Understanding growth mindset and chemistry mindsets of high-achieving students and the impact of influential language on learning motivation

Abstract

Students' mindsets about their intelligence can be fixed or malleable, but a general growth mindset does not ensure the same mindset in chemistry. Many factors influence success and perseverance in chemistry, leading to inconsistent experiences even among high-achieving students in specialized programs. This research examines the correlations between general growth mindset, students' perspective on their chemistry intelligence, gender, academic achievement, and family economic status, while identifying factors influencing motivation in learning chemistry and analyzing students' responses to challenging chemistry situations based on their general growth mindset. A cross-sectional survey was conducted with 338 high-achieving tenth graders nationwide using an 8-item growth mindset scale (Dweck, 1999, Self-theories: Their role in motivation, personality, and development) and the individual items from a modified chemistry mindset questionnaire (Santos et al., 2022, Chem. Educ. Res. Pract., 23(3), 742–757). Findings revealed that 232 students (68.64%) were categorized as having a growth mindset, 9 students (2.66%) were classified as having a fixed mindset, and 95 students (28.11%) were identified as having a mixed mindset. Students rated their chemistry mindset highest in applying chemical knowledge and learning new chemistry concepts. Most female students associated self-chemistry intelligence with applying chemistry knowledge, while male students associated it with learning new concepts. No correlations were found between general growth mindset, gender, GPA, and family socioeconomic status among high-achieving students. However, a moderate significant correlation was found between general growth mindset and all sub-aspects of chemistry intelligence. The study revealed that students themselves were the most influential factor in motivating their learning of chemistry, followed by chemistry teachers, parents, and close friends. Conversely, demotivation was primarily influenced by the students themselves, followed by other individuals, chemistry teachers, and classmates. Moreover, most students with a general growth mindset (82%) persisted and sought solutions when faced with challenging chemistry problems, but some students of this group felt hopeless (6%) or found the subject too difficult (9%). The study discusses implication for chemistry instruction to keep high-achieving students in chemistry tracks engaged.

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Introduction

The introduction of the Sustainable Development Goals (SDGs) by the United Nations in 2015 marked a significant milestone in global efforts to promote sustainable growth, particularly in education (Zhang, 2022). The SDGs incorporate 21st-century skills by integrating STEM education as an alternative approach to ending poverty, protecting the planet, and ensuring equitable education, aiming for societal prosperity by 2030 (Kennedy and Cherry, 2023). Despite these goals, the perceived shortage of the STEM workforce is complex, with countries like the United States and Lithuania advocating for more STEM graduates to meet industry demands (Hoshizaki, 2019; Vaitekaitis, 2020), while research suggests that the shortage in STEM fields may lead to an oversupply in some areas and a shortage in others, potentially due to the migration of labor (Camilli and Hira, 2019). This “STEM shortage” is linked to rapid technological changes that render some skills obsolete, reducing economic returns for STEM graduates (Deming and Noray, 2018, 2020), and a critical lack of STEM teachers exacerbates this issue (Ofem et al., 2021). Addressing this shortage requires a nuanced approach considering evolving industry needs, technological advancements, and diverse skill sets across STEM and non-STEM fields. Educational policies worldwide are adapting to make science education engaging and effective, exemplified by programs like the Junior Apprenticeship Advantage (JAA) in the U.S., Indonesia's “Merdeka” curriculum, and Canada's Discovery initiative, all aimed at developing advanced thinking and problem-solving skills (Malobicky, 2018; Noble et al., 2022; Sari et al., 2022). In the U.S., the NGSS curriculum and competency-based STEM education in college preparatory high schools emphasize real-world scenarios and problem-solving to prepare students for STEM careers (Barcelona, 2014; Roche and Manzi, 2019). Or even providing adequate STEM teacher training, increasing teacher retention in STEM subjects, and building a supportive environment for STEM teachers and teachers in general are crucial (Kumar and Brown, 2023). Thailand's education revised science and mathematics learning indicators and specialized science curricula to enhance problem-solving and integration skills (Thai Ministry of Education, 2017). These global educational initiatives are essential to connecting K-12 STEM education, a competitive workforce, and economic development.

Chemistry, a fundamental STEM discipline, is widely recognized as a difficult and challenging subject for learners (Johnstone, 2000, 2010; Treagust et al., 2000; De Jong and Taber, 2013; Taber, 2019). Research suggests several factors contributing to this difficulty. The first factor is information overload, which can overwhelm students and drive them away from the subject (Johnstone and Kellett, 1980; Johnstone, 1991; Reid, 2021). The abstract nature of chemical concepts makes it difficult to connect different representations to create meaningful learning, further exacerbating the problem (Hinton and Nakhleh, 1999; Kozma et al., 2000; Treagust et al., 2003; Gilbert and Treagust, 2009). Many students also struggle to see the relevance of chemistry to their lives, which diminishes their motivation to engage and attitude toward the subject (Gilbert, 2006; Barbera et al., 2008; Wang et al., 2021). Moreover, another factor is the use of symbolic language and the integral use of mathematics, including stoichiometry and thermodynamics (Maatman, 1995; Aje, 2005; Scott, 2012). Therefore, misconceptions and knowledge barriers are prevalent among students, particularly with complex materials that require a solid understanding of prerequisite concepts, which hinders learning outcomes (Taber, 2002; Barke et al., 2008; Tumay, 2016; Jusniar et al., 2020). These mentioned factors collectively contribute to the significant challenge students face in mastering chemistry and often lead to a decline in motivation to pursue the subject. The aforementioned factors significantly contribute to students' tendency to surrender to the challenges of studying chemistry.

Education, traditionally viewed as the transmission of knowledge, has undergone significant transformation due to technological advancements and shifting societal needs. Historically, educational systems in the 19th and 20th centuries focused primarily on cognitive and psychomotor domains, often neglecting the affective domain (Kahveci and Orgill, 2015). However, contemporary education emphasizes a holistic approach, recognizing the importance of accessibility to knowledge, process skills, and the development of attitudes towards self-improvement and resilience (Dweck and Leggett, 1988; Blackwell et al., 2007; Yeager and Dweck, 2012). This shift is influenced by research on mindsets, which demonstrates that individuals with a growth mindset—believing that abilities and intelligence can be developed—tend to achieve greater success than those with a fixed mindset, who view these traits as static (Dweck and Leggett, 1988; Blackwell et al., 2007; Yeager and Dweck, 2012). A growth mindset positively affects motivation, self-efficacy, and attitudes toward learning subjects (Dweck, 2002, 2009, 2011), such as chemistry (Wang et al., 2021). In contrast, students with a fixed mindset are more likely to attribute difficulties to a lack of inherent ability and experience fear and anxiety when facing challenges (Dweck et al., 1995). Despite the general benefits of a growth mindset, students may not always apply this mindset to specific subjects like chemistry (Miller and Srougi, 2021; Santos et al., 2021). For instance, while a general growth mindset is important for overall academic development, subject-specific mindsets can play different roles. In reading comprehension, a domain-general growth mindset is a predictor of growth, whereas a subject-specific growth mindset might not be as influential (Cho et al., 2021; Heslin et al., 2021). In the STEM context, fostering a growth mindset is particularly beneficial for students from disadvantaged backgrounds, helping to reduce academic inequality (Gardner, 2019). However, many students who abandon STEM majors, such as chemistry, often possess a fixed mindset, believing their intelligence is unchangeable and attributing their struggles to a lack of ability (Shedlosky-Shoemaker and Fautch, 2015). A mindset influences several factors contributing to academic success, including motivation, self-efficacy, and attitudes toward the subjects being studied. Students are more motivated to learn when they believe they can improve their skills and knowledge (Dweck, 1990, 2011; Dweck and Master, 2009). Chemistry is similar to other disciplines in science in that it requires motivation, a positive attitude towards learning, and a mindset in which students believe in their own ability to succeed.

There is a lack of evidence to determine whether individuals who endorse a general mindset also hold a growth mindset in domain-specific areas such as chemistry. Ensuring that high school students already in STEM tracks, like chemistry, remain engaged in these fields and choose to pursue them at the university level remains critically important. Several factors influence high school students' motivation as well as attitudes towards chemistry (e.g. student interest, classroom setting, curriculum relevance, teacher behaviour, perceived difficulty), and these elements need to be managed to foster positive attitudes towards chemistry among secondary students and to enhance their academic performance (Musengimana et al., 2021). Both internal and external influences play in shaping students' mindsets. The external influences, such as parental support, peers' mindsets, and family involvement in education, play crucial roles in shaping students' mindsets (King, 2020; Vince Cruz et al., 2015; Boncquet et al., 2022). Despite the recognized importance of a growth mindset in educational success, research focusing specifically on its application in chemistry education remains limited. Previous studies have primarily concentrated on general educational contexts or specific subjects like mathematics and reading, leaving a gap in our understanding of how growth mindset principles apply to chemistry learning (Kosmas, 2017; Fink, et al., 2018; Limeri et al., 2020; Santos et al., 2021; Santos et al., 2022). Addressing this gap, this research aims to delve into the exploration of both general and chemistry intelligence among high school students engaged in the Science, Mathematics, Technology, and Environment Enrichment Program in Thailand. One of effort that Thailand has shown its commitment to these goals by engaging with the United Nations Sustainable Development Cooperation Framework (UNSDCF) for 2022–2026, particularly highlighting the importance of education in achieving sustainable development (https://thailand.un.org). If mindset related to students' performance became more adaptable and resilient, then students would be better equipped to meet the evolving demands of the STEM workforce, attain learning in chemistry, and contribute meaningfully to achieving the SDGs. This study aims to explore the general mindsets of high-achieving students, their perceptions of their own chemistry intelligence, and other factors influencing their motivation to learn chemistry. The findings will contribute to a deeper understanding of how these elements impact educational practices and outcomes in the context of Thai education.

Theoretical background

Many terms in academic discourse share similarities For example, Dweck and Leggett (1988) introduced the term implicit theories of intelligence and implicit theories of personality as conceptual terms, and in the last decade the term mindset later appeared in the non-academic book Mindset (Dweck, 2006) instead of or interchangeably with implicit theories (Lüftenegger and Chen, 2017). The term “mindset” has become widely adopted, particularly in educational and developmental psychology, due to its accessibility and practical application. While “implicit theories” accurately describe the underlying psychological constructs, “mindset” provides a more relatable and concise term that resonates with both academic and non-academic audiences. Therefore, this literature review employs the terms implicit theories and mindset for measuring or classifying individuals based on implicit theories and motivation.

Implicit theory of intelligence

Mindset theory describes core assumptions about the malleability of personal qualities. Dweck and Leggett's (1988) implicit theories of intelligence propose two primary beliefs: the incremental theory, which views intelligence as a flexible and improvable quality, and the entity theory, which sees intelligence as a fixed and unchangeable trait. A fixed mindset reflects the belief that intelligence is beyond personal control and inherently stable. Conversely, a growth mindset is the belief that intelligence can be developed through effort and experience (Dweck, 1999). Research has linked a growth mindset to higher academic performance, the pursuit of more challenging courses, and increased college retention (Yeager et al., 2019). While mindsets are often discussed as stable traits, they are context-dependent (Dweck, 2007). Individuals may exhibit both growth and fixed mindsets in different circumstances, such as holding a growth mindset about general intelligence while doubting their ability to improve in specific areas. Based on implicit theories, this study defines a fixed mindset as the belief that one's intelligence is a fixed trait with minimal capacity for development, whereas a growth mindset refers to the belief that intelligence is malleable and can expand through effort and learning.

Categorization of entity and incremental theorists

Classifying learners as either entity or incremental theorists is a complex task. Dweck et al. (1995) proposed a method that categorizes individuals as entity theorists if their implicit theory score is 3.0 or below on a 6-point scale and as incremental theorists if their score is 4.0 or above. Those scoring in the middle range are classified as part of a mixed group, typically about 15% of the sample, which is often excluded from further analysis. This method involves reversing the scores of the entity theorist items (4 items) and combining them with the scores of the incremental theorist items (4 items) to calculate an average. This approach treats implicit theories as a unidimensional construct, with entity and incremental theories positioned at opposite ends of a bipolar spectrum. Additionally, Blackwell et al. (2007) classified participants into entity, incremental, and mixed groups based on scores 1 standard deviation above or below the mean. However, this approach has faced criticism due to concerns about the limitations of predefined group categorizations (Peterson, 2009), the variability in the distribution of groups across different samples and domains, and reduced generalizability resulting from fixed cutoff criteria. Moreover, excluding a significant portion of participants in larger samples leaves only the extreme groups for comparison, potentially biasing the analysis (Lüftenegger and Chen, 2017). As an alternative, some researchers suggest using a person-centered analytical method, such as cluster or latent profile analysis, to form groups based on actual data patterns rather than preset cutoffs (Lüftenegger and Chen, 2017; Burgoyne and Macnamara, 2021; Santos et al., 2021). In this study, general intelligence, as measured by Dweck's (1999) instrument, is viewed as a multidimensional construct consisting of both fixed and growth traits. Each dimension is measured and analyzed separately, with a cutoff point of 4.0 applied to each trait. Students were then categorized into three groups: growth mindset, fixed mindset, and mixed mindset, as described detail further in the data analysis.

Implicit theory and motivation

Implicit theory of intelligence or mindset theory is closely associated with theories of motivation (Dweck, 2017). The learning motivation and self-efficacy posits that an individual's motivation to learn and perform tasks is heavily influenced by their self-efficacy, or their confidence in their ability to achieve specific goals. Self-efficacy impacts the decisions individuals make, the effort they invest in activities, their persistence amidst difficulties, and their resilience to setbacks. High self-efficacy fosters greater intrinsic motivation, enabling individuals to tackle challenges with assurance and a proactive attitude, whereas low self-efficacy can lead to avoidance and reduced motivation (Bandura, 1977). Measuring self-efficacy frequently commence with phrases like “I believe that I can…,” while those assessing implicit theory traits can be like “I have a certain amount of intelligence and cannot change it.” There is ongoing debate about whether these measures reflect a mere belief or a stable trait within the learner. Researcher have posited that motivation and achievement have a reciprocal relation (Deci et al., 2001; Dweck and Master, 2009). It is important to recognize that mindsets are domain-specific; for instance, an individual may possess a growth mindset regarding their mathematical abilities but hold a fixed mindset about their basketball skills (Dweck, 2006). In other words, a student may have a growth mindset about general intelligence but hold differing beliefs or perspectives concerning their intelligence in specific domains. Therefore, when faced with more challenging tasks in learning chemistry, even students with a general growth mindset will respond differently to the same situation.

Scholars have identified three key mechanisms through which mindsets influence learning and behaviours: goals, effort beliefs, and attribution of failure (Dweck, 2017). Students with a growth mindset perceive challenges and failures as distinct from their inherent abilities or personality. They tend to benefit from mistakes and feedback, seek help when necessary, and learn from their failures. In contrast, students with a fixed mindset struggle to find opportunities in failure, as they fear it may expose their inadequacies. Consequently, they are more prone to helplessness upon encountering failure, as they do not believe their abilities can improve. These students often respond to setbacks with negative emotions or may even give up.

Research questions

Learners may have a growth mindset, believing in their overall intelligences. This mindset may not always apply to specific subjects like chemistry. Most research studies have identified correlations between growth mindset, gender, and family economic status, primarily within Western education settings. However, these findings may not be universally applicable in Asian contexts due to differing social and cultural factors. Additionally, most of these studies have been conducted with general student populations. The results might differ when examining students in specialized programs requiring high academic abilities from High-achieving students. The high-achieving students may have their own life goals and their mindset. Research examining the correlations between various factors and students' mindsets related to chemistry remains insufficient. There is a notable gap in detailed studies exploring how specific types of verbal encouragement or discouragement from close individuals impact students' motivation to pursue further studies in chemistry. This gap affects not only average students but also high-achieving ones, who might lose motivation and give up on studying chemistry. Therefore, this research aims to answer the following four research questions.

RQ1: How are high-achieving students' growth mindsets and perception on their chemistry intelligence characterized?

RQ2: Are there any correlations between a student's growth mindset, gender, chemistry learning achievement, family economic status, and their chemistry intelligence?

RQ3: What types of statements, and from whom, encourage and discourage high-achieving students in their study of chemistry?

RQ4: How do high-achieving students with different mindsets react to challenging situations in chemistry?

Methodology

This research has obtained ethical approval from the University's Research Ethics Committee under reference number 660132. This research was conducted as a survey using the cross-sectional survey method. Data were collected simultaneously, covering all nine collaborative networks nationwide. The data collection encompassed both quantitative and qualitative data.

Population. The population under consideration is the 10th-grade secondary students, equal to 6600 students who are studying in the SMTE program (Science, Mathematics, Technology, and Environment Program). To enter the SMTE program, students must pass an academic selection exam and undergo a review of their middle school grades. At present, this program is offered by 220 schools nationwide, with one classroom per grade. Each classroom has a maximum capacity of 30 students.

Sample group. The sample group comprises 338 10th-grade secondary students in specialized classrooms for SMTE program. The sampling procedure was conducted using a two-stage sampling method.
Stage 1: stratified sampling. Stratified sampling was employed by categorizing schools based on the collaboration network centres according to the list of names of project schools for specialized classrooms in science, mathematics technology, and environmental studies, as announced by the Office of the Basic Education Commission on April, 2017. These networks are comprised of nine cooperative regions: (1) Northern Upper Region, (2) Northern Lower Region, (3) Northeastern Upper Region, (4) Northeastern Lower Region, (5) Central Upper Region, (6) Central Lower Region, (7) Eastern Region, (8) Southern Upper Region, and (9) Southern Lower Region, totalling 220 schools. The random selection was performed separately for each collaborative network, taking into account the proportion of schools in each region (Table 1).

Table 1 School distribution and sampling in regional networks

Demographic variables	Frequency	Percentage (%)	Total
a Thailand living standard of 20 000–30 000 THB per month, which id around $600–$900 USD.
Sex
Male	147	43.0%	338
Female	191	55.8%
Cumulative grade point average (GPA)
1 (1.00–2.00)	0	0.0%	338
2 (2.01–2.50)	0	0.0%
3 (2.51–3.00)	2	0.6%
4 (3.01–3.50)	37	10.8%
5 (3.51–4.00)	299	87.4%
Family economic status^a
1.00 (<12 900 THB)	24	7.0%	338
2.00 (13 000–19 000 THB)	46	13.5%
3.00 (20 000–29 000 THB)	48	14.0%
4.00 (30 000–35 000 THB)	65	19.0%
5.00 (35 500–46 500 THB)	37	10.8%
6.00 (46 000–67 000 THB)	55	16.1%
7.00 (>67 000 THB)	63	18.4%
Missing data	4	1.2%	4

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Collaborative regional network	No. of schools		No. of questionnaires		Respond rate (%)
	Total	Sampling	Distributed	Responded
1. Upper Northern	23	2	60	37	62
2. Lower Northern	19	1	30	22	73
3. Upper N/E	32	2	60	16	27
4. Lower N/E	28	2	60	40	67
5. Upper Middle	29	2	60	60	100
6. Lower Middle	27	2	60	60	100
7. Eastern	20	1	29	28	97
8. Upper Southern	24	2	60	45	75
9. Lower Southern	18	1	30	30	100
Total	220	15	449	338	75

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Stage 2: cluster sampling. In Stage 2, cluster sampling was employed with schools as the sampling units, targeting Grade 10 SMTE classes, as previously mentioned. The sample size was calculated using Cochran's formula for finite populations, based on a total population of approximately 6600 students. The formula utilized a Z-score of 1.96 (reflecting the desired confidence level), an estimated population proportion (p) of 0.5 for maximum sample size, and a margin of error (E) of 0.05, resulting in a sample size of 364. School administrators and homeroom teachers from fifteen selected schools across the country were then contacted to explain the research objectives and secure consent for data collection. Consent forms were distributed to students for parental signatures, collected by homeroom teachers, and returned to the researcher by mail. The students were subsequently provided with a Google Form link to complete the survey. The data collection period extended from the initial school contact in July until survey completion in September 2023. Of the 449 students from the fifteen classes, 388 completed the questionnaire, yielding a response rate of 75%. The demographic data indicated that females comprised 55.8% of the participants, outnumbering males at 43.0%. A significant majority of students (87.4%) had high GPAs ranging between 3.51 and 4.00. Family economic status varied, with the largest percentage (19.0%) earning between 30 000 and 35 000 THB. Students were geographically diverse, with 17.5% each from the Northeastern Upper and Lower Regions. Only 1.2% of the data was missing.

Research instruments

An investigation was conducted, and a questionnaire designed to examine the general growth mindset and their perception on their chemistry intelligence in Thai high-achieving students. The first instrument in this study was translated from Dweck's (1999) original tool and underwent quality verification prior to its application. Similarly, the second instrument was translated and modified from Santos et al. (2022), with some components removed.

1. Growth Mindset Scale: based on the mindset scale originally developed by Dweck (1999), this instrument is a 6-point Likert-type rating scale used to assess beliefs in the ability to develop knowledge, abilities, or intelligence. It consists of 8 items, including 4 positively and 4 negatively framed questions. The scale was translated into Thai, and verified using the back-translation method (Brislin and Freimanis, 2001). This process involved three steps: (1) the researcher translated the original version into Thai, (2) an English linguistic expert translated the Thai version back into English, and (3) both parties analyzed the word-for-word equivalence. The comparison revealed minor revisions were needed, such as awkward Thai sentences and phrases like ‘quite a bit’ in question 7 of the mindset section (Appendix A), which were prone to mistranslation. The Thai version was revised until it was smooth and natural sounding. The final translated version was added preliminary questions including GPA, gender, and family's socioeconomic status.

Because the instrument had a clearly defined framework, students’ responses were analyzed using the confirmatory factor analysis (CFA) conducted with Jamovi 2.3.28, using packages in R. The data analysed in this study were continuous, and Maximum Likelihood (ML) was employed as the estimator for parameter estimation. Typically, research studies report model fit as either acceptable or good. Acceptable fit is indicated when RMSEA is <0.08, SRMR is approximately 0.06, [small gamma, Greek, circumflex] and CFI are >0.90, and χ²/df is <3.80. In contrast, good fit is indicated when RMSEA is <0.05, SRMR is <0.06, [small gamma, Greek, circumflex] and CFI are >0.95, and χ²/df is <3.00 (Cheung and Rensvold, 2002; Marsh, et al., 2004; Brown, et al., 2017). The CFA model in this study demonstrated an acceptable fit, with the following indices: χ²/df = 2.93, CFI = 0.95, SRMR = 0.07, and RMSEA = 0.08 (90% CI = 0.06–0.11). Additionally, coefficient H values were obtained to determine the reliability. The results indicated that the items used in the scale yielded good reliability (Growth Mindset = 0.89, Fixed Mindset = 0.77). Table 2 presents factors, items, standardize loading, and coefficient H.

Table 2 Factors, items, standardize loading, and coefficient H

Factor/item	Loading	Coefficient H
Growth mindset		0.89
G2 No matter who you are, you can significantly change your intelligence level.	0.767
G4 You can always substantially change how intelligent you are.	0.838
G6 No matter how much intelligence you have you can always change it quite a bit.	0.750
G8 You can change even your basis intelligence level considerably.	0.866
Fixed mindset		0.77
G1 You have a certain amount of intelligence, and can't really do much to change it.	0.427
G3 Your intelligence is something about you that you can't change very much.	0.845
G5 To be honest, you can't really change how intelligent you are.	0.451
G7 You can learn new things but you can't really change your basis intelligence.	0.486

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2. Chemistry Mindset: based on the development and validation of the Chemistry Mindset Instrument (CheMI) by Santos et al. (2022), which was initially inspired by Dweck's (1999) growth mindset concept but expanded the terminology to affect the interpretation of the scale, distinguishing between intelligence, chemistry intelligence, and chemistry ability. Santos et al. (2022) developed and revised the instrument across four versions. This study utilized the latest version, version 4. This final version employed a 10-point semantic differential scale with 7 aspects, each reflecting a distinct aspect of chemistry intelligence as identified by students in the exploratory phase: (1) problem-solving in chemistry, (2) understanding chemical concepts, (3) applying chemical knowledge, (4) learning chemistry content with understanding, (5) visualizing chemical structures and related processes, (6) using mathematical equations and logical reasoning, and (7) overall chemistry intelligence. This study adapts the final version and reduce ceiling effects and provide more precise measurements covering six aspects related to chemistry intelligence by removing some aspects before translation and revalidation in the Thai context. The deletion occurred because the content analysis by Santos et al. (2022) clearly defined six aspects, and their report indicated overlap among these areas. Therefore, the seventh aspect, “overall chemistry intelligence,” was removed from the Thai version, as the first six aspects were clear, and the wording of the seventh might have caused confusion. Therefore, this study utilizes six aspects of chemistry intelligence as follows: Aspect 1: problem-solving in chemistry, Aspect 2: learning new chemistry content with understanding, Aspect 3: applying chemical knowledge, Aspect 4: memorizing chemistry content, Aspect 5: visually conceptualizing chemical structures and related processes, and Aspect 6: using mathematical equations and logical reasoning. Henceforth, the abbreviations for each aspect, CIA1–CIA6, were used sequentially: CIA1 refers to Chemistry Intelligence Aspect 1.

Moreover, the chemistry intelligence assessment has been modified from the original version by adding questions in Sections 2 and 3 (Appendix B). The Section 1 of the chemistry mindset tool is a 10-point rating scale. Although reporting the reliability of a scale using the alpha coefficient has been wildly found in chemistry education research, alpha is not an indicator of unidimensionality (Komperda et al., 2018b). Reliability of chemistry mindset scale analyse by confirmatory factor analysis and H-coefficient is 0.92 indicating that the items have strong internal consistency and are highly reliable in measuring the intended construct, and it also implies a good fit of the data to the model.

Based on the standards for the validity of test score interpretations for intended uses within the target examinee population (American Educational Research Association, 2014; Komperda et al., 2018a; Lewis, 2022), the interpretation of key terms has been analyzed in Section 2 by examining how each student interprets the six key terms used in the questions, ensuring that the wording aligns with the intended constructs and supports the accuracy of the data collected from respondents. The Section 3 is not a scale; instead, the questions in this section are designed to collect qualitative data. The purpose is to explain the factors that influence a person's motivation and demotivation in learning chemistry. This section contains 7 questions. Only the questions in Section 3 were verified for content validity, assessed by three experts. The validity of the test content was based on expert judgments regarding the alignment between the items and the construct, as guided by the Standards for Educational and Psychological Testing (2014) and Lewis (2022). The experts, with backgrounds in science education, have extensive experience in developing measurement tools, particularly for affective dimensions such as motivation, attitudes toward science, and attitudes toward chemistry. Experts 1 and 2 specialize in chemistry education, while Expert 3 focuses on science education, measurement, and evaluation. They were asked to determine whether each question belonged to the two categories. First, extrinsic motivation refers to engaging in a behavior or activity due to external factors or rewards, rather than for intrinsic satisfaction or personal interest. These external factors can include incentives such as money, praise, grades, recognition, or the avoidance of punishment (Deci et al., 2001). Second, setback refers to how one reacts to an unexpected obstacle, delay, or reversal in progress that hinders the achievement of a goal or objective. It can refer to any situation where anticipated outcomes are disrupted. Questions 1, 2, 3, 4, and 6 from Section 3 of the chemistry mindset tool were categorized under extrinsic motivation. Question 5 was categorized under setback. Each expert rated whether the item was essential to the content domain (1 = Essential, 0 = Useful but not essential, −1 = Not necessary). All three experts considered the item essential (CVR = 1.00), indicating strong content validity.

Data collection and analysis

Data collection was conducted via online surveys on Google Forms, analyzed using SPSS version 28. This analysis included calculating mean, standard deviation, and correlation coefficients for the variables. Each item on the questionnaire is scored according to a pre-defined scoring rubric.

Students respond on a Likert scale to indicate their general growth mindset beliefs (e.g., strongly agree to strongly disagree). These statements assess students' beliefs about the malleability of intelligence and abilities. Research suggests that incremental and entity theories, traditionally seen as mutually exclusive, may coexist within individuals depending on the context, indicating they could be modeled as separate constructs rather than a single continuum (Lüftenegger and Chen, 2017). Based on Dweck et al. (1995), this study measures implicit theories of intelligence. The analysis is conducted separately for each dimension, following the results of the CFA, which identified two factors, each consisting of four items. The scoring reflects the students' mindset, with the incremental theory rated on a scale from 1 (strongly disagree) to 6 (strongly agree). The cutoff scores for each factor are set according to Dweck et al. (1995). Specifically, an overall score of 4.0 or above for the incremental theory indicates a stronger incremental theorist, while an overall score of 4.0 or above for the entity theory indicates a stronger entity theorist. The interpretation of the classification is established as follows:

A growth mindset: students who have an overall average score of 4.0 or higher on the incremental theory and an overall average score of less than 3.0 on the entity theory. This group of students is considered malleable and develops their general intelligence through effort.

A fixed mindset: students who have an overall average score of less than 3.0 on the incremental theory and greater than 4.0 on the entity theory. This group of students views their general intelligence as a stable and innate quality.

A mixed mindset: students who exhibit overall average scores that are either both above 4.0 or both below 3.0 on the incremental and entity theories. This group of students holds mixed beliefs, recognizing both innate ability and the potential for developing their general intelligence with effort.

Fig. 1 displays the frequency distributions of students' general intelligence beliefs, categorized into two mindsets: entity theorists (fixed mindset) and incremental theorists (growth mindset). In Fig. 1(a), most students scored between 1.5 and 3.5 on the entity general intelligence scale (M = 2.51, SD = 0.91), with the distribution skewed to the right, indicating fewer students with strong entity beliefs. Fig. 1(b) shows the distribution for incremental theorists, where most students scored between 4.0 and 6.0 (M = 4.69, SD = 0.93). The distribution peaks around 5.0 and 6.0, suggesting many students believe intelligence can grow and develop (growth mindset).

Fig. 1 The distribution of general mindset.

The self-chemistry mindset was analyzed by summing the scores for each item. The means and standard deviations were then calculated and reported separately for each gender. A Pearson correlation analysis was conducted to examine the relationships among the variables addressing RQ2. To control for Type I error, a Bonferroni correction was applied, resulting in an adjusted significance level of p < 0.00091.

In Section II, six questions were designed to explore how students interpret terms related to chemistry intelligence, with the objective of validating the data obtained from Section I. This section also examined the alignment between the predefined definitions and the students' interpretations, ensuring that the intended questions were understood as expected. The analysis was reported as the frequency of each term's interpretation. Coding was derived directly from student responses, with codes and frequencies emerging from the actual data rather than being predetermined. The research findings on students' interpretation of six key areas of chemistry intelligence (Appendix C) highlight several important insights. Regarding problem-solving in chemistry, students exhibit the ability to solve arithmetic problems, handle unexpected challenges in the lab, and apply their knowledge in both academic and everyday settings. They also emphasize the critical role of understanding when and how to apply formulas, alongside logical reasoning and attention to detail. In terms of learning and understanding new content, students express their ability to quickly grasp complex chemistry topics, apply their knowledge to real-life situations, and utilize logical thinking. Many students also self-assess their learning strategies, describing techniques such as independent study, reviewing content, and taking notes. When discussing the application of chemical knowledge, students reference using chemistry in daily life, laboratory experiments, interdisciplinary contexts, and science projects. Memorization in chemistry encompasses remembering general content, periodic table details, and chemical formulas, with students employing techniques like repeated review and visualization to support retention. Students' ability to visualize chemical structures involves mental imagery of chemical structures, self-reading, and visualizing 3D models. Lastly, their ability to use mathematical equations and logical reasoning in chemistry includes solving arithmetic problems, balancing equations, and applying logical reasoning to address challenges.

However, there is some ambiguity surrounding the term “problem-solving” since mathematical problem-solving appears in three of the six areas of chemistry intelligence: (1) problem-solving in chemistry, (3) the ability to apply chemical knowledge, and (6) the ability to use mathematical equations and logical reasoning in chemistry. In summary, overall, the students' interpretations align well with the intended meaning of the survey questions.

Section III of the questionnaire was not a scale but consisted of open-ended questions designed to gather ESI† on the individuals and statements that influence high-achieving students' motivation and demotivation in learning chemistry. The open-ended responses were analyzed using thematic analysis. This process involved two coders—the researcher and another science educator—who independently reviewed the responses, coding words that indicated emotions, while excluding irrelevant or off-topic responses.

The codes were generated organically from the data without a predetermined list. Following independent coding, the coders convened to discuss and finalize the themes. Cohen's kappa was calculated for the overall raters. Interrater reliability (IRR) was analyzed and found to be 0.97. The IRR value is high because the students provided short responses, which led to consistent interpretation by the two coders. The codes and their frequencies were then grouped and summarized into themes. The frequency of each code was based on the final consensus among the coders. Descriptions for each theme were developed, reviewed, and revised as necessary before drawing final conclusions.

Results and discussion

The RQ1 asked, “How are high-achieving students' growth mindsets and their perception on their chemistry intelligence characterized”?

The analysis of self-evaluation on each aspect of chemistry intelligence, as per questions 1–6 (Appendix B), and question 7, which asks students about their perspective on which aspects they consider most important for learning chemistry. The analysis for questions 1–6 was conducted by calculating the mean of all students' responses using SPSS v.29, and the results are shown in Table 3, which reveals that students had an average general growth mindset score of 4.59 (SD 0.78) (Table 3). Based on the previously mentioned criteria for classifying students into growth, fixed, and mixed mindsets, the results showed that 232 students (68.64%) were categorized as having a growth mindset, 9 students (2.66%) were classified as having a fixed mindset, and 95 students (28.11%) were identified as having a mixed mindset. Two students (0.59%) were excluded from the analysis due to incomplete responses. Clearly, high-achieving students were distributed across growth, mixed, and fixed mindsets, respectively. In summary, the results indicate that the majority of students in this group believe that their general intelligence can be developed and improved through effort. Some students, however, see their intelligence as a fixed, inherent trait. A small portion of the group holds the belief that their general intelligence remains stable and unchangeable.

Table 3 Descriptive statistics on students' self-evaluation (N = 338)

Growth mindset	Total
	Min	Max	M	SD
	1	6	4.59	0.78
	Min	Max	M	SD
CIA 1	1	10	6.88	1.93
CIA 2	1	10	7.25	1.80
CIA 3	1	10	6.56	1.91
CIA 4	1	10	6.81	1.88
CIA 5	1	10	6.49	1.97
CIA 6	1	10	6.76	2.13

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Students rated their chemistry mindset highest in CIA2 (M = 7.25, SD = 1.80), followed by CIA1 (M = 6.88, SD = 1.93), while they rated themselves lowest in CIA5 (M = 6.49, SD = 1.97). The analysis of students' perspectives on chemistry intelligence (Question 7, Section I, Appendix B) was conducted by counting the responses and calculating the frequency of each aspect as a percentage. The results showed that students ranked the aspects of chemistry intelligence from most to least related as follows: Application of Chemistry Knowledge (30.77%), Ability to Learn New Content with Understanding (27.81%), Ability to Memorize Chemistry Content (13.61%), Ability to Use Mathematical Equations and Logical Reasoning in Chemistry (12.13%), Problem Solving in Chemistry (7.99%), and Ability to Visualize Chemical Structures and Processes (7.69%). Furthermore, the independent t-test analysis, as shown in Table 4, revealed no significant difference in overall general growth mindset between male and female students [t(1336) = 0.237, p = 0.649]. Male students (N = 147) had an average general growth mindset score of 4.71 (SD = 0.94), while female students (N = 191) had an average score of 4.68 (SD = 0.92).

Table 4 Comparing growth mindsets of male and female students

Group	N	Mean	SD	df	t	p-Value
Male	147	4.71	0.94	336	0.237	0.649
Female	191	4.68	0.92

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Research on gender differences in self-evaluation of general intelligence yields mixed findings. Furnham and Ward (2001) reported that males typically rate their logical-mathematical intelligence higher than females, while Manger and Eikeland (1998) identified gender differences in mathematics performance. In contrast, Kornilova and Novikova (2013) found no significant gender differences in self-assessed intelligence. Regarding chemistry-specific intelligence, societal expectations and stereotypes may influence self-confidence and self-evaluations. However, there is limited direct evidence comparing gendered self-assessments of chemistry intelligence, as studies often generalize findings across STEM disciplines without isolating chemistry-specific trends. Fig. 2 illustrates how students of different genders rate their chemistry intelligence. Fig. 2 illustrates the percentage of overall agreement among students of each gender regarding their self-assessed chemistry intelligence across various aspects. Both male and female students view the application of chemical knowledge as the most closely related to chemistry intelligence, followed by the ability to learn new concepts with understanding as the second most related. However, the findings are consistent with previous research. Studies suggest that spatial visualization is a significant factor in problem-solving, with some students showing higher proficiency in using it (Fennema and Tartre, 1985). Research by Manger and Eikeland (1998) found differences in performance on challenging mathematical tasks, while Mundy (1980) highlighted the link between spatial visualization and calculus achievement. Additionally, Geary et al. (2023) noted that students with higher self-efficacy in advanced mathematics tended to excel in spatial and algebraic tasks, often experiencing less anxiety in these areas. In STEM education, Clark and Ernst (2008) emphasized the importance of spatial visualization in learning environments, particularly for tasks involving conceptual and physical modeling. Bodner (1983) also identified a relationship between visualization skills and chemistry performance. In conclusion, this study aligns with existing research, showing that students who emphasize visualization and mathematical skills tend to associate these abilities more closely with chemistry intelligence. Others, however, relate chemistry intelligence more to memorization and content retention.

Fig. 2 Percentage of students' perspectives toward chemistry intelligence.

Conversely, female students rate their perspective of chemistry mindset related to abilities in memorizing content (CIA4), applying chemical knowledge (CIA3), and learning new concepts higher than male students (CIA2) (Fig. 2). Both male and female students regard the application of chemical knowledge (CIA3) and the learning of new concepts (CIA2) as highly related to chemistry mindset.

Notably, female students place greater importance on memorizing chemical content (CIA4) and view visualization in chemistry (CIA5) as less association. In contrast, male students emphasize visualization (CIA5) and mathematical ability (CIA6) as more closely related to chemistry mindset compared to female students.

The findings of this study align with previous research. Scholars have found that boys utilize spatial visualization in problem-solving more effectively than girls (Fennema and Tartre, 1985). Manger and Eikeland (1998) noted that boys outperformed girls in challenging mathematical tasks, although spatial visualization was not the primary contributing factor. Furthermore, spatial visualization ability has been associated with calculus achievement, with boys outperforming girls in this domain (Mundy, 1980). Boys exhibited higher proficiency in algebra and spatial abilities, greater self-efficacy in higher mathematics, and lower levels of mathematics anxiety compared to girls (Geary et al., 2023). Moreover, Clark and Ernst (2008) observed that in STEM educational environments for grades 6–12, males exhibited higher initial spatial visualization achievements than females, highlighting the significance of conceptual and physical modelling. In chemistry, Bodner (1983) identified a correlation between visualization skills and chemistry performance, with males outperforming females.

In conclusion, consistent with previous research affirming that gender influences certain abilities such as visualization and mathematics, this study found that male students perceive chemical intelligence to be more closely related to these two abilities than female students do. While female students exhibit lower abilities in these two areas, they tend to view chemical intelligence as more associated with memorization skills compared to their male counterparts.

RQ2: Are there any correlations between a student's growth mindset, gender, chemistry learning achievement, family economic status, and their chemistry intelligence?

The findings of this study contribute to the growing body of literature on general growth mindset and its relationship with academic performance and socioeconomic status (SES). Previous research consistently shows that students with a growth mindset tend to achieve higher academic performance compared to those with a fixed mindset (Wang et al., 2020). Additionally, students from higher SES backgrounds typically exhibit a positive correlation with a growth mindset, benefiting from greater access to resources, supportive educational environments, and parental involvement in their learning processes (OECD, 2020).

This study, however, presents a nuanced perspective by revealing that among high-achieving students, no significant correlations were found between a general growth mindset and gender [r(336) = −0.013, p < 0.00091] or GPA [r(336) = 0.095, p < 0.00091], and there was weak correlation between a general growth mindset and economic status (SES), (r(336) = 0.180, p < 0.00091) among high-achieving students. However, it found a medium significant correlation with all subaspects of chemistry intelligence (Table 5). The correlation between a general growth mindset and GPA was negligible. This finding suggests that high-achieving students may possess a general growth mindset regardless of their GPA, potentially due to the already high level of academic success they have achieved.

Table 5 Pearson correlations among GPA, sex, economic status, growth mindset, and chemistry mindset (N = 338)

Measure	1	2	3	4	5	6	7	8	9	10	11
Note: ** p< 0.00091 (Bonferroni-adjusted significance threshold).
1. GPA		0.262**	0.036	−0.095	0.095	0.059	0.015	0.000	0.057	−0.014	0.025
2. Sex			−0.141	−0.105	−0.013	−0.088	−0.009	−0.076	0.018	−0.048	−0.137
3. SES				−0.101	0.180**	0.177	0.146	0.151	0.126	0.121	0.164
4. Entity					−0.452	−0.284	−0.313	−0.267	−0.270	−0.278	−0.189
5. Incremental						0.477**	0.532**	0.464**	0.442**	0.461**	0.470**
6. CIA1							0.673**	0.665**	0.569**	0.553**	0.532**
7. CIA2								0.742**	0.686**	0.660**	0.572**
8. CIA3									0.616**	0.680**	0.604**
9. CIA4										0.681**	0.500**
10. CIA5											0.560**
11. CIA6

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Even though the fact that students with high SES can have opportunity to get more supportive environment for developing a growth mindset (OECD, 2020, 2021). The study found a weak but statistically significant correlation between general growth mindset and family economic status (r(336) = 0.180, p < 0.00091), This finding suggests that SES had a negligible association with general growth mindset among this group of students. Moreover, fixed mindset is negatively correlated with growth mindset (r(336) = −0.452) and several CIA measures, showing a consistent negative relationship across these constructs. In contrast, there is a moderate positive correlation between having a growth mindset and students' self-assessment of their chemistry mindset in all aspects. There were moderate positive correlations with CIA1 (r(336) = 0.477, p < 0.00091), CIA2 (r(336) = 0.532, p < 0.00091), CIA3 (r(336) = 0.464, p < 0.00091), CIA4 (r(336) = 0.442, p < 0.00091), CIA5 (r(336) = 0.461, p < 0.00091), and CIA6 (r(336) = 0.470, p < 0.00091). These findings indicate that regardless of students' views on what constitutes chemistry mindset, a positive correlation exists between their general growth mindset and all aspects of chemistry mindset.

In summary, for high-achieving students, the analysis found that a general growth mindset is not correlated with GPA or gender but is weakly correlated with family economic status but non-significant. Moreover, all six aspects of chemistry mindset were positively correlated with students' general growth mindset, suggesting that a general growth mindset and chemistry mindset are correlated.

Numerous studies have shown a positive correlation between a general growth mindset and academic achievement. This research used GPA to represent academic achievement, which reflects students' knowledge and accumulated experience. The findings revealed that for high-achieving students, there was no correlation between academic achievement and a general growth mindset, consistent with Li and Bates (2020). One possible explanation is that the GPA of this group of students is relatively high (Table 1), with most students having a GPA between 3.51 and 4.00. As a result, no significant correlation was found between GPA and general growth mindset.

However, many studies have demonstrated that a general growth mindset is related to learning performance and affective domain variables such as motivation and engagement across all socioeconomic backgrounds. Its impact on academic achievement was moderated by socioeconomic status (SES), with positive effects observed only in students from higher SES families, not in those from lower SES families (King and Trinidad, 2021). Research has also found that SES, motivation, and achievement are correlated (Yeung et al., 2022). Additionally, some studies have reported a positive association between a general growth mindset and learning in mathematics and science, specifically among higher SES students, while no significant relationship was found among lower SES students (Bernardo, 2021). Therefore, this research suggests further studies on high-achieving students, as their academic achievement and gender might not be influenced by a general growth mindset. Given the characteristics of high-achieving students and the lack of correlation between the mentioned variables and chemistry intelligence, further investigation is warranted.

RQ3: What types of statements, and from whom, encourage and discourage high-achieving students in their study of chemistry?

Educational psychologists have provided valuable insights into understanding and supporting high-achieving students. A key aspect of this study is motivation. High-achieving students often have intrinsic motivation, driven by personal interest and satisfaction in their learning (Schick and Phillipson, 2009; Kover and Worrell, 2010; Garn and Jolly, 2014; Syskowski and Kunina-Habenicht, 2023). This includes sustaining and enhancing learning motivation through goal-setting, self-regulation, and positive reinforcement. While this research question may not be new to the field of education, the objective of this study is to examine this specific purpose further. The focus is on students with high academic achievement who have chosen a study track within a program emphasizing Science, Mathematics, Technology, and Environmental Enrichment. Among other things, one of the rigorous subjects in this program is Chemistry. This research question aims to expand the understanding of behaviors and the affective dimension effects on the academic performance of these students.

The aim of RQ3 is to investigate types of statements, and from whom, encourage and discourage high-achieving students in their study of chemistry? To address RQ3, part 3 of the questionnaire (Appendix B) comprises five questions (questions 1, 2, 3, 4, and 6) that were analyzed. In responding to the closed questions, students could identify more than one individual. Theme analysis of open-ended questions involved manually coding emotional words. The codes were generated organically from the data without a predetermined list. The codes and their frequencies were then grouped and summarized into themes. The frequency of each code was based on the final consensus among the coders. Descriptions for each theme were developed, reviewed, and revised as necessary before drawing final conclusions and create a graph (Fig. 3 and Tables 6, 7). This process help identifying common experiences, perceptions, and feelings affecting motivation in learning chemistry. The Fig. 3 illustrates that the individuals who most significantly boost motivation in learning chemistry, in order, are the students themselves, chemistry teachers, parents, close friends, classmates, homeroom teachers, and others.

Fig. 3 The frequency of responses identifying individuals influencing students’ motivation and demotivation in learning chemistry.

Table 6 Analysis sources of motivation for learning chemistry

Theme	Theme description	Example
Encouragement and support from others
1. General compliment	General mentions of support from parents, teachers, and friends suggesting external support in the students' learning Chemistry. [Code (frequency): compliment (35), very good or good (17), so smart (140), you are the best (3), great (5), congratulation/card (16)]	“Very good”
		“You are so smart”
		“Congratulation!”
		“I receive a congratulation card”
		“Getting this far is already very impressive.”
2. Speech from others that show confidence in the students	A praise or positive feedback given by someone else that reflects their belief in our abilities, potential, or competence. These compliments often highlight that the person sees and trusts in our capability to succeed or achieve something. [Code (frequency): you can do it (19), keep fighting (2), believe in one's inner potential (4), don’t give up (1)]	“Friend said that you can do it.”
		“Teacher said that you can do it, don't worry.”
		A friend said, “Try to believe in yourself, because if you don't have confidence in yourself, who will? “
		“Mom said that ‘keep fighting! you have to believe that you can do it.’”
3. Compliments from others that emphasize the importance of results	A praise or positive feedback focused on the outcomes or achievements rather than just the effort or process. [Code (frequency): high score (5), pass the test (5), got A grade (2), s/n said proud of myself (10)]	“Teachers often compliment when someone scores well in class.”
		“I received praise for getting a perfect score on the chemistry test.
		“A friend said ‘you're awesome! I wish I could be as clever as you are.’”
4. Positive words from others that encourage perseverance.	Supportive and uplifting comments or feedback aimed at motivating someone to keep going, even when faced with challenges or setbacks emphasizing the value of persistence, resilience, and determination. [Code (frequency): good/great effort (28), try again (5), word of encouragement (25), good responsibility (1), word of praise and guidance (1), constructive criticism (1), doing better (33), keep going (19)]	“Mother always says that it doesn't matter what the score is, we can find more knowledge, it's not necessary to be sad, try listening to clips to find knowledge instead.”
		Teacher said “If you don't understand, start over, try to understand anew, and concentrate.”
		“Me myself, I often use my free time or near exam times to review the content of Chemistry and learn more about parts I don't understand, and sometimes my brother often says I can do it if I try.”
		Mom said “was it worth the effort?”
5. Reward	Any tangible positive reinforcement given to encourage and reinforce desired behaviors or performance such as money, gifts, or food. [Code (frequency): reward (12)]	“Mother says if the exam is passed, she will give 1,000 baht.”
		“Great job! Do you want to eat something delicious?”
6. Positive interaction	A ‘positive interaction’ refers to an exchange between people, such as peers, parents, or chemistry teachers, that is constructive, supportive, and uplifting, leading to positive feelings toward learning chemistry. [Code (frequency): peer interaction (7), a pat on the head (1), a hug (1), a thumbs-up (1), applause (2)]	“Classmate is when we have a problem, or can't answer that question, we often ask for help from friends, and friends can teach us to understand very well.”
		“A friend helps tutor the parts that are not understood.”
		“Mom said ‘you worked really hard, didn't you? Great job!’ Hugs and pats on my head.”
		“The chemistry teacher gives a thumbs-up.”
Self-encouragement
7. Self-motivation	Many responses highlight the importance of self-confidence and belief in one's abilities. This theme intersects with motivation and effort, indicating that a student's belief in their capability is foundational to their success and willingness to engage with challenging subjects. [Code (frequency): I can do it (2), feeling proud of myself (1), thank to myself (1)]	“Everyone has different knowledge and abilities, but if we try and are diligent in doing it, we will increase our knowledge and abilities and become much better.”,
		“We can do it if we try.”
		“Being motivated by watching movies that spark interest in Chemistry has the greatest influence.”,
8. Academic achievement	Recognition of academic success is a significant motivator and point of pride for students like getting high scores, understanding of Chemistry. [Code (frequency): got high score (5), got A grade (2), pass the test (6)]	“Got full marks in Chemistry.”,
		“Got a lot of scores.”,
		“The teacher said I got high scores.”,
9. Learning and understanding	Direct mentions of studying, understanding lessons, and utilizing different learning resources indicate a deep engagement with the subject. [Code (frequency): understand concept (1), perform well in class (1), can solve chemistry problem (1)]	“Experiments that I can do well.”,
		“The teacher is dedicated to teaching, so I feel like wanting to learn, and when the exam comes out, the score is good.”
		I can answer questions during chemistry class.

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Table 7 Analysis sources of demotivation for learning chemistry

Theme	Theme description	Example
1. Self-pressure and self-expectation	References to pressuring oneself, high personal expectations, and the disappointment that follows from not meeting these expectations. [Code (frequency): self-pressure (18), self-expectation (24)]	“Pressuring myself, feeling not good enough.”
		“When feeling uncertain, fearing the results won't be good.”
		“When I receive my exam results and the scores aren't very good, I tend to put pressure on myself.”
		“Why can't I do even this much? Why can't I do it? If I can't do even this, then there's no point in doing anything at all.”
2. Comparisons with others and Judgments by others	Instances of comparing oneself to others, feeling judged, or being explicitly judged based on academic performance. [Code (frequency): chem score comparison (32), close people judgement (15), friend speech judgement (9), expectation from other (2), judgement from parent (4), negative word from parents (13), Negative word from other (not identify) (16), judgement from chemistry teacher (2), negative words from peer (8)]	“My friends are happy when leaving the exam room, but I'm not confident in the exam I've completed.”
		“The aunt next door said that I don’t study as well as her child.
		“I was pressured with the question, 'Why only this much?”
		Parents complain about low scores.
		A friend said I got less than him.
		A friend said, I am the worst in the class.
		A friend said “stupid.”
3. Emotional responses	Expressions of feelings such as frustration, disappointment, sadness, and inferiority related to challenges or failures. [Code (frequency): afraid of be criticized (1), feeling discouraged (12), dislike chemistry (3), stress from other pressure (2), Sadness (3), feeling stress (2), feeling frustrated (1), feeling disappointed (8), feeling fatigued (1), feeling lack of passion (3), feeling bored (2), deep in negative self-thinking (3), fear of failure (4), feeling overwhelmed (2), no motivation (1), no value in chemistry learning (3), poor mental state (2)]	Feeling sad if the exam isn't passed.
		Friend said “you think you're so smart?”
		Parents said “why not try at all?”
		“Because I didn't do as well as I should have, I'm disappointed.”
		“For myself, because when I can't do certain exercises, I feel discouraged, wondering why I can't do it while others can, which undermines my morale.”
		“‘Tired,’ but I'm someone who believes in myself more than feeling discouraged. I might just pressure myself to succeed, but in the end, I still don't do it.”
		“There's a lack of motivation, poor mental state, ineffective teaching, teaching that's too fast, and a situation that makes it unappealing to learn.”
4. Academic challenges	Descriptions of difficulties understanding material, studying effectively, or performing well on exams. [Code (frequency): cannot perform well on test (10), chemistry is difficult (13), fail chemistry test (3), high-stake test (1), too much content to remember (7), do not understand chemistry (14), got low score (26)]	“Stress from exams or over-memorizing.”
		“It's difficult, have to memorize the periodic table.”
		“The teacher announced the grades, and I wasn't good.”
		“I got a very low score on the chemistry exam.”
		“Chemistry is too difficult.”
		“Too much content to remember”
5. Teacher teaching and interactions	Mentions of interactions with teachers and peers that impact one's feelings and perceptions about academic performance. [Code (frequency): negative words from chemistry teacher (13), ineffective teaching (3)]	A teacher said “the test is difficult, you think you can do the exam?”
		A teacher said “any student who fails should reconsider themselves.”
		“I don't understand what the teacher is teaching, she teaches too fast.”
6. A lack of self-efficacy, self-confidence, and self-judgement	Statements reflecting on one's confidence, self-efficacy, or lack thereof in their ability to succeed academically. [Code (frequency): a lack of self-efficacy (10), self-judgement (42), a lack of self-confidence (21)]	“It's too hard, can I do it? Will I remember? There's so much content, afraid I won't be able to do it.”
		“For myself, because when I can't do certain exercises, I feel discouraged, wondering why I can't do it while others can, which undermines my morale.”
		“Myself, because I thought I couldn't do it.”
		“Sometimes, I feel a lack of confidence in myself, and I'm afraid of giving the wrong answer and taking risks.”
		“I keep saying over and over that I can't.”
		“I think I can't do it”

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Positive statements encouraging the study of chemistry (Table 6) can be categorized into: (1) Encouragement and Support from others: this includes general compliments (e.g., “You are so smart”), words from others that show confidence in the student (e.g., “The teacher said that you can do it, don't worry,” “Mom said, ‘Keep fighting! You have to believe that you can do it.”), compliments from others that emphasize the importance of results (e.g., “Teachers often compliment students when they score well in class,” “A friend said, ‘You’re awesome! I wish I could be as clever as you are.’”), positive words from others that encourage perseverance (e.g., “Mother always says that it doesn't matter what the score is, we can always learn more. There's no need to be sad; try listening to clips to find more knowledge instead.”), rewards (e.g., “Mother says if I pass the exam, she will give me 1000 baht.”), and positive interactions (e.g., “A friend helps tutor in parts that are not understood,” “Mom said, ‘You worked really hard, didn’t you? Great job!’ Hugs and pats me on the head,” “The chemistry teacher gives a thumbs-up.”).

(2) Self-encouragement: this encompasses self-motivation (e.g., “Everyone has different knowledge and abilities, but if we try and are diligent, we will increase our knowledge and abilities and become much better,” “We can do it if we try.”), academic achievement (e.g., “Got full marks in Chemistry,” “The teacher said I got high scores.”), and learning and understanding (e.g., “experiments that I can do well.” “the teacher is dedicated to teaching, so I feel like wanting to learn, and when the exam comes out, the score is good.”). In summary, positive statements that encourage the study of chemistry can be categorized into two main themes. First, the encouragement and support include general compliment, speech from others that show confidence in the students, compliment from others that emphasize the importance of results, positive words from other that encourage perseverance, reward, and positive reaction. Second, self-encouragement involves self-motivation, academic achievement, and learning understanding. The analysis reveals these sources of motivation for high-achieving students in chemistry stem from both extrinsic and intrinsic factors to succeed in learning chemistry.

Although RQ1 revealed that most students display either a general growth mindset (68.64%) or a mixed mindset (28.11%) about their intelligence, social interactions significantly contribute to their learning motivation. For individuals contributing to students' demotivation in learning chemistry (Fig. 3), the order is as follows: the students themselves, unspecified others, chemistry teachers, classmates, parents, close acquaintances, close friends, and homeroom teachers. Statements that students indicated as demotivating in learning chemistry (Table 7) can be categorized into six main themes: (1) self-pressure and self-expectation (e.g., “Pressuring myself, feeling not good enough.” “When feeling uncertain, fearing the results won't be good.”), (2) comparisons with others and judgements by others (e.g., “A friend said I got less than him.” “My friends are happy when leaving the exam room, but I'm not confident in the exam I've completed.” “The aunt next door said that I don’t study as well as her child.”), (3) Emotional responses (e.g., “Feeling sad if the exam isn't passed.” “A friend said, 'You think you're so smart?” “There's a lack of motivation, poor mental state, ineffective teaching, teaching that's too fast, and a situation that makes it unappealing to learn”), (4) Academic challenge (e.g., “Stress from exams or over-memorizing.” “It's difficult, have to memorize the periodic table.”), (5) Teacher teaching and interactions (e.g., “A teacher said any student who fails should reconsider themselves.” “I don't understand what the teacher is teaching, she teaches too fast.”), and (6) A lack of self-efficacy, self-confidence, and self-judgement (e.g., “It's too hard, can I do it? Will I remember?” “There's so much content, I'm afraid I won't be able to do it.” “I keep saying over and over that I can’t” “Sometime, I feel a lack of confidence in myself, and I’m afraid of giving the wrong answer and taking risks.”). Based on the analysis of demotivation in learning chemistry, the primary causes are attributed to the students themselves (Fig. 3). Theme analysis of the open-ended questions indicates that factors such as self-pressure, self-expectations, a lack of self-efficacy, self-confidence, self-judgment, and emotional responses to situations, words, or actions significantly contribute to demotivation in learning chemistry. Additionally, the academic challenges inherent in chemistry, such as students' perception that the subject is too difficult or requires excessive memorization, further exacerbate this issue. Moreover, external factors—including unspecified persons, parents, and chemistry teachers—play a crucial role in demotivation. These external factors include comparisons, judgment, and interactions that lead to negative feelings or confusion during learning. In other words, the words or expressions of close individuals can influence demotivation in learning chemistry.

The results of this research may not be solely explained by educational psychology; social psychology, which examines how individual behaviour is influenced by social interactions, can also provide explanations. Motivation is perhaps the indispensable element needed for school success without it the student never even tries to learn (Sternberg, 2017). There are different kinds of motivation; first, achievement motivation is that people who are high in achievement motivation seek moderated challenges and risks. From this motivation perspective, individuals are striving to better themselves and their accomplishments. Second, competence (self-efficacy) motivation, refers to persons’ beliefs in their own ability to solve the problem at hand (Sternberg, 2017). The latter kind of motivation can result from intrinsic and extrinsic rewards (Bandura et al., 1996; Sternberg, 2017). Based on the analysis (Table 6), whether it is positive reinforcement from external individuals (e.g., a teacher saying “you can do it, don’t worry,” or a friend saying “you can do it”) or promised rewards as motivation for studying chemistry (such as a mother saying if the exam is passed, she will give 1000 baht), both are considered extrinsic motivation (Bandura et al., 1996) that promotes learning chemistry.

The themes as in the Table 6 classified as self-motivation (e.g., “we can do it if we try”), academic achievement (e.g., “got full marks in Chemistry”), and learning and understanding (e.g., “I can answer questions during chemistry class”) are internal rewards that students set as small learning goals for themselves, which are considered intrinsic motivation. According to the research findings, whether the statements support extrinsic or intrinsic motivation, students' responses focus on achievement motivation or later changed to competence motivation (Elliot et al., 2017). This research confirms that high-achieving students need both intrinsic and extrinsic motivation to succeed in their chemistry studies.

Moreover, Fig. 3 indicates that motivation for learning chemistry is crucial. The most significant factors, whether they are sources of motivation or demotivation, stem from the students themselves. It is observed that the graph related to the students themselves is the highest, whether it pertains to motivation or demotivation. The results align with earlier studies, which highlight that a key source of motivation is individuals' desire to improve their intellectual abilities (Dweck, 1990, 2002, 2007). Additionally, various factors, such as gender, social class, race, social identity, parents, peers, teachers, and schools, all influence achievement or competence motivation (Elliot and Hulleman, 2017). It shows that the key individuals influencing motivation and demotivation include chemistry teachers, parents, and close friends (Fig. 3), and statements from these key persons, which greatly affect students' feelings (Tables 6 and 7). Parents, teachers, friends, and schools are considered external environments that influence students' learning. Engagement in learning chemistry at school is largely influenced by chemistry teachers and peers. However, recognizing the importance of students' efforts towards academic success is not limited to chemistry teachers alone; parents also play a significant role. The Fig. 3, beside the students themselves, the chemistry teacher significantly influences students' motivation, especially for high-achieving students. Teachers positively impact high-achieving students' motivation by building personal connections, demonstrating strong subject knowledge and classroom management skills, and using reward power, which includes positive reinforcement (Graefe, 2024).

At home, parents contribute to child's motivation and competence in school through their level of involvement in child's learning neither academic success nor academic failure (Grolnick, 2016; Pomerantz and Grolnick, 2017; Grolnick and Pomerantz, 2022), Raftery-Helmer and Grolnick, (2016). Parenting's styles was related to adolescents’ orientation toward particular peer group (Durbin et al., 1993). Mother's perceptions of child's academic competencies are more likely to act as self-fulling prophecies when they hold a fixed or growth mindset (Haimovitz and Dweck, 2017). Mother's aspirations and expectations for child are regard as parents’ involvement affecting to child's academic competence (Pomerantz and Grolnick, 2017). Moreover, parents' academic efficacy and aspirations for their children enhance their children's scholastic achievement by fostering the children's own academic self-beliefs and aspirations, which contribute to higher achievement and positive behaviours and reducing futility and depression (Bandura et al., 1996). However, the students' responses both negative and positive remarks from mothers impact their children's motivation to study chemistry (e.g., “mother always says that it doesn’t matter what the score is, we can find more knowledge, it's not necessary to be sad, try listening to clips to find knowledge instead (Table 6))” and “parents complain about low scores” (Table 7). The conclusion of this research urges parents to recognize that their words and actions significantly impact their children's academic motivation, especially in complex subjects like chemistry that require perseverance. Parents should use positive reinforcement and rewards for achievements, emphasize effort over outcomes, and avoid discouraging remarks. Additionally, they should not overly focus on test scores, as this can increase stress and reinforce the perception that the subject is too difficult to excel in. Peers also play a crucial role in influencing students' motivation. It can be seen from the statements from teachers and peers that both motivation and demotivation impact the students (e.g., a friend said, ‘try to believe in yourself, because if you don’t have confidence in yourself, who will? (Table 6)), or a friend said I am the worst in the class (Table 7). Children who enjoy positive relationships with their peers also tend to be more engaged in and excel at academic tasks compared to those who have peer relationship problems (Wentzel et al., 2017; Wentzel, et al., 2021; Wentzel, 2022) For example, Table 6 shows that good relationships with friends and mutual encouragement result in students responding positively towards learning chemistry. Research states that when peers communicate positive expectations and standards for achieving goals, provide direct assistance and help in achieving them, and create a climate of emotional support, it greatly benefits students (Wentzel et al., 2017).

Moreover, both teachers and the complexities of schools can significantly influence the development of competence motivation (Anderman and Gray, 2017) This research does not overclaim that the mindset of parents, teachers, and peers directly impacts their words in either encouraging or discouraging students' ability to learn chemistry. Instead, it confirms that both positive and negative remarks from surrounding individuals (such as parents, chemistry teachers, and friends) significantly affect students' motivation to learn chemistry, which is a challenging STEM subject. Keeping students engaged in the curriculum and leading them to pursue careers in STEM fields depends greatly on the words and actions of those around them.

RQ4: How do high-achieving students with different mindsets react to challenging situations in chemistry?

Based on the implicit theories as a framework for analysing and interpreting human actions. The theory refers two different assumptions people may make about the malleability of personal attributes. An entity theory of intelligence posits that intelligence is an unchangeable personal trait, with proponents believing that while people can acquire new knowledge, their fundamental level of intelligence stays constant. An implicit theory, in contrast, conceived of intelligence as cultivatable (i.e. individuals may become more intelligent through their efforts) (Dweck, Chiu and Hong, 1995). This research question aims to explore the extent to which students remain resilient in the face of obstacles encountered in learning chemistry. It involves students self-assessing through question number 5, which asks, “If there is an incident where you are unable to solve a chemical problem or apply chemical concepts to solve a problem in Chemistry, how would you feel and react to such an event?” The research findings (Fig. 4) reveal that students with a growth mindset have the highest percentage of those who “are not discouraged and try to find the answer using various methods,” accounting for 82% of this group. This is followed by students with a mixed mindset at 69%, and those with a fixed mindset at 33%. The determination to persist and seek solutions through different approaches reflects the students' belief in their own intelligence. Additionally, only 6% of students with a growth mindset reported feeling “despondent and hopeless about learning chemistry (9%)” or “despondent and feeling that the subject is too difficult to handle,” which is lower compared to students with a mixed mindset (11% and 19%, respectively) and those with a fixed mindset (0% and 56%).

Fig. 4 Student's reactions to chemistry challenges.

This study interprets the statements “not discouraged and try to find the answer using various methods” as resilience which encompasses the ability to recover quickly from chemistry difficulties, toughness, and adaptability in the face of adversity which highlights a crucial aspect of a growth mindset. Conversely, statements like “despondent and feel that the subject is too difficult to handle” may not reflect the authentic reactions of high-achieving students toward chemistry learning. It could merely be a sentiment, with other factors influencing their continued perseverance. Academic perseverance in high-achieving students is influenced by various factors. Previous research suggests that traits like grit, specifically the perseverance of effort, play a crucial role in shaping learning behaviors and academic performance (Tang and Neber, 2008; Jiang, et al., 2021). Factors such as academic self-efficacy act as significant mediators in the relationship between perseverance of effort and cognitive learning strategies, ultimately impacting academic success (Jiang, et al., 2021).

Tang and Neber (2008) discovered that motivation and self-regulation significantly impact the chemistry learning performance of high-achieving students and high-achieving girl students exhibit a stronger effort goal orientation in chemistry learning. Additionally, high-achieving students attribute their academic success to factors like attendance to lectures, early revision, deep learning, time management, and internal motivation, which collectively contribute to their perseverance and achievement (Abdulghani, et al., 2014).

This research interprets the statements “Despondent and hopeless about learning chemistry” and “Despondent and feels that the subject is too difficult to handle” to mean that the students who responded feel defeated and lack the will to persevere in learning chemistry. Additionally, the statement “Not worried, because I don’t use chemistry knowledge in future education” indicates that the students do not value of striving to learn chemistry and do not perceive its benefits for further education. Therefore, they do not feel anxious when faced with challenging chemistry situations.

Therefore, the responds indicate a sense of discouragement and a lack of willingness to persevere in learning chemistry. Most of the research findings on this point can be explained by the implicit theory. The majority of students with a fixed mindset chose to give up when faced with challenging chemistry situations. High-achieving students with a growth mindset chose the answer that they would not give up and would try to find other solutions (Fig. 4). According to the implicit theory (Dweck and Leggett, 1988; Henderson and Dweck, 1990; Dweck et al., 1995), the focus on traits versus specific mediators leads to different reactions to negative events. Entity theorists are more likely than incremental theorists to react helplessly when facing achievement setbacks (Dweck and Leggett, 1988; Henderson and Dweck, 1990). The entity theorists are more prone to making negative judgments about their intelligence from failures and are likely to exhibit negative emotions and debilitation, whereas the incremental theorists attribute negative outcomes to behavioural factors such as effort and problem-solving strategies, and responses by trying harder, developing better strategies, and working diligently towards mastering the task (Dweck et al., 1995). In summary, predominant reaction among high-achieving students with growth mindset is willing to persist in their efforts despite challenges.

However, some aspects of the findings cannot fully be explained by the implicit theory. For instance, high achieving students with a growth mindset (6, 9, and 3%) selected responses that showed they had given up on learning chemistry. Moreover, although the analysis from Fig. 4 shows that students with a growth mindset have the highest percentage of resilience (82%), students with a mixed mindset and fixed mindset also demonstrate resilience, with 69% and 33%, respectively. Regardless of how students perceive chemistry intelligence, the findings in RQ2 indicated a positive correlation between a general growth mindset and their self-chemistry intelligence. The RQ4 found that some high-achieving students with a growth mindset selected responses that showed they had given up on learning chemistry. According to the implicit theory, students with a growth mindset tend to choose not to give up when faced with challenging situations (Dweck et al., 1995). However, Henry and colleagues (2019) demonstrated that successfully navigating scientific challenges, persevering through difficulties, and coping with failure are key traits of a successful scientist. The unique cultures and practices in STEM classrooms significantly influence how students approach and respond to challenges and failures during their education and beyond. As students transitioned to junior high school, where schoolwork becomes more challenging and grading criteria stricter, it was anticipated that frequent achievement setbacks would occur, and their implicit theories of intelligence would predict their future academic outcomes (Henderson and Dweck, 1990).

This study collects data from 10th-grade students during a transitional phase from middle school, where science is taught as a unified subject. In high school, starting in 10th grade, students begin to study physics, chemistry, and biology as separate subjects. The significant increase in content volume can overwhelm students, potentially making their belief in their intelligence, as gauged by a general growth mindset, insufficient to explain their performance in chemistry, which demands specific knowledge and skills. Additional research is necessary to enhance chemistry education and understand the emotional factors affecting students' persistence and resilience in the curriculum. This understanding is essential for achieving the goal of expanding the STEM workforce, particularly in chemistry, which is a primary objective of 21st-century education.

Conclusions and implication

This research surveyed high-achieving students who may not be gifted in chemistry but entered the program based on their grades and standardized entrance exams. These students have chosen to study in a program focused on Science, Mathematics, Technology, and Environment (SMTE). The findings revealed that the majority of students identified with a general growth mindset, followed by a mixed mindset, and a fixed mindset. Male and female students did not differ in their general growth mindset. This result contrasts with several studies showing that male students generally have a higher growth mindset than female students, especially in STEM, or that in some cultures, such as Chinese, students exhibit a more fixed mindset than male students.

The study found that male and female students have different views on self-chemistry mindset. This aligns with previous research indicating that female students may not prioritize visual thinking, making visualization in chemistry the least important for them. In contrast, male students considered chemistry intelligence to be most related to learning new concepts and least related to problem-solving in chemistry. However, the study did not find significant correlations between a general growth mindset and gender or GPA among high-achieving students. There was, however, a weak correlation between a general growth mindset and economic status, and a moderate correlation with all sub-aspects of the self-chemistry mindset. This indicates a positive relationship between a growth mindset and students' views on chemistry intelligence, regardless of their specific perspective. Additionally, the study revealed that the most influential factor in motivating students to learn chemistry was the students themselves, followed by chemistry teachers, parents, and close friends. Conversely, the primary factor in demotivating students was also themselves, followed by other individuals, chemistry teachers, and classmates. This highlights that high-achieving students recognize their role in both motivating and demotivating their chemistry learning.

The implications for chemistry instruction, based on the results of RQ1 and 2 suggest the implication for chemistry learning should focus on chemistry-specific mindset development. Since this research suggests that a general growth mindset may not significantly influence academic achievement and gender among high-achieving students, chemistry instruction might benefit from focusing more on developing a subject-specific mindset. Teachers could design activities and discussions that emphasize the unique challenges and opportunities in chemistry, encouraging students to adopt a growth mindset specifically tailored to this subject area.

Based on the finding that a general growth mindset may not correlate with academic achievement among high-achieving students, it may suggest that these students already possess strong cognitive skills, chemistry instruction should still aim to support their continuous improvement. This could involve challenging these students with advanced topics, encouraging critical thinking, and promoting a mindset of lifelong learning, ensuring that they continue to grow intellectually even when they have already achieved high levels of success.

From the results of RQ3, the analysis reveals sources of motivation and demotivation for high-achieving students in chemistry stem from both extrinsic and intrinsic factors. The implication for learning chemistry is that educators should focus on fostering both extrinsic and intrinsic motivation in their students to optimize their learning. This means creating an environment that not only provides external rewards and recognition but also encourages self-motivation, curiosity, and a deep understanding of the subject matter. For high-achieving students, balancing these motivational factors can be particularly effective in sustaining their engagement and success in chemistry. Moreover, since many people around children influence their motivation to learn chemistry, attention should be given to fostering collaboration between schools, including chemistry teachers, homeroom teachers, classmates, and parents. It is important to create understanding among these individuals about how their words can impact students, potentially leading to discouragement and hopelessness in learning chemistry. Moreover, it is crucial to design chemistry learning experiences that maintain motivation for high-achieving students in SMTE tracks, ensuring they remain engaged with the curriculum and don't give up.

The findings support the implicit theory, showing that most high-achieving students with a general growth mindset (82%) persist and seek solutions when faced with challenging chemistry situations. However, some students feel hopeless (6%) or find the subject too difficult to handle (9%). Future research should explore why some high-achieving students with a general growth mindset still struggle and give up on chemistry. Ensuring that students in SMTE tracks continue their education in STEM fields at the university level and ultimately pursue STEM careers is a significant challenge for educators and chemistry education specialists, especially given the current shortage in STEM professions.

Limitation of the study

1. Despite the practical benefits of using multistage and cluster sampling, such as reduced cost and logistical feasibility, this method has significant limitations that can undermine the study's validity and reliability. One major issue is the potential for increased sampling error compared to simple random sampling. This error arises because clusters, such as students within the same high school class, tend to be more similar to each other and less diverse than a random sample drawn from the entire population. As a result, the homogeneity within clusters can lead to an underestimation of population variability, which in turn reduces the generalizability of the findings (Remler and Van Ryzin, 2021).

2. This study used online surveys for data collection, leveraging the ability to reach a large audience through an invitation link. However, many people are increasingly reluctant to participate in online surveys, and technological issues like unstable internet connections can prevent some respondents from completing the questionnaires (Remler and Van Ryzin, 2021). These challenges can reduce the accuracy of responses, especially in sections requiring detailed explanations. For example, many students did not answer questions about situations affecting their motivation or demotivation in learning chemistry. Future research should include additional interviews to improve data accuracy.

3. The investigation of the influences on students' motivation to study chemistry through open-ended questions of this study, focusing on both motivation and demotivation. However, the data collected was limited to willing participants, making it difficult to fully analyze or align the responses with theoretical frameworks (Ryan and Deci, 2020). Ryan and Deci's self-determination theory outlines a motivation continuum from extrinsic to intrinsic motivation. The survey-based approach of this study lacked the depth needed for comprehensive analysis. Future research should incorporate interviews to gain richer insights into the sources and processes of motivation development in chemistry and should rigorously test this continuum in the context of chemistry learning, exploring whether deeper internalization of extrinsic motivation indeed leads to greater autonomy in student behaviour.

4. Reporting single-administration reliability values of a research tool, including alpha, McDonald's omega, and the H-coefficient, provides information about the relationships between individual items and the composite score. When the tool has multiple dimensions, analyzing reliability using the H-coefficient is more robust than using Cronbach's alpha, provided the underlying statistical assumptions are met. However, while there are many benefits to using a factor analysis framework for reliability assessment, there are also limitations. These include the need for a larger sample size, additional analysis steps, and a stronger emphasis on the theoretical framework underlying the instrument (Komperda et al., 2018a, 2018b). Other studies that wish to use this tool may need to reanalyze it, as the H-coefficient value is appropriate for the population in this research. Therefore, the reliability of this measurement tool may need to be reassessed if it is used with a different population.

5. The study analyzed a population of 6600 10th-grade secondary students, with a final sample of 338 participants, revealing a small proportion classified under the fixed mindset category. This imbalance limits the generalizability of findings regarding responses to chemistry challenges compared to those in growth and mixed mindset groups. Drawing conclusions from a small proportion of the sample may lead to misinterpretations of students' experiences with chemistry challenges, potentially overlooking key insights. This limitation reduces the broader applicability and external validity of the study's conclusions.

Data availability

I would like to submit the following data availability statement for the following research article: RP-ART-07-2024-000218.R5 – Understanding Growth Mindset and Chemistry Mindsets of High-Achieving Students and the Impact of Influential Language on Learning Motivation Research tools and the results of the analysis from this study are publicly available. The research tools have been sent as a Word document (doc) in response to this email, and the results of the t-test, correlation, and CFA analyses have been uploaded as a PDF file to the system. However, raw data, including numeric responses to the growth mindset scale and chemistry intelligence, are not available because they cannot be disclosed to protect the identities of the participants. Qualitative data, which were obtained in Thai, are also not publicly available.

Conflicts of interest

There are no conflicts to declare.

Appendices

Appendix A: growth mindset survey

Appendix B: chemistry intelligence

Appendix C: chemistry intelligence term interpretation

Chemistry intelligent words	Summary of respondent interpretations [Code: frequency]
1. Problem-solving in chemistry	The ability to solve arithmetic problems in chemistry [Code: general problem-solving: 20; arithmetic problem-solving in chemistry: 25], handle unexpected challenges in laboratory settings [Code: immediate problem-solving in the laboratory: 15], and apply learned knowledge to solve problems in both academic and everyday contexts [Code: application of chemistry knowledge: 10]. Additionally, knowing which formulas to use and when to apply them [Code: calculation and formula use: 18], along with logical reasoning and attention to detail, enables students to address problems systematically and efficiently [Code: logical thinking and carefulness: 12].
2. Ability to learn and understand new content with understanding	The ability to quickly grasp new material [Code: fast learning ability: 35], understand complex chemistry content [Code: content comprehension: 45], apply learned knowledge to real-life situations [Code: application of chemistry knowledge: 20], and use logic and reasoning when learning new content [Code: logical thinking and reasoning: 15]. Additionally, most students shared self-assessments of their learning abilities and described their study techniques for chemistry, including learning independently or from available resources [Code: self-directed learning: 50], reviewing and repetition to reinforce understanding [Code: reviewing chemistry lessons: 30], and using listening and note-taking to support the learning process [Code: listening and note-taking: 25].
3. Ability to apply chemical knowledge	The ability to apply chemistry in daily life includes using chemical knowledge in everyday situations and addressing unexpected problems in real-time [Code: using chemicals and handling chemical problems at home: 75; immediate problem-solving: 20]. In experiments, it involves applying chemical knowledge in laboratory settings and ensuring safety by preventing chemical hazards [Code: solving problems in the lab: 55; preventing hazards from chemicals: 25]. It also encompasses applying chemical knowledge to other subjects [Code: applying chemistry in biology: 35] and in science/chemistry projects, using chemical knowledge in project work [Code: applying in projects: 15].
4. Ability to memorize information content in chemistry	The ability to memorize content in chemistry refers to: General content memorization [Code: remember chemistry content: 75], Periodic table memorization [Code: remember periodic table and element properties: 30], and Chemical formula and equation memorization [Code: remember chemical formulas or equations: 25]. Some students mentioned learning techniques for memorization, such as: Memorization through review [Code: remember by reviewing repeatedly: 40] and memorizing by visualizing the relationships between elements [Code: memorize by visualizing related elements: 20].
5. Ability to visualize chemical structures and related processes.	The ability to visualize chemical structures and related processes refers to the ability to visualize chemical structures [Code: ability to visualize chemical structures and related processes: 70], the ability to visualize from a teacher's explanation or self-reading [Code: ability to visualize what the teacher explains: 37, or from self-reading: 13], imagination in problem-solving [Code: using imagination to help with problem-solving in chemistry: 25], and the ability to imagining 3D model [Code: imagining 3D chemical structures: 20].
6. Ability to use mathematical equations and logical reasoning in chemistry	The ability to use mathematical equations and logical reasoning in chemistry refers to: Using mathematics in chemistry [Code: arithmetic chemistry problem-solving (e.g. calculate concentration, atomic mass, or energy): 91; calculating chemical equations: 17; applying mathematical equations: 30], Chemical equation balancing [Code: ability to balance chemical equations: 50], Logical reasoning in problem-solving [Code: ability to provide logical reasoning in problem-solving: 45].

Acknowledgements

This research project is supported by a grant for the development of new faculty staff, Ratchadaphiseksomphot Fund, Chulalongkorn University, Grant Number: DNS 66_060_27_005_1.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4rp00218k

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