Middle School Students’ Beliefs About Intelligence and Giftedness

Abstract

This study investigated middle school students’ beliefs about intelligence and differences in the development of intelligence across ages, beliefs about giftedness and the development of giftedness, and how beliefs about intelligence and giftedness were related. A total of 52 eighth graders from two regular classes (n = 36) and one gifted class (n = 16) at a public school in the U.S. Midwest completed a survey and a vignettes task. Results revealed that participants associated intelligence with school and non-school intelligence, knowledge and learning, smartness, and motivation. They associated academic giftedness with intelligence, motivation, school achievement, and high ability. Participants were more certain young children could grow intelligence. Most participants endorsed incremental views of intelligence and giftedness. This was more evident in students holding an incremental belief about intelligence, and in non-gifted students. Gifted participants and those holding an incremental belief about intelligence placed more value on motivation and learning. Theoretical and practical implications were discussed.

Keywords

perceptions of intelligence perceptions of giftedness beliefs about the development of intelligence beliefs about the development of giftedness

Students’ beliefs about intelligence are of particular interest to researchers and educators because these beliefs impact students’ achievement motivation (Dweck, 1986; Nicholls, 1990). Dweck’s (1986, 2000) theory of implicit beliefs about intelligence, or mindset theory, has been an influential framework in understanding students’ beliefs about intelligence and motivation across cultures, and has gained interest internationally in recent decades. This framework posits that at around age 10 to 12, children start to form a mature and stable conception of intelligence. They tend to either believe intelligence is malleable through time and effort (incremental view, growth mindset), or believe intelligence is fixed and cannot be changed (entity view, fixed mindset; Dweck, 2002; Dweck & Master, 2008). At this age, children begin to form a differentiated meaning system of ability, motivation, and achievement beliefs that are coherent with each other (Dweck, 2002). For example, if children believe intelligence is malleable, they are more likely to endorse a malleable view of personality and the world (Dweck, 2000). Students’ beliefs about intelligence and their meaning system play a causal role in their goals, attributions, effort beliefs, affect, intrinsic motivation, self-regulation, and performance especially in the face of difficulty (Dweck, 2002).

However, two issues relevant to the theory warrant further investigation. First, the theory does not clearly address beliefs about potential developmental differences in the development of intelligence across ages (e.g., intelligence can grow at 4 years old but not 18 years old). Dweck’s theory has generated much research and has served as a guide for educational interventions and reforms. Studies following this framework (e.g., Blackwell, Trzesniewski, & Dweck, 2007; Haimovitz, Wormington, & Corpus, 2011; Stipek & Gralinski, 1996) often categorize students into those who endorse an incremental belief about intelligence (henceforth referred to as “incremental students/participants”) and those who endorse an entity belief about intelligence (henceforth referred to as “entity students/participants”), and compare their learning behaviors and outcomes. Most of these studies have used the Theories of Intelligence Scale (TIS, Dweck, 2000), which identifies whether students believe intelligence can grow or not. However, the scale fails to capture whether and how much students believe intelligence can grow across ages, and whether intelligence is easier to grow at certain ages than others. If students believe the development of intelligence is bound to age, interventions need to be designed with more nuanced considerations beyond the idea of intellectual growth through practice and learning. This need is exemplified in Schmidt, Shumow, and Kackar-Cam’s (2017) study that implemented the same interventions to promote a growth mindset with middle and high school students and had mixed success. We might need to first closely examine students’ personal developmental beliefs about intelligence rather than convincing them to adopt beliefs incongruent with those they already hold.

Second, a closer look into students’ beliefs about giftedness and how such beliefs may be interconnected with their beliefs about intelligence is needed. Dweck (2000) cautioned that labeling children as “gifted” could promote an entity belief. Makel and colleagues (Makel, Snyder, Thomas, Malone, & Putallaz, 2015) suggested that this proposition assumed the term giftedness automatically implied an entity belief, and that intelligence and giftedness were synonymous constructs. Researchers have developed growing interests in exploring the malleability of giftedness (Horowitz, Subotnik, & Matthews, 2009; Subotnik, Olszewski-Kubilius, & Worrell, 2011). They have suggested that schools should examine students’ beliefs about giftedness and communicate a developmental view of giftedness while designing interventions and special service programs (Makel et al., 2015). Whether students believe giftedness and intelligence can be developed may directly impact their motivation to study and participate in gifted programs. To promote an incremental view of giftedness and intelligence, more evidence is needed to understand students’ beliefs about giftedness, intelligence, and the development of giftedness in the first place. According to the meaning system framework (Dweck, 2000), comparing their beliefs about intelligence and the development of intelligence could provide helpful insights.

The purpose of the current research is twofold. First, we aim to further investigate students’ perceptions of intelligence, and beliefs about the development of intelligence, specifically whether students believe there are age differences in the malleability of intelligence. The second aim is to explore students’ perceptions of giftedness and beliefs about the development of giftedness, and to determine whether and how those beliefs relate to their beliefs about intelligence. Furthermore, we compare the two threads of beliefs about intelligence and giftedness in two groups of students: gifted and non-gifted students (those who are not identified as gifted will be labeled as “non-gifted”), and students with incremental and entity beliefs about intelligence.

We focus on middle school students, as they have developed relatively mature and systematic views and meaning systems (Dweck, 2002). Moreover, developmental motivation research has shown that students’ achievement motivation often declines during middle school (Fredericks & Eccles, 2002; Wigfield, Eccles, MacIver, Reuman, & Midgley, 1991). As noted earlier, beliefs about intelligence and giftedness impact motivation; hence, it is important to examine these beliefs in middle school students.

Perceptions of Intelligence and the Stability of Intelligence

There are two general threads of inquiry of students’ beliefs about intelligence: first, conceptions or meanings of intelligence (i.e., what is intelligence?); and second, the stability or malleability of intelligence (i.e., how stable is intelligence?; Kinlaw & Kurtz-Costes, 2003). Attempts to answer these questions will be discussed as follows.

Regarding the meaning of intelligence, children in general associate intelligence with knowledge and intellectual abilities (Kinlaw & Kurtz-Costes, 2003). Several studies found that intelligence conceptions are similar for older children and adults, while younger children have more naïve conceptions due to developmental constraints (Dweck, 2002; Nicholls, 1990). For example, in Sternberg, Conway, Ketron, and Bernstein (1981), older children, by ages 11 or 12, shared similar views of intelligence with adults who perceived an ideally intelligent person as having high practical problem-solving ability, verbal ability, and social competence. Cain and Dweck (1989) found that older children and adults viewed knowledge, capacity, and effort as components of intelligence. Researchers also reported that, compared with young children, older children’s definitions of intelligence were more elaborate and homogeneous, and concerned more with cognitive and intellectual traits and academic abilities and skills (Kurtz-Costes, McCall, Kinlaw, Wiesen, & Joyner, 2005; Yussen & Kane, 1985). Moreover, Dweck (2000) proposed that the conceptions of intelligence operate under the function of students’ incremental or entity beliefs about intelligence. Both elementary and college students with an entity belief defined intelligence as a person’s inherent capacity or potential, and doing things quickly, easily, and better than others. Incremental students, however, put the emphasis on knowledge and effort in defining intelligence. Intelligence was also conceived by domains, such as social domain intelligence, academic domain intelligence, and everyday intelligence (Sternberg et al., 1981; Yussen & Kane, 1985).

In terms of the stability of intelligence, mindset theory does not specify students’ beliefs about the malleability of intelligence across ages. In the TIS scale, two items briefly touch upon the malleability of intelligence over time (i.e., “You can always greatly change how intelligent you are” and “No matter how much intelligence you have, you can always change it quite a bit”). However, students’ beliefs regarding developmental differences cannot be directly interpreted from these items. If middle school students believe only young children can grow intelligence, for example, then they have an incremental view of intelligence for young children but a fixed view of intelligence for students of their age. Therefore, we raise the question: What are students’ beliefs regarding whether and how much intelligence can grow at different ages?

Although research has not addressed this specific question, a small number of studies examined beliefs about the stability of intelligence in students of different age groups, providing some insight. Kinlaw and Kurtz-Costes (2003) followed Pomerantz and Ruble’s (1997) framework to categorize beliefs about the stability of intelligence into beliefs about constancy, controllability, and capacity of intelligence. They found that older children—above ages 7 to 9—were more likely than younger children to believe ability is both temporally and contextually constant. In contrast, Cain and Dweck (1995) reported that children’s theories of intelligence remained stable across elementary school years. They found no grade difference across first, third, and fifth graders in the endorsement of an entity versus incremental view, although there were clear individual differences. Likewise, in Kinlaw and Kurtz-Costes (2007), elementary school children across three grade levels—kindergarten, second grade, and fourth grade—reported similar beliefs that all children could get smarter over time. Clear difference was revealed in Ablard and Mills’s (1996) study in which high school high achievers held a more stable view of intelligence than their elementary school counterparts. Although the findings are somewhat inconsistent, these studies suggest that students at different ages may have different views on the stability of intelligence. Young children (e.g., elementary school, especially lower grades) tend to have more optimistic views of intelligence that intelligence is inconstant, unstable, and can always grow, while older students (e.g., high school) tend to view ability as more constant and intelligence as more stable. Questions remain regarding students’ beliefs about potential age differences in the development of intelligence when they have formed a mature and systematic belief of intelligence.

Perceptions of Giftedness and Beliefs About the Development of Giftedness

In students’ perceptions, giftedness was related to traits, performance, intelligence, ability, effort, and motivation. Student’s implicit beliefs about giftedness—beliefs about development and malleability of giftedness—are reflected in these perceptions. Both Kerr, Colangelo, and Gaeth (1988) and Manaster, Chan, Watt, and Wiehe (1994) found gifted students conceived of giftedness as traits- and performance-related. In Kerr et al. (1988), 64% of the gifted adolescents viewed giftedness as reflecting people’s performance and what they do, whereas 36% viewed it as a trait. In Manaster et al. (1994), 9% of the adolescent and young adult students viewed giftedness as reflecting performance, 72% viewed it as a trait, and 13% a combination of the two. Giftedness has also been perceived as innate ability and high intellectual ability. Long (1993) found that students aged 12 to 16 (a) perceived giftedness as higher intellectual ability than peers, (b) as greater speed and competence in completing schoolwork, and (c) as involving innate ability, reflecting a fixed view of giftedness. Long also compared gifted and non-gifted students, and found one third of students of both groups believed people were born gifted and could excel without practice. Similarly, Siegle, Rubenstein, Pollard, and Romey (2010) found that male freshmen from an honors program made stronger attributions to the role of natural ability to some talents, indicating a fixed view. In addition, giftedness has been associated with effort and motivation. Guskin, Okolo, Zimmerman, and Peng (1986) surveyed 295 gifted students 9 to 15 years old. Although acknowledging innate talent as a source of exceptional ability, half of the participants endorsed the idea giftedness could be attained through motivation and hard work, indicating an incremental view.

Although student’s implicit beliefs about giftedness have been touched upon in these studies, the discussion is unfocused and unsystematic. One exception is Makel et al. (2015) who specifically examined implicit beliefs about giftedness of 365 gifted adolescents from a talent search program with reference to implicit beliefs about intelligence. Participants generally perceived intelligence as malleable and giftedness as fixed. Their implicit beliefs about intelligence and giftedness were positively correlated while also having significantly different means.

Relevant to this discussion, various studies have examined implicit beliefs about intelligence in gifted students or students in general. A few studies compared gifted and non-gifted students for intervention purposes, and all indicated some belief differences in these two groups. Alexander (1985) found that gifted students were more likely to view intelligence as unstable, however, both gifted and non-gifted students also viewed intelligence as arising from hard work and positive attitudes. Snyder, Barger, Wormington, Schwartz-Bloom, and Linnenbrink-Garcia (2013) found that implicit beliefs about intelligence did not differ between high-ability college students who were identified as gifted and those who were not. But high-ability gifted students held a mildly stronger entity belief about intelligence than those of relatively lower ability. Similarly, van Bemmel (2015) reported gifted and non-gifted secondary students held statistically significant different beliefs about intelligence, with gifted students holding more fixed views.

In summation, little developmental literature on students’ perceptions of and beliefs about giftedness is available. Only a limited number of studies reported students’ beliefs about the development of giftedness or compared gifted and non-gifted students’ beliefs about the development of intelligence within Dweck’s general framework. The results and discussions are mixed, inconsistent, and muddy, which might be due to the different instrumentation employed. Our study is an effort to disentangle the developmental literature issues and to address the gap in literature.

Research Questions

The Mindset framework and existing research indicate that first, beliefs about intelligence and giftedness are related; second, beliefs often differ due to being incremental or entity believers of intelligence; third, beliefs also differ in a degree due to being identified gifted or not. Therefore, we explored students’ beliefs about intelligence and giftedness in general, and also whether these beliefs differed in incremental and entity students and in gifted and non-gifted students. The following research questions guided our inquiry:

Research Question 1: What are students’ perceptions of intelligence and beliefs on the malleability of intelligence at different ages?

Research Question 2: What are students’ perceptions of giftedness and implicit beliefs about the development of giftedness?

Research Question 3: How are students with incremental and entity views of intelligence similar or different in their beliefs about intelligence and giftedness?

Research Question 4: How are gifted and non-gifted students similar or different in their beliefs about intelligence and giftedness?

Method

Informed by a postpositivist paradigm, we employed Dweck’s mindset theory to frame our inquiry. Mindset theory is often used as a starting point for data analysis as well as an explanation or validation for establishing relationships among variables in previous studies that have been predominantly quantitative (e.g., Blackwell et al., 2007; Makel et al., 2015). Our questions ask about the “what” and “how” of individuals’ beliefs and aim to capture nuances, so we adopted a qualitative approach that allows a close examination of individuals’ perspectives (Denzin & Lincoln, 2018). We utilized open-ended questions to selected scenarios to implicitly elicit participants’ viewpoints so as to reduce social desirability and measurement biases.

Setting and Context of the Study

Data were gathered from a middle school in the U.S. Midwest. The school enrolls approximately 1,000 students, 60% White, 20% Hispanic, and 12% African American. A total of 70% of the students participate in the free/reduced lunch federal program. The school offers two gifted programs. The first-level program enrolls about 80 students whose curriculum is one grade above peers not identified as gifted. The second-level program enrolls about 20 students whose curriculum is two grades above the students’ grade level. They are placed in the same gifted class. This top-level gifted class (n = 16) was chosen to participate. Two regular classes were chosen where the students (n = 36) shared most teachers with that gifted class.

Participants

Students were in eighth grade with an age range of 12 to 14. In total, 33 students were girls, and 19 were boys. They were predominantly White. The gifted participants had initially been identified as gifted in kindergarten with subsequent evaluations occurring in fourth and fifth grades through an ability test. They had to maintain high grades to remain in the gifted program. Specifically, the identification criteria included Northwest Evaluation Association (NWEA) assessment (98% and above), the Otis-Lennon School Ability Test (OLSAT; score 120 and above), grades (A/B, usually A), Statewide Testing for Educational Progress (ISTEP; Pass +), and teacher and parent recommendation. All 52 participants completed the vignettes task, and 49 completed the survey.

Data Sources

Data sources included the Implicit Theories Survey for Children (Dweck, 2000) and a vignettes task (see Appendix). The survey was designed for children at age 10 or above to measure their implicit beliefs about intelligence. There are 6 items (6-point Likert-type scale; 1 = strongly agree, 6 = strongly disagree). Items 1 to 3 tap into entity beliefs. Items 4 through 6 represent incremental beliefs and were reverse-coded. High scores represent incremental beliefs, and low scores represent entity beliefs. Two open-ended questions about what intelligence means were included after the 6 items: “What came to your mind when you saw the word intelligence when doing the questions above?” and “In your opinion, what is intelligence?”

The vignettes were created by two authors to tap into beliefs across age. We referred to some case studies in a few gifted education textbooks as a starting point for developing the vignettes. The vignettes task consisted of short scenarios or profiles of six students at different ages. Two highlighted young children at age 4, two highlighted teenagers at ages 13 and 14 (the same age as participants), and two highlighted adults at ages 20 and 22. The intent was to see if participants considered the age factor when making attributions. The profiles showed examples of intellectual ability and academic giftedness, including information on performance at school, strength, interests, efforts, and goals. The design of the profiles focused on competence and motivation as they are two key elements in scientific conceptions of intelligence and giftedness (Dweck & Leggett, 1988; Renzulli, 2016; Sternberg et al., 1981). Following the scenarios were three sets of open-ended questions to implicitly elicit participants’ perceptions and beliefs about intelligence and giftedness.

The two open-ended questions in the survey and the first question in the vignettes tapped into students’ perceptions of intelligence and beliefs about the development of intelligence—including whether growth differs across developmental ages. The second question in the vignettes tapped into students’ perceptions of giftedness, what contributes to giftedness, and whether it is related to intelligence. The third question tapped into implicit beliefs about the development of giftedness. Students’ responses to the open-ended questions ranged from brief (one full page, double space) to rich and thorough (three full pages, double space).

Data Collection Procedures

Data were collected toward the end of the 2016 spring semester. Students took approximately 5 min to finish the survey, and 25 to 40 min to finish the vignettes task. It was emphasized that there were no right or wrong answers, and students’ true, original thoughts were the best answers. The researcher assured students’ understanding of the importance of thinking carefully and expressing thoughts clearly, but they did not have to write in complete sentences.

The researcher explained to students the idea of growth mindset after data collection, students reported they had never heard of it—providing an indication that participant responses were indigenous without the influence of prior knowledge or mindset interventions.

Data Analysis

We adopted the constant comparative method for data analysis, as it lends itself to different types of qualitative and quantitative research (Glaser & Strauss, 1967; Merriam, 1998). This method compares incidents applicable to each category, integrates categories and their properties, and delimits categories and concepts. Under this broad umbrella of data analysis, we followed the specific coding procedures of Creswell (2006) and Corbin and Strauss (2007).

We created a document of response to open-ended questions for each participant, and imported a total of 104 pages of data into NVivo. Each participant’s data were treated as one source. We analyzed the data of the entire sample first. We randomized our data and assigned numbers to participants, so as to be blind as to which class (gifted or non-gifted) or belief group (entity or incremental) participants belonged to during the coding process. We then assigned participants into two groups of cases—the incremental and entity cases and the gifted and non-gifted cases—and compared the cases within each group (selection criteria described later). To ensure trustworthiness, two authors coded the data together. Before coding, we discussed our potential biases and remained reflective of these biases in data interpretation.

Analysis of the entire sample

We coded the data deductively and inductively across the questions in the survey and the vignettes task. Following Creswell (2006), our first step was to develop a codebook that would remain stable and represent the coding analysis of two independent researchers. As a blueprint for the codebook, we first specified four broad categories naturally deriving from our research questions: perceptions of intelligence, beliefs about the development of intelligence, perceptions of giftedness, and beliefs about the development of giftedness. We then open-coded the first 14 sources to the four categories independently. A graduate student familiar with Dweck’s theory not involved in this study rated the degree of agreement of our coding on a 5-point scale for each category source by source. We defined interrater agreement as assigning the same codes or assertions to each category, and we reached a 90% interrater agreement. We then recoded the first 10 sources independently with greater depth and details, and developed a codebook with coherent code names and sample text segments for each code. We had “parent” and “child” codes in NVivo representing a hierarchical structure. Interrater agreement to us meant we assigned the same codes to text segments. We continued to code sources 11 to 20 separately and refined our codebook.

Then, we independently coded the rest of the sources based on the codebook as well as capturing new codes and categories that emerged beyond the predetermined ones. After a joint discussion, we conducted a second round of coding of each source for anything we missed. Then, we agreed we had reached saturation.

Following Corbin and Strauss’s (2007) coding process of open, axial, selective coding, a next and simultaneous step was to collapse codes into potential themes, a process called axial coding. With joint discussions, we first grouped our codes conceptually into different axial codes, which were generated based on number/percentage of participants supporting those axial codes. This use of frequency to generate axial codes is common in qualitative research following a postpositivist paradigm (e.g., Jen & Moon, 2015).

We constantly reflected on how relationships could be formed between the axial codes, and grouped them to broader, overarching themes, a process called selective coding. Thematic findings were generated accordingly. We went through several rounds of reviewing and refining the themes following the same procedure of coding based on Creswell (2006) to assure they formed coherent patterns and fit together to truthfully speak about the dataset.

Analysis of group comparisons

The survey results were used to group participants into an incremental case (n = 39) and an entity case (n = 10) with a cut-off score of 21 in the 6 to 36 score range. Information about cut-off scores assigning participants into groups is not clear in research using Dweck’s measures. For example, Blackwell et al. (2007) only reported that students scoring at the low end were assigned to an entity group and at the high end for the incremental group. Therefore, we adopted a simplistic way of dividing groups and used the median score of 21 in the score range. Three participants’ survey data were missing, so we used data from 49 students for the comparison of the entity-incremental dimension. Participants scoring below 21 were grouped into the entity case (score range: 6 to 20; average score: 16), participants scoring more than 21 were grouped into the incremental case (score range: 22 to 36; average score: 28). Participants were also grouped into a non-gifted case (n = 36, the two regular classes) and a gifted case (n = 16, the gifted class). We followed the same procedure for generating patterns and themes across the two cases of each group as for analyzing the entire sample.

Results

Results are organized by research questions. Summaries are presented in Tables 1 to 4.

Table 1.

Results for Research Question 1.

Thematic findings	Axial codes	Number of participants supporting the axial code
Intelligence is mostly related to school and non-school intelligence, motivation, and knowledge and learning	Intelligence means school intelligence	44
	Intelligence means non-school intelligence	44
	Intelligence means motivation	39
	Intelligence means knowledge and learning	31
	Intelligence means smartness	33
	Intelligence means ability	17
	Intelligence means skills	16
Intelligence can grow across ages, and it is more certain students at a young age would grow intelligence	Intelligence can grow across ages	37
	It is more certain that young children can grow intelligence	23
	Intelligence can grow by working hard	24
	Intelligence can grow by learning	23
	Intelligence can grow with time	16

Table 2.

Results for Research Question 2.

Thematic findings	Axial codes	Number of participants supporting the axial code
Giftedness is mostly related to motivation and intelligence	Gifted students are intelligent and smart	35
	Gifted students are motivated	33
	Gifted students have high ability	16
	Gifted students are high achievers	13
	Intelligence is necessary for being selected into a gifted program	35
	Hard work also matters for being selected into a gifted program	23
	Giftedness was not always distinguished from intelligence	17
Giftedness can be developed, and motivation matters	Students currently not in a gifted program can be selected later on	32
	Only some students currently not in a gifted program can be selected later on	15
	Giftedness can grow by increasing motivation and working hard	19
	Giftedness can grow by learning	12

Table 3.

Results for Research Question 3.

Thematic findings	Axial codes	Percentage of participants supporting the axial code
Incremental participants demonstrate more incremental views of intelligence and giftedness	Incremental participants were more likely to believe intelligence can grow across ages	Incremental: 80% Entity: 60%
	Incremental participants were more likely to think students currently not in a gifted program can be selected later on	Incremental: 72% Entity: 40%
Incremental participants place more value on motivation and learning	Incremental participants were more likely to perceive intelligence as related to knowledge and learning	Incremental: 69% Entity: 40%
	Incremental participants were more likely to believe intelligence can grow by learning	Incremental: 56% Entity: 10%
	Incremental participants were more likely to think both intelligence and hard work matter for being selected into a gifted program	Incremental: 46% Entity: 20%
	Entity participants were more likely to think gifted students have high ability	Incremental: 28% Entity: 50%

Table 4.

Results for Research Question 4.

Thematic findings	Axial codes	Percentage of participants supporting the axial code
Non-gifted participants demonstrate more incremental views of intelligence and giftedness	Non-gifted participants were more likely to believe intelligence can grow across ages	Gifted: 50% Non-gifted: 81%
	Non-gifted students were more likely to think students currently not in a gifted program can be selected later on	Gifted: 50% Non-gifted: 67%
Gifted participants place more value on motivation and learning	Gifted students were more likely to associate intelligence with knowledge and learning	Gifted: 75% Non-gifted: 53%
	Gifted students were more likely to associate intelligence with interests	Gifted: 50% Non-gifted: 22%
	Non-gifted students used the word “smartness” more to describe intelligence	Gifted: 38% Non-gifted: 75%
	Gifted students were more likely to perceive gifted students as motivated	Gifted: 94% Non-gifted: 50%
	Gifted students were more likely to believe motivation matters for being selected into a gifted program	Gifted: 50% Non-gifted: 31%

Research Question 1: What Are Students’ Perceptions of Intelligence and Beliefs on the Malleability of Intelligence at Different Ages?

Intelligence is mostly related to both “school” and “non-school” intelligence, motivation, and knowledge and learning

Overall, participants perceived intelligence as mostly related to both “school” and “non-school” intelligence, motivation, and knowledge and learning (survey and vignettes task, April 17, 2016). We used the code “school intelligence” when participants discussed school achievement, academic skills, being in a gifted program, and attending college. We used the code “non-school intelligence” when participants referred to practical intelligence, strengths not directly related to school, general skills, and when intelligence was explicitly noted that it was not necessarily related to school performance. The code “motivation” was used when participants associated intelligence with hard work, desire to learn, interests, dedication, and having goals. We grouped “knowledge and learning” as one code as both knowledge and learning relate to knowledge acquisition. When participants used the word “smart” to describe intelligence, we coded it as “smartness.”

More than half of the participants perceived intelligence as pertaining to both school intelligence (n = 44) and non-school intelligence (n = 44). For example, one participant commented, “When I saw the word intelligence, my mind went straight to different aspects of life they need to know about. For example academic smarts, street smarts, manner/humanity smarts.”

Most participants also associated intelligence with motivation (n = 39), knowledge and learning (n = 31), and smartness (n = 33). For example, one participant reported, “Mary is intelligent because [being] behind your classmates doesn’t make you dumb, it just means you haven’t learned the material yet.” Another said, “Ted is intelligent because he is a hard worker and will be an English teacher and he is very motivated about his job.”

About one third of the participants also linked intelligence to ability (n = 17) and skills (n = 16). One explained, “Intelligence is what someone is capable of, how much they know.” Another stated, “Intelligence is how smart and skilled you are at doing stuff.” In addition, although less salient in the dataset, participants’ perceptions of intelligence also included quick processing speed, creativity, brain functions, and rationality.

Intelligence can grow across ages, and it is more certain students at a young age would grow intelligence

Overall, participants believed intelligence could grow across different ages through effort (vignettes task, April 17, 2016). More than half of the participants (n = 37) believed the six students from the vignettes task could all grow intelligence. Participants also thought the students in the profiles could grow intelligence by working hard (n = 24), by learning (n = 23), and across time (n = 16). Other ways of increasing intelligence reported included improving performance, becoming more motivated, and finding out and working on areas of strength and interests.

However, about half of the participants (n = 23) were also more likely to believe younger students (specifically, those at age 4 in the vignettes task) could grow intelligence compared with older students (vignettes task, April 17, 2016). We coded it based on participants’ tones and wording and if they made attributions to age in reporting whether intelligence could grow. Participants often used more affirmative language to report that the 4-year-old students in our vignettes task could grow intelligence considering their age. Examples include, “Yes she [Mary] will grow intelligence, she is only four” or “very young,” and “He [John] will [grow intelligence] because he has time to grow.” Comparatively, some participants used less affirmative language that older students could increase intelligence. For example, one participant reported a student “is 4, and can increase intelligence vastly more” and a student at age 20 “will not get smarter if she doesn’t try hard.” Some students also explicitly commented that intelligence could not or was hard to grow at an older age. One commented, a student “will grow intelligence later on because she is only 4,” and a student at age 22 has “no intelligence increase because he is a senior and moving on to teach English.”

Research Question 2: What Are Students’ Perceptions of Giftedness and Implicit Beliefs About the Development of Giftedness?

Giftedness is mostly related to motivation and intelligence

Overall, participants associated giftedness with intelligence, motivation, high ability, and academic achievement (vignettes task, April 17, 2016). More than half of the participants thought gifted students were intelligent (n = 35) and motivated (n = 33). We coded it as “intelligence” when students talked about gifted students being intelligent or smart, “motivation” when they talked about hard work, perseverance, dedication, and motivation to learn. For example, one commented, “Gifted students are smart, hard worker, perseverant, and have good character.” Another responded, “A gifted student works hard, is determined, and generally quick-witted.”

About one third of the participants also linked gifted students to high ability (n = 16) and high achievers (n = 13). We coded it as “high ability” when participants discussed high learning ability, above average ability, problem-solving ability, and high processing speed. We coded it as “high achievers” when participants discussed excellent school performance. Two examples are “Gifted students have abilities that other students don’t have” and “I think a gifted student always has good grades.”

More than half of the participants (n = 35) also believed intelligence was necessary for being selected into a gifted program, and about half of the participants (n = 23) thought hard work also mattered. For example, one reasoned, “I think Mike has been smart in the first place but it took hard work to get into the gifted program. Intelligence comes with hard work.”

Along with this line, about one third of the participants (n = 17) also perceived intelligence and giftedness as being very similar. These terms were used interchangeably, and many responses indicated that students did not always distinguish these two. For example, when answering the question about whether students currently not in a gifted program can be selected later on, one participant reported, “Yes they can become more intelligent if they have the will/desire.” Another reported, “Yes, because they can get smarter later on.”

In addition, although less salient in the dataset, participants’ perceptions of giftedness also included quick processing speed, creativity, aptitude, and some social-emotional aspects such as kindness and helpfulness.

Giftedness can be developed, and motivation matters

Overall, participants endorsed an incremental belief about giftedness, and believed motivation mattered for developing giftedness (vignettes task, April 17, 2016). More than half of the participants (n = 32) believed all students currently not identified as gifted in the vignettes task could be selected into a gifted program later on. Participants reported giftedness could be developed by increasing motivation (n = 19) and learning (n = 12), and that all students had potential (n = 9). One participant responded, “I believe it is possible for them to be selected, it is really about the initiative to learn. All of them could be if they tried.” Similar comments include, “Yes they can be, if they work hard and study more” and “Yes they will possibly, because all have strive or potentials.”

About one third of the participants (n = 15) reported only some students not in a gifted program could be selected later on. Some explained it was because these students already had some basic characteristics such as “smart,” “excel academically,” “ahead of other students,” and “young.” One participant commented, “It is possible for like Ted and John because they are on the right path and are very smart. The other kids would really have to try to be able to learn more.” Another participant reasoned, “Some of these students could be selected. This is because they show some of the basic characteristics. The students that show very little of these or many negatives may not get selected.”

Research Question 3: How Are Students With Incremental and Entity Views of Intelligence Similar or Different in Their Beliefs About Intelligence and Giftedness?

Incremental participants demonstrate more incremental views of intelligence and giftedness

Participants in the incremental group were more likely to believe intelligence can grow across ages (80%, representing 31 out of the 39 incremental participants, i.e., 80% of them) compared with the entity group (60%, representing six out of the 10 entity participants, i.e., 60% of them) although the majority of the latter also held such beliefs. Participants in the incremental group were slightly more certain that young children would increase intelligence (incremental: 49%; entity: 40%), and that intelligence can grow through hard work (incremental: 51%; entity: 40%; vignettes task, April 17, 2016).

Incremental participants were more likely to believe students currently not in a gifted program could be selected later on (incremental: 72%; entity: 40%). Many of both incremental and entity participants also thought motivation mattered for being selected into a gifted program (38%-40%, representing percentage range for believing motivation mattered across both participants; vignettes task, April 17, 2016).

Incremental participants place more value on motivation and learning

Incremental participants were more likely to perceive intelligence as knowledge- and learning-related (69%) than their entity counterparts (40%). Incremental participants were also more likely to believe intelligence can grow through learning (incremental: 56%; entity: 10%). The associations of intelligence with school intelligence, non-school intelligence, motivation, and smartness were also salient in both incremental and entity participants (50%-94% for these categories from school intelligence to smartness across both participants; survey and vignettes task, April 17, 2016).

Incremental participants were more likely to believe both intelligence and hard work mattered for being selected into a gifted program (incremental: 46%; entity: 20%), although both incremental and entity participants perceived gifted students as intelligent and motivated (60%-69%). Entity participants were more likely to associate gifted students with high ability (incremental: 28%; entity: 50%; vignettes task, April 17, 2016).

Research Question 4: How Are Gifted and Non-Gifted Students Similar or Different in Their Beliefs About Intelligence and Giftedness?

Non-gifted participants demonstrate more incremental views of intelligence and giftedness

Non-gifted participants were more likely to believe intelligence can grow across ages compared with gifted participants (gifted: 50%, i.e., eight out of 16 gifted students; non-gifted: 81%, i.e., 29 out of 36 non-gifted students). Gifted participants were more certain than the non-gifted that young children would increase intelligence (gifted: 56%; non-gifted: 42%), and that intelligence could grow by learning and working hard (gifted: 50%; non-gifted: 42%), though the difference is very slight (vignettes task, April 17, 2016).

Differences in gifted and non-gifted participants’ beliefs about the development of giftedness were not evident. However, non-gifted participants were more likely to think students currently not in a gifted program could be selected later on (gifted: 50%; non-gifted: 67%), and gifted participants were more likely to think only some students could be selected (gifted: 38%; non-gifted: 25%; vignettes task, April 17, 2016).

Gifted participants place more value on motivation and learning

Gifted participants were more likely to associate intelligence with knowledge and learning (75%) and interests (50%) than non-gifted participants (knowledge and learning: 53%; interests: 22%). Gifted participants were also slightly more certain that intelligence can grow by learning and working hard (gifted: 50%; non-gifted: 42%). Non-gifted participants were more likely to use the word to describe intelligence (75%; gifted: 38%). Both gifted and non-gifted participants were more likely to associate intelligence with school intelligence, non-school intelligence, and motivation (75%-89%; survey and vignettes task, April 17, 2016).

Nearly all gifted participants referred to motivation as characteristic of gifted students, much more than the non-gifted (gifted: 94%; non-gifted: 50%). Gifted participants were also more likely to believe motivation was important for being selected into a gifted program (gifted: 50%; non-gifted: 31%). They both believed that gifted students were intelligent, and intelligence was necessary for being selected into a gifted program (63%-69%; vignettes task, April 17, 2016).

Discussion

Our study contributes to the mindset and gifted education literature by addressing two issues. The first is that individuals’ beliefs about the development of intelligence may differ according to the age range in question. Our participants believed students at a young age would be more likely to grow intelligence, demonstrating a belief that the development of intelligence is age relevant. Therefore, students’ incremental orientation might be differentiated for different ages, and stronger for a young age. The picture is more complicated than the mindset theory’s main proposition that students either believe intelligence can grow or believe it cannot grow. Dweck (2015) recently revisited her theory and clarified that she may have overly portrayed people of having one mindset or the other; rather, most individuals hold both entity and incremental beliefs in different situations. The more nuanced belief about developmental differences in the development of intelligence is aligned with this mixed nature of beliefs.

The second issue is the relationship between perceptions of and beliefs about intelligence and giftedness. Our results indicate that intelligence and giftedness are not synonymous constructs as suggested in Dweck (2000). Consistent with previous literature (e.g., Makel et al., 2015), participants’ perceptions of intelligence and giftedness overlapped, but were clearly different.

Akin to previous studies, participants perceived intelligence as pertinent to school performance (Kerr et al., 1988), knowledge, abilities, skills (Yussen & Kane, 1985), and hard work and effort (Alexander, 1985). Intelligence and smartness were also interchangeably used by our participants—also consistent with previous research (Dweck, 2000; Kurtz-Costes et al., 2005). Participants also associated giftedness with hard work (Guskin et al., 1986), intelligence, high ability (Long, 1993), and higher achievers (Kerr et al., 1988) as in the literature.

At the same time, participants did not always distinguish between intelligence and giftedness, sometimes seeming to equate the development of giftedness with the growth of intelligence. Participants associated both intelligence and giftedness with motivation, ability, school achievement, and, to a lesser extent, with quick processing speed and creativity. They also frequently referred to intelligence when discussing giftedness. One explanation is that these two constructs do share similarities in students’ perceptions (e.g., Long, 1993). Another explanation is that giftedness was implicitly confined to academic giftedness in our vignettes task. It was implied in the profile that the gifted program was for high academic achievers.

In contrast, a noteworthy difference in participants’ conceptions of intelligence and giftedness is that the former uniquely included knowledge and learning, and a domain-specific feature (different aspects of school intelligence and non-school intelligence). Although peripheral, participants also uniquely perceived giftedness as pertinent to aptitude and some social-emotional aspects, and intelligence uniquely pertinent to brain functions and rationality. Besides these conceptual differences, participants were also more likely to hold incremental beliefs toward intelligence than giftedness. Most students reported that intelligence could grow; fewer reported giftedness could develop. Also, some participants reported only some students could develop giftedness. Therefore, compared with intelligence, giftedness was perceived as relatively less changeable, consistent with the literature (e.g., Makel et al., 2015; Manaster et al., 1994).

Nevertheless, in general, most participants held incremental beliefs about intelligence as well as giftedness. This provides some counter-evidence to the assumption that giftedness itself implies an entity belief. Students may not think of giftedness as a trait but rather something that can be developed. This incremental orientation toward intelligence and giftedness can be explained from the meaning system framework of Dweck: If a student has an incremental orientation toward intelligence, he is also likely to have an incremental orientation toward giftedness. Another possible explanation is that participants may have implicitly received some growth mindset interventions. For example, their classroom walls contained logos such as “working hard, never give up.” Although we worked to control social desirability, it is possible that because of implicit messages provided by the larger educational context, participants came to understand that motivation and hard work were valued, which could have shaped their responses.

Several entity participants displayed incremental thoughts such as intelligence can grow across ages and students not in a gifted program can be selected later on. This finding should be interpreted with caution. Besides the social desirability issue, another explanation might be that we only had 10 participants in the entity case, and some had relatively high scores and were not the most representative of an entity orientation. By contrast, our incremental participants did demonstrate a clear incremental orientation, aligning with the mindset theory.

Aligned with previous findings (Dweck, 2012; Makel et al., 2015; van Bemmel, 2015), results revealed that non-gifted students were more likely to hold incremental views toward intelligence and giftedness than gifted students. Dweck (2012) has suggested gifted students are at risk of developing a fixed mindset, and hypothesized it could be a result of labeling gifted students and praising their intelligence. However, we found that giftedness does not necessarily imply an entity belief. Our gifted participants endorsed a developmental view of giftedness in general, and placed more value on motivation and learning for development than non-gifted peers. Similar results were also found in the literature (e.g., Guskin et al., 1986).

Limitations and Future Research

Our sampling method could be improved. We used the median score as our cut-off score and assigned participants to the entity and incremental groups dichotomously, rather than treating the beliefs as following a continuum. This could artificially reduce the variance of beliefs (Rucker, McShane, & Preacher, 2015). We had unequal numbers of participants in the gifted and non-gifted groups as well as the incremental and entity groups; we only had 10 participants in the entity group, and not all students in this group were representative. Although research has shown comparisons can be done with unequal numbers of participants in each group (Patton, 2015), the inequality limits our findings. In addition, a large number of participants had lower socioeconomic status and were predominantly White, which might potentially constrain the randomness of the sample and the generalizability of the results. Future research could use a larger sample (a) of various socioeconomic statuses and races and ethnicities, (b) well representing the gifted and non-gifted groups, and (c) well representing the whole belief range and investigate participants’ beliefs along the continuum.

The vignettes instrument has noted weaknesses. Although the vignettes task seemed to work fine for our sample, it could be enriched and further tested to improve validity and reliability. In a post-reflection phase, we noted the vignettes could lead to a number of confounding issues. Different academic domains, levels of achievement, interest and motivation, and gender of the students in the vignettes may result in a reliance on participants’ interpretations to generate usable data. The difficulty remains in terms of establishing a reliable and valid instrument to measure students’ developmental views of giftedness. However, our study serves as a pilot that can spark more substantial research in the future. Future studies with the vignettes could manipulate the variables (e.g., age, performance) one at a time to yield more focused analyses of the roles of each variable, to simplify the issue for participants, and to get clearer responses. More diverse age ranges could also be included to more adequately examine age-related beliefs. For example, vignettes could tap into whether students believe senior adults can still grow intelligence and whether there is a critical period. The instrument should also be more carefully piloted before use. Our investigation is still exploratory, future research could use in-depth interviews and the think-aloud method to understand students’ meaning-making of the questions in interest. This avoids relying on researchers’ interpretations of students’ responses, and potential biases of aggregated results from survey types of instruments (Kaplan & Maehr, 2007).

In addition, giftedness and development of giftedness were constricted to academic giftedness in this study. Future research could extend to broader domains of giftedness for more insights.

Conclusion

Our results have practical implications for educators and researchers in terms of promoting adaptive beliefs of intelligence and giftedness. For intelligence beliefs, although most participants held an incremental, adaptive view toward intelligence, this incremental orientation is stronger for young children. Our gifted participants’ beliefs were also less incremental than the non-gifted. It is necessary to be aware of those beliefs with more nuanced considerations to improve the effect of interventions. One commonly used strategy to promote growth mindset is addressing how the brain is like a muscle, and intelligence grows with practice (Education Week Research Center, 2016). We believe a small step forward could be helpful: acknowledging students’ beliefs that brain development does upsurge during early years, whereas emphasizing that brain plasticity is not limited to a given period, but over a lifetime. Dweck also suggested that educators of adolescents could stress that the brain is especially open to learning at their age, and that they could grow their brains by taking challenging tasks (Gross-Loh, 2016).

For giftedness beliefs, most of our participants also had an incremental view of giftedness, but more fixed than their view of intelligence, especially for gifted students. With that in mind, Makel et al. (2015) have suggested educational interventions be designed with examining implicit beliefs about intelligence and giftedness in the first place as these beliefs could act as schemas that can be activated in contexts. Likewise, as teachers’ beliefs directly impact their views of and practices toward students (Dweck & Master, 2008), we suggest that teachers also examine their own beliefs and potential biases toward intelligence and giftedness, avoiding incidentally promoting a fixed mindset. This is because teachers’ beliefs directly impact (a) their students’ beliefs and motivation (Dweck & Master, 2008), and (b) their own views of and practices toward students, such as practices related to gifted identification and education (Baudson & Preckel, 2013).

Moreover, to promote growth mindset beliefs in general, strategies researchers have previously recommended could all be helpful. For instance, praising for effort, encouraging students to try new strategies when they struggle, and helping students see failure as learning opportunities (Education Week Research Center, 2016). In addition, we believe the following practices will enhance the intervention effect. First, present to students concrete, real-life, relatable examples and evidence of how growth is achieved through growth mindset practices. In so doing, students could be more convinced that they are capable of cultivating a growth mindset. Second, show students how growth mindset does not only help them grow intelligence but also talents, character of perseverance, empathy, and so forth, and benefit them in all areas of life. In so doing, students could be more motivated to cultivate a growth mindset.

Footnotes

Appendix

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Dongyao Tan is a doctoral fellow of the Educational Psychology Program at Purdue University. Her research interests include students’ and teachers’ motivational beliefs and strategies, and the impact of classroom environment on students’ motivation and communication. Her research has focused on pre-service teachers, middle school students, and second language learners.

Mike Yough is an assistant professor and coordinator of the Educational Psychology Program. His research interests include teacher beliefs and social cognition, and their effects on student motivation—especially for those students who differ from themselves in terms of linguistic and cultural background. Specific areas of interest include teachers’ sense of efficacy, teachers’ sense of responsibility, social perspective-taking, and sense of school belonging. His work has been published in the Educational Psychologist, the Journal of Teacher Education, Motivation & Emotion, and The Counseling Psychologist.

Ophélie A. Desmet is a doctoral fellow at Purdue University. Her research interests include underachievement, twice exceptionality, creativity and underrepresentation.

Nielsen Pereira (PhD) is an assistant professor of Gifted, Creative, and Talented Studies at Purdue University. His research interests include conceptual, contextual, and measurement issues in the identification of gifted and talented populations, design and assessment of learning in varied gifted and talented education contexts, and understanding gifted and talented student experiences in talent development programs in and out of school. He is a regular presenter in national and international conferences on educational research, gifted education, and STEM education.

References

Ablard

K. E.

Mills

C. J.

(1996). Implicit theories of intelligence and self-perceptions of academically talented adolescents and children. Journal of Youth and Adolescence, 25, 137-148. doi:10.1007/BF01537340

Alexander

P. A.

(1985). Gifted and nongifted students’ perceptions of intelligence. Gifted Child Quarterly, 29, 137-143. doi:10.1177/001698628502900307

Baudson

T. G.

Preckel

(2013). Teachers’ implicit personality theories about the gifted: An experimental approach. School Psychology Quarterly, 28(1), 37-46. doi:10.1037/spq0000011

Blackwell

L. S.

Trzesniewski

K. H.

Dweck

C. S.

(2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246-263. doi:10.1111/j.1467-8624.2007.00995.x

Cain

K. M.

Dweck

C. S.

(1989). The development of children’s conceptions of intelligence: A theoretical framework. In Sternberg

R. J.

(Ed.), Advances in the psychology of human intelligence (pp. 47-82). Hillsdale, NJ: Lawrence Erlbaum.

Cain

K. M.

Dweck

C. S.

(1995). The relation between motivational patterns and achievement cognitions through the elementary school years. Merrill-Palmer Quarterly, 41(1), 25-52. Retrieved from http://www.jstor.org.ezproxy.lib.purdue.edu/stable/23087453

Corbin

Strauss

(2007). Basics of qualitative research: Grounded theory procedures and techniques (3rd ed.). Thousand Oaks, CA: Sage.

Creswell

J. W.

(2006). Qualitative inquiry and research design: Choosing among five approaches (2nd ed.). Thousand Oaks, CA: Sage.

Denzin

N. K.

Lincoln

Y. S.

(2018). The SAGE handbook of qualitative research (5th ed.). Thousand Oaks, CA: Sage.

10.

Dweck

C. S.

(1986). Motivational processes affecting learning. American Psychologist, 41, 1040-1048. doi:10.1037/0003-066X.41.10.1040

11.

Dweck

C. S.

(2000). Self-theories: Their role in motivation, personality, and development. Philadelphia, PA: Psychology Press.

12.

Dweck

C. S.

(2002). The development of ability conceptions. In Eccles

Wigfield

(Eds.), Development of achievement motivation (pp. 57-88). San Diego, CA: Academic Press.

13.

Dweck

C. S.

(2012). Mindsets and malleable minds: Implications for giftedness and talent. In Subotnik

R. F.

Robinson

Callahan

C. M.

Gubbins

E. J.

(Eds.), Malleable minds: Translating insights from psychology and neuroscience to gifted education (pp. 7-18). Storrs, CT: National Center for Research on Giftedness and Talent.

14.

Dweck

C. S.

(2015, September 22). Growth mindset, revisited. Education Week. Retrieved from https://www.edweek.org/ew/articles/2015/09/23/carol-dweck-revisits-the-growth-mindset.html

15.

Dweck

C. S.

Leggett

E. L.

(1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256-273. doi:10.1037/0033-295X.95.2.256

16.

Dweck

C. S.

Master

(2008). Self-theories motivate self-regulated learning. In Schunk

D. H.

Zimmerman

B. J.

(Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 31-51). New York, NY: Taylor & Francis.

17.

Education Week Research Center. (2016). Mindset in the classroom: A national study of K-12 teachers. Retrieved from http://www.edweek.org/media/ewrc_mindsetintheclassroom_sept2016.pdf

18.

Fredericks

J. A.

Eccles

J. S.

(2002). Children’s competence and value beliefs from childhood through adolescence: Growth trajectories in two male sex-typed domains. Developmental Psychology, 38, 519-533. doi:10.1037/0012-1649.38.4.519

19.

Glaser

B. G.

Strauss

A. L.

(1967). The discovery of grounded theory: Strategies for qualitative research. New York, NY: Aldine de Gruyter.

20.

Gross-Loh

(2016, December). How praise became a consolation prize. The Atlantic. Retrieved from https://www.theatlantic.com/education/archive/2016/12/how-praise-became-a-consolation-prize/510845/

21.

Guskin

S. L.

Okolo

Zimmerman

Peng

C.-Y. J.

(1986). Being labeled gifted or talented: Meanings and effects perceived by students in special programs. Gifted Child Quarterly, 30, 61-65. doi:10.1177/001698628603000203

22.

Haimovitz

Wormington

S. V.

Corpus

J. H.

(2011). Dangerous mindsets: How beliefs about intelligence predict motivational change. Learning and Individual Differences, 21, 747-752. doi:10.1016/j.lindif.2011.09.002

23.

Horowitz

F. D.

Subotnik

R. F.

Matthews

D. J.

(Eds.). (2009). The development of giftedness and talent across the life span. Washington, DC: American Psychological Association. doi:10.1037/11867-000

24.

Jen

Moon

(2015). Retrospective perceptions of graduates of a self-contained program in Taiwan for high school students talented in STEM. Gifted Child Quarterly, 59, 299-315. doi:10.1177/0016986215598001

25.

Kaplan

Maehr

(2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19, 141-184. doi:10.1007/s10648-006-9012-5

26.

Kerr

B. A.

Colangelo

Gaeth

(1988). Gifted adolescents’ attitudes toward their giftedness. Gifted Child Quarterly, 32, 245-247. doi:10.1177/001698628803200201

27.

Kinlaw

C. R.

Kurtz-Costes

(2003). The development of children’s beliefs about intelligence. Developmental Review, 23, 125-161. doi:10.1016/S0273-2297(03)00010-8

28.

Kinlaw

C. R.

Kurtz-Costes

(2007). Children’s theories of intelligence: Beliefs, goals, and motivation in the elementary years. The Journal of General Psychology, 134, 295-311. doi:10.3200/GENP.134.3.295-312

29.

Kurtz-Costes

McCall

R. J.

Kinlaw

C. R.

Wiesen

C. A.

Joyner

M. H.

(2005). What does it mean to be smart? The development of children’s beliefs about intelligence in Germany and the United States. Journal of Applied Developmental Psychology, 26, 217-233. doi:10.1016/j.appdev.2004.12.005

30.

Long

P. E.

(1993). Student perceptions of giftedness. European Journal of High Ability, 4, 62-69. doi:10.1080/0937445930040107

31.

Makel

M. C.

Snyder

K. E.

Thomas

Malone

P. S.

Putallaz

(2015). Gifted students’ implicit beliefs about intelligence and giftedness. Gifted Child Quarterly, 59, 203-212. doi:10.1177/0016986215599057

32.

Manaster

G. J.

Chan

J. C.

Watt

Wiehe

(1994). Gifted adolescents’ attitudes toward their giftedness: A partial replication. Gifted Child Quarterly, 38, 176-178. doi:10.1177/001698629403800404

33.

Merriam

S. B.

(1998). Qualitative research and case study applications in education: Revised and expanded from case study research in education. San Francisco, CA: Jossey-Bass.

34.

Nicholls

(1990). What is ability and why are we mindful of it? A developmental perspective. In Sternberg

Kolligian

(Eds.), Competence considered (pp. 11-40). New Haven, CT: Yale University Press.

35.

Patton

M. Q.

(2015). Qualitative research and evaluation methods: Integrating theory and practice (4th ed.). Thousand Oaks, CA: Sage.

36.

Pomerantz

E. M.

Ruble

D. N.

(1997). Distinguishing multiple dimensions of conceptions of ability: Implication for self-evaluation. Child Development, 68, 1165-1180. doi:10.1111/j.1467-8624.1997.tb01992.x

37.

Renzulli

J. S.

(2016). The three-ring conception of giftedness. In Reis

S. M.

(Ed.), Reflections on gifted education (pp. 55-86). Waco, TX: Prufrock Press.

38.

Rucker

D. D.

McShane

B. B.

Preacher

K. J.

(2015). A researcher’s guide to regression, discretization, and median splits of continuous variables. Journal of Consumer Psychology, 25, 666-678. doi:10.1016/j.jcps.2015.04.004

39.

Schmidt

J. A.

Shumow

Kackar-Cam

H. Z.

(2017). Does mindset intervention predict students’ daily experience in classrooms? A comparison of seventh and ninth graders’ trajectories. Journal of Youth and Adolescence, 46, 582-602. doi:10.1007/s10964-016-0489-z

40.

Siegle

Rubenstein

L. D. V.

Pollard

Romey

(2010). Exploring the relationship of college freshmen honors students’ effort and ability attribution, interest, and implicit theory of intelligence with perceived ability. Gifted Child Quarterly, 54, 92-101. doi:10.1177/0016986209355975

41.

Snyder

K. E.

Barger

Wormington

S. V.

Schwartz-Bloom

Linnenbrink-Garcia

(2013). Identification as gifted and implicit beliefs about intelligence: An examination of potential moderators. Journal of Advanced Academics, 24, 242-258. doi:10.1177/1932202X13507971

42.

Sternberg

R. J.

Conway

B. E.

Ketron

J. L.

Bernstein

(1981). People’s conceptions of intelligence. Journal of Personality and Social Psychology, 41(1), 37-55. doi:10.1037/0022-3514.41.1.37

43.

Stipek

Gralinski

J. H.

(1996). Children’s beliefs about intelligence and school performance. Journal of Educational Psychology, 88, 397-407. doi:10.1037/0022-0663.88.3.397

44.

Subotnik

R. F.

Olszewski-Kubilius

Worrell

F. C.

(2011). Rethinking giftedness and gifted education: A proposed direction forward based on psychological science. Psychological Science in the Public Interest, 12(1), 3-54. doi:10.1177/1529100611418056

45.

van Bemmel

A. W

. (2015). Implicit theories of intelligence of gifted students in secondary education (Master’s thesis). Retrieved from http://studenttheses.library.uu.nl/search.php?language=en.

46.

Wigfield

Eccles

MacIver

Reuman

Midgley

(1991). Transitions during early adolescence: Changes in children’s domain-specific self-perceptions and general self-esteem across the transition to junior high school. Developmental Psychology, 27, 552-565. doi:10.1037/0012-1649.27.4.552

47.

Yussen

S. R.

Kane

P. T.

(1985). Children’s concept of intelligence. In Yussen

S. R.

(Ed.), The growth of reflection in children (pp. 207-241). New York, NY: Academic Press.