Intelligence, Education, & Motivational Development

Development of Self-Understanding

Self-concept refers to beliefs about general personal identity (Seiffert, 2011). These beliefs include personal attributes, such as one’s age, physical characteristics, behaviors, and competencies. Children in middle and late childhood have a more realistic sense of self than do those in early childhood, and they better understand their strengths and weaknesses. This can be attributed to greater experience in comparing their own performance with that of others, and to greater cognitive flexibility. Children in middle and late childhood are also able to include other peoples’ appraisals of them into their self-concept, including parents, teachers, peers, culture, and media. Internalizing others’ appraisals and creating social comparison affect children’s self-esteem, which is defined as an evaluation of one’s identity. Children can have individual assessments of how well they perform a variety of activities and also develop an overall global self-assessment. If there is a discrepancy between how children view themselves and what they consider to be their ideal selves, their self-esteem can be negatively affected. 

Another important development in self-understanding is self-efficacy, which is the belief that you are capable of carrying out a specific task or of reaching a specific goal (Bandura, 1977, 1986, 1997). Large discrepancies between self-efficacy and ability can create motivational problems for the individual (Seifert, 2011). If a student believes that he or she can solve mathematical problems, then the student is more likely to attempt the mathematics homework that the teacher assigns. Unfortunately, the converse is also true. If a student believes that he or she is incapable of math, then the student is less likely to attempt the math homework regardless of the student’s actual ability in math. Since self-efficacy is self-constructed, it is possible for students to miscalculate or misperceive their true skill, and these misperceptions can have complex effects on students’ motivations. It is possible to have either too much or too little self-efficacy, and according to Bandura (1997) the optimum level seems to be either at, or slightly above, one’s true ability.

Theories of Intelligence 

Psychologists have long debated how to best conceptualize and measure intelligence (Sternberg, 2003). These questions include: How many types of intelligence there are, the role of nature versus nurture in intelligence, how intelligence is represented in the brain, and the meaning of group differences in intelligence. 

General (g) versus Specific (s) Intelligences. From 1904-1905 the French psychologist Alfred Binet (1857–1914) and his colleague Théodore Simon (1872–1961) began working on behalf of the French government to develop a measure that would identify children who would not be successful with the regular school curriculum. The goal was to help teachers better educate these students (Aiken, 1994). Binet and Simon developed what most psychologists today regard as the first intelligence test, which consisted of a wide variety of questions that included the ability to name objects, define words, draw pictures, complete sentences, compare items, and construct sentences.

Binet and Simon (Binet, Simon, & Town, 1915; Siegler, 1992) believed that the questions they asked the children all assessed the basic abilities to understand, reason, and make judgments. It turned out that the correlations among these different types of measures were in fact all positive; that is, students who got one item correct were more likely to also get other items correct, even though the questions themselves were very different. 

On the basis of these results, the psychologist Charles Spearman (1863–1945) hypothesized that there must be a single underlying construct that all of these items measure. He called the construct that the different abilities and skills measured on intelligence tests have in common the General Intelligence Factor (g). Virtually all psychologists now believe that there is a generalized intelligence factor, “g”, that relates to abstract thinking and that includes the abilities to acquire knowledge, to reason abstractly, to adapt to novel situations, and to benefit from instruction and experience (Gottfredson, 1997; Sternberg, 2003). People with higher general intelligence learn faster. 

Soon after Binet and Simon introduced their test, the American psychologist Lewis Terman at Stanford University (1877–1956) developed an American version of Binet’s test that became known as the Stanford-Binet Intelligence Test. The Stanford-Binet is a measure of general intelligence made up of a wide variety of tasks, including vocabulary, memory for pictures, naming of familiar objects, repeating sentences, and following commands. 

Although there is general agreement among psychologists that “g” exists, there is also evidence for specific intelligence or“s”, a measure of specific skills in narrow domains. One empirical result in support of the idea of “s” comes from intelligence tests themselves. Although the different types of questions do correlate with each other, some items correlate more highly with each other than do other items; they form clusters or clumps of intelligences. 

Triarchic Theory. One advocate of the idea of multiple intelligences is the psychologist Robert Sternberg. Sternberg has proposed a triarchic (three-part) theory of intelligence which holds that people may display more or less analytical intelligence, creative intelligence, and practical intelligence. Sternberg (1985, 2003) argued that traditional intelligence tests assess analytical intelligence, academic problem solving and performing calculations, but that they do not typically assess creative intelligence, the ability to adapt to new situations and create new ideas, and/or practical intelligence, the ability to demonstrate common sense and street-smarts. 

As Sternberg proposed, research has found that creativity is not highly correlated with analytical intelligence (Furnham & Bachtiar, 2008) and exceptionally creative scientists, artists, mathematicians, and engineers do not score higher on intelligence than do their less, creative peers (Simonton, 2000).

Furthermore, the brain areas that are associated with convergent thinking, thinking that is directed toward finding the correct answer to a given problem, are different from those associated with divergent thinking, the ability to generate many different ideas or solutions to a single problem (Tarasova, Volf, & Razoumnikova, 2010). On the other hand, being creative often takes some of the basic abilities measured by “g”, including the abilities to learn from experience, to remember information, and to think abstractly (Bink & Marsh, 2000). Ericsson (1998), Weisberg (2006), Hennessey and Amabile (2010) and Simonton (1992) studied creative people and identified at least five components that are likely to be important for creativity as listed in Table 7.1. 

Table 7.1 Important Components for Creativity

adapted from Lally & Valentine-French, 2019

The last aspect of the triarchic model, practical intelligence, refers primarily to intelligence that cannot be gained from books or formal learning. Practical intelligence represents a type of “street smarts” or “common sense” that is learned from life experiences. Although a number of tests have been devised to measure practical intelligence (Sternberg, Wagner, & Okagaki, 1993; Wagner & Sternberg, 1985), research has not found much evidence that practical intelligence is distinct from “g” or that it is predictive of success at particular tasks (Gottfredson, 2003). Practical intelligence may include, at least in part, certain abilities that help people perform well at specific jobs, and these abilities may not always be highly correlated with general intelligence (Sternberg et al., 1993). 

Theory of Multiple Intelligences. Another champion of the idea of specific types of intelligences rather than one overall intelligence is the psychologist Howard Gardner (1983, 1999). Gardner argued that it would be evolutionarily functional for different people to have different talents and skills, and proposed that there are eight intelligences that can be differentiated from each other. Table 7.2 lists Gardner’s eight specific intelligences. 

Table 7.2 Howard Gardner's Eight Specific Intelligences

Adapted from Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century. New York, NY: Basic Books.

Gardner identified these 8 intelligences using multiple sources of evidence. He conducted psychometric analyses of tests designed to capture different kinds of intelligence. He also examined evidence from studies of children who were talented in one or more areas, and from studies of adults who suffered brain damage from strokes that compromised capacities in some areas, but not in others. Gardner also noted that some evidence for multiple intelligences comes from the abilities of autistic savants, people who score low on intelligence tests overall, but who nevertheless may have exceptional skills in a given domain, such as math, music, art, or in being able to recite statistics in a given sport (Treffert & Wallace, 2004). A potential ninth intelligence; that is, existential intelligence, still needs empirical support.

The idea of multiple intelligences has been influential in the field of education, and teachers have used these ideas to try to teach differently to different students. For instance, to teach math problems to students who have particularly good kinesthetic intelligence, a teacher might encourage the students to move their bodies or hands according to the numbers. On the other hand, some have argued that these “intelligences” sometimes seem more like “abilities” or “talents” rather than real intelligence. There is no clear conclusion about how many intelligences there are. Are sense of humor, artistic skills, dramatic skills, and so forth also separate intelligences? Furthermore, and again demonstrating the underlying power of a single intelligence, the many different intelligences are, in fact, correlated and thus represent, in part, “g” (Brody, 2003). 

Measuring Intelligence: Standardization and the Intelligence Quotient 

The goal of most intelligence tests is to measure “g”, the general intelligence factor. Good intelligence tests are reliable, meaning that they are consistent over time, and also demonstrate validity, meaning that they actually measure intelligence rather than something else. Because intelligence is such an important individual difference dimension, psychologists have invested substantial effort in creating and improving measures of intelligence, and these tests are now considered the most accurate of all psychological tests. In fact, the ability to accurately assess intelligence is one of the most important contributions of psychology to everyday public life. 

Intelligence changes with age. A 3-year-old who could accurately multiply 183 by 39 would certainly be intelligent, but a 25-year-old who could not do so would be seen as unintelligent. Thus, understanding intelligence requires that we know the norms or standards in a given population of people at a given age. The standardization of a test involves giving it to a large number of people at different ages and computing the average score on the test at each age level. 

It is important that intelligence tests be standardized on a regular basis, because the overall level of intelligence in a population may change over time. The Flynn effect refers to the observation that scores on intelligence tests worldwide have increased substantially over the past decades (Flynn, 1999). Although the increase varies somewhat from country to country, the average increase is about 3 IQ points every 10 years. There are many explanations for the Flynn effect, including better nutrition, increased access to information, and more familiarity with multiple-choice tests (Neisser, 1998). Whether people are actually getting smarter, however, is debatable (Neisser, 1997). Most of the increase in IQ occurred during the second half of the 20th century. Recent research has found a reversal of the Flynn effect in several nations around the world, although some nations still show an increase in IQ scores (Dutton, van der Linden, & Lynn, 2016). 

Once the standardization has been accomplished, we have a picture of the average abilities of people at different ages and can calculate a person’s mental age, which is the age at which a person is performing intellectually. If we compare the mental age of a person to the person’s chronological age, the result is the Intelligence Quotient (IQ), a measure of intelligence that is adjusted for age. A simple way to calculate IQ is by using the following formula: 

IQ = mental age ÷ chronological age × 100. 

Thus a 10-year-old child who does as well as the average 10-year-old child has an IQ of 100 (10 ÷ 10 × 100), whereas an 8-year-old child who does as well as the average 10-year-old child would have an IQ of 125 (10 ÷ 8 × 100). Most modern intelligence tests are based on the relative position of a person’s score among people of the same age, rather than on the basis of this formula, but the idea of an intelligence “ratio” or “quotient” provides a good description of the score’s meaning. 

Wechsler Scales. A number of scales are based on the IQ. The Wechsler Adult lntelligence Scale (WAIS) is the most widely used intelligence test for adults (Watkins, Campbell, Nieberding, & Hallmark, 1995). The current version of the WAIS, the WAIS-IV, was standardized on 2,200 people ranging from 16 to 90 years of age. It consists of 15 different tasks, each designed to assess intelligence, including working memory, arithmetic ability, spatial ability, and general knowledge about the world. The WAIS-IV yields scores on four domains: verbal, perceptual, working memory, and processing speed. The reliability of the test is high (more than 0.95), and it shows substantial construct validity. The WAIS-IV is correlated highly with other IQ tests such as the Stanford-Binet, as well as with criteria of academic and life success, including college grades, measures of work performance, and occupational level. It also shows significant correlations with measures of everyday functioning among people with intellectual disabilities. 

The Wechsler scale has also been adapted for preschool children in the form of the Wechsler Primary and Preschool Scale of Intelligence-Fourth Edition (WPPSI-IV) and for older children and adolescents in the form of the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V).

Bias in Tests of Intelligence.  Intelligence tests and psychological definitions of intelligence have been heavily criticized since the 1970s for being biased in favor of Anglo-American, middle-class respondents and for being inadequate tools for measuring non-academic types of intelligence or talent. Intelligence changes with experience, and intelligence quotients or scores do not reflect that ability to change. What is considered smart varies culturally as well, and most intelligence tests do not take this variation into account. For example, in the West, being smart is associated with being quick. A person who answers a question the fastest is seen as the smartest, but in some cultures being smart is associated with considering an idea thoroughly before giving an answer. A well-thought out, contemplative answer is the best answer. 

• This required video explores the history of intelligence tests, including their initial creation, their use to justify eugenics practices, and their inherent flaws.

Education 

Remember the ecological systems model (Bronfenbrenner, 1979) that we explored in chapter one? This model helps us understand an individual by examining the contexts in which the person lives and the direct and indirect influences on that person’s life. School becomes a very important component of children’s lives during middle and late childhood, and parents and the culture contribute to children’s educational experiences through their interaction with teachers and schools. 

Gender. The stereotypes held by parents and teachers can influence children’s self-efficacy in various domains. For example, teachers who hold the view that girls are better at reading (Retelsdorf, Schwartz, & Asbrock, 2015) or boys are better at math (Plante, de la Sablonnière, Aronson, & Théorêt, 2013) often find that their students’ performance in these areas mirror these stereotypes, despite the children’s actual ability, or the ability of children in the classrooms of teachers who do not hold such stereotypes. While not all children will internalize the views of others, those who do are more likely to show declines in their performance consistent with the stereotypes (Plante, et al., 2013; Retelsdorf et al., 2015). 

Parental Involvement in School. Parents vary in their level of involvement with their children’s schools. Teachers often complain that they have difficulty getting parents to participate in their child’s education and devise a variety of techniques to keep parents in touch with daily and overall progress. For example, parents may be required to sign a behavior chart each evening to be returned to school or may be given information about the school’s events through websites and newsletters. There are other factors that need to be considered when looking at parental involvement. To explore these, first ask yourself if all parents who enter the school with concerns about their child will be received in the same way? 

Horvat (2004) found that teachers seek a particular type of involvement from particular types of parents. While teachers thought they were open and neutral in their responses to parental involvement, in reality teachers were most receptive to support, praise, and agreement coming from parents who were most similar in race and social class with the teachers. Parents who criticized the school or its policies were less likely to be given voice. Parents who have higher levels of income, occupational status, and other qualities favored in society have family capital. This is a form of power that can be used to improve a child’s education. Parents who do not have these qualities may find it more difficult to be effectively involved. The authors suggest that teachers closely examine their biases about different kinds of parents. Schools may also need to examine their ability to dialogue with parents about school policies in more open ways. Any efforts to improve effective parental involvement should address these concerns. 

Cultural Differences in the Classroom 

Bilingualism in Schools. In 2013, approximately 20% of school aged children and adolescents spoke a language other than English in the home (Camarota & Zeigler, 2014). The majority of bilingual students speak Spanish, but the rest represent more than three hundred different language groups from around the world. In larger communities throughout the United States, it is therefore common for a single classroom to contain students from several different language backgrounds at the same time. In classrooms, as in other social settings, bilingualism exists in different forms and degrees. At one extreme are students who speak both English and another language fluently; at the other extreme are those who speak only limited versions of both languages. In between are students who speak their home (or heritage) language much better than English, as well as others who have partially lost their heritage language in the process of learning English (Tse, 2001). Commonly, a student may speak a language satisfactorily, but be challenged by reading or writing it. That is, children can be bilingual without being biliterate. Whatever the case, each bilingual student brings unique strengths and poses unique challenges to teachers. 

The student who speaks both languages fluently has a definite cognitive advantage. As you might suspect, and research confirms, a fully fluent bilingual student is in a better position to express concepts or ideas in more than one way, and to be aware of doing so (Jimenez, Garcia, & Pearson, 1995; Francis, 2006). Unfortunately, the bilingualism of many students is unbalanced in the sense that they are either still learning English, or else they have lost some earlier ability to use their original, heritage language. Losing one’s original language is a concern as research finds that language loss limits students’ ability to learn English as well or as quickly as they could do. Having a large vocabulary in a first language has been shown to save time in learning vocabulary in a second language (Hansen, Umeda & McKinney, 2002). Preserving the first language is important if a student has impaired skill in all languages and therefore needs intervention or help from a speech-language specialist. Research has found, in such cases, that the specialist can be more effective if the specialist speaks and uses the first language as well as English (Kohnert, Yim, Nett, Kan, & Duran, 2005). 

Cultures and ethnic groups differ not only in languages, but also in how languages are used. Since some of the patterns differ from those typical of middle class American classrooms, they can create misunderstandings between teachers and students (Cazden, 2001; Rogers, et al., 2005). Consider these examples:  

• Speaking. In some cultures, it is considered polite or even intelligent not to speak unless you have something truly important to say. Chitchat, or talk that simply affirms a personal tie between people, is considered immature or intrusive (Minami, 2002). In a classroom, this habit can make it easier for a child to learn not to interrupt others, but it can also make the child seem unfriendly. 
• Eye contact. Eye contact varies by culture. In many African American and Latin American communities, it is considered appropriate and respectful for a child not to look directly at an adult who is speaking to them (Torres-Guzman, 1998). In classrooms, however, teachers often expect a lot of eye contact (as in “I want all eyes on me!”) and may be tempted to construe lack of eye contact as a sign of indifference or disrespect. 
• Social distance. Social distance varies by culture. In some cultures, it is common to stand relatively close when having a conversation; in others, it is more customary to stand relatively far apart (Beaulieu, 2004). Problems may happen when a teacher and a student prefer different social distances. A student who expects a closer distance than does the teacher may seem overly familiar or intrusive, whereas one who expects a longer distance may seem overly formal or reserved. 
• Wait time. Wait time varies by culture. Wait time is the gap between the end of one person’s comment or question and the next person’s reply or answer. In some cultures wait time is relatively long, as long as three or four seconds (Tharp & Gallimore, 1989). In others it is a negative gap, meaning that it is acceptable, even expected, for a person to interrupt before the end of the previous comment. In classrooms the wait time is customarily about one second; after that, the teacher is likely to move on to another question or to another student. A student who habitually expects a wait time longer than one second may seem hesitant, and not be given many chances to speak. A student who expects a negative wait time, on the other hand, may seem overeager or even rude. 
• Questions. In most non-Anglo cultures, questions are intended to gain information, and it is assumed that a person asking the question truly does not have the information requested (Rogoff, 2003). In most classrooms, however, teachers regularly ask test questions, which are questions to which the teacher already knows the answer and that simply assess whether a student knows the answer as well (Macbeth, 2003). The question: “How much is 2 + 2?” for example, is a test question. If the student is not aware of this purpose, he or she may become confused, or think that the teacher is surprisingly ignorant. Worse yet, the student may feel that the teacher is trying deliberately to shame the student by revealing the student’s ignorance or incompetence to others. 

• 

  Preference for activities that are cooperative rather than competitive. Many activities in school are competitive, even when teachers try to de-emphasize the competition. Once past the first year or second year of school, students often become attentive to who receives the highest marks on an assignment, for example, or who is the best athlete at various sports or whose contributions to class discussions gets the most verbal recognition from the teacher (Johnson & Johnson, 1998). A teacher deliberately organizes important activities or assignments competitively, as in “Let’s see who finishes the math sheet first”. Classroom life can then become explicitly competitive, and the competitive atmosphere can interfere with cultivating supportive relationships among students or between students and the teacher (Cohen, 2004). For students who give priority to these relationships, competition can seem confusing at best and threatening at worst. A student may wonder, “What sort of sharing or helping with answers is allowed?” The answer to this question may be different depending on the cultural background of the student and teacher. What the student views as cooperative sharing may be seen by the teacher as laziness, freeloading, or even cheating.

The Development of Motivation: Mindsets 

Mindsets are organized sets of beliefs people have about the nature of ability and what they themselves are capable of learning. They are convictions people come to hold about how competent they are and whether there are limits to how much more competent they can become. 

Where do mindsets come from? 

According to theories of mastery motivation, babies are born active and curious, ready to learn about the world and see how it works. As a result, infants are highly motivated and busy trying to make things happen—they love to “create effects,” for example, by waving their arms around, dropping spoons, splashing in the bath, pulling on earrings, and so on. Mastery motivation (sometimes called intrinsic motivation) is like a motor that sets in motion thousands of these exploratory interactions, and through them, babies learn an enormous amount about how to be effective in producing desired and preventing undesired outcomes. 

How do mindsets develop? 

As children learn about their environments, however, they are also learning something about themselves: that they are competent, efficacious little people, capable of making things happen. They take these beliefs with them into other learning contexts, like school, and such beliefs provide an underlying source of confidence, determination, and persistence, especially when children run into problems or setbacks. 

This sense of confidence and competence is called a mastery orientation, and it is one basis for children’s constructive engagement with challenging learning activities. When children with a mastery orientation make mistakes or can’t solve problems right away, they roll up their sleeves and work harder, their concentration and strategizing increases, they turn on the effort and don’t give up. As a result, they learn from their mistakes and benefit from challenges and difficulties. They not only feel more competent and efficacious, they actually become more competent as a result. Over time, these experiences strengthen their mastery orientation. 

Do all children have a mastery orientation? 

No, unfortunately, many infants and young children grow up in environments where they do NOT have experiences of competence and control. Their parents are not responsive, they do not come when babies call, or comfort them when they are upset. Parents may even be downright hostile. Children soon learn that their actions don’t matter, that they have no control over their little worlds. This is called a learned helplessness orientation, and it can be seen in infants as young as 4 months old.  

What are the effects of a learned helplessness orientation? 

Children take helpless attitudes with them into learning contexts, too. Unlike the mastery oriented children, however, children with a learned helplessness orientation react to obstacles or setbacks with helplessness, which means that they behave as if there is nothing they can do to solve the problem: They become upset and anxious, they give up and don’t even try. They avoid challenges and don’t want to try anything new or difficult. As a result, they don’t learn very much. These experiences undermine their confidence even more. Over time, by avoiding challenge and giving up when the going gets tough, they learn less and start to lose ground. Eventually, they not only feel less competent, they actually become objectively less competent. It is a vicious cycle. 

What are the mindsets that underlie mastery and helpless orientations? 

A researcher named Carol Dweck (Dweck, 2006) has done a lot of research on the kinds of mindsets that children (and adults) develop. She has argued that the experiences that we have in achievement contexts (like schools) communicate to us the meaning of “intelligence” or “smart-ness.” 

According to her work, people tend to develop one of two kinds of mindsets based on their cumulative history of experiences: 

• Fixed Mindset (aka an entity view of intelligence). In this mindset, people view intelligence as an unchangeable thing (an entity). Each of us has a certain amount of ability or talent, and these traits are “fixed,” meaning that they can’t be expanded or improved. In this mindset, children are always trying to “measure up,” and they worry about revealing how big (or small) their intelligence actually is. Such children never want to let anyone see when they don’t understand something, so they don’t ask questions. Mistakes and failures are to be avoided because they show how “dumb” you are, and having to exert extra effort means that you must not be as “naturally” smart. Since every low performance is considered a shameful failure, individuals with this view tend to prefer tasks that they can already do well and to avoid those where they might have to try hard, or where they might make mistakes. 
• Growth Mindset (aka an incremental view of intelligence). In this mindset, people view intelligence and abilities as expandable with effort. There is no fixed amount of intelligence that people come with. Instead, there’s just the level of competence we have currently attained. Everyone can always get “smarter,” through effort, hard work, practice, and more effective strategies. In this view, effort expands the capacity to learn, and mistakes are an opportunity to learn even more. Such children do not need to worry about whether they “measure up;” they focus instead on figuring out how to take the next steps to improve their skills. Since “failure” is considered an opportunity to learn more, individuals with this view tend to prefer tasks that are challenging, even if it means they make mistakes at first, because that is how they will learn the most.  

Why do mindsets matter? 

Even throughout adulthood, mindsets profoundly affect your life and the way you approach the world. For example, they affect your goals, how you strive to achieve them, and your motivations for pursuing them. They also impact your definitions of success versus failure and your reactions to obstacles and challenges. 

Learning Goals (associated with a growth mindset). For those with a learning orientation, the goal is to acquire/improve new skills and knowledge. In general, individuals who hold these views enjoy challenges, set high goals for themselves, exert high effort, and concentrate on the task at hand. When failure is encountered, they tend to view it as information about how they can improve their performance in the future rather than as an assault on their personal abilities. When dealing with obstacles, people with learning goals tend to respond with more determination and persistence, show less distress, and initiate more proactive patterns of action such as planning, studying, and practicing. 

Performance Goals (associated with a fixed mindset). For those with a performance orientation, the goal is to gain approval from others (e.g., the teacher) by demonstrating one’s high ability or hiding one’s low ability. In general, individuals who hold these views often avoid challenge, set less specific goals for themselves, and are easily distracted. They tend to do just enough to get by and experience more self-derogatory thoughts. When dealing with these setbacks, people with performance goals tend to give up quickly, avoid help, ruminate on their failures, and give excuses for their performance. 

Can mindsets be changed? 

Absolutely! The key idea of a growth mindset is that we can develop our abiities through effort. Change doesn’t happen over night, but beliefs and mindsets can slowly be changed. When you encounter challenge, what can be learned from it? If we can give up the desire to always appear to be “smart” and embrace the struggles and setbacks that are an essential part of the learning process (and teach our children/students to do so as well), we will be more likely to pursue more challenging and fulfilling goals, and to become more competent over time. 

It’s important to note that having a growth mindset doesn’t mean you can never feel bad about things that have gone wrong. You can, for a little while. But, feeling bad does not need to keep you from taking the next, growth-minded steps of figuring out how to improve in the future and trying again. 

How can I promote a growth mindset in my children or students? 

Feedback from parents and teachers can play a big role in the development of children’s beliefs about themselves. There is a lot of research about how to set up classrooms so that they promote “learning goals” and a growth mindset. Perhaps the most important thing is the mindset of the adult. If a teacher believes that children’s abilities are fixed, then he or she focuses on meauring intelligence, sorting children accordingly, and offering different opportunities to each group. Parents who label their children as this is my “smart child” and this is my “artistic child” communicate to both of them that their fixed talents have been measured and that they should limit themselves to what they would be good at. 

Adults can help by providing a wide range of learning opportunities (especially in areas that children aren’t already good at), accompanied by lots of encouragement for effort, hard work, and practice. When children encounter setbacks, they can benefit from cooperative examination of their mistakes, supportive coaching, and suggestions about more effective strategies for learning. Even subtle things can make a difference: Praising a child for getting a perfect score on an exam can send a message about the importance of getting the correct answer the first time (associated with a fixed mindset), whereas praising their effort can emphasize the importance of developing and learning (associated with a growth mindset). Likewise, expressing sympathy at a low performance or encouraging children to drop an activity when they do not excel right away suggests that there is nothing they can do (associated with a fixed mindset), whereas mild irritation and support for continued practice can communicate the expectation that children can improve if they apply themselves (associated with a growth mindset). 

Reflect on your own development (or the development of someone you know): 

1.  What tends to motivate you? 

• Getting a good grade? 
• Learning new things? 

2.  How do you feel when you make a mistake? 

• Like an idiot? 
• Like you are about to learn something? 

3.  How do you cope with obstacles and setbacks? 

• Do you give up? 
• Do you try harder the next time? 

4.  What are your beliefs about intelligence? 

• Is it fixed? 
• Can it change with effort? 

5.  Can you influence your own development? 

• In what ways could you be your own (positive or negative) social context? 

Supplemental Materials

• This video illustrates Erikson’s stage of Industry. It features a 9-year old girl in Minneapolis who makes and sells bracelets with the proceeds going to support building black businesses & those in need bc of covid-19.
  

• This video shows an 8-year-old child, nick-named Kid President, who inspires people to be industrious and contribute to a better world.
• This article discusses the unique challenges of navigating the U.S. school system for minoritized students whose families do not subscribe to the White, middle class culture socialized in most schools.

Phelan, P., Davidson, A.L., & Cao, H.T. (1991). Students’ multiple worlds: Negotiating the boundaries of family, peer, and school cultures. Anthropology & Education Quarterly, 22, 224-250.

• This article provides an overview of the history of research on children’s mindsets, as told by one of the researchers who uncovered the concept.

Dweck, C. S. (2017). The journey to children’s mindsets—and beyond. Child Development Perspectives, 11(2), 139-144.

• This documentary by Shakti Butler explores the school-to-prison-pipeline and the impact of the criminal legal system on minoritized populations.

https://www.world-trust.org/healing-justice

• This article discusses how harsh discipline school policies impact Black girls.

Hines-Datiri, D., & Carter Andrews, D. J. (2017). The Effects of Zero Tolerance Policies on Black Girls. Urban Education, 0042085917690204. https://doi.org/10.1177/0042085917690204

References

Aiken, L. R. (1994). Psychological testing and assessment (8th ed.). Needham Heights, MA: Allyn and Bacon.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Beaulieu, C. (2004). Intercultural study of personal space: A case study. Journal of Applied Social Psychology, 34(4), 794-805. Binet, A., Simon, T., & Town, C. H. (1915). A method of measuring the development of the intelligence of young children (3rd ed.) Chicago, IL: Chicago Medical Book.

Bink, M. L., & Marsh, R. L. (2000). Cognitive regularities in creative activity. Review of General Psychology, 4(1), 59–78. Brody, N. (2003). Construct validation of the Sternberg Triarchic Abilities Test: Comment and reanalysis. Intelligence, 31(4), 319–329.

Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard Univeristy Press.

Camarota, S. A., & Zeigler, K. (2015). One in five U. S. residents speaks foreign language at home. Retrieved from https://cis.org/sites/default/files/camarota-language-15.pdf

Cazden, C. (2001). Classroom discourse, (2nd ed.). Portsmouth, NH: Heineman Publishers.

Cohen, E. (2004). Teaching cooperative learning: The challenge for teacher education. Albany, NY: State University of New York Press.

Dutton, E., van der Linden, D., Lynn, R. (2016). The negative Flynn effect: A systematic literature review. Intelligence, 59, 163-169.

Dweck, C. S. (2006). Mindset : the new psychology of success (1st ed.). Random House.

Ericsson, K. (1998). The scientific study of expert levels of performance: General implications for optimal learning and creativity. High Ability Studies, 9(1), 75–100.

Erikson, E. (1982). The life cycle completed. NY: Norton & Company.

Flynn, J. R. (1999). Searching for justice: The discovery of IQ gains over time. American Psychologist, 54(1), 5–20.

Francis, N. (2006). The development of secondary discourse ability and metalinguistic awareness in second language learners. International Journal of Applied Linguistics, 16, 37-47.

Furnham, A., & Bachtiar, V. (2008). Personality and intelligence as predictors of creativity. Personality and Individual Differences, 45(7), 613–617.

Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books.

Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century. New York, NY: Basic Books.

Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history and bibliography. Intelligence, 24(1), 13–23.

Gottfredson, L. S. (2003). Dissecting practical intelligence theory: Its claims and evidence. Intelligence, 31(4), 343–397.

Hansen, L., Umeda, Y., & McKinney, M. (2002). Savings in the relearning of second language vocabulary: The effects of time and proficiency. Language Learning, 52, 653-663.

Hennessey, B. A., & Amabile, T. M. (2010). Creativity. Annual Review of Psychology, 61, 569–598.

Horvat, E. M. (2004). Moments of social inclusion and exclusion: Race, class, and cultural capital in family-school relationships. In A. Lareau (Author) & J. H. Ballantine & J. Z. Spade (Eds.), Schools and society: A sociological approach to education (2nd ed., pp. 276-286). Belmont, CA: Wadsworth.

Jimenez, R., Garcia, G., & Pearson. D. (1995). Three children, two languages, and strategic reading: Case studies in bilingual/monolingual reading. American Educational Research Journal, 32 (1), 67-97.

Johnson, D., & Johnson, R. (1998). Learning together and alone: Cooperative, competitive, and individualistic learning, (5th ed.). Boston: Allyn & Bacon.

Kohnert, K., Yim, D., Nett, K., Kan, P., & Duran, L. (2005). Intervention with linguistically diverse preschool children. Language, Speech, and Hearing Services in Schools, 36, 251-263.

Macbeth, D. (2003). Hugh Mehan’s Learning Lessons reconsidered: On the differences between naturalistic and critical analysis of classroom discourse. American Educational Research Journal, 40 (1), 239-280.

Minami, M. (2002). Culture-specific language styles: The development of oral narrative and literacy. Clevedon, UK: Multilingual Matters.

Neisser, U. (1997). Rising scores on intelligence tests. American Scientist, 85, 440-447.

Neisser, U. (1998). The rising curve. Washington, DC: American Psychological Association.

Plante, I., De la Sablonnière, R., Aronson, J. M., & Théorêt, M. (2013). Gender stereotype endorsement and achievement-related outcomes: The role of competence beliefs and task values. Contemporary Educational Psychology, 38(3), 225-235.

Retelsdorf, J., Asbrock, F., & Schwartz, K. (2015). “Michael can’t read!” teachers’ gender stereotypes and boys’ reading self-concept. Journal of Educational Psychology, 107, 186-194.

Rogers, R., Malancharuvil-Berkes, E., Mosely, M., Hui, D., & O’Garro, G. (2005). Critical discourse analysis in education: A review of the literature. Review of Educational Research, 75 (3), 365-416.

Rogoff, B. (2003). The culture of human development. New York: Oxford University Press.

Seifert, K. (2011). Educational psychology. Houston, TX: Rice University.

Siegler, R. S. (1992). The other Alfred Binet. Developmental Psychology, 28(2), 179–190.

Simonton, D. K. (2000). Creativity: Cognitive, personal, developmental, and social aspects. American Psychologist, 55(1), 151–158.

Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. New York, NY: Cambridge University Press.

Sternberg, R. J. (2003). Contemporary theories of intelligence. In W. M. Reynolds & G. E. Miller (Eds.), Handbook of psychology: Educational psychology (Vol. 7, pp. 23–45). Hoboken, NJ: John Wiley & Sons.

Sternberg, R. J., Wagner, R. K., & Okagaki, L. (1993). Practical intelligence: The nature and role of tacit knowledge in work and at school. In J. M. Puckett & H. W. Reese (Eds.), Mechanisms of everyday cognition (pp. 205–227). Hillsdale, NJ: Lawrence Erlbaum Associates.

Tarasova, I. V., Volf, N. V., & Razoumnikova, O. M. (2010). Parameters of cortical interactions in subjects with high and low levels of verbal creativity. Human Physiology, 36(1), 80–85.

Tharp, R. & Gallimore, R. (1989). Rousing minds to life. New York: Cambridge University Press.

Torres-Guzman, M. (1998). Language culture, and literacy in Puerto Rican communities. In B. Perez (Ed.), Sociocultural contexts of language and literacy. Mahwah, NJ: Erlbaum.

Treffert, D. A., & Wallace, G. L. (2004). Islands of genius. Scientific American, 14–23. Retrieved from http://gordonresearch.com/articles_autism/SciAm-Islands_of_Genius.pdf

Tse, L. (2001). Why don’t they learn English? New York: Teachers’ College Press.

Wagner, R., & Sternberg, R. (1985). Practical intelligence in real-world pursuits: The role of tacit knowledge. Journal of Personality and Social Psychology, 49(2), 436–458.

Watkins, C. E., Campbell, V. L., Nieberding, R., & Hallmark, R. (1995). Contemporary practice of psychological assessment by clinical psychologists. Professional Psychology: Research and Practice, 26(1), 54–60.

Weisberg, R. (2006). Creativity: Understanding innovation in problem solving, science, invention, and the arts. Hoboken, NJ: John Wiley & Sons.

OER Attribution:

“Lifespan Development: A Psychological Perspective, Second Edition” by Martha Lally and Suzanne Valentine-French is licensed under a CC-BY-NC-SA-3.0

The Development of Motivation: Mindsets by Jennifer Pitzer Graham is licensed CC-BY-NC-SA-4.0

Video Attribution:

9-year-old makes bracelets to raise money for Minneapolis by Good Morning America is licensed All Rights Reserved and is embedded here according to YouTube terms of service.

The dark history of IQ tests by TED is licensed CC-BY-NC-ND 4.0