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mariapatricia@erinkentconsulting.com

www.linkedin.com/in/maria-patricia-mesa

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Estructuras de enseñanza de la lectura - Jennifer Serravallo

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Estructuras de enseñanza de la lectura - Jennifer Serravallo

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Formatos de planeación JSerravallo en español

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Libros decodificables español - CKLA/Caminos

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Libros decodificables español - CKLA/Caminos

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Una Guía Amigable para Maestros sobre la Investigación Científica

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Una Guía Amigable para Maestros sobre la Investigación Científica

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por Jan Burkins y Kari Yates

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SOR: Una Guía Amigable para Maestros sobre la Investigación Científica

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¿Cómo cambia el cerebro con la lectura? Evidencia desde las neurociencias cognitivas

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Enseñar a leer es ciencia espacial - Louisa Moats

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Enseñar a leer es ciencia espacial - Louisa Moats

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TLa responsabilidad más fundamental de las escuelas es enseñar a los estudiantes a leer. Debido a que la lectura afecta a todos los demás logros académicos y está asociada con la salud social, emocional, económica y física, ha sido el aspecto más investigado de la cognición humana. Para el año 2000, después de décadas de investigación multidisciplinaria, la comunidad científica había logrado un amplio consenso con respecto a estas preguntas: ¿Cómo aprenden a leer los niños? ¿Qué causa las dificultades de lectura? ¿Cuáles son los componentes esenciales de una instrucción de lectura efectiva y por qué cada uno es importante? ¿Cómo podemos prevenir o reducir las dificultades de lectura? Dos décadas después, cientos de estudios adicionales han refinado y consolidado lo que sabemos sobre el refuerzo del rendimiento en lectura, especialmente para los estudiantes en riesgo.

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La semana pasada os hablábamos sobre The Science of Reading o La ciencia de la lectura, un conjunto interdisciplinario de investigaciones científicas relacionadas con la lectura y la escritura. Hoy queremos ofrecer algunos tips para implementar T he Science of Reading en el aula.

Desde las edades más tempranas, el enfoque de The Science of Reading puede ofrecer una serie de beneficios significativos:

Establecimiento de una base sólida. Al enseñar habilidades como la fonética y el reconocimiento de letras desde el principio, se establece una base sólida para el desarrollo futuro de la lectura.

Prevención de dificultades de lectura. La enseñanza temprana y sistemática de habilidades lectoras puede ayudar a identificar y abordar posibles dificultades de lectura antes de que se conviertan en problemas mayores.

Mayor fluidez de lectura. Al enfocarse en la decodificación y comprensión desde el principio, los niños y las niñas pueden desarrollar una fluidez de lectura más rápida y efectiva a medida que avanzan en su educación.

Mejora de la comprensión lectora. Al proporcionar una base sólida en habilidades de lectura, los estudiantes tienen más probabilidades de desarrollar una comprensión lectora profunda y significativa a medida que avanzan en su aprendizaje.

En resumen, The Science of Reading ofrece un enfoque sólido y basado en la evidencia para la enseñanza de la lectura desde las edades más tempranas, que puede tener beneficios duraderos en el desarrollo académico y el éxito futuro de los estudiantes.
¿Cómo podemos implantarla en clase?

Usar una variedad de actividades que trabajen en el desarrollo de la conciencia fonética y el reconocimiento del alfabeto es fundamental para el éxito en la lectura temprana. Prioriza la conciencia fonética (comprender que las palabras están compuestas por sonidos) y el reconocimiento del alfabeto para asegurarte de que los estudiantes tengan una base sólida. Estas habilidades son cruciales para el éxito en la lectura temprana.

Aprovecha el poder de la revisión. Comienza con conceptos simples y avanza hacia conceptos más complejos, incorporando frecuentemente la revisión del conocimiento previo.

Incorpora los dictados en el aula. Introduce la ortografía guiada con explicaciones verbales desde las edades más tempranas de escolarización. Esta actividad puede acelerar las habilidades ortográficas de niños y niñas y su comprensión de los patrones de ortografía.

Fomenta la lectura de textos. Utiliza textos que sean fácilmente decodificables para los niveles iniciales de la instrucción de la lectura. Esto ayudará a los estudiantes a ganar confianza y control en sus habilidades de lectura. Lee, relee, discute y escribe sobre el texto para reforzar las habilidades de lectura.

Continuar con esta variedad de actividades ayudará a los estudiantes a desarrollar una base sólida en habilidades de lectura y escritura. Además, es importante proporcionar retroalimentación constante y apoyo individualizado para cada alumno y alumna, adaptando las actividades según sus necesidades específicas. Al integrar estas estrategias de enseñanza, los docentes pueden promover un ambiente de aprendizaje inclusivo y efectivo que fomente el éxito de todos los peques en el desarrollo de habilidades de lectura y escritura.

En Smile and Learn tenemos una serie de vídeos sobre fonética, vocabulario y audiolibros. En ellos, niños y niñas aprenderán a pronunciar los fonemas y las sílabas. Además, trabajamos la comprensión lectora de los más pequeños con actividades elaboradas por profesionales de la docencia, como lecturas comprensivas o dictados. Si aún no has probado nuestra plataforma educativa puedes hacerlo a través del siguiente enlace.

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The Science of Reading (La ciencia de la lectura) - Cómo implementarla en el aula -

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Hoy queremos ofrecer algunos tips para implementar The Science of Reading en el aula.

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Tilde Podcast: La ciencia de la lectura en español

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Tilde Podcast: La ciencia de la lectura en español

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Tilde podcast | La ciencia de la lectura, por Valeria Abusamra

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The Reading League: Phonics practices aligned with SoR

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The Reading League: Phonics practices aligned with SoR

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Protocol for Identifying Needs from Assessments

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Protocol for Identifying Needs from Assessments

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EKC Community Resources Rec.: Lincoln Sem.1 2024

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Literacy Consultants & Consulting Services | Erin Kent Consulting

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About the Community | Erin Kent Consulting (EKC)

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A few special gifts for signing up now: a strategy call with Erin Kent or an EKC Senior Strategist to get started using the EKC Community to support your current literacy initiatives; and 5 free spots in our live, virtual winter courses ($1495 value!)

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Tier 1 Checklist - MAKE A COPY for your grade level

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Tier 1 Checklist - MAKE A COPY for your grade level

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Lots of Resources available on this website! You may have to register...

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The Six Shifts Where Science of Reading Meets Balanced Literacy. We provide resources, professional development, and literacy audits for busy literacy educators like you.

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Literacy Consultants & Consulting Services | Erin Kent Consulting

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Literacy Consultants & Consulting Services | Erin Kent Consulting

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EKC provides literacy consultants, consulting services, and literacy professional learning for schools.

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CORE Phonics Survey Español, Recording Form

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CORE Phonics Survey Español, Recording Form

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Morpheme Magic – Lessons to Build Morphological Awareness

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Teachers and students alike, will become captivated by words as they explore together the world of language and morphology, and build awareness of morphemes, the meaningful parts in words.

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Consideraciones para la instrucción del vocabulario escalonado

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Consideraciones para la instrucción del vocabulario escalonado

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Conversations with researchers and scholars and how to apply what research suggests in classrooms.

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conversations with researchers and Educators

The collection of knowledge and advice about best practices in literacy education is always evolving. This is both a gift and a challenge. In my new podcast for educators,I interview leading researchers and scholars to understand current, relevant, and digestible answers to all things literacy, learning, and best-practice, regardless of an educator's approach to literacy instruction. I'm joined by colleagues from my consulting team, and classroom educators, to debrief the researcher interview and answer the question, "How will we take what we've learned to the classroom?"

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To the Classroom: Conversations with Researchers and Educators - Jennifer Serravallo Author & Consulting

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Jennifer Serravallo interviews leading researchers and scholars about important findings, and has post-interview conversations about practical takeaways with colleagues from her consulting team.

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Printable Resources | Reading Writing Templates | Jennifer Serravallo - Jennifer Serravallo Author & Consulting

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Printable literacy resources for teachers & students, from Jennifer Serravallo. Writing & reading templates, assessments, checklists, goal-setting, study guides.

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Palabras de uso frecuente: Jardín (Kinder)

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6 Rutinas de pensamiento para la comprensión (Shifting the Balance)

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6 Rutinas de pensamiento para la comprensión (Shifting the Balance)

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Planeador para un pequeño grupo de lectura de un texto decodificable

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Planeador para un pequeño grupo de lectura de un texto decodificable

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Planificador de grupos estratégicos - Plantilla

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Planificador de grupos estratégicos - Plantilla

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Cambios en la instrucción del vocabulario

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Cambios en la instrucción del vocabulario

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El cerebro lector: Últimas noticias de las neurociencias sobre la lectura

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El cerebro lector: Últimas noticias de las neurociencias sobre la lectura

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Ustedes están leyendo este texto como si fuera lo más natural del mundo. Todos damos por hecho que un conjunto de marcas, líneas y espacios sobre el papel significan algo... y tendemos a olvidar que el acto de leer es una auténtica hazaña. Más asombroso resulta si consideramos que leemos usando un cerebro que evolucionó mucho tiempo antes de que se inventara la escritura, y lo hizo para fines completamente diferentes. Stanislas Dehaene, uno de los líderes mundiales en neurociencia cognitiva, desentraña esta inquietante paradoja y nos enseña cómo nuestro cerebro de primates logra traducir esos signos llamados letras en lenguaje. Recorriendo los últimos descubrimientos de la psicología de la lectura y los más sorprendentes hallazgos de las neurociencias, Dehaene responde con absoluta maestría a las preguntas centrales sobre esta deslumbrante habilidad humana: ¿Cómo es posible que un invento cultural reciente se haya instalado con tanta naturalidad en nuestro cerebro? ¿Qué operaciones realizamos para convertir un conjunto de trazos aprehendidos por nuestro sistema visual en conceptos y sonidos? ¿Son estas operaciones universales o dependen de la lengua, el alfabeto o la cultura? ¿Cómo hacen los niños, o más bien sus cerebros, para aprender a leer? ¿Da igual qué sistema de enseñanza se utilice? ¿Qué es la dislexia y cuáles son sus causas? ¿Es posible superarla? El cerebro lector es un relato a la vez fascinante y riguroso en el que tanto simples curiosos como especialistas en el tema encontrarán todo lo que hoy se sabe sobre lo que ustedes están haciendo en este preciso momento: leer.

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El cerebro lector: Últimas noticias de las neurociencias sobre la lectura, la e

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Ustedes están leyendo este texto como si fuera lo más natural del mundo. Todos damos por hecho que un conjunto de marcas, líneas y espacios sobre el papel significan algo... y tendemos a olvidar que el acto de leer es una auténtica hazaña. Más asombroso resulta si consideramos que leemos usando un ...

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Posibilidades de ideas para escribir acerca de la lectura

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Posibilidades de ideas para escribir acerca de la lectura

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Posibilidades de ideas para escribir acerca de la lectura

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Desarrollo del vocabulario 3-5: Shifting the Balance

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Desarrollo del vocabulario 3-5: Shifting the Balance

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Redefining the Science of Reading - Natalie Wexler

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Redefining the Science of Reading - Natalie Wexler

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The term science of reading (SoR) is often interpreted to mean "more phonics." This interpretation has led to significant pushback, with some educators and literacy experts arguing that reading involves more than just sounding out words-and that requiring children to spend hours on daily phonics drills is both misguided and potentially harmful.

But SoR advocates aren't necessarily calling for more phonics instruction. In many cases, they emphasize the need for more effective phonics instruction. They also acknowledge that skilled reading requires other crucial components. In fact, both sides of the phonics debate point to the same key elements: phonemic awareness, phonics, fluency, vocabulary, and comprehension.

Still, many in the SoR movement haven't focused on problems with reading instruction beyond those related to phonics. As a result, many educators and policymakers have come to assume that schools need to change only their approach to phonics in order to align with science. That assumption overlooks the fact that the standard approach to teaching reading comprehension, which focuses heavily on skills, also conflicts with a substantial body of evidence.

That argument rests on the recognition that our ability to understand or produce text reflects whatever we've been able to learn. At the same time, reading is a way of learning, and writing can deepen and reinforce knowledge. It follows that scientific evidence related to how we learn in general is inextricably linked to evidence on how we learn to read and write-and instruction in those areas should be linked as well.

Comprehension instruction has students practice an isolated skill using texts on random topics they may not know anything about. And writing curricula or prompts often provide only limited information on a topic before asking students to write about it.

If a student is not yet a proficient reader, the task of reading imposes an additional burden on working memory. That explains why it's important to introduce new topics through teacher read-alouds and class discussion. Because listening and speaking don't in themselves impose a cognitive load, students' ability to absorb new information and transfer it to long-term memory is expanded. Once students have acquired background knowledge of a topic, evidence that they can read and write about that topic at a higher level. Eventually, as they continue to acquire more academic knowledge, they become better able to read and write about a .

In this kind of lesson, there is no common content for students to practice retrieving. And even if students diligently practice the supposed skill of making inferences using a text on one topic, they're unlikely to be able to make inferences about a text on another topic that is unfamiliar, especially if the vocabulary and syntax are more complex. An iconic cognitive science text is called , but the problem with comprehension instruction is that there's no "it" that can actually be made to stick.

Understanding and applying the science of reading, the science of literacy, and the science of learning might sound like a daunting task for educators. But it doesn't have to be. The first step is to adopt a curriculum that is rich in content, ideally beginning in the early grades. Rather than jumping quickly from one topic to the next, because the focus is on teaching comprehension skills, effective literacy instruction requires deep dives into topics in history, science, and full-length novels. That content both builds the background knowledge essential for comprehension of complex text and also provides the context students need to develop their writing skills and critical thinking abilities.

The catch is that writing imposes such a heavy cognitive load that inexperienced writers won't get these benefits if they're simply asked to write without much guidance, which is often the case in typical literacy instruction. But explicit writing instruction that begins at the sentence level can make the cognitive load manageable, enabling writing to become a powerful lever for learning.

If teachers combine rich content with explicit writing instruction, they'll have a good chance of implementing the science of learning-and they're likely to see all their students, especially those who struggle the most, blossom and thrive.

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It's not just about phonics-literacy depends on rich content, explicit writing instruction, and cognitive science.

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An introduction to cognitive load theory 

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An introduction to cognitive load theory 

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Cognitive load theory helps us to understand how people generally learn and store new information, and the types of instructional practices that best support learning. It draws on the characteristics of working memory and long-term memory and the relationship between them to explain how people learn. Cognitive load theory emerged in the late 1980s from the work of John Sweller and his colleagues. The theory is based on our knowledge of the structure and processes of the human mind, known as human cognitive architecture. Human cognitive architecture helps us understand how we learn, think, and solve problems. It is considered to be a natural information processing system that generates various procedures designed to reduce cognitive load and facilitate the acquisition of biologically secondary knowledge held in long-term memory.

The distinction between biologically primary and biologically secondary knowledge

There are two basic categories of knowledge, biologically primary and biologically secondary[ii]. Biologically primary knowledge is information humans have evolved to acquire over thousands of generations. It is acquired unconsciously without instruction because it is necessary for the survival of humans and their societies. Biologically primary knowledge includes general problem-solving and thinking skills such as learning to speak and listen in a native language at a young age, generalising, transferring, and performing simple social skills like recognising faces. Because we have evolved to acquire such information automatically, it does not need to be taught and, since we can learn biologically primary information easily, it does not impose a heavy cognitive load.

In contrast, biologically secondary knowledge relates to information that needs to be explicitly taught and not left for students to discover. It requires conscious effort, and most subjects taught in formal education belong in this category of information. Reading, writing, mathematics, history, science, and other subjects traditionally taught in schools and universities are examples of biologically secondary knowledge. Since the acquisition of biologically secondary information requires conscious effort because it does not happen easily and automatically in the same way as listening and speaking, it requires mental effort. In other words, it imposes a cognitive load.

Generic-cognitive skills and domain-specific skills

According to Sweller, most biologically primary knowledge results in generic-cognitive skills. These are basic skills that can be applied across a variety of domains. On the other hand, most of what is taught in educational institutions consists of domain-specific skills. Even though generic-cognitive skills are far more important than domain-specific skills because they contribute to an individual's overall ability to adapt, learn and think independently, domain-specific skills are what need to be taught because they are not acquired easily and unconsciously. Biologically secondary, domain-specific skills need explicit instruction[iii] because, without it, most students will have to use a trial-and-error strategy to find the solution to a problem, and that kind of strategy imposes a heavy cognitive load.

Take as an example the specific problem-solving skills required to effectively solve the algebraic equation: 2 x - 40 = 60. One way to solve this equation would be to follow two solution steps. Firstly, to add 40 to both sides of the equation to arrive at the simplified equation: 2 x = 100; and secondly, to divide both sides of the simplified equation by 2 to arrive at the answer: x = 50. However, if a student attempts to solve the problem by using a trial-and-error strategy - for example, by substituting multiple values for x to eventually reach a solution - this is likely to impose a heavier burden on the working memory (or cognitive load), than if they are provided with the solution steps. This biologically secondary, domain-specific skill - in this case, the mathematical method for solving the equation - should be explicitly taught.

The relationship between working memory and long-term memory

Cognitive load theory explains the way that new knowledge is constructed in working memory, and the way that permanent knowledge is built up and held in long-term memory - it explains how we learn. Figure 1 below represents the aspects of the human memory system used to describe the separate processes associated with working memory and long-term memory.

The general memory structure in Figure 1 shows that the memory system is not a unitary entity. In other words, it is not just one system but rather two memory components that do not function separately. These two components have links between them that represent separate processes.

Working memory is the conscious component of our memory where novel information is temporarily stored and actively manipulated for reasoning, learning and comprehension. For learning to occur, information must be processed in working memory for meaning before it is passed into long-term memory. Long-term memory is the unconscious component of our memory. If we have truly learnt something, it means we have stored it indefinitely in knowledge structures known as schemas in our long-term memory. Schemas are activated when learners deal with familiar information. When this trigger occurs, schemas are transferred from long-term memory to be consciously processed in working memory for a specific purpose. This happens automatically and easily because it is a biologically primary skill. A more knowledgeable learner in a domain can effortlessly transfer large amounts of organised schematic information from long-term memory to working memory to assist with carrying out complex problem-solving tasks because their schemas are more sophisticated in magnitude, complexity and refinement than those of a less knowledgeable learner.

Working memory has a limited capacity and can only remember roughly five to nine items or chunks of novel information at any one time[iv]. In addition, only two to four chunks of novel information can be simultaneously worked on or thought about in our working memory[v], which means that we can work on only a very limited number of items when a task requires working memory resources to process a new task. Furthermore, we can only hold this limited amount of information in working memory for about 20 seconds without needing to prompt our memory[vi]. The limitations of our working memory result in cognitive load whenever we are presented with new information.

Unlike working memory, long-term memory has no known limits. Furthermore, when familiar information is transferred from long-term memory back to working memory to be used for reasoning or problem solving, working memory has no limits on its capacity or duration. Working memory only has limits when dealing with novel information, not familiar information from long-term memory. Accordingly, when information is stored in long-term memory, the contents of our mind change - this is the process of learning.

Why is it so important for teachers to know about cognitive load theory?

Cognitive load theory is vital for teachers to know about and understand because it helps them reduce unnecessary cognitive load on students' working memory in order to promote and optimise learning. A student's competence in a specific domain is dependent on what is stored in their long-term memory. However, when a student does not have the prior knowledge to complete a task, and information about how to complete the task has not been provided, this creates the need for problem solving where novel information must be constructed. The newly generated information must be tested for its effectiveness, with ineffective information being removed and effective information kept, making the process of generating new information in working memory slow and difficult. This is not a very effective way of learning, but it is the only procedure available when solution paths such as information from teachers are unavailable.

This means that engaging in inquiry-based learning is usually an ineffective process for novice or less experienced learners because it imposes a considerable cognitive load on their working memory. Learners are forced to resort to weak, unguided methods such as problem solving, as in the example above where a novice algebra student attempts to solve 2 x - 40 = 60 by randomly guessing the value of x. By contrast, when a less experienced learner in a specific domain studies the solution steps contained in a worked example prior to attempting a similar problem-solving task, this diminishes cognitive load and therefore efficiently assists the student to transfer knowledge of the method for solving the problem to long-term memory. A student who already holds domain-specific knowledge in long-term memory can use that knowledge to accurately categorise the problem and its solution. This is why problem solving (or inquiry-based learning) is better suited for more experienced learners, because they can perform the entire solution in a single mental step that imposes a minimal working memory load.

Cognitive overload occurs when the working memory resources needed to process a task are greater than available working memory resources. When working memory is overloaded, content is hard to understand, learning becomes slow or ineffective, and transferring knowledge into long-term memory becomes difficult. Students might experience cognitive overload when:

they are exposed to complex learning material without being provided with sufficient guidance from teachers or educational materials (see the worked example effect below)

they are required to split their attention among multiple sources of interrelated information (see the split-attention effect below)

they are forced to process unnecessary information because it is integrated with essential information (see the redundancy effect below)

the level of instructional guidance has not been adjusted to take into account their prior knowledge (see the expertise reversal effect below)

they are exposed to a highly variable task, but they do not have a sufficient level of expertise to enable them to process the task (see the variability effectbelow)

What is the state of the evidence regarding cognitive load theory?

Over the last three decades, instructional techniques (known as cognitive load effects) have been developed within the framework of cognitive load theory to help people learn effectively. Each effect is based on multiple, replicated, randomised, controlled trials. Most of the early research on cognitive load effects was conducted in mathematics and science-based domains, although increasing amounts of research have been conducted more recently in non-scientific content domains such as music, literature, and foreign language acquisition. Consequently, cognitive load theory is well grounded in robust empirical evidence based on hundreds of randomised, controlled trials carried out by many researchers from around the world. This evidence suggests that cognitive load effects can be directly transferred to most learning environments. By taking a scientific approach to the way learning materials are designed, cognitive load theory and its associated effects help to alleviate cognitive overload and maximise learning.

Useful cognitive load effects to help you design and implement effective teaching strategies

As teachers, you can help overcome your students' working memory limitations and foster effective learning by factoring in some of the following cognitive load effects.

The worked example effect

The worked example effect demonstrates that it is not effective to have novice learners (less experienced learners in a specific domain) attempt to solve problems without any instructional guidance. In the absence of substantial prior knowledge, novice learners will randomly generate their own solutions and test the effectiveness of them. This approach is likely to overload working memory capacity, cause a high cognitive load, and inhibit learning. Teachers can reduce unnecessary cognitive load and make learning more efficient by providing worked examples. Novice learners who are exposed to worked examples during the initial stages of cognitive skill acquisition learn more easily and more rapidly than they would when trying to solve the problem by randomly generating solutions: this is the worked example effect. It has been replicated on many occasions in technical domains such as mathematics, science, engineering, and computer programming, and to some extent in non-technical domains such as English literature, foreign language acquisition, athletics, music instruction, history, and social sciences.

Worked examples in technical domains essentially consist of a problem statement followed by a well-structured representation of solution steps that clearly specify how to solve a particular problem or perform a particular task. The most efficient way to present learning material for skill acquisition and application is to link the worked example to a similar target practice problem (known as a near transfer problem), rather than presenting an entire set of different worked examples followed by a set of varied practice problems. Alternating worked examples and problem solving enables the student to derive the full benefit of using appropriate prior information from the example to build rules for the similar problem to be solved. Solving a problem immediately after studying a similar worked example is also likely to generate more incentive for the student to study the original example.

Worked examples in non-technical domains are similar to the classical worked examples in technical domains. In the domain of English literature, for example, a worked example may consist of an essay question (which is analogous to the problem statement found in a mathematics problem) and a model answer to the essay question (analogous to the solution steps that specify how to solve a mathematics problem). The model essay can therefore be used as a general guide to help answer a similar type of essay question (analogous to studying a mathematics worked example and attempting to solve a near transfer problem).

The split-attention effect

Split attention occurs when multiple sources of information cannot be understood at the same time because the items of information are in a split-source format - in other words, disparate sources of information are not integrated to form one intelligible task. An example is shown in Figure 2 below, where a student is asked to solve an unknown angle in a geometric diagram and the explanatory written text is not placed next to the unknown angle nor is there an arrow pointing to the unknown angle. To make sense of the instructional material, the student will have to work out the relevant relationships by searching and matching between the written text and the corresponding parts of the diagram. This kind of cognitive activity will impose an unnecessary cognitive load because the written text must be mentally integrated with the diagram by reading it and then storing it in working memory temporarily before searching for the correct reference in the diagram. This unnecessary activity can overburden working memory and interfere with learning because of the intensive search-and-match process that is required for mental integration. In other words, the student's working memory is fully occupied by integrating the visual and written information provided in the question and is unable to attend to solving the mathematical problem.

The heavy split-attention situation in Figure 2 can be reduced if the corresponding textual explanations are physically embedded into the components of the diagram as shown in Figure 3 below. When multiple representations of interdependent sources of information are integrated into a unified source of information, the student is not required to unnecessarily search for and match different sources of information to complete a task: this benefit is the split-attention effect. It applies not only to diagrams and texts but equally to any two interdependent sources of information such as text and tables, tables and diagrams, and so on.

The redundancy effect
When teachers provide additional information to students to help reduce their mental effort and alleviate unnecessary cognitive load, they may unwittingly include redundant information that has already been presented or is unnecessary for learning. The redundancy effectshows that providing students with redundant information can have a negative effect on learning because students are forced to process multiple lots of information together to make sense of it when they do not need to. Redundant information could include identical words in written and auditory form. For example, when a teacher reads out the exact words that appear on PowerPoint slides, this forces students to unnecessarily alternate between the written and spoken text, resulting in the text being read out of phase with the teacher's speech. Removing some of the written text on PowerPoint slides and keeping the spoken text increases the likelihood of more information being absorbed.

Another case of redundancy occurs when teachers attempt to elaborate by providing additional information that is lengthy and unhelpful, such as integrating explanatory written text in a diagram when the diagram is intelligible on its own. Unlike the split-attention effect, where different sources of information that are unintelligible on their own should be integrated, teachers should avoid replicating necessary information into instructional materials. For example, any information that repeats what is being presented, such as simultaneous spoken and written text or written text that merely describes a diagram, should be eliminated. Similarly, any unnecessary information such as cartoons or music should be eliminated because, while this material may be interesting or engaging for students, it is unlikely to lead to learning and may in fact detract from the learning. Redundant information should be eliminated so that scarce working memory resources are not wasted on processing it.

The expertise reversal effect (prior knowledge effect)

There is a plethora of strong evidence to suggest that the effectiveness of many cognitive load effects depends on the level of learner expertise in a specific domain. For example, using worked examples is most effective for novice learners because they have not yet stored in their long-term memory the appropriate problem-solving knowledge structures relevant to the area being studied. However, worked examples lose their effectiveness when studied by more experienced learners in a domain, and may even hinder learning due to redundancy. The expertise-reversal effect shows that teaching strategies should be matched to an individual student's level of expertise in order to promote knowledge acquisition - novice learners learn best from clear, explicit instructions (such as worked examples) and, as expertise increases, more experienced learners learn best from inquiry-based learning (such as unguided problem solving).

The variability effect

Studying multiple worked examples with the same solution structure can help students solve novel and difficult problems if they are able to draw an analogy between the examples. This is because learning can be enhanced if the tasks differ in their surface features (for example: 2 y = 20; 2 x = 20; 3 x = 30; 4 x = 40; 0.4 x = 1) and not their structural features (for example: 2 x = 20; 2 x + 60 = 4 x - 40; (2x) ² = 100.

When worked examples differ only in their surface features (in other words, they have a common solution structure), they enable students to recognise and reinforce the same solution steps.

Practising the same procedure with different variants of the task leads to the making of generalisations about the task. Generalising helps with the construction of (or the further development of previously constructed) schemas. These schemas enable learners to recognise the same task in future problems, despite any superficial elements that may vary the problem from those previously practised.

Consider a primary school mathematics problem: 'Chloe has two marbles and Peter has three marbles. How many marbles do they have together?' A low-variability problem would be: 'Soula has two hats and Steve has three hats. How many hats do they have together?' A high-variability problem would be: 'A box has five small blue balls, four large green balls, six large red balls, and two small yellow balls. How many large balls are there altogether?' In this example, the low-variability mathematical task varies only by way of the object (the marbles are replaced by hats), while the addition calculation of the numbers remains the same; and the high-variability mathematical task varies by way of the objects and the numbers. This enhances learning because students are able to create a generalised rule that can be transferred to a wider class of tasks that contain different surface features. Exposure to high-variability example-based instruction, compared to low-variability homogeneous examples, enables learners to engage in deeper processing which enhances the ability to transfer knowledge.

Research has confirmed that it is important to adapt instructional procedures to a student's existing schematic knowledge base in their long-term memory (according to the expertise reversal effect discussed above). Accordingly, students should be initially presented with low-variability problems (to avoid cognitive overload), with variability increased as their levels of knowledge advance - in other words, more experienced learners should be presented with high-variability learning tasks.

Consider a science lesson, where students are taught that energy in the form of food is transferred from one organism to another, and that scientists represent this transfer by using an arrow. Given that a fox is the predator and its prey is a rabbit, students could be given the example: carrots→rabbit→ fox. It is more effective to give a less experienced learner a low-variability problem such as: 'Which animal is the predator and which is the prey in the food chain grains→mice→owl?' On the other hand, a more experienced learner could be asked to study the diagram in Figure 4 and be given a high-variability problem such as: 'Many food chains can be joined together to make a food web. Which animal is the prey AND the predator in this food web?' The example provided for the low-variability problem allows the novice learner to learn which is the prey (rabbit) and which is the predator (fox). Once the novice learner can understand the food chain (which is just one single line), the teacher can move onto the food web diagram in Figure 4, which is much more challenging because of the many interconnected food chains that require the student to work out that the bird is a predator (grasses →grasshopper →bird) but is also the prey (grains →bird → fox).

References and further reading
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioural and Brain Sciences, 24(1), 87-114.

Garnett, S. (2020). Cognitive load theory: A handbook for teachers. Crown House Publishing.

Geary, D. C. (2008). An evolutionary informed science. Educational Psychologist, 43(4), 179-195.

Likourezos, V., Kalyuga, S., & Sweller, J. (2019). The variability effect: When instructional variability is advantageous. Educational Psychology Review, 31(2), 479-497.

Lovell, O. (2020). Sweller's cognitive load theory in action. John Catt Educational.

Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63, 81-97.

Peterson, L. R., & Peterson, M. J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58(3), 193-198.
Endnotes
[i] https://www.youtube.com/watch?v=gOLPfi9Ls-w&t=1s (Emeritus Professor John Sweller - UNSW, Sydney)

[ii] Geary, 2008.

[iii] Explicit instruction is the teaching of skills and concepts that uses direct, structured instruction which is designed and delivered to novices to help them develop specific knowledge about a topic.

[iv] Miller, 1956.

[v] Cowan, 2001.

[vi] Peterson & Peterson, 1959.

[vii] Likourezos, Kalyuga & Sweller, 2019.

[viii] Retrieved from https://desertoasisgarden.wordpress.com/2015/05/13/understanding-our-garden-better-with-a-food-web/#:~:text=A%20food%20web%20is%20a,5%20major%20levels%20of%20organisms.&text=Primary%20Consumers%3A%20Organisms%20that%20feed,autotrophs%2C%20also%20known%20as%20herbivores.

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Early data is showing trends of increased decoding performance for schools who have shifted to a Science of Reading model with an emphasis on explicit phonics and morphology instruction. However, data is also showing slowed second language proficiency development for multilingual learners. Recent research on language development, specifically translanguaging instructional practices, has documented the benefits of drawing on multilingual students' linguistic knowledge in multiple languages with increased literacy and language performance in workshop settings. How can we take the best of workshop and Science of Reading and infuse them with evidence-based practices to support multilingual students?

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Dr. Lindsey Moses

Lindsey grew up in Billings, Montana. She completed her B.S. degree in Elementary Education with an emphasis in Early Childhood Education at Montana State University. Lindsey began teaching second grade at a bilingual elementary school in Colorado. During that time, she became interested in how to best support the needs of young bilinguals acquiring English. She received her M.A. in Interdisciplinary Studies: ESL, Multicultural, Bilingual Education from the University of Northern Colorado. Lindsey received her doctorate from the University of Northern Colorado in Educational Studies with a dual cognate of Reading and Culturally and Linguistically Diverse Education. Lindsey began working as an assistant professor of Early Childhood Education at the University of Northern Colorado in 2009. During this time, she taught classes in Early Childhood Education, Elementary Education, Literacy, Linguistically Diverse Education, and Teaching English as a Second Language. Lindsey also worked at the University of Vermont as an assistant professor of Early Childhood/Elementary Literacy. She is currently an associate professor of Literacy Education at Arizona State University. Her research interests include examining how young children and young bilinguals construct meaning with text in various instructional settings. She has published articles on her classroom research, student inquiry and caring relations in schools settings. Lindsey is a co-author of the Heinemann book, Comprehension and English Language Learners: 25 Oral Reading Strategies That Cross Proficiency Levels, with Michael F. Opitz. Lindsey also authored the Heinemann book Supporting English Learners in the Reading Workshop. Her latest book, What are the REST of my Kids Doing? Fostering Independence in the K-2 Reading Workshop, was co-authored with an amazing first-grade teacher, Meridith Ogden. This book provides practical, classroom-based ideas for developing meaningful independent reading and learning opportunities for primary-aged students. Lindsey enjoys working with school districts, schools, and teachers across the country and internationally as an educational consultant to provide workshops and professional development to support their reading and writing instructional needs. She enjoys everything from long-term residencies to one-day workshops.

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Supporting Multilingual Learners in the Face of New Trends: Science of Reading and Translanguaging (K-8) Investment: $299 What do the new trends in research and practice mean for our multilingual students? Early data is showing trends of increased decoding performance for schools who have shifted to a Science of Reading model with an emphasis on explicit phonics and morphology instruction.