Просмотр кода страницы Обсуждение:The ABCs of How We Learn

== Analogy ==
DRAWING AN ANALOGY involves finding the underlying similarity between diverse instances. Analogies help people learn principles and apply those principles in new situations.

The second way to use analogies is to provide students with two (or more) examples and ask them to induce the underlying structure. This turns out to be extremely powerful for learning. In fact, having students find the analogous structure is much better than giving them one example and explaining the structure! 

The key outcome of learning through analogy is the ability to transfer the key concept to a new situation. This is a critical outcome because we want students to problem solve on their own, even when there is no teacher around to tell them which concept they should be using...

=== What is the core learning mechanic? ===
Finding the similarity between two or more examples despite differences on the surface. 

What is an example, and what is it good for? What is the same about an insect that looks like a stick and a golden lion that crouches in tall dry grass? Asking students to find the analogy helps them learn the key idea (e.g., camouflage), rather than focusing on irrelevant features like how majestic a lion looks. Analogies improve understanding of the underlying principle and increase the chances that students will spontaneously transfer that principle to a new situation. This is a top goal of education: enabling students to take what they learn in school and use it outside of school. 

=== Why does it work? ===
Drawing analogies allows students to make sense of a new concept by relying on a familiar one, taking advantage of their prior knowledge. Analogies are also powerful because they help students to find the common principle despite surface differences of examples.

== Belonging == 
Other studies have found that helping people to think about themselves as having multiple identities, in particular, focusing on those facets of their identity that are in-group (e.g., college student) rather than out-group (e.g., female), improves performance for those at risk of stereotype threat (Rydell, McConnell, & Beilock, 2009; for more examples, see http://www.reducingstereotypethreat.

=== What is the core learning mechanic? ===
; Feeling that one belongs will increase effort and decrease distracting thoughts of inadequacy or alienation.

=== Why does it work? ===
People try harder when they belong, and they are not distracted by a sense of alienation. Sometimes people feel they do not belong because they already belong to a group that is negatively stereotyped, such as “girls are bad at math.” Increasing a student’s sense of belonging to the learning community can soften the negative effects of stereotype threat.

== CONTRASTING CASES == 
ARE close examples that help people notice features they might otherwise overlook. They increase the precision and usability of knowledge.

=== What is the core learning mechanic? ===
; Noticing the difference between two or more examples that seem the same at a glance. 

What is an example, and what is it good for? 
Comparing a square knot to a granny knot. The close comparison can help people see the differences, which in turn can prepare them understand an explanation of why the square knot is preferable to the granny knot. Contrasting cases help people notice subtle but important details that they might otherwise overlook. These details help people recognize one thing from another, and they prepare people to understand why the difference is important. Contrasting cases increase the chances that people will use the right knowledge at the right time, because people learn to recognize cues in the environment. 

===  Why does it work? ===
People learn to perceive patterns in sensation. This occurs by discerning what makes one thing different from another. Contrasting cases juxtapose “near misses” to help students pick out distinctive features. What

== DELIBERATE PRACTICE == 
IS characterized by a high degree of focused effort to develop specific skills and concepts beyond one’s current abilities. 

http://thedanplan.com.
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=== What is the core learning mechanic? === 
; Applying focused and effortful practice to develop specific skills and concepts beyond one’s current abilities. 

What is an example, and what is it good for? 
A basketball player starts practice by shooting one hundred free throws. On each shot she focuses on her balance and knee bend, striving for perfect form. Engaging in deliberate practice is necessary to develop exceptional expertise. Yet, even for those who do not seek to be world experts, it can increase improvement. For example, a student in a physics course might engage in deliberate practice by mixing up problems from different chapters of the textbook, solving each problem by first trying to determine which concept it embodies and which formula to use. Contrast this with another student who solves lots of problems simply by plugging in numbers to the given formula. While the second student may solve more problems, the quality of the first student’s practice will make it more valuable. 

=== Why does it work? === 

Deliberate practice automatizes skills and concepts so they become faster, more accurate, less variable, and less effortful to execute. This allows people to see new patterns and frees cognitive resources so people can attempt more complex tasks. Additionally, deliberate practice leads to a reorganization of knowledge about a domain, such as a reorganization of physics formulas based on conceptual similarities rather than perceptual similarities. What

== ELABORATION == 
IMPROVES MEMORY by making connections between new information and prior knowledge. 

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=== What is the core learning mechanic? === 
; The process of elaboration involves explicitly connecting new information to what one already knows. Elaboration increases the chances of remembering the material later. 

What is an example, and what is it good for? A student needs to memorize a problem-solving cycle that has the following elements: identify problems, define goals, explore strategies, anticipate outcomes, look back to learn. An elaboration strategy is to find a way to connect each of these steps and relate them to one’s prior knowledge. One solution is to generate the acronym IDEAL and connect it to the idea of an ideal problem solver. 

=== Why does it work? === 
Human memory is vast. Remembering depends on finding the right memory at the right time. Elaboration makes connections among memories when learning, so it is easier to find a path to the stored information later. For instance, when asked how a “good” problem solver operates, one might think good → ideal → IDEAL → identify problems, define goals….

== FEEDBACK == 
IS INFORMATION that flows back to learners about the quality of their ideas and behaviors. Learners can then use the feedback to make adjustments.


For instance, an iPad game called Critter Corral lets students see how far their answer is from correct In one of the games, children need to decide how much food to serve to the restaurant patrons, and they can see if they served too much or too little. Ideally, this helps children learn the relative sizes of the numbers while also providing some guidance for how to revise. With only right/wrong feedback, learners can only guess at how to fix a mistake.

But this is a short-term effect that depends on putting the learner in the “right mood.” Can we help people learn to seek constructive criticism more generally? We addressed this question by making a game-based assessment called Posterlet. In the game, players create posters for booths at a funfair. They choose a booth and then design a poster. When done, they select a focus group of animal characters to assess their design. Each member of the focus group shows up with two thought bubble
One says, “I don’t like …” and one says, “I like …” Players can choose either the constructive negative feedback or the positive feedback about their graphic design for each character, but not both. Students then get a chance to revise if they want. Finally, they send their poster to the booth, and they learn how many tickets sold. Students repeat the cycle three times. All told, players have nine chances to choose between constructive criticism and praise (three per poster). The assessment is unique because the goal is not to test students’ factual or procedural knowledge but, rather, to assess students’ free choices relevant to learning (see Schwartz & Arena, 2013).

=== What is the core learning mechanic? === 

; Feedback allows people to sense the discrepancy between what they did and what they should have done, which enables them to adjust future actions. 

What is an example, and what is it good for? When a child incorrectly claims that 9 – 7 = 3, the teacher uses blocks to lay out the correct answer and the child’s answer and then points out the difference between them (“Three is too many!”). Feedback helps people identify a discrepancy and ideally points out what to fix. 

=== Why does it work? === 
People would have a hard time learning something new if they never knew whether they were on the right track. They could perhaps copy a model, but even so, there would be aspects they would miss. Feedback, particularly constructive negative feedback, guides people toward what they can do to improve and learn.

== Elaboration ==

Elaboration is a strategy specialized for memorizing declarative information—things about which one can talk. The amount of declarative information that people know is astonishing. Newspaper articles, movies, a bully from second grade, sundry math facts, your friend’s preferences, alphabetical order, favorite dinners—it’s all in there. It is a good thing people do not remember everything at once! The great trick of memory is to remember the right thing at the right time. Elaboration helps. To understand how elaboration works, we need to consider two of our many memory systems. One is called working memory. It enables the conscious manipulation of information, for example, when thinking through a problem. Working memory has only temporary storage. Information moves in and out of working memory depending on the problem at hand. Working memory cannot hold information very long, so people need to keep refreshing the information, for example, by repeating the name of a person they just met. Refreshing the information keeps it available for immediate processing in working memory, but it is a poor technique for storing the information for later use.

=== What is the core learning mechanic? === 

The process of elaboration involves explicitly connecting new information to what one already knows. Elaboration increases the chances of remembering the material later.

===  What is an example, and what is it good for? === 
A student needs to memorize a problem-solving cycle that has the following elements: identify problems, define goals, explore strategies, anticipate outcomes, look back to learn. An elaboration strategy is to find a way to connect each of these steps and relate them to one’s prior knowledge. One solution is to generate the acronym IDEAL and connect it to the idea of an ideal problem solver. 

=== Why does it work? === 
Human memory is vast. Remembering depends on finding the right memory at the right time. Elaboration makes connections among memories when learning, so it is easier to find a path to the stored information later. For instance, when asked how a “good” problem solver operates, one might think good → ideal → IDEAL → identify problems, define goals….


== GENERATION == 
IS A memorization technique that relies on the fact that remembering something makes it easier to remember the next time. 

=== What is the core learning mechanic? === 
Practicing the retrieval of target memories given partial cues or hints improves future retrieval. 

=== What is an example, and what is it good for? === 
Flash cards are the original example. On one side a card says jocund, and on the other side it says, cheerful and lighthearted. To work on memorizing the definition, people read the vocabulary word (the cue) and practice generating the definition (the target) without looking at the other side. This improves memory for the definition. If people simply flip the card over to read the definition, instead of trying to remember the definition first, there will be little improvement in memory. 

=== Why does it work? === 
Retrieving a memory increases the strength of the memory, so it is easier to retrieve later. Spreading out memorization practice over several days increases memory strength compared with memorizing in only one session.
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== HANDS-ON == 
LEARNING OCCURS when people use their bodies and senses in the learning process. It recruits perceptual-motor intelligence to give meaning to words and symbols.

Does a hands-on activity need to be hands on? There are many computer simulations of hands-on activities that include mathematical and science manipulatives 
* National Library of Virtual Manipulatives at http://nlvm.usu.edu/en/nav/vlibrary.html
* PhET Interactive Simulations at http://phet.colorado.edu

The answer to this question depends on whether learners can elicit the right perceptual-motor experiences without physically touching.
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=== What is the core learning mechanic? === 
; Making sense of abstract concepts through perceptual-motor activities. 

=== What is an example, and what is it good for? === 
Students sit in a spinning chair while stretching their arms in and out. They feel how they speed up and slow down (like a twirling ice skater). This experience can anchor a discussion of angular momentum. Without the perceptual-motor experience, students would learn about angular momentum only through a series of declarative statements and equations. 

=== Why does it work? ===

The perceptual-motor system contains tremendous intelligence. This intelligence provides meaning for simple symbols and words. For example, without perceptual experience, it would be hard to understand the concepts of large and small. Hands-on learning recruits the perceptual-motor system to coordinate its meaning with symbolic representations.

==  IMAGINATIVE PLAY == 
INVOLVES creating a story that is different from the world at hand, often letting one thing stand for another (e.g., a stick becomes a swooshing plane). Theoretically, imaginative play should improve a number of developmental outcomes, such as verbal abilities, symbolic creativity, intelligence, cognitive control, and social competence. 

How to Use Imaginative Play to Enhance Learning There is a prevailing hypothesis that improving children’s executive functioning, a major component of socioemotional functioning, will have cumulative effects on future learning. Children will be better able to control their attention, concentration, and impulsivity when learning and interacting with others. People have looked to play-centered curricula to strengthen executive functioning in four- and five-year-olds. The Tools of the Mind curriculum wraps executive function exercises around imaginative play 
* http://www.toolsofthemind.org/

For example, children may be asked to play specific roles (e.g., doctors) and behave like doctors (and not patients). This differs from immature play where children do not try to play within rules. Rule-based behavior, by its very nature, is not stimulus driven.

[[Game play]] for learning has received increasing attention lately. For instance, the Quest to Learn schools in New York and Chicago frame a public school curriculum around games 

* http://www.instituteofplay.org/work/projects/quest-schools/quest-to-learn/

=== What is the core learning mechanic? === 

Imaginative play involves creating a story that is different from the world at hand. In pretend play, people let one thing stand for another. 


=== What is an example, and what is it good for? === 
A child pretends a fork (a mother) is scolding a spoon (a child) for not eating all her peas. Theoretically, imaginative play should help children develop symbolic and social abilities, as well as cognitive control.

=== Why does it work? === 
Imaginative play involves two key moves. The first is that it requires preventing the stimulus from driving one’s responses to the environment (a fork is not a fork). The second move is to construct an alternative, cognitively controlled interpretation (a fork is a mother). Exercising these core human abilities should spur their maturation. However, it has been difficult to develop definitive evidence about the causes and consequences of any form of play, despite its ubiquity across mammals. In the meantime, play, which is typically fun, can serve as a great vehicle for delivering activities known to support maturation and learning. What problems

== JUST-IN-TIME TELLING ==

ENABLES students to first experience problems before they hear or read the solutions and explanations. Lectures and readings are more effective when they address a problem students have experienced. This chapter considers how to provide those problem-solving experiences. When students appreciate the details of the problem, they learn the expository information more precisely, and they can use it to learn future material efficiently and solve novel problems effectively.
Freely available science simulations 
* see, e.g., PhET Interactive Simulations at http://phet.colorado.edu 
: can also set the stage for just-in-time telling. If this is the intent, do not ask students to only explore a simulation on their own, because they may not experience a useful problem to be solved. Instead, let them explore for a few minutes, so they can learn the various controls and general simulation behavior


A second risk is that people may discount compelling experiences, because they do not show an immediate benefit. For instance, Arena (2012) showed that commercial video games, such as [[Civilization]] and [[Call of Duty]], prepare students to learn about World War II. Students who had played the games for several hours did not initially exhibit any more WWII knowledge than students who had not, so the games seemed useless. However, this would have been a premature conclusion. While the video games do not portray WWII history per se, students do engage in strategic and tactical problems that can prepare them to learn about those types of issuerom a lecture on WWII. The video games revealed their value only when they were coupled with a subsequent explanation. Students who had played the games learned more from a lecture on WWII than those who had not. The benefit of many experiential activities is that they prepare people for future learning, and not that they are a complete lesson on their own.

Blikstein, P., & Wilensky, U. (2010). MaterialSim: A constructionist agent-based modeling approach to engineering education. In M. J. Jacobson & P. Reimann (Eds.), Designs for learning environments of the future: International perspectives from the learning sciences (pp. 17–60). New York: Springer. Bonawitz,

=== What is the core learning mechanic? ===

Enabling students to first experience problems before they hear or read the solutions. 

=== What is an example, and what is it good for? === 

Students complete a simulated battle, and afterward there is a debriefing. The simulation provides students with rich experiences, and the debriefing provides an explanation or framework for organizing those experiences. Without the experience, the explanation would be too abstract. Without the explanation, the experiences would just be a collection of memories. Together, they produce usable knowledge. 

=== Why does it work? === 

People learn from expository materials by integrating the explanations with their prior knowledge. Often, students do not have sufficient prior knowledge to integrate the explanations meaningfully. Providing learners an opportunity to develop prior knowledge of the problem helps make the lecture more meaningful, because students have experienced aspects of the problem that need to be solved. What

== KNOWLEDGE == 
Prior knowledge enables people to make sense of new information, and “post” knowledge enables people to imagine and achieve goals they previously could not. Because of knowledge’s central importance, we decided to break form. Rather than presenting a chapter for the letter K in our usual style, we offer a brief essay on the broad outcomes of knowledge. Our goal is to help untangle a tacit dichotomy that leads to confusion about the design of educational experiences and desirable learning outcomes.


== LISTENING  ==
AND sharing learners try to construct joint understandings. Listening and sharing are the cornerstones of collaborative learning. We can learn more working together than working alone.

Determining whether students have learned to cooperate is daunting. Gillies (2002) recorded group member behaviors every 10 seconds—it is not an easily adopted methodology. People are working on more efficient solutions. [[Basketball]] has a solution. It is possible to measure the performance of teammates when player X is on the floor versus not. For example, do the teammates score more points when player X is on the floor? It is fun to think of how to do something similar for learning settings. The Programme for the International Student Assessment * http://www.oecd.org/pisa is a common test taken by many nations (and causes consternation among officials when their country does poorly). 

The test makers are trying to measure cooperative skills in negotiation, consensus building, and divide-and-conquer tasks. In a fascinating sample item, a student interacts with three simulated team members in a computer-based task. The team is planning a welcoming activity for visitors. One of their tasks is to decide among several options offered by their simulated teacher, Ms. Cosmo. One of the simulated students, Brad, says, “Who cares? All thehoices are boring. Let’s take our visitors someplace they’ll actually enjoy.” 

=== What is the core learning mechanic? ===
; Listening and sharing are the cornerstones of collaborative learning, where students work together to complete projects, solve problems, and learn. 

What is an example, and what is it good for? Small groups of students collaborate on a class project to make a school system for fair decision making. With guidance, students can learn to cooperate more effectively, and they will learn about the topic of governance more deeply. 

=== Why does it work?=== 
Doing things with others can be very motivating, but it also takes cooperative skills. Done well, students maintain joint attention, listen, share, coordinate, and try to understand one another’s points of view. This can help learners exchange information and develop a multifaceted understanding.

== MAKING ==

CREATING shareable products, such as gardens, works of art, and computer programs. Making is motivating, yields practical knowledge, and may lead to sustained interest. Being able to create things is both useful and satisfying. Dale Dougherty, who started Make magazine, clairs, ''“The maker movement has come about in part because of people’s need to engage passionately with objects in ways that make them more than just consumers”.'' Making starts with production, not consumption. People make many things. Beer brewing is a fine example. 

Karl [[Marx]] (yes, we really are bringing in Marx) wrote of two great forces that constitute a person. One is appropriation—we become what we are by taking up the ideas and artifacts of those around us. The second is production. With [[Aristotle]], he viewed humans quintessentially as builders. We want to produce and create ourselves in the world, whether through ideas or products. This way we can put our element in the social matrix, and other people may appropriate our ideas. Marx did not advocate for a welfare state in which people only had access to appropriation. He advocated for a productive state where people could contribute and impress themselves upon the world. For Marx, the critical political issue was always who owned the means of production, which is oddly consistent with arguments for student-centered classrooms. This does not imply that student-centered teachers are communists! But Marx did capture the essence of making and how it can contribute to learning.

PRODUCTIVE AGENCY 
; Making follows the idealized cycle shown in Figure 

<graphviz>
digraph ProductiveAgency {
 
node [shape=ellipse];

subgraph cluster_subject {
 //         style=filled;
//       color=lightgrey;
    label="Цикл продуктивной агентности";


Make  [label="Создать"];
Fruits  [label="Увидеть результаты труда\n#1 Удовлетворение"];
Share [label="Поделиться сделанным\n#2 Удовлетворение"];
Overcome [label="Преодолеть новый вызов\n#3 Удовлетворение"];
Learn [label="Изучить стратегии и методы\n#4 Удовлетворение"];

Make -> Fruits ;
Fruits -> Share ;
Share -> Overcome ;
Overcome -> Learn ;
Learn -> Make ;
}
  }
</graphviz>

The cycle comprises four of the top five motivations found in the Pfaffman survey. These are not just any motivations. They are motivations for more learning. People want to see the fruits of their labor. When people see their ideas rendered into practice, they receive feedback on what did and did not work, including surprising outcomes. People also like to share their creations, which in turn generates additional feedback about the features that others especially liked and disliked, along with suggestions for variations. These contribute to the appropriation of other people’s ideas. The feedback further motivates makers to set new goals and challenges that create needs for new learning. This tilts the cycle into an upward spiral, because new challenges require new learning. Finally, given new goals, makers are motivated to seek out new methods that give them the means of production to achieve those goals. Who knew that hobbies were such a good play pattern for learning! One reason the cycle is powerful is that people are exceptionally attentive to feedback that comes from their own productions. Okita and Schwartz (2013), for example, reported research in which high school students created a computer agent and taught it logical reasoning rules that would let it solve problems. Students either observed their agent solving problems or solved the same problems themselves, getting otherwise identical feedback about the answers. Those who watched their agent learned more from the feedback than those students who solved the problems themselves. They were better able to solve complex logic problems on a posttest. People pay special attention to feedback directed at their creations. LEARNING AN INTEREST A second powerful feature of the cycle is that it captures interest. There is an important distinction between situational interest and individual interest (Renninger, Hidi, & Krapp, 2014). Situational interest is driven by the immediate context. Museums are purveyors of situational interest—people become captured by an intriguing exhibit or activity. Situational interest is transient. When the situation is no longer there, the interest often is gone as well. Situational interest can eventually evolve into individual interest when there are sufficient resources, including expanding opportunities, access to expertise, and a community of sharing (see Chapter P). Ito and colleagues (2009) describe an interest trajectory for creating digital artifacts that graduate from hanging out and messing around to “geeking out.” When people have individual interest, they seek relevant opportunities, and they show resilience in the face of temporary failures and even boring presentations (Renninger et al., 2014). People learn to be interested, and making helps.
Making happens in clubs, museums, homes, and even school. 
One fun example is the SparkTruck (see http://sparktruck.org —a big mobile truck carrying tools and materials that delivers maker projects to kids across the country, logging over 20,000 miles as of 2015. 

We take the example of [[Scratch]], a visual programming language developed at the MIT Media Lab Scratch includes the essential ingredients of productive agency. There are resources for making, namely, the learner-friendly programming

{{#ask: [[Scratch]] | ?Description }}

=== What is the core learning mechanic? ===
; Producing an artifact or performance and taking up feedback and setting new goals. 

What is an example, and what is it good for? 
Brewing beer at home and tasting it; writing a poem to perform at the local spoken-word festival. Makers learn practical knowledge and interest. 

=== Why does it work? ===
Making has motivations that naturally produce a learning cycle that expands one’s means of production. Motivations include the desire for feedback on the realization of one’s ideas, and the creation of new challenges that motivate makers to learn more skills and methods. With support, making can evolve from a situational interest driven by the environment to an individual interest where people independently pursue making opportunities.

== NORMS ==

ARE RULES of social interaction. They are often culture specific and can vary by intellectual tradition and setting. Good norms enable productive learning interactions. Norms of intellectual engagement shape what people learn and what they value.

As Richard Posner put it, “''If you don’t play chess by the rules, you’re not playing it at all''”  Norms allow people to play the game of social life. If players have different rules of the game, it is a recipe for frustration and no game play at all. In a learning setting, productive norms increase the effective exchange of information and ideas. Shared goals and methods of interaction ensure that learners are playing the same game, smoothing out the process of learning and problem solving together. Shared norms reduce the need to enforce rules of engagement.

=== What is the core learning mechanic? ===
; Social norms are informal rules that regulate social interaction. Social interaction determines what and how people learn. 

What is an example, and what is it good for? 
An elementary math class adopts the norm of class discussions in which ideas and answers need to be justified with mathematical arguments, rather than just saying an answer and letting the teacher evaluate. This norm helps students learn what it means to do math and think mathematically.

=== Why does it work? ===
People want to fit in, and following social norms is the way. People are likely to follow a social norm when they believe society expects them to, and they believe other people also follow the norm. Good norms help coordinate learning interactions, both at the level of good behaviors and at the level of the way different disciplines engage their topics.

== Observation ==
PEOPLE LEARN BY watching other people’s attitudes and behaviors ==

Learning by watching is called observational learning. It is especially effective for overt procedural skills, affective responses, and social values. Observational learning occurs naturally without explicit instruction—people learn to imitate both good and bad behaviors by watching those around them.

Providing a human model is an exceptionally effective way to teach people how they should feel about an event, a situation, or other people. Learning affective responses through observation comes naturally. One example comes from social referencing (Walden & Ogan, 1988). When an infant falls, he may look to his mother to see her reaction: if the mother laughs, the child will laugh about the fall; if the mother shows alarm, the child will cry about the fall. Social referencing is a powerful way that children learn how to feel about strangers and novelty. The reactions of an adult (or even a video game character) to children’s failure should be an important consideration when designing instruction. 

=== What is the core learning mechanic? ===
; Learning by observation involves watching and imitating other people’s behaviors and affective responses, as well as vicariously seeing the consequences of other people’s behaviors. 

=== What is an example, and what is it good for? ===
 
A child is not sure how to play a game on the playground. She stands on the sideline and watches the other children play for several minutes, until she figures out the rules and decides to join in. Learning by observation is especially good for learning overt behaviors. It is also a powerful way in which people learn affective responses.

=== Why does it work? ===
Human brains are wired to learn by observing others. The brain shows similar patterns of activation when people observe others as when acting themselves. People can learn physical skills or emotional reactions by imitating the behaviors they observe in others. Additionally, seeing the consequences of other people’s actions allows learners to determine which behaviors should be favored or avoided.

== PARTICIPATION ==
What Is Participation? PARTICIPATION REFERS TO engaging in an existing cultural activity. The major benefit of learning through participation is that it involves a rich and purposeful social context, often with a trajectory of continued participation and growth.

Developmental psychologist Lev [[Vygotsky]] stated, “What a child is able to do in collaboration today he will be able to do independently tomorrow” (1934/1987, p. 211). This thought undergirds his most influential idea, the zone of proximal development (the [[ZPD]], as it is fondly called), a region along a trajectory of growth where, with a little social help, learners can begin participating in an activity. Participating, in turn, further drives the processes of development and learning so that eventually the learner can participate without help.

This brings up an important point. Good learning environments provide a trajectory for continued learning and deeper involvement. Sports provide an impressive example: children can begin in little leagues and move through clubs of increasing abilities until they become professional athletes. The best video games make extensive use of the [[ZPD]] to create trajectories for more complex participation (Gee, 2003). At early levels, the computer accomplishes many of the tasks of the game, such as maneuvering additional players and offering a lax opposition. Players have a chance to experience why one would play such a game while also learning some of the basic game mechanics. As the player moves up in skill level, the game reveals more complexity along a “Goldilocks” ramp that is neither too hard nor too easy. (See Chapter R for why this is also very motivating.) II. How to Use Participation to Enhance Learning

* Gee, J. (2003). What video games have to teach us about learning and literacy. New York: Palgrave Macmillan. 
* Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.

=== What is the core learning mechanic? ===
; Participating in a socially contextualized activity provides learners with access to the goals, consequences, methods, and interpretations that render learning meaningful. The challenge involves finding a way to help beginners to start participating. 

What is an example, and what is it good for? 
Learning to surf: The instructor tows the beginner out to sea and pushes the surfboard at the right moment to catch the wave. Meanwhile, the surfer can focus on balancing and experiencing what it means to surf. With improvement, the instructor can fade the support, and the beginner can participate in other aspects of surfing such as being very cool. 

=== Why does it work? ===
With just the right amount of social or physical supports, beginners can start to participate in an activity that they could not engage in on their own. Over time, learners come to manage the complexity of the activity and no longer need special supports.

== QUESTION-DRIVEN LEARNING ==

The Jasper Woodbury videos are carefully constructed problem-based learning activities with two key features: they introduce a rich and compelling video-based problem-solving context that does not handicap children with poor reading skills, and they are self-contained and provide sufficient information to address the question. This latter feature is in contrast to more open-ended, project-based approaches that have students work on real-world problems, such as, “How can we increase the percentage of students who recycle at school?” This is more authentic, in the sense that it has a potential impact on people’s lives. Projects can be very rewarding, but they are also more demanding for the instructor, because the space of subproblems and possible relevant resources can become unruly.

* http://wise.berkeley.edu


Some additional examples of K-12 question-driven cycles are the Web-Based Inquiry Science Environment (WISE; see http://wise.berkeley.edu) and the STAR.Legacy software (Schwartz, Brophy, Lin, & Bransford, 1999). Simpler visual organizations can also be helpful. Consider a whiteboard with the following columns (Hmelo-Silver, 2004). FACTS

=== What is the core learning mechanic? ===
; Learning in the service of answering a driving question increases curiosity, purpose, attention, and well-connected memories and may develop problem-solving skills. 

=== What is an example, and what is it good for? ===
As part of a unit on humans and the environment, a class investigates how noise pollution is affecting the wildlife around their school. The teacher supports students in constructing an answerable question, figuring out what they know and what they need to find out, and deciding how to evaluate and integrate different sources of information. Students are engaged with learning content from various disciplines, including science, math, and social studies. 

=== Why does it work? ===
Question-driven learning draws on several useful mechanisms. Curiosity drives reward and motivation systems. Learning with a problem-solving orientation can help students apply what they have learned to solve problems in the future. A focusing question allows disparate information to build into a network of related ideas, which should support memory retrieval.


* Personal Inquiry: Orchestrating Science Investigations Within and Beyond the Classroom 
** https://www.tandfonline.com/doi/full/10.1080/10508406.2014.944642#d1e328
** SCI-WISE Inquiry Cycle engages students in a six-step inquiry (Question, Hypothesize, Investigate, Analyze, Model, and Evaluate)
** Scanlon, E., Anastopoulou, S., Kerawalla, L., & Mulholland, P. (2011). How technology resources can be used to represent personal inquiry and support students’ understanding of it across contexts. Journal of Computer Assisted Learning, 27, 516–529. -

<graphviz>
digraph InqueryCycle {
layout = "circo";
 node [shape=none];
   edge [dir=none, color=gray] ;
// edge [arrowType=dot]
 
Find  [label="Find\nmy topic"];
Decide [label="Decide\nmy hypothesis"];
Plan [label="Plan\nmy methods"];
Collect [label="Collect\nmy evidence"];
Analyze [label="Analyze\nand represent\nmy evidence"];
Respond [label="Respond\nto my hypothesys"];
Share [label="Share\ndiscuss my inquiry"];
Reflect  [label="Reflect\nOn my progress"];

Find -> Decide -> Plan -> Collect -> Analyze -> Respond -> Share -> Reflect  ;

Find -> Plan ;
Find -> Collect ;
Find -> Analyze ;
Find -> Respond ;
Find -> Share ;

Reflect -> Find ;
Reflect -> Decide;
Reflect -> Plan; 
Reflect-> Collect ;
Reflect -> Analyze ;
Reflect -> Respond ;

 Decide -> Find ;
  Decide -> Collect ;
   Decide -> Analyze; 
    Decide -> Respond ;
     Decide-> Share ;
      Decide -> Reflect ;

 Plan -> Analyze ;
 Plan -> Respond ;
 Plan -> Share ;
 Plan -> Reflect ;
 
 Collect -> Share ;
 Collect -> Respond ;

  }
</graphviz>

----

<graphviz>
digraph InqueryCycle {
layout = "circo";
    node [shape=none];
    edge [dir=none, color=gray];

    Find [label=<1 <b>Найти</b><br/>мою тему >];
    Decide [label=<2 <b>Выбрать</b><br/>мою гипотезу >];
    Plan [label=<3 <b>Спланировать</b><br/>методы >];
    Collect [label=<4 <b>Собрать</b><br/>доказательства >];
    Analyze [label=<5 <b>Проанализировать</b><br/>и представить<br/>доказательства >];
    Respond [label=<6 <b>Ответить</b><br/>на мою гипотезу >];
    Share [label=<7 <b>Поделиться</b><br/>обсуждением моего исследования >];
    Reflect [label=<8 <b>Обдумать</b><br/>мой прогресс >];

    Find -> Decide -> Plan -> Collect -> Analyze -> Respond -> Share -> Reflect;

    Find -> Plan;
    Find -> Collect;
    Find -> Analyze;
    Find -> Respond;
    Find -> Share;

    Reflect -> Find;
    Reflect -> Decide;
    Reflect -> Plan; 
    Reflect -> Collect;
    Reflect -> Analyze;
    Reflect -> Respond;

    Decide -> Find;
    Decide -> Collect;
    Decide -> Analyze; 
    Decide -> Respond;
    Decide -> Share;
    Decide -> Reflect;

    Plan -> Analyze;
    Plan -> Respond;
    Plan -> Share;
    Plan -> Reflect;

    Collect -> Share;
    Collect -> Respond;
}
</graphviz>

== REWARD ==

IS a desirable outcome received in response to behavior. As might be expected, rewarding a behavior drives people to adopt that behavior. Punishing a behavior leads to the opposite. People often think about learning in terms of improved understanding. Another important outcome of learning involves simply taking on good behaviors. If only parents could get their teenagers to do homework instead of playing video games all the time!

B. F. [[Skinner]] (1986), a major proponent of behaviorism, tells the story of nurturing art appreciation. Two undergraduates wanted to place a painting on their dormitory wall, but their roommate wanted to put up his sports awards. The undergraduates decided to change their roommate’s behavior by rewarding pro-art behaviors surreptitiously. During a party, they paid a young woman to ask the roommate about art and to hang on his every word. They later took him to a museum and covertly dropped a $5 bill on the floor next to a painting he was observing. They also paid more attention to him when he discussed art. As the story goes, a month later the roommate had bought his first painting for the dorm room.

Different people find different things intrinsically rewarding. A cook likes making a good meal, whereas a sports fan likes cheering a good game. One ideal is personalizing instruction to match each student’s individual interests. This is a good idea, but can be hard to achieve when there are many different students, each of whom finds personal relevance and interest in different things. Fortunately, we can rely on situations that nearly everyone finds intrinsically motivating. Ryan and Deci (2000) have proposed three foundational intrinsic motivators: 
# autonomy, 
# competence
# social relatedness. 

Autonomy relates to a feeling of control over your own decisions and actions. The authors found that classrooms where students had more autonomy showed higher levels of curiosity, desire for challenge, and sustained motivation than did classrooms where teachers were very directive and controlling. Competence relates to the feeling that one is capable of achieving desired goals and gaining mastery. Positive feedback that one’s free-throw percentage is improving can motivate further practice. Social relatedness taps into the human desire to connect with others. While relatedness is not necessary for intrinsic motivation (many solitary activities are inherently motivating), it can help support motivation. For example, many hobbyists find the opportunity to share the fruits of their labor, such as a painting or a brewed beer, a strong source of motivation

Activities that foster any or all of these feelings will be highly motivating because they tap into human psychological needs...

Good video games are a wonder of reward. They employ multiple extrinsic reinforcement schemes. These include several simultaneous point schemes, juicy graphics, and rewards of new powers and levels. They further use variable reinforcement so that players sometimes lose but eventually always win. If that were not enough, they also include multiple intrinsic reward schemes, including narrative, fantasy, customization (choice), and optimal challenges. The powerful motivation schemes of video games have led to the idea of “gamifying” otherwise unappealing tasks (Reeves & Read, 2009). For instance, call centers have a terrible employee turnover problem—nobody wants a job where strangers yell at them on the phone. One solution could be a rich video game where answering calls is part of the game. Employees receive points, complete quests, and advance levels by effectively answering calls.

=== What is the core learning mechanic? ===
; Rewarding a behavior leads people (and animals) to repeat that behavior. People can learn new behaviors by rewarding successive approximations until they achieve the desired the behavior. 

What is an example, and what is it good for? Reward can lead people to behaviors they might not otherwise engage in. A teacher wants a child to complete his homework, but the child never does it, so the teacher cannot deliver a motivating reward. To shape the child’s behavior, the teacher gives a red star if he turns in a paper with just his name. After a few times, the teacher gives a green star if he turns in a paper with his name and one completed problem. The teacher then provides a gold star for turning in five problems, and so forth. 

=== Why does it work? ===
Rewards, whether delivered as a prize or generated through internal satisfaction, motivate people to repeat rewarded behaviors in similar situations.

== Self-explanation ==

Self-explanation is a constructive process. People build knowledge that goes beyond the information given. For example, self-explainers generate inferences to fill in missing information. They also make connections to their own knowledge (e.g., the lungs oxygenate blood). Ineffective readers do not take these extra efforts to construct an interpretation of a text. If anything, they merely paraphrase or reread sentences. Ineffective readers are learning a text, whereas effective readers are learning from a text.

; What is the core learning mechanic? 
*Silently talking through expository materials to improve comprehension.
----
What is the core learning mechanic? Silently talking through expository materials to improve comprehension. What is an example, and what is it good for? “Dark matter is a type of matter hypothesized in cosmology to account for a large part of the mass that appears to be missing from the universe.” Effective readers work to make sense of this sentence, for example, by asking how matter can be missing from the universe yet still be there. They try to construct a mental model of the text by explicitly looking for connections across sentences and connections to their own knowledge. This includes looking for gaps in understanding. The resulting mental model improves memory for meaning and makes it easier to draw inferences and have insights. Why does it work? People often think learning means memorizing, and their study habits reflect it—repeatedly reading a text and reciting facts verbatim. However, a text cannot state all the relevant connections among ideas, so students need to construct an understanding that goes beyond memorizing sentences. Self-explanation works because people fill in missing information to make a coherent explanation.

== Teaching ==

LEARNING BY TEACHING occurs when people accept the responsibility of teaching others and develop their own understanding so they can teach well. Teaching is not just for pupils; the teacher learns too. Teaching creates a confluence of felicitous conditions for learning. These include a motivating sense of responsibility, a need to organize and explain information, and feedback based on one’s pupils’ performances. When learning by teaching, people develop chains of connected ideas. Professors often say they never really understood a topic until they had to teach it. Students can also benefit when they have a chance to teach. In peer tutoring, students teach other students face-to-face. In a review of thirty-eight peer-tutoring studies, Cohen, Kulik, and Kulik (1982) found that 87 percent of the studies exhibited a learning benefit for the tutors that was nearly as large as the benefit for the tutees! Engaging in teaching can be a powerful way for students to learn.

In [[Jigsaw]], all the students have an opportunity to teach. This arrangement is nice because there is interdependence within the group—no student can complete the task alone, and success depends on the teaching of the other students.

A technology called a [[Teachable Agent]] (TA) fosters chains of ideas in the extreme (Blair, Schwartz, Biswas, & Leelawong, 2007). With TA, students teach a computer character. F To teach a TA, students build their agent’s brain by adding nodes and connecting them. - [[Betty's Brain]]

[[Teachable Agent]]. Students teach a computer character by creating a [[concept map]] that shows the causal relations among nodes in the map. Once they have taught their agent, they can ask it questions, as shown in the foreground panel. The agent visually reasons through the concept map to determine the answer.

== UNDOING ==
 HELPS TO weaken mistaken ideas that are often resistant to change. People regularly develop beliefs and ways of reasoning that work much of the time but are still formally incorrect and can occasionally cause problems. This includes bad habits, misconceptions, and faulty ways of reasoning. It is important to undo this prior learning lest it interfere with future learning. Undoing requires identifying and replacing the source of the incorrect thinking, rather than just correcting each mistaken answer.

How Undoing Works Jean [[Piaget]], the famous developmental psychologist who proposed stages of cognitive development, theorized that children improve their reasoning about the world through processes of assimilation and accommodation. Assimilation occurs when people make new information fit their current ideas. Accommodation occurs when people change their ideas to fit new information. Undoing depends on accommodation, which can be difficult—changing beliefs is harder than confirming them. 


=== What is the core learning mechanic? ===

; Teaching improves the teacher’s own knowledge. 

What is an example, and what is it good for? Teaching is not just good for pupils; it is good for the teacher, too. Professors often say they never really understood a topic until they had to teach it. Asking older students to tutor younger students is an excellent example of learning by teaching. Tutors improve their understanding nearly as much as the tutees. The outcome of teaching is well-connected ideas. 

=== Why does it work? ===

Teaching brings strong social motivations that cause teachers to engage content carefully. Teachers need to organize the information and be prepared to answer any question that might arise. Pupil questions lead teachers to elaborate and explain how ideas fit together. Teachers observe their pupils use what they have been taught, which provides useful feedback on how well the teachers connected their own ideas.

==  V - VISUALIZATION ==
IS THE process of making an external spatial representation of information. Visualizing is a useful strategy for discovering structure and organizing information efficiently. People often want to know the best way to communicate effectively with visualizations (McElhaney, Chang, Chiu, & Linn, 2014). In this chapter, we discuss a different matter: getting learners to create their own visualizations. It has many benefits and is rarely included in instruction, so we emphasize it here. Maps, diagrams, sketches,

=== What is the core learning mechanic? ===
; Drawing a spatial representation helps organize ideas and information. Some examples include maps, diagrams, sketches, graphs, Venn diagrams, trees, and matrices. 

What is an example, and what is it good for? In the early 1900s, Harry Beck created a visualization of the London subway that sacrificed exact geographic detail for a structure more relevant to the subway passenger. It led to the modern subway map used by nearly every subway system. Visualization is a strategy for organizing complex information. It works for inherently spatial information, as may be found in many science topics. It also works for nonspatial information, as in the case of a calendar, which uses space to represent time. Visualization can help people discover new structure that improves learning and future problem solving. 

=== Why does it work? ===
Creating a spatial organization of ideas helps the visual system find patterns. Visual patterns support the discovery of structure, new interpretations, and the efficient search of information.

== W - WORKED EXAMPLES ==
ARE models of expert solutions. Novices can follow the expert’s procedures and explanations to learn how to solve similar problems on their own. A
What is the core learning mechanic? Worked examples involve demonstrating step by step how to complete a procedural task. What is an example, and what is it good for? A self-help video shows how to install a faucet and explains each step of the repair. Watching the video can save novices a great deal of time learning how to install the faucet compared with the inevitable doing and undoing that happens when they try on their own. A second application is showing the solution steps for an algebra problem. Solve for a: (a + b)/c = d a + b = dc a = dc – b When people do not know how to solve problems, worked examples are useful for initial learning. They help novices attend to the key steps, which helps them solve highly similar problems later. Why does it work? Worked examples build on observational learning. They allow the learner to observe and imitate well-defined steps. Ideally, they also share expert thinking processes, particularly how and why to segment complex problems into subgoals. A worked example can be more efficient than problem solving for initial learning. The worked example reduces unnecessary floundering and distractions, so people can focus on the actual steps that give the right solution.

== X -  EXCITEMENT == 
IS A physiological state of heightened arousal. It comes with increased heart rate, increased blood pressure, moist palms, focused attention, higher emotion. Moderate levels of arousal improve performance and memory encoding.

People perform better when other people are around, called social facilitation. Cyclists go faster with a peer (Triplett, 1898). Billiards players shoot better with an audience (Michaels, Blommel, Brocato, Linkous, & Rowe, 1982). Even ants excavate more tunnels in the company of other ants (Chen, 1937). To explain social facilitation, Zajonc (1965) proposed that the mere presence of others induces general arousal. In turn, arousal enhances prepotent responses, or behaviors that are well known. He drew an unhappy implication for learning: arousal activates prepotent responses, which interfere with learning new responses. Zajonc concluded, “One practical suggestion … advise the student to study all alone, and to arrange to take his examinations in the company of many other students” (1965, p. 274). So much for collaborative learning! But wait … Recent research has amended Zajonc’s conclusion. Babies learn language better from a real person than from a video of a person (Kuhl, Tsao, & Liu, 2003). Adults learn science concepts better in the presence of real people.

=== What is the core learning mechanic?  ===
Excitement increases physiological arousal, which focuses attention and improves memory acquisition. What is an example, and what is it good for? In the middle of a long lecture, a professor asks students to stand up and look around. Activity increases arousal, which in turn improves attention and memory for the material. Why does it work? Excitement is a physiological change associated with primitive flight-or-fight responses. Heart rates increase, palms become moist, attention focuses. Emotion colors arousal with positive or negative feelings. Positively arousing emotions activate the brain’s reward system and the amygdala, both of which help lay down memory traces. The exciting event does not have to be relevant to the instructional content. Arousal after the fact can sometimes improve memory for what happened earlier. What

== Y - YES I CAN ==
YES I CAN refers to [[self-efficacy]] — people’s belief that they have what it takes to accomplish a goal. When people believe success is within reach, they approach activities more readily, persist longer, persevere in the face of failures, and accomplish more. How do you convince yourself to try something? One argument is the payoff—a million-dollar lottery has an attractive payoff, so you try it. This part of the calculus is often called the utility or value. Sometimes, students do not perceive any utility in what they are supposed to learn, so they don’t bother. (Chapters M, P, Q, and R address this issue.) The second part of the calculus is whether you can succeed or not—your expectancy. This partially involves estimating the probability of success: maybe buying the lottery ticket wasn’t such a good idea after all. People also have expectations about their abilities to cause success (self-efficacy): maybe you believe you can rig the lottery, so you play. If the utility of an endeavor is high and your expectancy for accomplishing that endeavor is high, you approach and persist.

In [[Bandura]]’s formulation, self-efficacy differs from self-esteem and self-confidence, which both involve people’s overall sense of worth. People can have different feelings of self-efficacy for different tasks. Chase (2013) studied relatively accomplished scholars in mathematics and literature. She gave both groups of scholars an accessible but difficult problem in math (a tricky math problem) and in literature (a tricky poem). The mathematicians persisted on the math problem and noted the components of the problem that made it tricky. But when they received the poem, they said, “I’ve never been good at this stuff.” Conversely, the literature scholars persisted on the poem and blamed the poet for being bloated, but blamed themselves for not being able to do the math problems, which they said they were never good at anyway. Perceived self-efficacy in one domain does not entail self-efficacy in another. This is a good thing, because few things are more dangerous than ignorant overconfidence. 

Bandura nails the point again: “The objective of education is not the production of self-confident fools” (1997, p. 65). 
Bandura (1997) described four factors that influence people’s self-efficacy: 
#  mastery experiences: having had past success; 
# vicarious experiences: seeing others like you achieve the goal; 
# social persuasion: hearing you are efficacious; and 
# physiological signals: noticing the effort and time involved while doing an activity. 

Since Bandura’s original work, most contemporary versions of self-efficacy include some form of self-attribution. People attribute a causal role to themselves for successes and failures. An example of attributional thinking comes from the fundamental attribution error (Ross, 1977): Americans tend to believe that other people’s bad behavior is caused by bad personalities, whereas their own bad behavior is caused by the environment. For example, when driving to work, a car cuts you off, and you think the driver is a jerk and a bully, but when you cut someone off, it is because your boss would fire you for being late again. People regularly make motivation-relevant attributions that reveal the fundamental attribution error: “I cannot do this task well because my teacher is being mean.”

=== What is the core learning mechanic? ===
Enabling learners to believe that they can succeed helps them take on a challenging activity, persist longer, persevere in the face of failures, take on more challenges, and ultimately accomplish more. 

=== What is an example, and what is it good for? ===
As Albert [[Bandura]], the father of self-efficacy, wisely notes, “''Self-belief does not necessarily ensure success, but self-disbelief assuredly spawns failur''e” (1997, p. 77). A novice rock climber with high self-efficacy will choose to tackle paths that look difficult and will get right back on the climbing wall when she falls. A novice with low self-efficacy will attempt new paths only when necessary and respond to a fall by giving up and trying an easier route. 

=== Why does it work? ===
One part of the motivation equation is the perceived utility of success. The second part is whether people believe they can succeed. People make attributions about whether they can cause their own success. Improving these attributions increases the likelihood they will engage and persist in a challenging task.


HAVE YOU TAUGHT? That is a rhetorical question. Of course you have. Humans have a basic need to teach one another. Teachers do it; so do parents, friends, siblings, gossips, and employers. People even teach themselves. Today alone, among our many teaching experiences, we showed a toddler how to peel a banana, familiarized an out-of-town visitor with the commuter train, returned written feedback on a statistics assignment, and coaxed a puppy to sit, yet again. Given that you have taught, you might also recall a time when it did not work very well. So, you tried another approach and it worked better. Here is a common example: Someone asks you for directions to a building or store, and you reply, “Sure!” because you certainly know where it is. But soon enough, you find yourself gesturing ineffectually as confusion crosses the listener’s face. Finally, you just draw a map. What is the moral of this story? It is not that perseverance pays off, though that is often true. The moral is that there are many different ways we teach one another. Moreover, different ways of teaching are suited to different types of learning. For instance, the visual system is very good at learning spatial material, and that is why a map usually works better than words when it comes to spatial directions. Learning is not a single thing—there is no central processing unit responsible for all learning, and the brain is not a homogenous lump of neurons. The brain has many learning systems each of which has a different neural structure and a unique appetite. Effective instruction depends on choosing pedagogical moves that nourish the right learning system for the desired outcomes. If you want people to learn to respond appropriately to frustration, give them a chance to observe a role model, don’t just tell them to buck up. If you want people to change their bad habits, use reinforcement, not willpower

Core mechanics of learning.