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Disclaimer

The simplified model presented here is just one of many ways to categorize types of memory. There are other models too, and some of them use slightly different definitions for some of the terms used here.

This model highlights just a few of the key brain regions involved in different memory functions. Since no part of the brain exists in isolation, most types of memory include more areas than the ones listed here. The big idea to take away is that different areas of the brain have different functions.

If you wish to delve more deeply into the types of memory or the brain anatomy involved, we encourage you to explore the references linked at the bottom of the page.

When we learn, our brain takes in information about the world that causes us to change our behavior in some way.

For example, a young baby doesn't understand that fire can hurt them. But thanks to learning, if they touch a candle flame even one time, they will probably not do it again. The information that feeds into the brain-candle, flame, hot, pain-is physically recorded in the connections between cells. And those connections influence our future behavior-don't touch!

As the brain takes in any information and stores it, there are physical changes to the connections between brain cells. And those connections influence our behavior.

Neurons that fire together wire together

The brain is an enormous interconnected network of specialized cells called neurons and supporting cells called glia. To transmit information in the brain, neurons pass signals to one another. This passing of signals can cause physical changes to the connections between cells.

When we learn a skill (like riding a bike) or new information (like multiplication tables), we are building brain connections. The more we use those brain connections, the stronger they get. It's the reason "practice makes perfect."

In the activity above, you can read about how injuries to certain brain areas cause certain memory functions to stop working. But just as important are the connections between them. If two brain areas can no longer communicate, then learning and memory can break down.

Like trails through the wilderness, brain connections get stronger the more they're used

Learning is a process that relies on long-term memory systems in the brain. People who study learning typically divide it into 4 steps: attention, storage, consolidation, and retrieval. These steps dovetail with the Types of Memory described above, and they provide a slightly different framework for thinking about what happens when one or more of these steps go wrong.

Attention - Our sensory systems are constantly bombarded with information, yet most of it fades before we are even aware of it. Attention is like a filter that lets important information into the brain while keeping the irrelevant information out. Without attention, we would remember very little.

Encoding - As sensory information comes in, the brain processes it. Strings of sounds become words, flashes of light and color become images. The brain begins connecting this new information with what it already knows about the world. Some information that is encoded will fade, and some will stick around.

Storage - Long-term storage of information requires new brain connections. The brain has its own way of rehearsing experiences (there's evidence that this is what dreaming is all about), which helps strengthen brain connections. But we can also consciously repeat information or skills to help the brain form strong memories.

Retrieval - Retrieval is the process of accessing stored memories. We use it when we recall a fact or reimagine an experience. But we also use it when we take in new information. It's part of what helps us connect new information to old.

Even when memory systems function normally, memory is imperfect. To learn more about the fallibility of memory, visit The Seven Sins of Memory.

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The simplified model presented here is just one of many ways to categorize types of memory. There are other models too, and some of them use slightly different definitions for some of the terms used here. This model highlights just a few of the key brain regions involved in different memory functions.

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One of the most widely used ways of organizing levels of expertise is according to Bloom's Taxonomy of Educational Objectives. (Bloom et al., 1994; Gronlund, 1991; Krathwohl et al., 1956.) Bloom's Taxonomy (Tables 1-3) uses a multi-tiered scale to express the level of expertise required to achieve each measurable student outcome. Organizing measurable student outcomes in this way will allow us to select appropriate classroom assessment techniques for the course.

There are three taxonomies. Which of the three to use for a given measurable student outcome depends upon the original goal to which the measurable student outcome is connected. There are knowledge-based goals, skills-based goals, and affective goals (affective: values, attitudes, and interests); accordingly, there is a taxonomy for each. Within each taxonomy, levels of expertise are listed in order of increasing complexity. Measurable student outcomes that require the higher levels of expertise will require more sophisticated classroom assessment techniques.

The course goal in Figure 2-"student understands proper dental hygiene"-is an example of a knowledge-based goal. It is knowledge-based because it requires that the student learn certain facts and concepts. An example of a skills-based goal for this course might be "student flosses teeth properly." This is a skills-based goal because it requires that the student learn how to do something. Finally, an affective goal for this course might be "student cares about proper oral hygiene." This is an affective goal because it requires that the student's values, attitudes, or interests be affected by the course.

Level of Expertise

Description of Level

Example of Measurable
Student Outcome

Table 1: Bloom's Taxonomy of Educational Objectives for Knowledge-Based Goals

Level of Expertise

Description of Level

Example of Measurable
Student Outcome

Table 2: Bloom's Taxonomy of Educational Objectives for Skills-Based Goals

Level of Expertise

Description of Level

Example of Measurable
Student Outcome

Table 3: Bloom's Taxonomy of Educational Objectives for Affective Goals

To determine the level of expertise required for each measurable student outcome, first decide which of these three broad categories (knowledge-based, skills-based, and affective) the corresponding course goal belongs to. Then, using the appropriate Bloom's Taxonomy, look over the descriptions of the various levels of expertise. Determine which description most closely matches that measurable student outcome. As can be seen from the examples given in the three Tables, there are different ways of representing measurable student outcomes, e.g., as statements about students (Figure 2), as questions to be asked of students (Tables 1 and 2), or as statements from the student's perspective (Table 3). You may find additional ways of representing measurable student outcomes; those listed in Figure 2 and in Tables 1-3 are just examples.

Bloom's Taxonomy is a convenient way to describe the degree to which we want our students to understand and use concepts, to demonstrate particular skills, and to have their values, attitudes, and interests affected. It is critical that we determine the levels of student expertise that we are expecting our students to achieve because this will determine which classroom assessment techniques are most appropriate for the course. Though the most common form of classroom assessment used in introductory college courses-multiple choice tests-might be quite adequate for assessing knowledge and comprehension (levels 1 and 2, Table 1), this type of assessment often falls short when we want to assess our students knowledge at the higher levels of synthesis and evaluation (levels 5 and 6).4

Multiple-choice tests also rarely provide information about achievement of skills-based goals. Similarly, traditional course evaluations, a technique commonly used for affective assessment, do not generally provide useful information about changes in student values, attitudes, and interests.

Thus, commonly used assessment techniques, while perhaps providing a means for assigning grades, often do not provide us (or our students) with useful feedback for determining whether students are attaining our course goals. Usually, this is due to a combination of not having formalized goals to begin with, not having translated those goals into outcomes that are measurable, and not using assessment techniques capable of measuring expected student outcomes given the levels of expertise required to achieve them. Using the CIA model of course development, we can ensure that our curriculum, instructional methods, and classroom assessment techniques are properly aligned with course goals.

Note that Bloom's Taxonomy need not be applied exclusively after course goals have been defined. Indeed, Bloom's Taxonomy and the words associated with its different categories can help in the goals-defining process itself. Thus, Bloom's Taxonomy can be used in an iterative fashion to first state and then refine course goals. Bloom's Taxonomy can finally be used to identify which classroom assessment techniques are most appropriate for measuring these goals.

References

Bloom, B. S., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). The Taxonomy of educational objectives, handbook I: The Cognitive domain. New York: David McKay Co., Inc.

Gronlund, N. E. (1991). How to write and use instructional objectives (4th ed.). New York: Macmillan Publishing Co.

Krathwohl, D.R., Bloom, B.S., & Masia, B.B. (1964). Taxonomy of educational objectives, the classification of educational goals, handbook II: Affective domain. New York: David McKay Co., Inc.

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Bloom’s Taxonomy of Educational Objectives – The Center for Teaching and Learning

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One of the most widely used ways of organizing levels of expertise is according to Bloom’s Taxonomy of Educational Objectives. (Bloom et al., 1994; Gronlund, 1991; Krathwohl et al., 1956.) Bloom’s Taxonomy (Tables 1-3) uses a multi-tiered scale to express the level of expertise required to achieve each measurable student outcome. Organizing measurable student outcomes in this way will allow us to select appropriate classroom assessment techniques for the course.

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Differences between long-term, short-term, and working memory

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Differences between long-term, short-term, and working memory

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And how can it improve the way you learn?

Cognitive load theory explains how we process and remember information, and how too much (or too complex) information is harmful to learning. Here's how we can manage cognitive load in our lessons, and 5 principles to reduce cognitive load for our students.

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Cognitive load theory explains how we process and remember information, and how too much (or too complex) information is harmful to learning. Here's how we can manage cognitive load in our lessons, and 5 principles to reduce cognitive load for our students.

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Quick Links What is Inclusive Design? Cal Poly’s Commitment to Inclusive Design Related Resources What is Inclusive Design?

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The animated guide to inclusive design (Explanimators: Episode 9)

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When You Design for Everyone, EVERYONE Benefits from the Design

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The arts, in its countless forms, transform our minds and bodies

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Art Enhances Brain Function and Well-Being

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Art Enhances Brain Function and Well-Being

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Art Enhances Brain Function and Well-Being

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An increasing amount of scientific evidence proves art enhances brain function and well-being. It impacts brain wave patterns, emotions, the nervous system.

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Teaching and Learning in Higher Education

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This module touches on topics such as inclusive education, student diversity, interculturalizing the curriculum and teaching across cultures in various academic settings.

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Growing Brains, Nurturing Minds—Neuroscience as an Educational Tool to Support Students’ Development as Life-Long Learners

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Growing Brains, Nurturing Minds—Neuroscience as an Educational Tool to Support Students’ Development as Life-Long Learners

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Growing Brains, Nurturing Minds—Neuroscience as an Educational Tool to Support Students’ Development as Life-Long Learners

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Compared to other primates, humans are late bloomers, with exceptionally long childhood and adolescence. The extensive developmental period of humans is thought to facilitate the learning processes required for the growth and maturation of the…

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This theory suggests that learning happens best under conditions that are aligned with human cognitive architecture. The structure of human cognitive architecture, while not known precisely, is discernible through the results of experimental research. Recognizing George Miller's information processing research showing that short term memory is limited in the number of elements it can contain simultaneously, Sweller builds a theory that treats schemas, or combinations of elements, as the cognitive structures that make up an individual's knowledge base. (Sweller, 1988)

The contents of long term memory are "sophisticated structures that permit us to perceive, think, and solve problems," rather than a group of rote learned facts. These structures, known as schemas, are what permit us to treat multiple elements as a single element. They are the cognitive structures that make up the knowledge base (Sweller, 1988). Schemas are acquired over a lifetime of learning, and may have other schemas contained within themselves.

The difference between an expert and a novice is that a novice hasn't acquired the schemas of an expert. Learning requires a change in the schematic structures of long term memory and is demonstrated by performance that progresses from clumsy, error-prone, slow and difficult to smooth and effortless. The change in performance occurs because as the learner becomes increasingly familiar with the material, the cognitive characteristics associated with the material are altered so that it can be handled more efficiently by working memory.

From an instructional perspective, information contained in instructional material must first be processed by working memory. For schema acquisition to occur, instruction should be designed to reduce working memory load. Cognitive load theory is concerned with techniques for reducing working memory load in order to facilitate the changes in long term memory associated with schema acquisition.

Application

Sweller's theories are best applied in the area of instructional design of cognitively complex or technically challenging material. His concentration is on the reasons that people have difficulty learning material of this nature. Cognitive load theory has many implications in the design of learning materials which must, if they are to be effective, keep cognitive load of learners at a minimum during the learning process. While in the past the theory has been applied primarily to technical areas, it is now being applied to more language-based discursive areas.

Example

In combining an illustration of blood flow through the heart with text and labels, the separation of the text from the illustration forces the learner to look back and forth between the specified parts of the illustration and the text. If the diagram is self-explanatory, research data indicates that processing the text unnecessarily increases working memory load. If the information could be replaced with numbered arrows in the labeled illustration, the learner could concentrate better on learning the content from the illustration alone. Alternatively, if the text is essential to intelligibility, placing it on the diagram rather than separated will reduce cognitive load associated with searching for relations between the text and the diagram (Sweller, 1999).

Principles

Specific recommendations relative to the design of instructional material include:

1. Change problem solving methods to avoid means-ends approaches that impose a heavy working memory load, by using goal-free problems or worked examples.

2. Eliminate the working memory load associated with having to mentally integrate several sources of information by physically integrating those sources of information.

3. Eliminate the working memory load associated with unnecessarily processing repetitive information by reducing redundancy.

4. Increase working memory capacity by using auditory as well as visual information under conditions where both sources of information are essential (i.e. non-redundant) to understanding.

References

Sweller, J., Cognitive load during problem solving: Effects on learning, Cognitive Science, 12, 257-285 (1988).

Sweller, J., Instructional Design in Technical Areas, Camberwell, Victoria, Australia: Australian Council for Educational Research (1999).

This article was provided by Howard Soloman.

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Cognitive Load Theory (John Sweller) - InstructionalDesign.org

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This theory suggests that learning happens best under conditions that are aligned with human cognitive architecture. The structure of human cognitive architecture, while not known precisely, is discernible through the results of experimental research. Recognizing George Miller’s information processi…

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6 Education Theorists All Teachers Should Know Infographic - e-Learning Infographics

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The 6 Education Theorists All Teachers Should Know present 6 people that did some of the major research in education.

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Anchored instruction is a major paradigm for technology-based learning that has been developed by the Cognition & Technology Group at Vanderbilt (CTGV) under the leadership of John Bransford. While many people have contributed to the theory and research of anchored instruction, Bransford is the principal spokesperson and hence the theory is attributed to him.

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Script Theory (R. Schank) - InstructionalDesign.org

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The central focus of Schank's theory has been the structure of knowledge, especially in the context of language understanding. Schank (1975) outlined contextual dependency theory which deals with the representation of meaning in sentences. Building upon this framework, Schank & Abelson (1977) introduced the concepts of scripts, plans and themes to handle story-level understanding.

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The Science Behind The Art Of Storytelling

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The Science Behind The Art Of Storytelling

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This is the first of two posts co-written by Lani and Vanessa Boris, Senior Manager, Video Solutions at Harvard Business Publishing Corporate Learning.
Storytelling has the power to engage, influence, teach and inspire listeners. That's why we argue for organizations to build a storytelling culture and place storytelling at the heart of their learning programs. There's an art to telling a good story, and we all know a good story when we hear one. But there's also a science behind the art of storytelling.

Here's how it works, starting with the science of the non-story:

We've all listened to (and suffered through) long PowerPoint presentations made up of bullet points - bullet points that may be meaningful to the presenter, but lack the same punch for the audience. Even if the presenter is animated, when we hear information being ticked off like this, the language processing parts in our brain, known as Broca's area and Wernicke's area, get to work, translating those bullet points into story form where we can find our own meaning. The problem with this, however, is that the story we come up with in our mind may not be the same one the speaker is intending to convey through data.

When a speaker delivers those same facts within a story, however, something else happens in the brain. In his essay "The Science of Storytelling: What Listening to a Story Does to Our Brains", entrepreneur and storyteller Leo Widrich noted that there's research to suggest that when we hear a story, "not only are the language processing parts in our brain activated, but any other area in our brain that we would use when experiencing the events of the story are, too." For example, sensory details like the client was as excited as if he had won the lottery engage a listener's sensory cortex. Action words like drive this project home engage the motor cortex, all leading to a more connected and richer experiencing of the message. In short, the more a speaker conveys information in story form, the closer the listener's experience and understanding will be to what the speaker actually intended.

Neuroscientists are still debating these findings, but we know from experience that when we're listening to a good story - rich in detail, full of metaphor, expressive of character - we tend to imagine ourselves in the same situation. Just think about all those scary stories told around the campfire. Your heart rate increases, you get goosebumps, the hair on the back of your neck stands on end. The stories told in a business setting might not be quite as dramatic (or hair-raising), but nevertheless can be more impactful than data alone.

Lisa Cron, in Wired for Story, speaks to additional benefits of sharing stories in business settings, "Stories allow us to simulate intense experience without having to actually live through them. Stories allow us to experience the world before we actually have to experience it." Leo Widrich, citing Princeton neuroscientist Uri Hasson, writes that "a story is the only way to activate parts in the brain so that a listener turns the story into their own idea and experience." The potential value here for managers to use story to mentor and coach is clear. Through stories, we can utilize vicarious experience, mentally rehearsing how we might handle a situation before we have to face it. Internal data banks, so full of what if's and how to's, are refreshed with new options, without our having to live through an experience and all the risk that might entail.

There are additional scientific elements at play. Scientists are discovering that chemicals like cortisol, dopamine and oxytocin are released in the brain when we're told a story. Why does that matter? If we are trying to make a point stick, cortisol assists with our formulating memories. Dopamine, which helps regulate our emotional responses, keeps us engaged. When it comes to creating deeper connections with others, oxytocin is associated with empathy, an important element in building, deepening or maintaining good relationships.

Perhaps most importantly, storytelling is central to meaning-making and sense-making. It is through story that our minds form and examine our own truths and beliefs, as well as discern how they correlate with the truths and beliefs of others. Through story listening, we gain new perspectives and a better understanding of the world around us. We challenge and expand our own understanding by exploring how others see and understand the world through their lens.

By sharing and listening to each other's stories, we all get a little bit closer to what's true.

Ultimately, storytelling is about the exchange of ideas, about growth - and that's learning. That's why we believe that it's important that we embed storytelling in our organizational cultures and in our learning programs. Storytelling is essential. If you're trying to engage, influence, teach, or inspire others, you should be telling or listening to a story, and encouraging others to tell a story with you. You'll have plenty of science to back you up.
Lani Peterson, Psy.D. is a psychologist, professional storyteller and executive coach who specializes in the use of story as a powerful medium for personal growth, connection and change. Drawing on her broad experience with individuals, teams and organizations in the profit and nonprofit worlds, Lani brings a unique combination of personal stories, knowledge of the theory behind stories, and deep experience helping people use stories to transform their understanding of themselves and others. Lani's professional training includes a doctorate in psychology from William James University, a master's in counseling psychology from Lesley University, and bachelor's degree in literature from Smith College. She is a member of the National Speakers Association, the National Storytelling Network, and serves on the Executive Committee of the Healing Story Alliance, which she recently chaired for five years.

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The Science Behind The Art Of Storytelling - Harvard Business Publishing

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This is the first of two posts co-written by Lani and Vanessa Boris, Senior Manager, Video Solutions at Harvard Business Publishing Corporate Learning. Storytelling has the power to engage, influence, teach and inspire listeners.

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Storytelling and Executive Functioning | Montessori Foundation | MFA | IMC

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All classroom teachers have had experiences with children who have difficulty in school; often, it’s unclear why. We might try a number of things and still find frustration. Often, the students clearly want to do well and evince great potential, but their difficulties or challenges aren’t attributable to any sort of obvious learning disability or special need.

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People love stories. We find it easier to remember events when they are part of an overarching narrative. But in real life, the chapters of a story don’t follow smoothly one from another. Other things happen in between. A new brain imaging study from the Center for Neuroscience at the University of California, Davis, shows that the hippocampus is the brain’s storyteller, connecting separate, distant events into a single narrative. The work is published Sept.

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Paulo Freire's work has influenced people working in education, community development, community health and many other fields. Freire developed an approach to education that links the identification of issues to positive action for change and development. While Freire's original work was in adult literacy, his approach leads us to think about how we can 'read' the society around us.

For Freire, the educational process is never neutral. People can be passive recipients of knowledge - whatever the content - or they can engage in a 'problem-posing' approach in which they become active participants. As part of this approach, it is essential that people link knowledge to action so that they actively work to change their societies at a local level and beyond.

In the video above, Freire talks about the importance of curiosity, of critical thinking and ultimately of hope. It is a profound reflection on learning.

Many of Freire's writings are available in English. The most well known of these, Pedagogy of the Oppressed (1972) has been very influential but new readers of Freire may find The Politics of Education (1985) a more accessible text. Key concepts associated with Freire include the contrast between "banking" education (in which facts are deposited into the minds of passive students) and problem-posing education; the notion of conscientization (which is much more than simply awareness-raising); and the idea of the "culture of silence", in which people are unable to reflect critically upon their world - they become fatalistic and dominated.

A typical feature of Freire-type education is that people bring their own knowledge and experience into the process. Training is typically undertaken in small groups with lively interaction and can embrace not only the written word but art, music and other forms of expression.

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Paulo Freire's work has influenced people working in education, community development, community health and many other fields. Freire developed an approach to education that links the identification of issues to positive action for change and development. While Freire's original work was in adult literacy, his approach leads us to think about how we can 'read' the society around us.

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Reading Comprehension

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Paulo Freire - An Incredible Conversation

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What is the Science of Reading? How the Human Brain Learns to Read | Lexia

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What is the science of reading? More than just phonics, this body of research covers the different skills that make up reading, how the human brain learns to read, and the most effective way to teach reading. Read on to learn more.

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What We Know About Reading and the Brain

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Our brains are the command center for reading and coordinate interrelated processes that allow us to use spoken and written language. In this section, you’ll learn about the brain regions that are related to reading.

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The Science of Reading Basics, Part 1: The Reading Brain

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What Are Decodable Books and Why Are They Important?

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Decodable books play an important role in phonics instruction and building confidence in young readers. Learn more about decodable books, how they differ from predictable texts, and how to select high-quality texts that align with the scope and sequence of your phonics program.

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The Science Behind Decodable Books - MetaMetrics Inc.

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By Neena Saha, Ph.D., Vice President, Science of Reading on December 14, 2022

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Playbooks® is the leading social-interactive, role-play reading enrichment program for Reader’s Theater in the nation, with its patented Roleplay Reader® format of color-coded dialogue text and easier and harder roles in the same story. Help teach Reading Fluency with this fun method of choral reading.

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Developing the Big 5 of Reading Using Reader’s Theatre

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Discover how Reader’s Theatre helps develop the Big 5 of reading. Engage students and boost literacy skills with this fun, interactive approach!

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This article is based on a talk by Daniela Kaufer, associate professor in the Department of Integrative Biology, for the GSI Teaching & Research Center's How Students Learn series in Spring 2011.

On this page:
Key Learning Principles
Research Fundamentals
Applications to Teaching
Further Reading

Also available:
Video and full summary of Daniela Kaufer's talk "What Can Neuroscience Research Teach Us about Teaching?"

Key Learning Principles

From the point of view of neurobiology, learning involves changing the brain.

Moderate stress is beneficial for learning, while mild and extreme stress are detrimental to learning.

Adequate sleep, nutrition, and exercise encourage robust learning.

Active learning takes advantage of processes that stimulate multiple neural connections in the brain and promote memory.

Research Fundamentals

Changing the brain: For optimal learning to occur, the brain needs conditions under which it is able to change in response to stimuli (neuroplasticity) and able to produce new neurons (neurogenesis).

The most effective learning involves recruiting multiple regions of the brain for the learning task. These regions are associated with such functions as memory, the various senses, volitional control, and higher levels of cognitive functioning.

Moderate stress: Stress and performance are related in an "inverted U curve" (see right). Stimulation to learn requires a moderate amount of stress (measured in the level of cortisol). A low degree of stress is associated with low performance, as is high stress, which can set the system into fight-or-flight mode so there is less brain activity in the cortical areas where higher-level learning happens. Moderate levels of cortisol tend to correlate with the highest performance on tasks of any type. We can therefore conclude that moderate stress is beneficial for learning, while mild and extreme stress are both detrimental to learning.

Moderate stress can be introduced in many ways: by playing unfamiliar music before class, for example, or changing up the format of discussion, or introducing any learning activity that requires individual participation or movement. However, people do not all react the same way to an event. The production of cortisol in response to an event varies significantly between individuals; what constitutes "moderate stress" for one person might constitute mild or extreme stress for another. So, for example, cold-calling on individual students in a large-group setting might introduce just the right amount of stress to increase some students' performance, but it might produce excessive stress and anxiety for other students, so their performance is below the level you know they are capable of. Any group dynamic that tends to stereotype or exclude some students also adds stress for them.

Adequate sleep, good nutrition, and regular exercise: These common-sense healthy habits promote optimal learning performance in two ways. First, they promote neuroplasticity and neurogenesis. Second, they keep cortisol and dopamine (stress and happiness hormones, respectively) at appropriate levels. All-night cramming sessions, skipped meals, and skipped exercise can actually reduce the brain's capacity for high academic performance. (This is true for instructors as well as students.)

Active learning: Cognitive functions associated with the lower levels of Bloom's taxonomy (see diagram at left), such as understanding and remembering, are associated with the hippocampus (the area of the brain responsible for memory and spatial awareness). The higher-level cognitive functions of Bloom's taxonomy, such as creating, evaluating, analyzing, and applying, involve the cortical areas responsible for decision-making, association, and motivation.

More complex thought processes are more beneficial for learning because they involve a greater number of neural connections and more neurological cross-talk. Active learning takes advantage of this cross-talk, stimulating a variety of areas of the brain and promoting memory.

Applications to Teaching

Classroom Activities, from the Teaching Guide for GSIs

Some Basic Active Learning Strategies, from the University of Minnesota Center for Educational Innovation

Further Reading
Please note that some links may require Library proxy access. Please see the Library's page Connect from Off Campus.
Blakemore, Sarah-Jayne and Uta Frith (2005). The Learning Brain: Lessons for Education. Malden, MA: Blackwell.

Felder, Richard M. and Rebecca Brent (1996). " Navigating the Bumpy Road to Student-Centered Instruction." An abridged version of this article was published in College Teaching 44: 43-7.

Tokuhama-Espinosa, Tracey (2011). Mind, Brain, and Education Science: A Comprehensive Guide to the New Brain-Based Teaching. New York: W. W. Norton.

Walker, J. D. et al. (2008). " A Delicate Balance: Integrating Active Learning into a Large Lecture Course." CBE Life Sciences Education 7.4: 361-67.

Winter, Dale et al. (2001). " Novice Instructors and Student-Centered Instruction: Identifying and Addressing Obstacles to Learning in the College Science Laboratory." The Journal of Scholarship of Teaching and Learning 2.1: 14-42.

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This article is based on a talk by Daniela Kaufer, associate professor in the Department of Integrative Biology, for the GSI Teaching & Research Center's How Students Learn series in Spring 2011. On this page: Key Learning Principles Research Fundamentals Applications to Teaching Further Reading Also available: Video and full summary of Daniela Kaufer's talk "What Can Neuroscience Research Teach Us about Teaching?"

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Findings from the science of learning and development contain powerful lessons for improving education to help every student access opportunities and reach their full potential.