Two books – Rethinking Innateness and The Language Game – provide us with some fascinating perspectives on language learning that bears implications for how we think about learning to read and write, and furthermore, for how we talk about the power and limitations of AI.

A Review of Where We’ve Been

In a previous series, we pursued an interesting debate about whether learning to read is more unnatural than learning oral or signed languages. We also investigated the notion, frequently stated by “science of reading” proponents, that “our brains were not born to read,” while our brains are “hard-wired” for language.

While I agree with researchers Gough, Hillinger, Liberman and others that written language is more complex and abstract than oral language and—hence—more difficult to acquire, I’m not convinced that calling it unnatural is most accurate. Instead, I suggest terming it effortful.

In one of the earlier papers we examined, Liberman argued that oral language is pre-cognitive, meaning that it requires no cognition to learn and thus is more natural to acquire. He used this claim to counter the Goodmans’ assertion that oral and written language were largely synonymous, and that kids therefore could learn to read merely through exposure to literacy, rather than explicit instruction in the alphabetic principle (“whole language”). While I most definitely don’t agree with the Goodmans, I paused on Liberman’s claim with some skepticism, as there are a subset of kids who also struggle to develop speech and language skills, just as there are a subset of kids who struggle to develop reading and writing skills.

Liberman also made another strong claim that I paused on: that the evolution of oral language is biological, while written language is cultural (which parallels arguments that language is “biologically primary” while reading and writing are “biologically secondary,” which I have also questioned, given that making the distinction is harder than it seems when social and cultural advancements are deeply interwoven with human existence over generations of time). But I mostly accepted this premise, as it seems to be self-evident that language is baked into our brains. After all, babies begin to attune to languages spoken around them even while still in the womb.

Liberman does not stand on his own in these assertions, I should hasten to add. I just bring one of his papers up because we spent time with it here. Noam Chomsky, for example, has long argued for a universal grammar, which is taught in foundational courses on linguistics, and the related study of generative grammars is alive and well.

Why is this important? It’s important because whether we consider language “natural” or written language “unnatural” bears implications for how we decide to teach them (or not). If we think of language as completely innate, then perhaps we don’t think it requires much of any teaching that is explicit, systematic, or diagnostic. Or conversely, if we think of written language as wholly unnatural, we may not consider how to strategically design opportunities for implicit learning, volume, and exposure.

Yet I have just read two books, written in two different decades, that provide some really interesting critiques against the widely adopted supposition that language is innate.

Language Models

The first book, Rethinking Innateness: A Connectionist Perspective on Development, by Elizabeth Bates, Jeffrey Elman, Mark H. Johnson, Annette Karmiloff-Smith, Domenico Parisi and Kim Plunkett, was published in 1996, and approaches language from the lens of neuroscience, explaining connectionist models and their implications for neural development and learning. These models are not only part of the lineage of the current renaissance of Large Language Models, such as ChapGPT, but also part of a lineage of models that have informed our theoretical understanding of how children learn to read, and may continue to inform explorations of “statistical learning.”

I was led to this book from a recommendation by Marc Joanisse, a researcher at Western University, when he commented on my tweet (are we still calling them that?) about research on artificial neural networks that suggests they can accurately model language learning in human brains.

It was a great recommendation, and I found the book extremely relevant to ongoing conversations about AI and LLMs today, in addition to providing key insights from connectionist models into language and literacy development that challenge assumptions around innateness, such as:

• Simulations show that simple learning algorithms and architectures can enable rapid learning and sophisticated representations, such as those seen in younger infant competencies, without any innate knowledge.
• U-shaped learning and discontinuous change also occur in neural networks without innate knowledge, due to architecture, input, and time spent on learning. This parallels studies of the development of linguistic abilities in children, such as the learning of past-tense and pronouns.
• The way in which neural networks learn new things can be simple, yet the learning yields surprisingly complex results. This complexity emerges as the product of many simple interactions over time (this point, written in 1996, seems incredibly prescient to me as a reader in 2023 using Claude2 to distill and summarize my notes from each book for this post).
• Connectionist models show global effects can emerge from local interactions rather than centralized control. Connectionist models also show how structured behaviors can emerge in neural networks through exposure to and interactions with the environment, without explicit rules or representations programmed in (which makes me think of statistical learning).

Language Games

The second book, The Language Game: How Improvisation Created Language and Changed the World, by Morten H. Christiansen and Nick Chater, was published last year in 2022, and focuses more on cultural evolution and social transmission of language, arguing that language is akin to a game of charades that is honed and passed on from generation to generation. I happened to check it out from the library and read it concurrently with Rethinking Innateness, and there was some great synergy between the two, especially around challenging the notion that language is innate. Some of the key points of the book:

• Language relies on and recruits existing cognitive mechanisms, becoming increasingly specialized through extensive practice and use.
• Language evolves culturally to fit the human brain, not the reverse.
• Language is shaped for learnability and for coordinating with other learners, not for abstract principles and rules. Children follow paths set by previous generations.
• This cultural transmission across generations shapes language to be more learnable through reuse of memorable chunks (“constructions”).
• Due to working memory limitations, more memorable chunks survive, causing a design without a designer. These chunks become increasingly standardized over time.
• Language input must be processed immediately before it is lost (what the authors call the “Now-or-Never” bottleneck).
• Chunking sounds into words and phrases buys more time to process meaning.
• Gaining fluency with increasingly larger and more complex constructions of language requires extensive practice.

Across Connectionism and Charades

Together, these books provide a picture of language as an emergent, complex cultural and statistical phenomena that has evolved from simple learning mechanisms across generations. Rather than an innate universal grammar baked into children’s brains, language itself has adapted and molded over time to become essential to our human inheritance, as with clothing, pottery, or fire. Language emerges through social human communication and interaction. It becomes increasingly complex, yet also streamlined and standardized, without any explicit rules governing it beyond the constraints of our brains, tongues, and cognition.

This isn’t to say there isn’t something unique about the human brain architecture in comparison to our closest animal brethren—there clearly is—but rather that language has adapted symbiotically to that architecture, like a parasite, rather than specific parts of our brain that are genetically pre-determined for language.

Like reading, using language drives increasing specialization of our brain—and this specialization, in turn, drives greater cognitive ability and communicative reach.

There’s a lot here to unpack and synthesize, but I wanted to begin bringing these together, because just as I feel myself pushing against the zeitgeist when I argue that calling learning to read “unnatural” isn’t quite right, so too are arguments that learning language is not “innate” swimming against the tide. These two counterclaims are interwoven, and I think worth further exploring.

Consider this post the first in an exploratory series. We’ll geek out on language development and its similarities and differences to literacy development, maybe dig into the relation of cognition and language and literacy a little, and riff on the implications for AI, ANNs, and LLMs.

#language #literacy #natural #innateness #unnatural #reading #neuralnetworks #research #brains #linguistics #models

There is a concept termed diglossia worth exploring in relation to dialects of African American English used in the United States.

What is diglossia?

Diglossia can be defined as “the coexistence of two varieties of the same language throughout a speech community. Often, one form is the literary or prestige dialect, and the other is a common dialect spoken by most of the population.”

Britannica, T. Editors of Encyclopaedia. diglossia. Encyclopedia Britannica. https://www.britannica.com/topic/diglossia Diglossia is often discussed in the context of Arabic nations or China, in which there are formal, higher status forms of each language (learned and studied as the standardized system for reading and writing), while many divergent dialectal forms of Arabic and Chinese are also used simultaneously in everyday life. Regional, vernacular forms of Chinese can be so varied and distinct there’s an argument they can’t even be properly understood as dialects of one language, given not all are mutually intelligible. I will argue in this post that diglossia* can also be a useful frame for understanding African American English (AAE) use and its relation to literacy instruction in the United States. Though AAE use in the U.S. is not a specific example of diglossia, there are similarities worth exploring in relation to diglossic contexts and concepts, and the broader challenges that can arise when language varieties interact within a society**.

African American English (AAE)—also termed African American Vernacular English or Black American English—refers to dialectal forms of the English language used within African American communities that are distinct from General American English (GAE) dialects. When you think of GAE, think of the English news anchors speak.

AAE is a rule-governed linguistic system that can, according to linguist John McWhorter (as presented at a 2021 virtual Everyone Reading conference), be considered even more complex than GAE, in the way that Old English is considered more complicated: “it does things in grammar that standard English doesn’t.” Yet some social perceptions of AAE can be one of classist disdain, with an overlay of racism. As linguist George Pullum put it, “The majority of English speakers think that AAVE is just English with two added factors: some special slang terms and a lot of grammatical mistakes.”

A Bold Resolution

In the late 1990s, an “Ebonics” furor erupted when the Oakland school board adopted a strong resolution that suggested that African American students who use AAE as their primary home language, which they termed “‘Ebonics’ (literally Black sounds) or Pan-African Communication Behaviors or African Language Systems,” should have the same formal supports provided as students whose primary home languages are other than English (English language learners or ELLs). Such supports would include a home language identification process, teachers certified to teach students with AAE as a home language, and a matched instructional approach and program.

In the Oakland resolution, “Ebonics” was presented as more than a mere dialect of English, but rather a language of its own, unique to those of African descent who were enslaved and to their descendants.

It was a bold resolution, and it generated a backlash disturbingly similar to the current anti-CRT wave that has resulted in book banning and volatile school board elections and meetings.

Yet if you take a step back from the assertion that AAE is its own language entire, there is a falsifiable hypothesis implicit in that school board resolution: if we provide students who use primarily AAE at home with the instructional supports we would provide to students who speak a language other than English (i.e. ELLs), than we can improve their literacy achievement.

This is an empirically testable hypothesis — and some initial evidence supports it. Why would that be?

ELL & Bidialectal Instructional Supports

Let’s consider the kinds of supports that ELL policies have been developed to enact:

When you support students who speak (or sign) a language that is different than the language of academic instruction in core classrooms, you make adaptations and enhancements to your instructional resources and methods. You learn about some of the unique linguistic features of your students’ home languages. You invite and teach explicit contrasts and comparisons between your students’ home language and the language of instruction. This pedagogical method is called contrastive analysis. You highlight the sounds that are similar and directly teach (with clear articulation) the sounds that are different. You ask students to make connections between newly introduced vocabulary related to a topic or theme to words they already know, both conceptually and in terms of sound or spelling patterns. What other words do you know that look like or sound like this word? You amplify morphological and etymological features that are shared between languages, and those that are unique. You provide direct and explicit instruction on the meaning of new words, along with concrete and plentiful examples, never assuming that any given word is already a part of your students’ lexicon.

And you integrate, continuously, input and production across modalities in the language of instruction, ensuring sufficient and abundant opportunities for hearing and saying the sounds within words, and pairing them to their written forms, reading aloud written sentences and savoring them with echo and choral repetitions, selecting sentences that can then be analyzed as mentors for students’ own similarly patterned sentences. Plurals and verb tenses are enunciated and practiced with key words in varying contexts. Pronouns are explicitly connected to their referents. Conjunctions are studied and utilized to invite and extend complex reasoning and deepen knowledge.

In addition, because it is understood that an ELL is learning the English language simultaneous to the content of core instruction, additional instruction in small groups is either provided for targeted language supports or, even better, students receive paired literacy instruction in their home language within a bilingual program.

Some of the emerging evidence that supports this approach for speakers of AAE:

• Gatlin-Nash, B., Terry, N.P. (2022). Theory-Based Approaches to Language Instruction for Primary School Poor Readers Who Speak Nonmainstream American English. In: Saiegh-Haddad, E., Laks, L., McBride, C. (eds) Handbook of Literacy in Diglossia and in Dialectal Contexts. Literacy Studies, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-80072-7_20
• Johnson, L., Terry, N. P., Connor, C. M., & Thomas-Tate, S. (2017). The effects of dialect awareness instruction on nonmainstream American English speakers. Reading and Writing, 30, 2009-2038.
• Fogel, H., & Ehri, L. C. (2000). Teaching elementary students who speak Black English to write in standard English: Effects of dialect transformation practice. Contemporary Educational Psychology, 25, 212–235.

Diglossia & AAE

Let’s return to that idea of diglossia* we started out this post with. The concept—and growing body of related research—is useful as a basis for exploring the idea of linguistic distance between the spoken variations and forms of a language and its written form**.

When there is a greater distance between the forms of a language that are spoken and written, does that make it more challenging and complex for learners to acquire?

In other words, if you speak Cantonese, is it more challenging for you to learn to read and write in standard Chinese, given that it is based on Mandarin?

If you speak a dialect of Arabic that is further from the written form, is it more challenging for you to learn to read and write in Modern Standard Arabic?

If you speak primarily African American English, is it more challenging for you to learn to read and write in General American English?

As cited in the links above, the answers thus far appear to be ‘yes,’ with the caveat that there are many complicating factors well beyond any given features of a language that can make learning to read and write for individual students difficult.

That said, the emerging research certainly bolsters the approach taken by the Oakland school board. The written form of English is more closely matched to GAE, whereas there is a greater distance between AAE and written English. Acknowledging this distance and then providing direct and systematic instruction to bridge it, while building flexible language use and metalinguistic awareness, can be a potentially powerful support for students who speak primarily AAE at home.

Acknowledging differences and challenges around bidialectism does not mean, however, that AAE use should be viewed as a problem. Instead, AAE should be welcomed and affirmed for its unique history, complexity, and vibrant role in African American families and communities, in addition to celebrating its creative influence on U.S. culture.

As with students who are learning English as a new language, for students who speak primarily AAE at home, welcoming and affirming their home language, while providing targeted and sustained supports to classroom language and content, can provide a sense of belonging, while leveraging their linguistic knowledge and resources as a scaffold.

Further Information on AAE or Diglossia

• Teaching Reading to African American Children by Julie Washington and Mark Seidenberg in American Educator
• An Amplify Science of Reading podcast interview with Jasmine Rogers, Celebrating many meanings: Language comprehension and bidialectal students
• Many references cited in this post were drawn from this fascinating Handbook of Literacy in Diglossia and in Dialectal Contexts edited by Elinor Saiegh-Haddad, Lior Laks, and Catherine McBride in Psycholinguistic, Neurolinguistic, and Educational Perspectives
• Talking Black in America, a documentary from The Language & Life Project at NC State University (they also have a video on African American sign language, Signing Black in America)

#dialects #bidialectal #multilingualism #ebonics #language #linguistics #reading #literacy #AAE #diglossia #linguisticdistance

*Technically, the context in which AAE is used in the U.S. may not be properly termed diglossic, but more accurately, something like a “standard-with-dialects” context—but for the non-technical purposes of this post, I refer to the term diglossia as an interesting global example of how different forms of a spoken and written language can interact and co-exist in some tension.

**Updated 3/5/23 thanks to a critique from Dr. Angus Grieve-Smith that I needed to be clearer and explicit that AAE use within the U.S. is not a specific example of diglossia. In the Handbook of Literacy in Diglossia and in Dialectal Contexts, they term AAE use in the U.S. as an example specifically of “standard-with-dialects,” and they do note that there are specific denotations of diglossia that differ. That said, they also note that “Despite some differences in sociolinguistic features…both diglossia and standard-with-dialects contexts share fundamental aspects of language use, exposure, and input that might exert similar effects on literacy development.”

Case in point: whilst engaged in aforementioned spelunking, I discovered an absolutely wonderful paper synthesizing various theories of language, and I say wonderful because it manages to thread together varying theoretical perspectives from a stance of learning and curiosity, rather than pitting them against one another, as is so often the case. The paper is Essentials of a Theory of Language Cognition by Nick Ellis, and while it may be heady and academic, there’s something playful, even poetic, in the author’s use of language (so meta!).

By example, here’s a couple of gems:

“Language and usage are like the shoreline and the sea. Usage affects learning and it affects languages too. So, our understanding of language learning requires the detailed investigation of usage, its content, its participants, and its contexts—the micro level of human social action, interaction, and conversation; the meso level of sociocultural and educational institutions and communities; and the macro level of ideological structures.”

“Language is the quintessence of distributed cognition. Language is ever situated, either in the moment and the concrete context or by various means of mental extension to reflect prior or imaginary moments.”

Dear reader, you may or may not be aware that I have another (not updated any more) blog entitled, Schools & Ecosystems, wherein I geeked out about complex adaptive systems and how ecological concepts relate to the physical and social environment of schools. So you can imagine my nerdy delight when I discovered a connection in this paper between complex adaptive systems thinking and LANGUAGE! Oh my. It was like two previously schizophrenically disparate selves suddenly merged into one.

Here’s a couple of quotes regarding language as a complex adaptive system:

Language as a CAS [complex adaptive system] involves the following key features: The system consists of multiple agents (the speakers in the speech community) interacting with one another. The system is adaptive; that is, speakers’ behavior is based on their past interactions, and current and past interactions together feed forward into future behavior.

De Bot, Lowie, and Verspoor (DBL&V) present a persuasive case for language as a complex dynamic system where cognitive, social, and environmental factors continuously interact, where creative communicative behaviors emerge from socially co-regulated interactions, where there is little by way of linguistic universals as a starting point in the mind of ab initio language learners or discernable end state, where flux and individual variation abound, where cause-effect relationships are nonlinear, multivariate and interactive, and where language is not a collection of rules and target forms to be acquired, but rather a by-product of communicative processes.

In a previous post, we looked at some interesting findings from neuroscience that suggested language in the brain is mostly associated with parts for communication, rather than thinking. So this idea of language as a complex adaptive system that emerges based on social use within a particular community makes quite a bit of sense.

One of the other things that jumped out at me as a theme emerging from these various theories of language was the idea of language as an ecology: something dynamic and situated within a particular time, place, and community of relationships. It’s a beautiful — and more accurate — way to think of language that allows us to acknowledge the unique language ecologies we can each have as individuals and as members of communities — most especially for multilinguals who bring a rich repertoire of linguistic experiences and cultural knowledge.

This paper is also a wonderful companion to Annie Murphy Paul’s book, The Extended Mind, which isn’t focused on language per se, but connects to many of the theories in this paper and also has some Easter eggs for language focused nerds, such as an exploration of the use of gestures as a precursor and accelerator of language.

Somehow I had not stumbled across “usage-based” linguistic research or theory previously, so I’m excited to dig more into this realm. Seems like it has a lot to offer, especially as the reading research crowd begins to unpack more the language connection to reading (importance of phonology, morphology, incidental learning, statistical learning, etc).

#linguistics #language #ecology #complexadaptivesystem #interaction

Discuss...

You might assume I know something about teaching kids to read. I studied English at UCLA and obtained my master’s in education at The City College of NY. I taught special education grades 5-8 for 7 years, and I’ve supported schools and teachers throughout the Bronx with K-8 ELA instruction over the past 3 years.

Yet you’d be wrong. I’ve come to realize I know next to nothing.

In case you haven’t been aware, there’s been a firestorm of educators on platforms like Twitter gaining newfound awareness of the science of reading, with an urgent bellows inflamed by the ace reporting of Emily Hanford. For a great background on this movement, with links, refer to this post by Karen Vaites.

Impelled by this burgeoning national and international conversation, I’ve sought to educate myself about the science of reading. I began with Mark Seidenberg’s Language at the Speed of Sight, took a linguistics course, and have just completed David Kilpatrick’s Essentials of Assessing, Preventing, and Overcoming Reading Difficulties. Seidenberg is not only pithy, but furthermore impassioned, while Kilpatrick is deeply versed in both the research and application in practice as a former school psychologist. Both experts provide an incendiary takedown of more than a few sacred cows in the educational establishment.

It’s been fascinating to learn more about the science of reading while simultaneously working with a school where I could see problems elucidated by reading researchers and advocates play out in real-time. It has made what I’m learning gain an even greater sense of urgency. I would read pages critiquing the “three-cueing system” and balanced literacy approaches on the bus in the morning, then walk into classrooms where I saw teachers instructing students, when uncertain about a word, to use guessing strategies such as “look at the picture” and the “first letter of the word,” rather than stress the need to be able to decode the entire word (for more on the problems with current classroom practice, listen to Hanford’s podcast).

There’s so much to digest and apply from all of this. This post is my attempt to begin synthesizing the information I’ve read. I’ll start general and then focus on the word-level reading aspect of the research in this post. And there’s so much more I want to cover, but I’ll be leaving tons of stuff out that I would love to explore further. Someday . . .

Reading Can Be Simple

First off, though reading is complicated, it can be outlined by a simple model, known aptly enough as The Simple View of Reading. It can even be put into the form of an equation. The theory was first developed in 1986 by researchers Gough and Tunmer. The original formulation was D (decoding) X LC (linguistic or language comprehension) = Reading Comprehension.

After years of further research, this distinction has mostly held up, though it has become greatly expanded, especially in our understanding of what constitutes language comprehension.

Decoding has been clarified as one umbrella aspect of word-level reading, which is composed of many sub-skills. A more updated formula, courtesy of Kilpatrick, is:

Word Recognition X Language Comprehension = Reading Comprehension.

If you struggle with word recognition (such as with dyslexia), or if you struggle with language comprehension (like when you are learning the language), then you will have difficulty reading.

Protip: if you are an educator in NY, know that this distinction can be framed around the language from Advanced Literacy as code-based (word recognition) and meaning-based (language comprehension) skills. And if you are a NYC educator, you can furthermore align this to the Instructional Leadership Framework. Bonus points for alignment to state and city initiatives! Yay!

Within each of these two domains lie the various sub-skills and knowledge that make reading so very complicated. Here’s a chart I made to visualize the “Expanded” Simple View:

Protip: Most educators are already familiar with the “five pillars” or “Big 5” of reading instruction: phonemic awareness, phonics, fluency, vocabulary, and comprehension, so it can be helpful to build a bridge between that knowledge and the Simple View. At a recent session I facilitated, I asked teachers to consider the Big 5, introduced the notion that they are composed of subskills, then asked them to sort those subskills into code-based or meaning-based groups. Here’s a print-out you could use to create the sorting strips.

Note that word recognition skills are mostly mastery-based. And a key point experts like Seidenberg and Kilpatrick make about word recognition is that word recognition can be acquired by all children. IQ discrepancy is not a factor.

Here’s Seidenberg:

“For children who are poor readers, IQ is not a strong predictor of intervention responses or longer-term outcomes. Moreover, the behavioral characteristics of poor readers are very similar across a wide IQ range. . . Within this broad range of IQs, poor readers struggle in the same ways, need help in the same areas, and respond similarly to interventions. In short, the skills that pose difficulties for children are not closely related to the skills that IQ tests measure. The primary question is about children’s reading—whether it is below age-expected levels—not their intelligence.”

Here’s Kilpatrick:

“Discrepancies between IQ and achievement do not cause word-reading problems. Rather, deficits in the skills that underlie word-level reading cause those problems. The component skills of word reading can be strong or weak, independently of IQ test performance.”

“A common belief that continues to be recommended is that some students with severe reading disabilities simply cannot learn phonics and they should be shifted to a whole-word type of approach. This recommendation is inconsistent with the accumulated research on the nature of reading development and reading disabilities

“The simple view of reading applies to poor readers with IDEA disabilities (SLD, SLI, ID, ED/BD, TBI) and poor readers not considered disabled. Thus, when asked the question, ‘Why is this child struggling in reading?’ we would no longer answer, ‘because the child has an intellectual disability (or SLI or ED/BD or whatever).’ Those disability categories do not cause reading difficulties—specific reading-related skill deficits cause reading difficulties.”

What this means for educators: there is simply no excuse for any student to graduate from any of our schools without the ability to decode words in print. As Kilpatrick stated in a presentation (thanks to Tania James for her wonderful notes), “If a child can speak, they can learn phonics.”

Language comprehension, on the other hand, may be a tougher beast to tackle. Linguistic skills and knowledge are cumulative and on-going. Most importantly, a core component of language comprehension is background and topical knowledge, in addition to grammatical and syntactical knowledge — both which are inadequately taught in most schools due to the lack of a strong and coherent core curriculum.

I should note that Siedenberg doesn’t seem to fully subscribe to the Simple View, and that by no means should we begin to think any one model can adequately describe something so complex as reading. In his endnotes he states, “The main weakness in Gough’s theory is that it did not make sufficient room for the ways that the components influence each other. Vocabulary, for example, is jointly determined by spoken language and reading. Vocabulary can also be considered a component of both basic skills and comprehension.”

Kilpatrick contradicts this view when he states, “In the context of the simple view of reading, it appears that vocabulary belongs primarily on the language comprehension side of the simple view equation, not necessarily on the word-reading side.”

Seidenberg proposes his own model, based on computational simulations, which looks something like this (Figure 6.2 from Chapter 6):

I think this model is useful for conveying why reading is complicated and can be hard to learn, but maybe not quite as useful for guiding school-based assessment and instruction.

Why is The Simple View of Reading important?

Having a clear model for reading comprehension means we have a guide for aligned assessment, prevention, and intervention. Unfortunately, many schools base ELA instruction primarily on state assessments, which tell you very little about a student’s reading needs. People seem to forget that the function of a state assessment is for school, district, and state level accountability, not to direct classroom instruction.

Protip: One “research snapshot” I found useful from Nonie Lesaux and Emily Galloway’s Advanced Literacy framework is the distinction they make between “literacy performances” and “specific skills and competencies.”

A state literacy assessment is a “literacy performance.” Here’s an explanation in their book, Teaching Advanced Literacy Skills:

“There is a tendency to examine the results of outcome assessments at the item level—to figure out the types of items groups of students struggled with and then go back and teach to support this understanding. Perhaps the most universal example is ‘finding the main idea’ in a passage . . . the problem is that finding the main idea—among many other similar performances or exercises—is just that—a reading performance. It is not a specific skill. That is, to perform the task at hand, in this case to find the main idea, the reader draws on many component skills and composite competencies and initiates those in concert with one another. In turn, when a student is not able to find the main idea, we still do not know why.”

In order to know why, we need assessments that can better pinpoint where the breakdown occurs, whether in word recognition or in language comprehension, or both. And then we need to do something about it. This is where it gets hard.

Reading is Hard

Though we can draw on a simple model to explain it, in actuality reading is complicated.

First of all, it’s completely unnatural. While we acquire spoken language organically, reading requires the imposition of an abstract system onto that language, a grafting of a fragmented alphabet onto a river of sound. Writing is something our species invented, an ingenious mechanism to convey information across space and time. While the first writing appeared around 3,200 BC, humans have been speaking for anywhere between 50,000 to 2 million years prior (we don’t know for sure because we couldn’t record anything yet, duh).

There are many irregular words in the English language, which would appear to make the teaching of something like phonics a daunting endeavor. We assume that kids need to be taught the rules, and then memorize the exceptions (these are known as “sight words.”) Makes sense, right?

Yet research has made it clear we don’t acquire most sight words through memorization. Instead, we draw upon our letter-sound knowledge and phonological analysis skills to recognize new written words and unconsciously add them to our “orthographic lexicon.”

What’s interesting on this point is there appears to be some disagreement between the models Seidenberg and Kilpatrick use to explain this process. Seidenberg calls it statistical learning, meaning that we learn to recognize patterns in common words, from which we then can recognize many others, including ones with irregularities. Kilpatrick, on the other hand, terms it orthographic mapping, which is the process of instantaneously pulling apart and putting back together the sounds in words, drawing upon letter-sound knowledge and phonemic awareness. In either model, what is acknowledged is that children learn to recognize a large volume of new words primarily on their own, but that such an ability is founded upon a strong understanding of sounds (phonology) and their correspondences in written form (orthography).

Honestly, I find both concepts—statistical learning and orthographic mapping—hard to wrap my head around.

It’s also possible they describe different things. Seidenberg’s term seems more global, explaining how we acquire vocabulary, while orthographic mapping refers more specifically to the relationship between decoding and acquiring vocabulary. I should note here that Kilpatrick did not come up with the term, “orthographic mapping,” but rather draws on the research of Linnea Ehri.

Here’s Seidenberg on statistical learning:

“…learning vocabulary is a Big Data problem solved with a small amount of timely instruction and a lot of statistical learning. The beauty part is that statistical learning incorporates a mechanism for expanding vocabulary without explicit instruction or deliberate practice. The mechanism relies on the fact that words that are similar in meaning tend to occur in similar linguistic environments.”

Here’s Kilpatrick on orthographic mapping:

“Roughly speaking, think of phonic decoding as going from text to brain and orthographic mapping as going from brain to text. This is, however, an oversimplification because orthographic mapping involves an interactive back and forth between the letters and sounds. However, it is important that we do not confuse orthographic mapping with phonic decoding. They use some of the same raw materials (i.e., letter-sound knowledge and phonological long-term memory), but they use different aspects of phonological awareness, and the actual process is different. Phonic decoding uses phonological blending, which goes from “part to whole” (i.e., phonemes to words) while orthographic mapping requires the efficient use of phonological awareness/analysis, which goes from “whole to part” (i.e., oral words to their constituent phonemes).”

Kilpatrick notes that “The vast majority of exception words have only a single irregular letter-sound relationship.” This means that if a reader knows their letter-sound relationships well, they will be able to negotiate the majority of words with exceptions and irregularities.

What this means is that students need to be provided with sufficient practice to master phonological awareness and phonics skills. And we can not blame the failure of a student to learn to decode on the irregularity of the English language.

Phonology: What We Can Hear and Speak is the Root of Written Language

In Seidenberg’s book, he argues that phonemes are the first abstraction on the road to the written word. A phoneme is the individual sound that a letter can represent (e.g. the sound of “p”). While we learn many such sounds as we acquire spoken language, the need to disaggregate a single component sound into a phoneme only becomes necessary in the translation of speech into the written form. As Seidenberg puts it:

“Phonemes are abstractions because they are discrete, whereas the speech signal is continuous. . . The invaluable illusion that speech consists of phonemes is only completed with further exposure to print, often starting with learning to spell and write one’s name.”

No wonder “phonemic awareness” is central to learning to read! The ability to know and discern individual sounds, and then to be able to play with them and put them back together, is the core skill of reading. In other words, if you struggle with blending and manipulating the sounds in words, you struggle with reading.

And indeed, this is why far too many of our kids have problems with reading. As Kilpatrick puts it, “The phonological-core deficit is far and away the most common reason why children struggle in word-level reading.”

Once I grasped this deceptively simple idea—that fluent reading is dependent on the ability to hear and speak the sounds of letters within words—prevention and intervention began to make more sense to me.

So What Can We Do?

The great thing about Kilpatrick’s book—and why you should buy it—is that unlike many writers in the field of education, he actually goes through what assessments you can use and what you can do instructionally, both for prevention (K-1) and for intervention (grades 2 and up), to address reading needs. He calls out programs by name and praises or critiques them based on key understandings from the research, and some of it was pretty surprising to me.

But I’m going to stop here for this post before it gets overlong.

Afterword

You’ll notice I didn’t mention what I learned from my linguistics course, which was just an online series. It was fine, but I only found it useful insofar as it equipped me with some terms like lexicon, morphology, semantics, or pragmatics. If you have any recommendations for further learning in linguistics, please let me know.

Also, if I’ve demonstrated any misconceptions in this piece or you would like to challenge or add to anything I wrote, please share!

And thank you for reading.

#reading #literacy #learning #linguistics #SVR #Seidenberg #Kilpatrick #AdvancedLiteracy #research

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