Knowledge Gap"?Just a Few Questions with Marc Sims
Education is Power with Chris Stewart (Facebook Live)

Melissa and Lori Love Literacy (second appearance)
More Teacher Talk: Transforming Teaching and Learning for Disadvantaged Pupils Full Prefrontal: A Podcast About Exposing the Mysteries of Executive Function Ed's Not Dead: A podcast covering today's hottest education issues Off the Assembly Line: A Podcast with Rebecca Reed ExtraOrdinary Districts: Ordinary school districts that get extraordinary results Vrain Waves: Teaching Conversations with Minds Shaping Education Science of Reading: The Podcast The Learning Curve with Bob Bowdon and Cara Candel The Report Card with Nat Malkus Education Research Reading Room with Ollie Lovell Principal Center Radio with Justin Baeder Melissa and Lori Love Literacy

PRINT INTERVIEWS AND ARTICLES:
EdSource/KQED, This Literacy Expert Reveals the Key Problem Undermining American Education, April 25, 2024 Edutopia, "Why Our Youngest Readers Need 'Knowledge,' Not 'Skills,' July 28, 2022 Edutopia, "Is It Time to Drop 'Finding the Main Idea' and Teach Reading in a New Way?, July 28, 2020 American Educator, "Building Knowledge: What an Elementary Curriculum Should Do", Summer 2020 EdNC, "Reading instruction needs to improve. Who will lead the charge, and how?", February 17, 2020 Perspectives on Language and Literacy, "The Importance of Knowledge" (International Dyslexia Association), Fall 2019 MindShift (KQED) ,"Why Deeply Diving Into Content Could Be the Key to Reading Comprehension," October 22, 2019 Chalkbeat, "Want better readers? Spend less time teaching kids to find the main idea, 'Knowledge Gap' author Natalie Wexler argues", September 16, 2019 District Administration, "Closing the knowledge gap: Why schools are failing to teach fundamental reading and comprehension skills-and how to change it", October 2019
Click HERE to order The Knowledge Gap through the publisher's website or HERE to order from Amazon. Listen to an excerpt from the audio version, read by the author, below. To order the audio book, click HERE.

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Winner of a silver medal from the Nautilus Book Awards in the category of Social Sciences and Education "Is it possible to create a more inclusive and fairer education system? The Knowledge Gap shows the way." Daisy Christodoulou, tes. "The Knowledge Gap is not just a scholarly work.

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ASFM MS Foundations Review - November 2023 (1)

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ASFM MS Foundations Review - November 2023 (2)

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AI in the Classroom from Ditch that Textbook

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AI in the Classroom from Ditch that Textbook

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AI in the classroom: What’s cheating? What’s OK?

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Now that generative AI tools like ChatGPT are available, classwork is bound to change as a result. How do we preserve academic integrity? What’s cheating,

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Artificial intelligence image tools have a tendency to spin up disturbing clichés: Asian women are hypersexual. Africans are primitive. Europeans are worldly. Leaders are men. Prisoners are Black.

These stereotypes don't reflect the real world; they stem from the data that trains the technology. Grabbed from the internet, these troves can be toxic - rife with pornography, misogyny, violence and bigotry.

Every image in this story shows something that doesn't exist in the physical world and was generated using Stable Diffusion, a text-to-image artificial intelligence model.

Stability AI, maker of the popular image generator Stable Diffusion XL, told The Washington Post it had made a significant investment in reducing bias in its latest model, which was released in July. But these efforts haven't stoppedit from defaulting to cartoonish tropes. The Post found that despite improvements,the tool amplifies outdated Western stereotypes, transferring sometimes bizarre clichés to basic objects, such as toys or homes.

"They're sort of playing whack-a-mole and responding to what people draw the most attention to," said Pratyusha Kalluri, an AI researcher at Stanford University.

Christoph Schuhmann, co-founder of LAION, a nonprofit behind Stable Diffusion's data, argues that image generators naturallyreflect the world of White people because the nonprofit that provides data to many companies, including LAION, doesn't focus on China and India, the largest population of web users.

[ Inside the secret list of websites that make AI like ChatGPT sound smart]

When we asked Stable Diffusion XL to produce a house in various countries, it returned clichéd concepts for each location: classical curved roof homes for China, rather than Shanghai's high-rise apartments; idealized American houses with trim lawns and ample porches; dusty clay structures on dirt roads in India, home to more than 160 billionaires, as well as Mumbai, the world's 15th richest city.

United States

"This will give you the average stereotype of what an average person from North America or Europe thinks," Schuhmann said. "You don't need a data science degree to infer this."

Stable Diffusion is not alone in this orientation. In recently released documents, OpenAI said its latest image generator, DALL-E 3, displays "a tendency toward a Western point-of-view" with images that "disproportionately represent individuals who appear White, female, and youthful."

As synthetic images spread across the web, they could give new life to outdated and offensive stereotypes, encoding abandoned ideals around body type, gender and race into the future of image-making.

Predicting the next pixel

Like ChatGPT, AI image tools learn about the world through gargantuan amounts of training data. Instead of billions of words, they are fed billions of pairs of images and their captions, also scraped from the web.

Tech companies have grown increasingly secretive about the contents of these data sets, partially because the text and images included often contain copyrighted, inaccurate or even obscene material. In contrast, Stable Diffusion and LAION, are open source projects, enabling outsiders to inspect details of the model.

Stability AI chief executive Emad Mostaque said his company views transparency as key to scrutinizing and eliminating bias. "Stability AI believes fundamentally that open source models are necessary for extending the highest standards in safety, fairness, and representation," he said in a statement.

Images in LAION, like many data sets, were selected because they contain code called "alt-text," which helps software describe images to blind people. Though alt-text is cheaper and easier than adding captions, it's notoriously unreliable - filled with offensive descriptions and unrelated terms intended to help images rank high in search.

[ AI can now create images out of thin air. See how it works. ]

Image generators spin up pictures based on the most likely pixel, drawing connections between words in the captions and the images associated with them. These probabilistic pairings help explain some of the bizarre mashups churned out by Stable Diffusion XL, such as Iraqi toys that look like U.S. tankers and troops. That's not a stereotype: it reflects America's inextricable association between Iraq and war.

Misses biases

Despite the improvements in SD XL, The Post was able to generate tropes about race, class, gender, wealth, intelligence, religion and other cultures by requesting depictions of routine activities, common personality traits or the name of another country. In many instances, the racial disparities depicted in these images are more extreme than in the real world.

For example, in 2020, 63 percent of food stamp recipients were White and 27 percent were Black, according to the latest data from the Census Bureau's Survey of Income and Program Participation. Yet, when we prompted the technology to generate a photo of a person receiving social services, it generated only non-White and primarily darker-skinned people. Results for a "productive person," meanwhile, were uniformly male, majority White, and dressed in suits for corporate jobs.

a person at social services

a productive person

Last fall, Kalluri and her colleagues also discovered that the tools defaulted to stereotypes. Asked to provide an image of "an attractive person," the tool generated light-skinned, light-eyed, thin people with European features. A request for a "a happy family" produced images of mostly smiling, White, heterosexual couples with kids posing on manicured lawns.

Kalluri and the others also found the tools distorted real world statistics. Jobs with higher incomes like "software developer" produced representations that skewed more White and male than data from the Bureau of Labor Statistics would suggest. White-appearing people also appear in the majority of images for "chef," a more prestigious food preparation role, while non-White people appear in most images of "cooks" - though the Labor Bureau's statistics show that a higher percentage of "cooks" self-identify as White than "chefs."

Cleaner data, cleaner results

Companies have long known about issues with the data behind this technology. ImageNet, a pivotal 2009 training set of 14 million images, was in use for more than a decade before researchers found disturbing content, including nonconsensual sexual images, in which women were sometimes easily identifiable. Some images were sorted into categories labeled with slurs such as "Closet Queen," "Failure," "mulatto," "nonperson," "pervert," and "Schizophrenic."

ImageNet's authors eliminated most of the categories, but many contemporary data sets are built the same way, using images obtained without consent and categorizing people like objects.

Efforts to detoxify AI image tools have focused on a few seemingly fruitful interventions: filtering data sets, finessing the final stages of development, and encoding rules to address issues that earned the company bad PR.

For example, Stable Diffusion drew negative attention when requests for a "Latina" produced images of women in suggestive poses wearing little to no clothing. A more recent system (version 2.1) generated more innocuous images.

a Latina (version 1.5)

a Latina (version 2.1)

Why the difference? A Post analysis found the training data for the first version contained a lot more pornography.

Of the training images captioned "Latina," 20 percent of captions or URLs also included a pornographic term. More than 30 percent were marked as almost certain to be "unsafe" by a LAION detector for not-safe-for-work content. In subsequent Stable Diffusion models, the training data excluded images marked as possibly "unsafe," producing images that appear markedly less sexual.

The Post's findings track with prior research that found images of sexual abuse and rape in the data set used for Stable Diffusion 1, as well as images that sexualized Black women and fetishized Asian women. In addition to removing "unsafe" images, Ben Brooks, Stability AI's head of public policy, said the company was also careful to block child sexual abuse material (CSAM) and other high-risk imagery for SD2.

Filtering the "bad" stuff out of a data set isn't an easy fix-all for bias, said Sasha Luccioni, a research scientist at Hugging Face, an open source repository for AI and one of LAION's corporate sponsors. Filtering for problematic content using keywords in English, for example, may remove a lot of porn and CSAM, but it may also result in more content overall from the global north, where platforms have a longer history of generating high-quality content and stronger restrictions on posting porn, she said.

"All of these little decisions can actually make cultural bias worse," Luccioni said.

Even prompts to generate photos of everyday activities slipped into tropes. Stable Diffusion XL defaulted to mostly darker-skinned male athletes when we prompted the system to produce images for "soccer," while depicting only women when asked to show people in the act of "cleaning." Many of the women were smiling, happily completing their feminine household chores.

playing soccer

Stability AI argues each country should have its own national image generator, one that reflects national values, with data sets provided by the government and public institutions.

Reflecting the diversity of the web has recently become "an area of active interest" for Common Crawl, a 16-year-old nonprofit that has long provided text scraped from the web for Google, LAION, and many other tech firms, executive director Rich Skrenta told The Post. Its crawler scrapes content based on the organization's internal ranking of what's central to the internet, but is not instructed to focus on a specific language or country.

"If there is some kind of bias in the crawl and if it's not probing as deeply into, say, Indian websites," that is something Common Crawl would like to measure and fix, he said.

The endless task of eradicating bias

The AI field is divided on how to address bias.

For Kalluri, mitigating bias in images is fundamentally different than in text. Any prompt to create a realistic image of a person has to make decisions about age, body, race, hair, background and other visual characteristics, she said. Few of these complications lend themselves to computational solutions, Kalluri said.

Kalluri believes it's important for anyone who interacts with the technology to understand how it operates. "They're just predictive models," she said,portraying things based on the snapshot of the internet in their data set.

[ See why AI like ChatGPT has gotten so good, so fast]

Even using detailed prompts didn't mitigate this bias. When we asked for a photo of a wealthy person in different countries, Stable Diffusion XL still produced a mishmash of stereotypes: African men in Western coats standing in front of thatched huts, Middle Eastern men posed in front of ancient mosques, while European men in slim-fitting suits wandered quaint cobblestone streets.

the Middle East

Abeba Birhane, senior advisor for AI accountability at the Mozilla Foundation, contends that the tools can be improved if companies work hard to improve the data - an outcome she considers unlikely. In the meantime, the impact of these stereotypes will fall most heavily on the same communities harmed during the social media era, she said, adding: "People at the margins of society are continually excluded."

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These fake images reveal how AI amplifies our worst stereotypes

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AI image generators like Stable Diffusion and DALL-E amplify bias in gender, race and beyond, despite efforts to detoxify the data fueling these results.

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NCTE 2023 - Now the Hard Work Begins: Inviting Writers to Use AI Tools

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‎The Generative Age: AI in Education on Apple Podcasts

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Generative Age (powered by NYSCATE) explores the rapidly evolving world of generative artificial intelligence and its impact on education. Host Alana Winnick is joined by practitioners and thought leaders who are shaping the discourse around integrating generative AI into the classroom. Whether you...

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Aouad, J. and Savage, R. (2009). The component structure of preliteracy skills: Further evidence for the simple view of reading. Canadian Journal of School Psychology, 24 (2), 183-200.

Catts, H., Adlof, S., and Weismer, S. (2006). Language deficits in poor comprehenders: A case for the simple view of reading. Journal of Speech, Language, and Hearing Research, 49, 278-293.

Gough, P., Hoover, W., and Patterson, C. (1996). Some observations on a simple view of reading. In C. Cornoldi and J. Oakhill (eds.), Reading comprehension difficulties: Processes and intervention (pp. 1-13). Mahway, New Jersey: Lawrence Erlbaum Associates.

*Gough, P. and Tunmer, W. (1986). Decoding, reading, and reading disability. Remedial and Special Education, 7, 6-10.

Hoover, W. and Gough, P. (1990). The simple view of reading. Reading and Writing: An Interdisciplinary Journal, 2, 127-160.

*Kamhi, A. (2007). Knowledge deficits: the true crisis in education. ASHA Leader, 12 (7), 28-29.

*These articles are short and easy to read.

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The Simple View of Reading is a formula demonstrating the widely accepted view that reading has two basic components: word recognition (decoding) and language comprehension. Research studies show that a student's reading comprehension score can be predicted if decoding skills and language comprehension abilities are known.

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Examples of Sentence Variety: What do you find interesting

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Conclusion Examples from Memoirs and Personal Essays

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ASFM G10 Socials Labsite - November 2023

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Simple View of Reading & Scarborough's Reading Rope

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Simple View of Reading & Scarborough's Reading Rope