View PDF VersionPrevious ArticleNext Article
 
DOI: 10.1039/D2RP00169A (Paper) Chem. Educ. Res. Pract., 2023, 24, 203-216

Gender differences in high school students’ perceived values and costs of learning chemistry†

Xiaoyang Gong *a, Bradley W. Bergey b, Ying Jin a, Kexin Mao a and Yan Cheng a
aFaculty of Education, Tianjin Normal University, 300387 Tianjin, China. E-mail: xgongumd@163.com
bDepartment of Secondary Education and Youth Service, City University of New York – Queens College, 11367 New York, NY, USA

Received 8th June 2022 , Accepted 10th October 2022

First published on 11th October 2022

Abstract

Students’ perceived values and costs of learning chemistry influence their performance and intentions of choosing chemistry-related majors or careers. Based on Situated Expectancy-Value Theory, this study adopted a mixed method approach to examine the conceptualization of values and costs among Chinese high school students and identify their relations with chemistry test performance across gender. Qualitative content analyses revealed that students’ perceived values for chemistry could be categorized into five broad categories: utility value, epistemic value, intrinsic value, aesthetic value, and social value. Chi-square tests and multidimensional scaling revealed that boys and girls perceived values and costs in different ways: relational utility value was more salient to boys while practical utility value and epistemic value were more salient to girls; Girls perceived greater distinctions among different types of values (i.e., epistemic- and emotional-related values) and costs (i.e., emotional and ego cost) than boys. Independent t tests showed that boys reported mostly higher values and lower costs than girls. Hierarchical multiple regression found that gender, intrinsic value, social value and cost significantly predicted students’ chemistry exam scores. In addition, the interaction between gender and social value was identified. This study highlights the complexity of perceived values and costs of learning chemistry and provide implications for developing activities or interventions that foster student engagement in chemistry learning.

Introduction

Despite the significant role of chemistry education in national development, recent research has reported that students around the world are reluctant to choose chemistry-related majors or careers (Avargil et al., 2020; Wang et al., 2021), a pattern exacerbated for women and girls (Grunert and Bodner, 2011). In the Chinese context, this reluctance has become more pronounced after a reform of the national college entrance examination in which chemistry has become an optional course in high school (Zuo, 2021). Understanding factors that influence student choice of chemistry majors and careers remains an important task for researchers, educators, and policy makers wishing to increase student interest in chemistry. Situated Expectancy-Value Theory (SEVT) (Wigfield and Eccles, 2020), which posits that choice behaviors are closely associated with the perceived value and cost of tasks, provides a useful theoretical lens to understand students’ achievement, choices, or retention in STEM-related fields (Nagy et al., 2006; Chow et al., 2012; Watt et al., 2012; Perez et al., 2014; Chang, 2015; Guo et al., 2017; Guo et al., 2018; Jiang et al., 2020; Fong et al., 2021; Wan, 2021; Wang et al., 2021). To date, little work has been done to conceptualize values and costs of learning chemistry in Eastern countries, despite its potential to advance understanding about students’ academic choices in these contexts. Accordingly, the main purpose of this study was to investigate the conceptualization and importance of values and costs of learning chemistry among Chinese high school students and identify relations with chemistry test performance across gender. In doing so, this investigation sheds light on how educators can strengthen student performance and motivation through designing interventions targeting influential values and costs of learning chemistry.

Values and costs in situated expectancy-value theory

SEVT provides an integrative framework for understanding how individuals develop achievement motivations in particular academic domains (Wigfield and Eccles, 2020). The theory identifies cultural and social foundations that leads to two sets of perceptions that are most proximal to academic choices: expectancy for success—whether one expects successful outcome if effort is invested—and subjective task values, which refer to perceived values or reasons for task engagement (Wigfield and Eccles, 2000). Eccles and her colleagues have outlined three positive subjective task values: intrinsic value, attainment value, and utility value (Eccles et al., 1983; Eccles, 2005). Intrinsic value refers to personal interests towards the subject or enjoyment in doing domain-related tasks. Attainment value refers to the personal importance of performing well on the task and is closely associated with the individual's identity or self-schema (Wigfield et al., 2009). Utility value refers to the perceived usefulness of tasks for attaining current or future goals. Some researchers combine attainment value and utility value together and term it as importance value (Watt et al., 2012) while other researchers have made finer distinctions, such as different types of utility values including utility for daily life, job, school and social utility, or the usefulness of knowledge for retaining relations with peers (Gaspard et al., 2015, 2017, 2020).

In contrast to positive subjective task values, costs refer to perceived negative aspects of engaging in activities. Researchers have conceptualized a variety of cost types; these include excessive effort taken to accomplish the task (i.e., effort cost), ego threats associated with task failures (i.e., ego cost), loss of opportunities due to choosing one option than another (i.e., opportunity cost), and negative emotions emerged from engaging in the task such as stress (i.e., emotional cost) (Eccles et al., 1983; Luttrell et al., 2010; Perez et al., 2014; Flake et al., 2015; Jiang et al., 2020). Different types of costs and their relations to educational outcomes has received less attention until recently (González and Paoloni, 2015). The extant literature indicates costs are distinguishable from values (Barron and Hulleman, 2015; Jiang et al., 2020) and typically have negative associations with choice, persistence, and achievement. For example, Perez et al. (2014) found that effort and opportunity cost positively predicted students’ intentions of leaving STEM majors. Further, costs were found to negatively predict students’ academic outcomes, controlling for positive task values (Perez et al., 2014; Jiang et al., 2018).

Differences in construct operationalization, contexts, and domains have revealed somewhat mixed findings in the relation among value and cost constructs. Positive subjective task value (i.e., attainment, intrinsic and utility value) have been found to positively correlate with each other in math and science, as do different types of costs (Trautwein et al., 2012; Gaspard et al., 2018; Perez et al., 2019; Fong et al., 2021). Typically, positive subjective task values are negatively correlated with costs. For example, Gaspard et al. (2018) reported that among German grades 5–12 students various types of value were negatively correlated with effort, emotional and opportunity cost in physics and biology. In other cases, some cost types have been found to have no statistically significant correlation with some values or positive correlations. Perez et al. (2019) reported that among US undergraduate biology students three types of value (i.e., intrinsic, attainment and utility value) were negatively correlated with effort and opportunity cost while their correlations with psychological cost were not statistically significant. Conley (2012) reported that among US seventh grade math students three types of value were positively correlated with opportunity cost. Further, the correlations between utility value and cost have been found to be negative (Gaspard et al., 2015) and positive (Conley, 2012). Mixed findings highlight a need to understand how values and costs interrelate in different contexts.

The value and cost constructs described above are closely associated with student engagement, achievement and academic-related choices (Berndt and Miller, 1990; Bong, 2001; Trautwein et al., 2012; Luo et al., 2016; Galla et al., 2018; Gaspard et al., 2018; Jiang et al., 2020). Students’ course grades have been reported to be positively associated with three types of value while negatively associated with three types of cost (effort, emotional and opportunity cost) in physics, chemistry and biology (González and Paoloni, 2015; Gaspard et al., 2018; Perez et al., 2019). Chang (2015) analyzed survey data collected from Asian students (i.e., Japan, Korea, Taiwan and Hong Kong) and treated value as a composition of interest, enjoyment, importance and utility value. They found that values were positively correlated and predicted student performance in science tasks. Jiang et al. (2018) reported that among South Korean middle school students cost significantly predicted math achievement after controlling for task value. There remains a scarcity of studies addressing students’ perceived values and costs in chemistry domain, especially in Eastern cultural contexts.

The construct of value in science education

Besides intrinsic, attainment and utility value, researchers in theoretical traditions other than SEVT have identified additional values associated with the study of science, including attention to epistemic, aesthetic, and social values. Following definitions of values in previous research (e.g., Rokeach, 1979), epistemic and aesthetic value derive from personal appreciation of logical and intellectual development and of beauty, respectively. Social value refers to contribution at macroscopic social problems and advancement. Above three additional values have been discussed in previous science education literature. First, learning science is a knowledge-seeking process integrated with rich cognitive activities. Corrigan (2007, 2015) described value of science in different themes including learning science process skills (e.g., experimental method), improving human qualities or cognition (e.g., fairness and rational thinking), and better societal outcomes (e.g., impact on or contribution to society). Galloway (2017) reported that UK undergraduate students attached value to attaining a degree in chemistry to the acquisition of knowledge and skills, such as chemical principles, manipulative practical skills, and independent learning abilities. Nieswandt (2007) indicated that the value of chemistry was represented in its personal relevance and importance of chemical products in industry.

Stuckey et al. (2013) have attempted to integrate values defined in SEVT with above three additional values described in science education literature. They elaborated the definition of relevance, distinguishing between individual, societal and vocational levels. The individual level refers to the value of science in satisfying personal curiosity, developing intellectual skills and getting good grades. The societal level refers to valuing the contribution of science to cultivating responsible citizen who actively engage in societal discourses. The vocational level refers to values for promoting the orientation about potential careers and getting a well-paid job. Compared with SEVT, some values classified at the individual level and vocational level are similar to intrinsic and utility value while other values at the individual level and societal level emphasize personal cognitive development and social responsibility, which could relate to utility and attainment value within SEVT. To our knowledge, Stuckley et al. is the only theoretical model that has integrated values defined in SEVT with values emphasized in science education. However, as students’ perceived values of learning science are interdependent in nature, boundaries between some levels are vague, which increases the difficulty of differentiation. For example, the value of “contributing to society's economic growth” at the vocational level (p. 19) is closely related to values described at the societal level. What is more, applying Stuckey et al.'s model in an 8th-grade Finland context, Salonen et al. (2018) reported students distinguished between these relevance levels, perceiving a chemistry topic (i.e., water) as highly relevant for the world and society but irrelevant at personal level. Above theoretical considerations and empirical findings call for more research that examine how different values influence students’ chemistry performance.

Epistemic, social and aesthetic values of learning chemistry are also highlighted in Chinese high school chemistry curriculum standards and textbooks. These standards have emphasized that the goal of chemistry curriculum is to cultivate students’ core literacies for individual cognitive outcomes (e.g., the notion of change and balance, evidence and modelling) as well as social ones (e.g., social responsibilities) (The Ministry of Education of the People's Republic of China, 2020), which reflect unique cultural norms or social expectations in Eastern contexts. Confucian tradition treats wisdom as a virtue and advocates utilizing one's intelligence to make right decisions (Woods and Lamond, 2011). Simultaneously, collectivist values encourage individuals to fulfill their social obligations for maintaining group harmony (Dreamson, 2018). These orientations are in accordance with epistemic and social values. Chemistry's aesthetic value is often featured in school textbook photography. Girod et al. (2003) defined aesthetic value of learning science as the “deep appreciation for the beauty and power of science ideas that transform one's experiences and perceptions of the world” (p. 577). Müller (2003) connected beauty with symmetry because such characteristic is exemplified in molecule structure (e.g., C60). Ling et al. (2020) proposed four stages of integrating aesthetics elements in chemistry education through perceiving, appreciating, exploring and creating the beauty of chemistry in photography activity. The appreciation of the beauty of scientific phenomena can incite students’ interests of learning chemistry and retain continuous engagement (Wickman et al., 2021).

At present, there are few studies systematically investigating students’ perceptions of the value of learning chemistry. Nieswandt (2007) called for more research that conceptualize and measure student attitudes within science disciplines. As values and costs are multifaceted and shaped by social and cultural background, the interplay between different kinds of values and costs (e.g., related or independent) in different contexts merits further investigation (Perez et al., 2014). Therefore, we investigate multiple value dimensions of chemistry by integrating SEVT value with additional conceptualizations that capture the broad spectrum of value. Incorporating and distinguishing various types of value contributes to understanding potential gender differences across these dimensions (Gaspard et al., 2020).

Gender differences in science values and costs

The Eccles et al. SEVT model was originally developed to explain gender differences in math and science motivation and achievement (Eccles et al., 1983). SEVT posits that cultural and social factors, such as the representation of women in science and math, gendered popular cultural messages, and beliefs of key socializers can unfavorably shape girls’ identification with science and math fields, leading to lower expectancies for success, lower positive values, and higher costs for tasks in these domains (Eccles, 2005; Wigfield and Eccles, 2020). In past years, there has been an increasing amount of empirical research that provide evidence for this hypothesis (e.g., Nagy et al., 2006; Chow et al., 2012; Watt et al., 2012).

Studies that have compared gender differences on perceived values and costs of learning science in Western contexts suggest disciplinary differences (e.g., Chow et al., 2012). Gaspard et al. (2017) reported that in the field of physics, boys reported higher intrinsic value, personal importance, utility value for daily life, and utility for job, along with lower effort and emotional costs; no gender differences were found for utility for school, social utility, importance of achievement, or opportunity cost. By contrast, in biology, the authors found that girls reported higher intrinsic value, personal importance, importance of achievement, daily utility value, job utility, and school utility along with lower effort, emotional and opportunity cost; no gender differences were found in social utility. Similarly, in a German sample of tenth grade secondary students, Nagy et al. (2006) found that girls reported higher intrinsic value for biology than boys, while the opposite patterns emerged in the field of math.

For comparison, in the field of math, studies with Australian, Canadian, German, and United States samples have revealed that boys tend to report higher values and lower costs than girls, though many non-statistically significant differences are also reported (Nagy et al., 2006; Watt et al., 2012; Gaspard et al., 2015, 2017, 2020). Fong et al. (2021) classified US high school students into five groups based on expectancy and three types of value and found that girls were underrepresented in high-math/high science profile and were more likely to be members of high-math/low science profile.

The few studies that have been conducted in Eastern contexts parallel Western studies, with boys reporting higher values and lower costs in math or science than girls. Wan (2021) reported that 10th-grade boys in Hong Kong had significantly higher intrinsic and utility value in science than girls. However, Guo et al. (2015) found no gender differences in the utility value for math when examining the TIMSS data from Hong Kong Grade 8 students. Gaspard et al. (2020) found that Chinese boys reported higher intrinsic value, personal importance, social utility for math than girls. Further, the authors found that Chinese and Korean boys reported lower effort and emotional cost in math than girls (Gaspard et al., 2015, 2020). We were unable to find any prior studies in Eastern contexts that have examined perceived values and cost in learning chemistry. As student academic motivation is domain-specific and culturally-situated, investigating Chinese students’ perceived values and costs of learning chemistry across gender contributes to providing evidence for the validity of SEVT and providing implications for developing school interventions designed to influence students’ perceptions of chemistry values and costs. Further, understanding potential gender differences in perceived values and costs and their relations with achievement is essential for targeting interventions towards groups who most need them.

Current study

Increasing student involvement in chemistry-related majors and jobs requires pedagogical support targeting student perceptions of values and costs. Trends indicting girls are less likely than boys to choose a chemistry major (Grunert and Bodner, 2011) underscore the importance of understanding gender differences in how students make academic choices. Thus, there is a need to understand how boys and girls perceive values and costs in different contexts and how these perceptions influence choice and performance. The domain-specific nature of values, costs, and their relations to achievement also merits investigation (Jiang et al., 2020), yet remain under-researched in the domain of chemistry. Given the scarcity of such research, particularly in non-western settings (Gaspard et al., 2020), it is worthwhile to examine student perceptions in an Eastern cultural context. Finally, integrating values described in SEVT and in science education literature enables teachers and education researchers to better understand which types of values matter most to boys or girls and to their achievement. Therefore, the current study aims to investigate the following three research questions:

1. How do male and female Chinese high school students conceptualize the value of learning chemistry? Which types of values are salient for each group?

2. How do boys and girls differ in their perceived values and costs of learning chemistry?

3. How do values and costs predict students’ chemistry exam scores? Are there gender differences in these relations?

We pursue these questions with a mixed method design, including analysis of open-ended survey responses about perceived values for learning chemistry and multiple quantitative analyses (multidimensional scaling, t tests, and multiple regression analyses) of items assessing value and cost constructs.

Method

Participants and procedure

The sample were 464 tenth grade and eleventh grade students (M = 15.66 years old, SD = 0.73; 43.6% female; 3.3% minority) from a private high school in a northern province in China. Data were collected at the end of fall semester during the 2019–2020 academic year. Participants voluntarily reported demographic information (e.g., age, gender and grade), six values (see Appendix 1, ESI†) and cost scales, and chemistry exam scores. Even though there are no institutional review boards either in high schools or in higher education institutions, researchers have seriously taken ethical standards into consideration. First, informed consent was obtained from school principals and classroom teachers before sending out the Chinese version of the self-report survey. Students were asked to assent to using their responses for research purposes. Second, participants completed the survey using pencil and paper anonymously in regular chemistry classrooms administered by school teachers. Third, survey data was treated confidentially. Students received no compensation for the participation.

Measures

Before the data collection, authors first invited four chemistry education researchers and three experienced chemistry teachers to provide feedback for the content validity and the expression of Chinese language after translation. Then five high school students were selected through convenience sampling to fill out the survey. A focus group interview was conducted to examine whether students had difficulty in understanding survey items and whether survey items were comprehended in expected ways. Survey items were revised accordingly and final measures assessed six types of values (i.e., intrinsic, attainment, utility, social, epistemic, and aesthetic values) and cost with 6-point Likert-style items, using a response scale ranging from 1 (strongly disagree) to 6 (strongly agree). For all values and cost measures, which are provided in the Appendix (ESI†), scale scores were created by calculating the mean score across items, with higher values representing greater endorsement of the measured construct. At the end of the survey, students responded to an open-format question: “What is the value of learning chemistry?”
Intrinsic value. Intrinsic value was measured with four items (α = 0.951): three items from the mathematics value inventory (MVI; Luttrell et al., 2010) and one item was adopted from TIMSS2019. Survey items were modified for the subject of chemistry by replacing ‘math’ with ‘chemistry’. One example item was “I find many topics in chemistry to be interesting.”
Attainment value. Attainment value was measured with five items (α = 0.834): three items from TIMSS2019 and two items from the MVI developed by Luttrell et al. (2010). One example item was “Getting high grades in chemistry is important to me.”
Utility value. Utility value was measured with five items (α = 0.850) from TIMSS2019. One example item was “Doing well in chemistry helps me get a good grade in the exam.”
Social value. Social value was assessed with five researcher-developed items (α = 0.939). Items assessed perceived value of chemistry in promoting the development of human beings and society and were developed based on Nieswandt's (2007) survey of the chemistry's importance for chemical products and personal relevance. One example item was “Learning chemistry can help me protect the environment and achieve sustainable development.”
Epistemic value. Epistemic value was assessed with nine items (α = 0.953) measuring perceived value of learning chemistry to promote self-awareness of safety, thinking and inquiry skills. Items were first developed by the research team based on high school chemistry curriculum standards. Then items were revised based on suggestions from three chemistry education researchers and two experienced chemistry teachers. One example item was “Learning chemistry help me make inferences informed by evidence.”
Aesthetic value. Aesthetic value was assessed with four researcher-developed items (α = 0.927) and followed the similar revision procedure above. They measured the aesthetic value of learning chemistry including the beauty of symmetry, phenomena, rationality and conservation. One example item was “When learning chemistry, I can feel the beauty of symmetry.”

As some value items were adapted from previous literature while other value items were researcher-developed, we conducted confirmatory factor analysis using maximum likelihood estimation of a six-factor model. Model fit was assessed by examining standardized root mean square residual (SRMR), the root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker Lewis index (TLI). The six-factor solution produced an adequate model fit (SRMR = 0.06; RMSEA = 0.06; CFI = 0.91; TLI = 0.90), according to Hu and Bentler's (1999) criteria. Standardized loading estimates for 32 items on six factors are provided in the Appendix 3 (ESI†).

Cost. Cost was assessed with eight items (α = 0.793) adopted from the questionnaire developed by Jiang et al. (2016). We modified survey items for the chemistry subject by changing ‘math’ to ‘chemistry’. Item 4 referred to the general meaning of cost while the other items measured four types of costs including effort cost (item 1), opportunity cost (item 2 and 3), emotional cost (item 5 and 6), and ego cost (item 7, and 8). One example item was “I have to sacrifice a lot of free time to be good at chemistry.”
Chemistry scores. Students reported their chemistry scores in the last final exam with five options (≥90, 80–89, 70–79, 60–69, <60). In this school, students at different grade levels took final exams in the same week. Students at the same grade level took the same chemistry exam including multiple-choice questions and open-response questions.

Planned analysis

We adopted a mixed-method approach to investigate gender differences in Chinese high school students’ understanding of the value and cost of learning chemistry. We first conducted a qualitative content analysis of participants’ open-ended responses and then used quantitative analyses to analyze Likert scale survey data using multi-dimensional scaling, t tests, and multiple regression analyses.
Qualitative content analysis. The analytic sample for the open-ended response excluded 109 students who left the question blank; 2 participants did not report gender, and nine students provided answers unrelated to the question. We used qualitative content analysis (Braun and Clarke, 2006) to identify types and frequencies of students’ perceived value of learning chemistry. A coding scheme was developed in two stages. First, the research team examined a larger dataset from a related study that included the same open-response question with a similar population to develop possible codes. Initial codes were based on conceptualization of values in existing literature, including getting high grades, the need of major and job, daily life, learning other subjects, and interest (e.g., Eccles, 2005; Trautwein et al., 2012; Gaspard et al., 2018; Perez et al., 2019). Through coding procedures in the larger dataset, we added new code including promoting the development of knowledge and skills, discovering new things, and social contribution. Second, three members of the research team collectively coded a subset (20%) of open-ended responses in this study. After initial codes were applied, the team elaborated and refined code definitions and boundaries, resulting in a codebook with descriptions and examples under each code. Codes mentioned more than once in a single response were coded only once. More than one code could be applied to a response if applicable. Then, two members of the research team independently coded the remaining 80% of open-ended responses. Values for Cohen's Kappa statistics demonstrated relatively good inter-rater agreement with each code indicating acceptable-to-excellent inter-rater reliability (Table 1): Kappas for six codes >0.8 indicated almost perfect agreement, Kappas for two codes >0.6 indicated substantial agreement, and Kappas for two codes >0.4 indicated moderate agreement (Landis and Koch, 1977). Finally, the research team discussed each open-ended response with inconsistent codes and reached agreement for the whole dataset. Chi-square tests were used to compare the frequency of codes between boys and girls.
Table 1 Frequencies of different types of value, inter-rater reliabilities and chi-square tests
Types Codes Example quotations Kappa Frequency (N) Chi-square p Effect size
Male (N = 190) Female (N = 144)
a p values were derived from the Fisher's exact test.
Utility value Academic Learning chemistry can help me get good grades and get into a good university. 0.916 63 35 3.096 0.078 0.096
Practical Learning chemistry can help me solve problems in daily life. 0.912 43 49 5.331 0.021 0.126
Job It can increase my job opportunities. 0.898 34 29 0.270 0.604 0.028
Relation I want to learn chemistry well and do not disappoint others. 1.000 6 0 — 0.039a 0.118
Research It can help us create new materials. 0.544 5 2 — 0.703a 0.043
Epistemic value It can enrich my knowledge and broaden my horizon. 0.635 82 80 5.041 0.025 0.123
Intrinsic value Chemistry is interesting. I feel happy when learning chemistry. 0.763 25 21 0.140 0.708 0.020
Social value Learning chemistry enables me contribute to the country and society. 0.588 24 10 2.898 0.089 0.093
Aesthetic value It shows the nature's magic and beauty. 1.000 7 12 3.300 0.069 0.099
No value It is meaningless. 1.000 2 8 0.022a 0.131

Non-metric multidimensional scaling. To compare boys’ and girls’ meaning of perceived value and cost of learning chemistry, we used nonmetric multidimensional scaling (MDS). MDS in an analytic technique that visually represent the rank-order proximities (correlations) among survey items (Stalans, 1995). As MDS maps display relative positions of survey items, they provide insight into how students understand the meaning of a set of perceptions, including perceived similarities or dissimilarities among types of value and cost. The greater the perceived similarity of meaning between any two items, the higher their relation, and the closer their locations in the map space (Schwartz, 2007). Thus, items that clusters closely together are interpreted to have similar meanings, while related items (e.g., items measuring the same construct) that contain greater distance indicate broader meaning of the construct. Unlike factor analysis, which prioritizes identifying discrete factors, MDS allows for the overlap of latent constructs, thereby providing a less restrictive view of the meaning of individual items and latent factors. In this study, we separately conducted nonmetric MDS with 32 items from six scales of value and 8 items from the scale of cost. To facilitate comparing similarities and differences among boys and girls, separate maps were created for each group. Items were grouped in the maps and we selected the appropriate MDS solution based on the Stress level. Kruskal Stress coefficients were used to assess the correspondence between the visual distribution and the data matrix of proximities. At present, there are no absolute cut-off criteria. While maps with higher numbers of dimensions have lower stress values indicating more accurate representations, maps with fewer dimensions are easier to interpret. Based on the previous literature discussing and using MDS (e.g., Mair et al., 2016; Bergey et al., 2019), this study adopted the following criteria: appropriate and good fit is indicated by coefficient below 0.20 and 0.15, respectively. Items measuring a latent factor were connected with lines and presented in the same color. Patterns underlying visual representations of survey items are identified based on spatial dimensions.
Independent t tests and hierarchical multiple regression. Independent t tests were conducted to compare the perceived values and cost of boys versus girls. Before conducting the hierarchical multiple regression, bivariate correlations were first used to describe correlations among above seven variables and chemistry grade for male and female groups. Then hierarchical multiple regression was adopted to identify individual or interaction effects of demographic variables (e.g., gender), perceived values and cost on students’ chemistry grades.

Results

Content analysis and chi square test

Participants identified a wide array of values for learning chemistry. Codes, example quotations of each type of value, and frequencies for boys and girls are listed in Table 1. Effect sizes (i.e., Cramer's V) were calculated to measure the magnitude of gender differences.
Utility value. Many students emphasized the utility of learning chemistry for achieving other goals. We observed five types of utility values: academic utility (getting good grades and learning other subjects), practical utility (explaining phenomena and solving problems in daily lives), occupational value (major choice and job opportunities), relational utility (maintaining relations with others), and research utility (discovering new things). Among five types of utility value, 29.3% students indicated the utility for academic. They thought that learning chemistry contributed to getting high grades, learning other school subjects (e.g., biology), or entering universities. 27.5% students indicated the practical utility of learning chemistry. They noted that chemistry knowledge could help explain phenomena, solve problems and make right decisions (e.g., weight-loss products). 18.9% students indicated the utility for job and they thought that learning chemistry was beneficial for choosing university majors and future jobs (e.g., medicine). Six boys indicated the relational value of learning chemistry. For example, learning chemistry well could “strengthen the connection with classmates and teachers” and “fulfill parents’ expectations”. A few students indicated the utility for research and argued that learning chemistry was useful for creating new materials or products, which can be treated as a unique utility value of chemistry subject.
Epistemic value. The most common (48.5%) value of learning chemistry was to develop the individual's knowledge and various skills, which we labeled “epistemic value”. These students noted that learning chemistry could “enrich knowledge”, “strengthen safety awareness”, and “broaden horizon”. As a science subject, participants reported that learning chemistry could help them become “rational”, “logical” and “objective.” Other students noted that chemistry held value because it enabled them to “understand world at macro- and micro- levels.” All these advantages contributed to one individual's self-development and personal success in this field or in the future.
Intrinsic value – interest and positive feelings. Some students (13.8%) indicated the intrinsic value in learning chemistry, or their enjoyment and satisfaction arising from chemistry-related activities. For example, students characterized that learning chemistry as “interesting”, “enjoyable” and “comfortable”, especially during chemistry experiment or reaction process.
Social value. Social value emphasizes the value of learning chemistry at the macroscopic level – the betterment of country, society, human being as a whole. 10.2% students described that learning chemistry could “improve the life quality of human beings”, “promote the development of country and society” and “benefit environmental protection”.
Aesthetic value. Some participants (5.7%) described aesthetic value in that learning chemistry evoked aesthetic feelings and experience. For example, students noted that chemical phenomena such as reactions were “miraculous” and “beautiful,” and that learning chemistry could further help learners “feel the beauty of the world”.
No value. Ten students indicated that studying chemistry held no value and was useless.

A set of 2 (gender) × 2 (mentioned/did not mention) chi square tests were conducted to examine the relation between gender and perceptions of chemistry value. As some cells had expected frequencies less than five (i.e., research utility, relational utility and no value), Fisher's exact tests were adopted to compare proportions (Kim, 2017). Results revealed three statistically significant differences by gender for three values (see Table 1). However, effect sizes for two types of utility values (i.e., practical and relational) and epistemic value were relatively small. More boys than girls reported the relational value of learning chemistry. In contrast, higher percentage of girls than boys reported the practical utility value and epistemic value. In addition, more girls than boys perceived chemistry as a meaningless subject. These results suggested the perceived importance of learning chemistry in maintaining social relationships was more salient for boys while girls emphasized the usefulness of learning chemistry in daily lives and self-development, or were more likely to see no value.

Non-metric multidimensional scaling (MDS)

MDS for perceived value of learning chemistry. MDS analyses showed that the three-dimensional solution for perceived value of learning chemistry had lower stress values than the two-dimensional solution. However, MDS maps with three dimensions were difficult to interpret. Therefore, we selected the two-dimensional solution in light of the need for visualization.
Boys. For the male sample, the MDS analysis suggested that a two-dimensional solution showed an acceptable fit (stress = 0.164; see Fig. 1). The results showed that six value constructs were organized in four distinct regions. In a small region (labeled social development, lower left), items related to the societal value of learning chemistry clustered tightly together. In an adjacent region (labeled cognition and emotion, center bottom), items tapping intrinsic, epistemic, and aesthetic values grouped together along with some items tapping utility and attainment value. A third region (labeled interpersonal relationships, center right) included two attainment value items that described importance of learning chemistry for relations with parents or classmates. In the fourth region (labeled academic and occupational purposes, center top), utility and attainment value items grouped together and substantially overlapped. Items in the fourth region described the value of getting high grades, benefiting university major choices and increasing job opportunities. A main feature of the map was the centrality and breadth of attainment value, suggesting attainment value had broad meaning for boys and overlapped with other values. Similarly, utility value captured a sizable region and overlapped substantially with attainment value. Utility value also mostly subsumed clusters of intrinsic value, epistemic value, and aesthetic value, suggesting the meaning of utility value was closely related to the meanings of intrinsic, epistemic, and aesthetic values. The map was also characterized by the marginality of two attainment-value items relating to interpersonal relationships. This arrangement suggested somewhat separate regions within the attainment value, with the perceived value of maintaining relations with others constituting a distinct region from others.
image file: d2rp00169a-f1.tif
Fig. 1 MDS maps of chemistry values (left: boys; right: girls).

Girls. The MDS analysis for girls indicated that a two-dimensional solution had appropriate fit (stress = 0.174; see Fig. 1). The results showed that six value constructs were organized in four distinct regions. Similar to the boys’ map, one region (labeled social development, left) included items related to the social value of learning chemistry grouped together. Also similar to boys, an adjacent region (labeled cognition, bottom center) contained items related to epistemic and aesthetic values. Unlike the boys’ map, however, in the girls’ map aesthetic values did not overlap with epistemic values. Also contrasting with the boys’ map, most intrinsic value items in the girls’ map appeared in an adjacent region (labeled emotion, bottom right) along with one utility and one attainment items. In a fourth region (labeled academic and occupational purposes, top center), four items related to utility value and three items related to attainment value grouped together and overlapped considerably. Indeed, a prominent feature of this map was the phenomenological overlap of attainment value and utility value, suggesting closeness in meaning of these value sets of values, consistent with relations found in previous research (e.g., Durik et al., 2006).
MDS for perceived cost of learning chemistry. The MDS analysis of cost items suggested that a two-dimensional solution showed a good fit for boys (stress = 0.072) and girls (stress = 0.010). The results showed that four cost constructs were organized in three distinct regions for boys while in four distinct regions for girls. Both opportunity and effort cost items appeared to be distinct from each other and had similar interrelations for boys and girls. In contrast, emotional and ego cost items grouped together in map for boys while these items appeared in distinct regions in the map for girls (Fig. 2).
image file: d2rp00169a-f2.tif
Fig. 2 MDS maps of chemistry costs (left: boys; right: girls).

Independent samples t tests

Independent samples t tests were conducted to examine gender differences in perceived values and cost of learning chemistry (see Table 2). Effect sizes (i.e., Cohen's d) were calculated to measure the magnitude of gender differences. To balance Type 1 and Type 2 errors with multiple tests, we corrected alpha levels using Benjamini–Hochberg's procedure. Results showed that boys reported significantly higher levels of intrinsic value, utility value, attainment value, epistemic value and aesthetic value than girls. Simultaneously, boys reported significantly lower levels of cost than girls. The effect size for intrinsic value was medium (d = 0.52) while other significant differences had small effects (0.23 ≤ d ≤ 0.39). Groups were not statistically significantly different in perceived social value.
Table 2 Independent t tests and descriptive statistics for boys and girls
Variables Independent t tests Descriptive statistics Cohen's d
t df p Male Female
M SD M SD
1. Intrinsic 5.507 398.860 <0.001 4.81 1.22 4.13 1.39 0.52
2. Utility 4.151 448 <0.001 4.74 1.04 4.30 1.17 0.39
3. Attainment 2.682 380.762 0.008 4.70 1.01 4.41 1.19 0.26
4. Aesthetic 3.924 443 <0.001 4.71 1.18 4.25 1.27 0.37
5. Epistemic 2.354 437 0.019 4.92 1.00 4.69 1.03 0.23
6. Social 0.760 448 0.447 5.24 0.93 5.17 0.94 0.07
7. Cost −2.719 433 0.007 3.13 1.06 3.40 0.93 0.26

Correlations and hierarchical multiple regression

Bivariate correlations for all variables by gender are presented in Table 3. As expected, six types of perceived value were moderately or highly correlated for both boys (0.43 ≤ r ≤ 0.81) and girls (0.35 ≤ r ≤ 0.76). For boys but not girls, cost was weakly but significantly negatively correlated with intrinsic value, utility value and social value. Contrary to expectations, cost had a weak but significant positive correlation with attainment value for both boys and girls. Regarding their relations with achievement, boys’ chemistry performance was positively and significantly correlated with six types of value and significantly negatively correlated with cost. Girls shared similar patterns, with one exception: the correlation between achievement and social value was not statistically significant.
Table 3 Bivariate correlations between value types, cost and score in chemistry for boys (below the diagonal) and girls (above the diagonal)
1 2 3 4 5 6 7 8
1. Intrinsic — 0.74** 0.70** 0.70** 0.60** 0.35** −0.12 0.46**
2. Utility 0.75** — 0.76** 0.61** 0.65** 0.49** 0.03 0.27**
3. Attainment 0.62** 0.63** — 0.68** 0.64** 0.49** 0.15* 0.29**
4. Aesthetic 0.67** 0.59** 0.61** — 0.76** 0.54** −0.10 0.32**
5. Epistemic 0.69** 0.65** 0.62** 0.81** — 0.72** −0.05 0.28**
6. Social 0.43** 0.49** 0.47** 0.56** 0.70** — −0.01 0.13
7. Cost −0.14* −0.16* 0.17** −0.09 −0.09 −0.13* — −0.21**
8. Score 0.44** 0.42** 0.32** 0.38** 0.39** 0.36** −0.29** —

We conducted a hierarchical multiple regression to examine unique and collective relations among demographic variables, values, cost, and chemistry performance. Besides gender, age has also been included as the second demographic variable in the regression analysis because relations between academic motivation (e.g., value and cost beliefs) and achievement change across age (Wigfield, 1994). Such effort allows future researchers to compare the effect of age on student test performance along with other variables. Therefore, age and gender (coded female = 1, male = 0) were entered in step 1, values and cost were added in step two, and value by gender interactions were added in step 3 (see Table 4). Results showed that gender, age, intrinsic value, social value, cost, and a social value by female interaction explained 32.1% of the variance in students’ chemistry grades, F (15, 366) = 12.991, p < 0.001, R2 = 0.347, RAdjusted2 = 0.321. Among above independent variables, age (β = 0.141, p = 0.002), intrinsic value (β = 0.262, p = 0.019) and social value (β = 0.188, p = 0.025) were positive predictors while female (β = −0.162, p < 0.001) and cost (β = −0.198, p < 0.001) were negative predicators. The social value × female interaction emerged as a nearly significant and negative predictor (β = −0.169, p = 0.051). Thus, results indicated that older boys who perceived higher intrinsic value, higher social value, or lower costs of learning chemistry were more likely to get higher chemistry exam grades.

Table 4 Results of hierarchical multiple regression model
Variables B Std. error Beta
R 2 = 0.133 for step 1; ΔR2 = 0.197 for step 2; ΔR2 = 0.017 for tep 3. *p < 0.05.**p < 0.01. ***p < 0.001.a p = 0.051.
Step 1
Age 0.370 0.090 0.197***
Female −0.769 0.130 −0.284***
Step 2
Age 0.229 0.083 0.122**
Female −0.476 0.120 −0.176***
Intrinsic value (IV) 0.306 0.078 0.313***
Utility value (UV) 0.001 0.089 0.001
Attainment value (AtV) 0.007 0.086 0.006
Aesthetic value (AeV) 0.038 0.086 0.036
Epistemic value (EV) −0.002 0.113 −0.001
Social value (SV) 0.102 0.088 0.073
Cost −0.285 0.062 −0.214***
Step 3
Age 0.265 0.084 0.141**
Female −0.439 0.125 −0.162***
IV 0.256 0.108 0.262*
UV 0.136 0.122 0.116
AtV −0.002 0.113 −0.001
AeV 0.095 0.115 0.089
EV −0.134 0.161 −0.105
SV 0.264 0.117 0.188*
Cost −0.264 0.062 −0.198***
IV × female 0.134 0.215 0.068
UV × female −0.299 0.210 −0.151
AtV × female 0.038 0.194 0.019
AeV × female −0.148 0.220 −0.073
EV × female 0.270 0.233 0.137
SV × female −0.330 0.168 −0.169a

The interaction between social value and gender approached significance, aligning with a substantial descriptive difference in bivariate correlations. We were curious whether the large number of predictors in the model had reduced the statistical power to observe a significant gender by value interaction. Thus, in an exploratory analysis, we conducted a second hierarchical multiple regression with only significant values and cost predictors in the above model, along with their interactions with gender. In this exploratory model (see Appendix 4 for full reporting, ESI†), social value × female interaction (β = −0.155, p = 0.005) emerged as a significant negative predicator. This significant negative interaction between female and social value indicated that while social value predicted exam grade for boys, there was no similar effect for girls. We further confirmed the gender by social value interaction with a post hoc Fisher's r-to-z transformation of bivariate correlations, which indicated stronger correlations between social value and exam score for boys compared to girls (z = 2.58, p = 0.010).

Discussion

The current study adopted a mixed method approach to examine Chinese high school students’ conceptualization of values and costs and their relations with performance on a chemistry exam. Our results revealed a wide range of values related to learn chemistry as well as gender differences in mean levels or roles of values and cost in predicting chemistry achievement.

Conceptualization of values and cost

One goal of the study was to investigate students’ conceptualization of values and costs in a Chinese high school chemistry context. Consistent with previous research conducted in Western contexts, our findings suggested the multidimensional, domain-specific, and culturally situated nature of values and costs for chemistry. Qualitative analyses revealed that students’ values for chemistry could be categorized into five broad categories: utility value, epistemic value, intrinsic value, aesthetic value, and social value. Utility values, the most commonly mentioned value, consisted of an array of values, including academic utility, practical utility, occupation utility, relational utility, and research utility. These distinctions—with the exception of research utility—roughly align with distinctions made by Gaspard et al. (2017, 2020), with distinctions drawn between utility for daily life, utility for job, utility for school and social utility. High frequencies of academic utility and epistemic value might result from Confucius culture's emphasis of academic success and intellectual development. Our results identified an additional dimension of utility in chemistry–research utility–which referred to the perceived value of chemistry for creating new materials or products. It should be noted that attainment value – the sense of importance and fulfilment – was not identified in students’ open responses. This does not mean that students did not perceive attainment value. Instead, students’ perceived importance of learning chemistry was often represented in the perception of other types of values (e.g., utility value and epistemic value). As attainment value is more comprehensive than other values and is closely associated with one's identity, students during high school transition may be unaware of such value or be reluctant to describe such personal connections in the open response question.

Notably, our findings suggest the salience of values that have received less attention in the SEVT literature and which align with values highlighted in science education literature and Chinese curriculum standards. These include epistemic value (Galloway, 2017), social value (Stuckey et al., 2013) and aesthetic value (Müller, 2003; Ling et al., 2020), pointing to the importance of these values in this context. For example, a great portion of students in this sample emphasized epistemic and social values of learning chemistry. In cultural contexts in which collectivist values are prominent, it is important to understand the salience and importance of values beyond personal relevance such as social value which emphasizes the individual's role in the whole group (e.g., country, society, human being). Further, MDS maps and bivariate correlations suggest that students in this sample conceptualized these values as distinct from the SEVT values of intrinsic, attainment, and utility values.

What is more, MDS maps illustrated the overlapping conceptualization of attainment value and utility value items, which coincides with prior research (e.g., Perez et al., 2014). The high correlation between attainment and utility values was also identified in this sample and in previous research of East Asian samples (Gaspard et al., 2020). One possible explanation is that the importance of learning chemistry (i.e., attainment value) for students in this sample was embodied or manifested in achieving current academic goals or distal career goals. This study provides evidence for the rationality of combing utility value and attainment value as importance value (Durik et al., 2006; Watt et al., 2012).

Gender differences

Another goal of the study was to understand potential gender differences in perceived values and costs for learning chemistry. Content analyses and multidimensional scaling revealed several differences in how male and female Chinese students perceived values and costs. Chi-square tests of frequencies of values mentioned in an open-ended survey question showed that relational utility value was more salient to boys while practical utility value and epistemic value were more salient to girls. The importance of relational utility value for boys was further supported with evidence in MDS maps, which showed that interpersonal relationships constituted a distinct region. These gender differences may have implications for developing diverse classroom activities that target these values to foster engagement for both boys and girls. Previous research has reported mixed results about gender differences in preference of school activities within the science domain. Some researchers reported that girls were more interested in realistic, artistic, and social school activities (Blankenburg et al., 2016; Dierks et al., 2016) or purely cognitive activities (Swarat et al., 2012) while other researchers found that boys showed higher interests in social activities in physics than girls (Blankenburg et al., 2016). Therefore, further investigation is needed to examine how boys and girls perceive various school activities that focus on different values.

Besides gender differences in salience of values, MDS maps also demonstrated similarities and differences in boys’ and girls’ conceptualization of other value and cost constructs. Regarding MDS maps of values, attainment value had broader meaning for boys compared to girls. Epistemic- and emotional-related values were more integrated in boys’ conceptualization than for girls. In other word, girls may perceive greater distinctions among different types of values than boys. Such phenomena might be explained by potential gender differences that exist in cognitive-emotional interplay in neuropsychology: the processing of emotion and cognition is predominately parallel among girls but is mutually interactive among boys (Koch et al., 2007). Girls are usually thought to be “more emotionally reactive and expressive” than boys while boys are more adept at integrating emotion and cognition through utilizing cognitive resources to regulate emotions (Whittle et al., 2011). Existing neuropsychological findings provide insights about possible mechanisms underlying gender differences in perceiving values of learning chemistry. Regarding MDS maps of cost, both boys and girls were able to differentiate opportunity cost and effort cost, which are compatible with previous findings (Flake et al., 2015; Gaspard et al., 2015). However, in our sample, the boundary between emotional cost and ego cost was less marked for boys; emotional and ego cost were more distinct for girls than boys. One explanation for this pattern is that items measuring ego cost described negative effects of task failures on retaining social relationships (e.g., disappoint others). Given that the utility value of retaining social relations was salient to boys in the sample, such consequence may lead to perceptions of negative emotions (e.g., stress).

Gender differences also manifest in mean level differences in values and cost evidenced in independent t tests which showed that boys reported mostly higher values (except for social value) and lower costs than girls. This finding extends prior literature (e.g., Nagy et al., 2006; Watt et al., 2012; Guo et al., 2015; Gaspard et al., 2015, 2017, 2020) by examining students’ perceived values and costs of learning chemistry in Eastern contexts. Except for the social value, boys reported higher values and lower costs than girls, which are in line with gender stereotypes reported in previous research (Stoet and Geary, 2018; Wan, 2021). One possible explanation is that girls might acknowledge the significance of chemistry subject at the societal level but they may perceive it as less enjoyable, less personally important, or less relevant to future goals, which was consistent with Gaspard et al. (2015, 2020) finding. The chi-square test of practical utility value implied that personal relevance matters more for girls.

Correlations with chemistry performance

With respect to relations among values, cost and chemistry score, our findings both replicate and contradict previous research. Multiple regression showed that intrinsic value and cost were stronger predictors than other types of values and the former had the largest standardized regression coefficient. Empirical findings examining relationships between values and academic outcomes in Eastern contexts are inconsistent: Wan (2021) reported that intrinsic value was a significant predictor of Hong Kong 10th-grade students’ behavioral tendency to learn science. In contrast, Guo et al. (2015) reported that for Hong Kong 8th-grade students, paths from perceived intrinsic value to math achievement and aspirations were not significant while paths from utility value to above variables was significant and positive. Such inconsistency might result from differences in measured or controlled variables in each study, which suggests that more research is need to investigate the mechanism of how different value orientations influence students’ learning behaviors and performances.

Regarding the cost variable, we observed the unexpected finding of positive bivariate correlations between cost and attainment value for boys and girls, which joins mixed findings in previous research (Gaspard et al., 2018). Gaspard et al. (2020) reported that attainment and utility value was negatively correlated with cost in German and Chinese samples but were positively correlated in the Korean sample. In our sample, this suggests that both boys and girls perceive a task that brings the feeling of accomplishment would require more effort, time and foregoing alternatives. Students may experience high emotional costs when completing such tasks. What is more, the multiple regression showed that cost negatively and significantly predicted students’ chemistry scores after controlling for task values, which were consistent with recent findings (Jiang et al., 2018). Our study joins a growing body of research (Gaspard et al., 2020; Jiang et al., 2020) that indicates the importance of cost in predicting students’ achievement, suggesting that interventions should not merely improve perceptions of value but also reduce perceptions of cost (Rosenzweig et al., 2020).

Besides intrinsic value and cost, this study also provides empirical evidence that gender negatively predicted students’ chemistry scores while social value positively predicted student achievement and such relations differed across gender. These findings indicate that girls in the sample had lower performance than boys, consistent with previous findings on gender differences in math and science domains (Guo et al., 2015; Wan, 2021). Regarding the interaction between gender and social value, this finding suggests that, for boys more than girls, perceived value for social outcomes may be an important source of motivation in their decision to study chemistry.

Implications and limitations

The main findings have implications for theory and practice. First, our study applied Situated Expectancy-Value Theory in the chemistry domain and highlighted the complexity or multidimensional nature of perceived value of learning chemistry. The qualitative data demonstrated some salient values (e.g., research utility) that elaborate existing theory. Second, our study detected gender differences in correlations among value, cost and chemistry score in non-Western contexts. Although this study was a theoretical investigation of students’ perceptions of values and costs of learning chemistry, it still provides implications for chemistry teachers’ instructional interventions for fostering students’ value beliefs in chemistry. As such, it calls for research that developing effective instructional support or curriculum resources with appropriate cognitive load and personal relevance for boys and girls across cultural contexts. Student motivation and performance can be enhanced by developing interventions (e.g., reflections on the personal relevance) or providing teacher support targeting the sense of value and cost of learning chemistry. For example, utility interventions usually encourage students to connect class content with personal lives through writing prompts in homework assignment while cost reduction interventions typically help students interpret and address course challenges in positive ways. The above two strategies have been reported to positively influence students’ attitudes, performance, and persistence in physics, biology and chemistry, particularly for underrepresented students (Rosenzweig et al., 2020; Hulleman and Harackiewicz, 2021; Wang et al., 2021).

Results should be interpreted in light of the following limitations. First, correlational analyses of single time point data cannot evaluate causal relationships. Future research that analyzes multiple time point data holds promise for evaluating claims about causality and directionality. Second, as the open-ended question was placed at the end of a paper-pencil survey, student responses were potentially influenced by Likert-scale items and relatively short responses captured only a limited number of values per participant. Methods such as interviewing that yield richer data hold promise for reducing possible response bias and deepening insight about conceptualization of costs and values. Future research might also use open-ended response to examine students’ conceptualization of cost. Third, while the measure of exam performance had high ecological validity, basic psychometric data were unavailable, and therefore findings should be interpreted with caution. Fourth, we opted not adjust p value for multiple tests in order to prioritize identifying possible gender differences. Doing so increases the possibility of Type I error (false positives) and underscores the need for additional researcher to corroborate current findings. Finally, our study was conducted with participants from one high school in China and may capture idiosyncratic aspects of the context. As the regression approach was exploratory in nature, subsequent research that seeks to replicate findings with similar or different approaches (e.g., mediation analysis) in other institutional contexts is needed.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

The authors would like to thank Dr Lichun Gai (Hebei Normal University), teachers and students who made the data collection possible. This material is based upon work supported by the Youth Project of Tianjin Social Science Fund under grant number TJJXQN19-003.

References

  1. Avargil S., Kohen Z. and Dori Y. J., (2020), Trends and perceptions of choosing chemistry as a major and a career, Chem. Educ. Res. Pract., 21(2), 668–684 10.1039/c9rp00158a.
  2. Barron K. E. and Hulleman C. S., (2015), Expectancy-value-cost model of motivation, in International encyclopedia of social and behavioral sciences: Motivational psychology, Eccles J. S. and Salmelo-Aro K. (ed.), New York, NY: Elsevier, 2nd edn, pp. 503–509.
  3. Bergey B. W., Ranellucci J. and Kaplan A., (2019), The conceptualization of costs and barriers of a teaching career among Latino preservice teachers, Contemp. Educ. Psychol., 59, 101794 DOI:10.1016/j.cedpsych.2019.101794.
  4. Berndt T. J. and Miller K. E., (1990), Expectancies, Values and Achievement in Junior High School, J. Educ. Psychol., 82(2), 319–326 DOI:10.1037/0022-0663.82.2.319.
  5. Blankenburg J. S., Höffler T. N. and Parchmann I., (2016), Fostering Today What is Needed Tomorrow: Investigating Students’ Interest in Science, Sci. Educ., 100(2), 364–391 DOI:10.1002/sce.21204.
  6. Bong M., (2001), Between- and within-domain relations of academic motivation among middle and high school students: self-efficacy, task-value and achievement goals, J. Educ. Psychol., 93(1), 23–34 DOI:10.1037/0022-0663.93.1.23.
  7. Braun V., and Clarke V., (2006), Using thematic analysis in psychology, Qualitative Res. Psychol., 3(2), 77–101.
  8. Chang Y., (2015), Science Motivation Across Asian Countries: Links among Future-Oriented Motivation, Self-Efficacy, Task Values, and Achievement Outcomes, Asia-Pacific Education Researcher, 24(1), 247–258 DOI:10.1007/s40299-014-0176-3.
  9. Chow A., Eccles J. S. and Salmela-Aro K., (2012), Task value profiles across subjects and aspirations to physical and it-related sciences in the United States and Finland, Dev. Psychol., 48(6), 1612–1628 DOI:10.1037/a0030194.
  10. Conley A. M. M., (2012), Patterns of motivation beliefs: combining achievement goal and expectancy-value perspectives, J. Educ. Psychol., 104(1), 32–47 DOI:10.1037/a0026042.
  11. Corrigan D. J., (2015), Values and Learning Science, Encyclopedia of Science Education, Gunstone R. (ed.), Dordrecht: Springer, pp. 1091–1093.
  12. Corrigan D. J. and Gunstone R., (2007), Values in school science and mathematics education: issues and tensions, The re-emergence of values in science education, Corrigan D. J. and Gunstone R. (ed.), Rotterdam: Sense, pp. 133–148.
  13. Dierks P. O., Höffler T. N., Blankenburg J. S., Peters H. and Parchmann I., (2016), Interest in science: a RIASEC-based analysis of students’ interests, Int. J. Sci. Education, 38(2), 238–258 DOI:10.1080/09500693.2016.1138337.
  14. Dreamson N., (2018), Culturally inclusive global citizenship education: metaphysical and nonwestern approaches, Multicultural Education Rev., 10(2), 75–93.
  15. Durik A. M., Vida M. and Eccles J. S., (2006), Task values and ability beliefs as predictors of high school literacy choices: a developmental analysis, J. Educ. Psychol., 98(2), 382–393 DOI:10.1037/0022-0663.98.2.382.
  16. Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L. and Midgley, C., (1983), Expectancies, values and academic behaviors, Achievement and achievement motives, Spence J. T. (ed.), San Francisco, CA: W. H. Freeman, pp. 74–146.
  17. Eccles J. S., (2005), Subjective task value and the Eccles et al. model of achievement-related choices, Handbook of competence and motivation, Elliot A. J. and Dweck C. S. (ed.), London: Guildford Press, pp. 105–121.
  18. Flake J. K., Barron K. E., Hulleman C., McCoach B. D. and Welsh M. E., (2015), Measuring cost: the forgotten component of expectancy-value theory, Contemp. Educ. Psychol., 41, 232–244 DOI:10.1016/j.cedpsych.2015.03.002.
  19. Fong C. J., Kremer K. P., Hill-Troglin Cox C. and Lawson C. A., (2021), Expectancy-value profiles in math and science: a person-centered approach to cross-domain motivation with academic and STEM-related outcomes, Contemp. Educ. Psychol., 65(March), 101962 DOI:10.1016/j.cedpsych.2021.101962.
  20. Galla B. M., Amemiya J. and Wang M. T., (2018), Using expectancy-value theory to understand academic self-control, Learn. Instr., 58(April), 22–33 DOI:10.1016/j.learninstruc.2018.04.004.
  21. Galloway K. W., (2017), Undergraduate perceptions of value: degree skills and career skills, Chem. Educ. Res. Pract., 18(3), 435–440 10.1039/c7rp00011a.
  22. Gaspard H., Dicke A. L., Flunger B., Schreier B., Häfner I., Trautwein U. and Nagengast B., (2015), More value through greater differentiation: gender differences in value beliefs about math, J. Educ. Psychol., 107(3), 663–677 DOI:10.1037/edu0000003.
  23. Gaspard H., Häfner I., Parrisius C., Trautwein U. and Nagengast B., (2017), Assessing task values in five subjects during secondary school: measurement structure and mean level differences across grade level, gender, and academic subject, Contemp. Educ. Psychol., 48, 67–84 DOI:10.1016/j.cedpsych.2016.09.003.
  24. Gaspard H., Jiang Y., Piesch H., Nagengast B., Jia N., Lee J. and Bong M., (2020), Assessing students’ values and costs in three countries: gender and age differences within countries and structural differences across countries, Learn. Individual Differences, 79, 101836 DOI:10.1016/j.lindif.2020.101836.
  25. Gaspard H., Wigfield A., Jiang Y., Nagengast B., Trautwein U. and Marsh H. W., (2018), Dimensional comparisons: how academic track students’ achievements are related to their expectancy and value beliefs across multiple domains, Contemporary Educ. Psychol., 52, 1–14 DOI:10.1016/j.cedpsych.2017.10.003.
  26. Girod M., Rau C. and Schepige A., (2003), Appreciating the Beauty of Science Ideas: Teaching for Aesthetic Understanding, Sci. Education, 87(4), 574–587 DOI:10.1002/sce.1054.
  27. González A. and Paoloni P. V., (2015), Perceived autonomy-support, expectancy, value, metacognitive strategies and performance in chemistry: a structural equation model in undergraduates, Chem. Educ. Res. Pract., 16(3), 640–653 10.1039/c5rp00058k.
  28. Grunert M. L. and Bodner G. M., (2011), Finding fulfillment: Women's self-efficacy beliefs and career choices in chemistry, Chem. Educ. Res. Practice, 12(4), 420–426 10.1039/C1RP90050A.
  29. Guo J., Marsh H. W., Parker P. D., Morin A. J. S. and Dicke T., (2017), Extending expectancy-value theory predictions of achievement and aspirations in science: dimensional comparison processes and expectancy-by-value interactions, Learn. Instr., 49, 81–91 DOI:10.1016/j.learninstruc.2016.12.007.
  30. Guo J., Marsh H. W., Parker P. D., Morin A. J. S. and Yeung A. S., (2015), Expectancy-value in mathematics, gender and socioeconomic background as predictors of achievement and aspirations: a multi-cohort study, Learn. Individual Differences, 37, 161–168 DOI:10.1016/j.lindif.2015.01.008.
  31. Guo J., Wang M. T., Ketonen E. E., Eccles J. S. and Salmela-Aro K., (2018), Joint trajectories of task value in multiple subject domains: from both variable- and pattern-centered perspectives, Contemp. Educ. Psychol., 55(October), 139–154 DOI:10.1016/j.cedpsych.2018.10.004.
  32. Hu L. T. and Bentler P. M., (1999), Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives, Struct. Equ. Modeling, 6(1), 1–55.
  33. Hulleman C. S. and Harackiewicz J. M., (2021), The utility-value intervention, Handbook of wise interventions: How social psychology can help people change, Walton G. M. and Crum A. J. (ed.), Guilford Press, pp. 100–125.
  34. Jiang Y., Kim S. and Bong M., (2016), Measuring cost in the learning context: scale development and initial validation, Paper presented at the annual meeting of the American Educational Research Association, Washington, DC.
  35. Jiang Y., Kim S. and Bong M., (2020), The role of cost in adolescent students’ maladaptive academic outcomes, J. School Psychol., 83(July 2019), 1–24 DOI:10.1016/j.jsp.2020.08.004.
  36. Jiang Y., Rosenzweig E. Q. and Gaspard H., (2018), An expectancy-value-cost approach in predicting adolescent students’ academic motivation and achievement, Contemp. Educ. Psychol., 54(June), 139–152 DOI:10.1016/j.cedpsych.2018.06.005.
  37. Kim H. Y., (2017), Statistical notes for clinical researchers: chi-squared test and Fisher's exact test, Restor. Dent. Endod., 42(2), 152–155.
  38. Koch K., Pauly K., Kellermann T., Seiferth N. Y., Reske M., Backes V., Stöcker T., Shah N. J., Amunts K., Kircher T., Schneider F. and Habel U., (2007), Gender differences in the cognitive control of emotion: an fMRI study, Neuropsychologia, 45(12), 2744–2754 DOI:10.1016/j.neuropsychologia.2007.04.012.
  39. Landis J. R. and Koch G. G., (1977), The measurement of observer agreement for categorical data, Biometrics, 33(1), 159–174.
  40. Ling Y., Ling Y., Ling Y., Xiang J., Xiang J., Chen K., Zhang J. and Ren H., (2020), Integrating Aesthetics Education into Chemistry Education: Students Perceive, Appreciate, Explore, and Create the Beauty of Chemistry in Scientific Photography Activity, J. Chem. Educ., 97(6), 1556–1565 DOI:10.1021/acs.jchemed.9b00857.
  41. Luo W., Ng P. T., Lee K. and Aye K. M., (2016), Self-efficacy, value, and achievement emotions as mediators between parenting practice and homework behavior: a control-value theory perspective, Learn. Individual Differences, 50, 275–282 DOI:10.1016/j.lindif.2016.07.017.
  42. Luttrell V. R., Callen B. W., Allen C. S., Wood M. D., Deeds D. G. and Richard D. C. S., (2010), The Mathematics Value Inventory for General Education Students: Development and Initial Validation, Educ. Psychol. Meas., 70(1), 142–160 DOI:10.1177/0013164409344526.
  43. Mair P., Borg I. and Rusch T., (2016), Goodness-of-Fit Assessment in Multidimensional Scaling and Unfolding, Multivariate Behavioral Res., 51(6), 772–789 DOI:10.1080/00273171.2016.1235966.
  44. Müller A., (2003), Chemistry: the beauty of symmetry, Science, 300(5620), 749–750.
  45. Nagy G., Trautwein U., Baumert J., Köller O. and Garrett J., (2006), Gender and course selection in upper secondary education: effects of academic self-concept and intrinsic value, Educ. Res. Evaluation, 12(4), 323–345 DOI:10.1080/13803610600765687.
  46. Nieswandt M., (2007), Student affect and conceptual understanding in learning chemistry, J. Res. Sci. Teach., 44(7), 908–937 DOI:10.1002/tea.20169.
  47. Perez T., Cromley J. G. and Kaplan A., (2014), The role of identity development, values, and costs in college STEM retention, J. Educ. Psychol., 106(1), 315–329 DOI:10.1037/a0034027.
  48. Perez T., Dai T., Kaplan A., Cromley J. G., Brooks W. D., White A. C., Mara K. R. and Balsai M. J., (2019), Interrelations among expectancies, task values, and perceived costs in undergraduate biology achievement, Learn. Individual Differences, 72(April), 26–38 DOI:10.1016/j.lindif.2019.04.001.
  49. Rokeach M., (1979), Introduction, Understanding human values, Rokeach M. (ed.), New York: Free Press, pp. 1–11.
  50. Rosenzweig E. Q., Wigfield A. and Hulleman C. S., (2020), More useful or not so bad? Examining the effects of utility value and cost reduction interventions in college physics, J. Educ. Psychol., 112(1), 166–182.
  51. Salonen A., Kärkkäinen, S. and Keinonen, T., (2018), Career-related instruction promoting students’ career awareness and interest towards science learning, Chem. Educ. Res. Pract., 19(2), 474–483 10.1039/c7rp00221a.
  52. Schwartz S. H., (2007), Universalism values and the inclusiveness of our moral universe, J. Cross Cult. Psychol., 38(6), 711–728 DOI:10.1111/j.1541-0420.2012.01802.x.
  53. Stalans L. J., (1995), Multidimensional scaling, Reading and understanding multivariate statistics, Grimm L. G. and Yarnold P. R. (ed.), Washington, DC, US: American Psychological Association, pp. 137–168.
  54. Stoet G. and Geary D. C., (2018), The Gender-Equality Paradox in Science, Technology, Engineering, and Mathematics Education, Psychol. Sci., 29(4), 581–593 DOI:10.1177/0956797617741719.
  55. Stuckey M., Hofstein A., Mamlok-Naaman R. and Eilks I., (2013), The meaning of “relevance” in science education and its implications for the science curriculum, Stud. Sci. Educ., 49(1), 1–34 DOI:10.1080/03057267.2013.802463.
  56. Swarat S., Ortony A. and Revelle W., (2012), Activity matters: understanding student interest in school science, J. Res. Sci. Teach., 49(4), 515–537 DOI:10.1002/tea.21010.
  57. The Ministry of Education of the People's Republic of China, (2020), Chemistry Curriculum Standards of Compulsory Education, Beijing: Beijing Normal University Press.
  58. Trautwein U., Marsh H. W., Nagengast B., Lüdtke O., Nagy G. and Jonkmann K., (2012), Probing for the multiplicative term in modern expectancy-value theory: a latent interaction modeling study, J. Educ. Psychol., 104(3), 763–777 DOI:10.1037/a0027470.
  59. Wan Z. H., (2021), Exploring the Effects of Intrinsic Motive, Utilitarian Motive, and Self-Efficacy on Students’ Science Learning in the Classroom Using the Expectancy-Value Theory, Res. Sci. Educ., 51(3), 647–659 DOI:10.1007/s11165-018-9811-y.
  60. Wang Y., Rocabado G. A., Lewis J. E. and Lewis S. E., (2021), Prompts to Promote Success: Evaluating Utility Value and Growth Mindset Interventions on General Chemistry Students’ Attitude and Academic Performance, J. Chem. Educ., 98(5), 1476–1488 DOI:10.1021/acs.jchemed.0c01497.
  61. Watt H. M. G., Shapka J. D., Morris Z. A., Durik A. M., Keating D. P. and Eccles J. S., (2012), Gendered motivational processes affecting high school mathematics participation, educational aspirations, and career plans: a comparison of samples from Australia, Canada, and the United States, Dev. Psychol., 48(6), 1594–1611 DOI:10.1037/a0027838.
  62. Whittle S., Yücel M., Yap M. B. H. and Allen N. B., (2011), Sex differences in the neural correlates of emotion: evidence from neuroimaging, Biol. Psychol., 87(3), 319–333 DOI:10.1016/j.biopsycho.2011.05.003.
  63. Wickman P. O., Prain V. and Tytler R., (2021), Aesthetics, affect, and making meaning in science education: an introduction, Int. J. Sci. Educ., 1–18 DOI:10.1080/09500693.2021.1912434.
  64. Wigfield, A., (1994), Expectancy-value theory of achievement motivation: a developmental perspective, Educ. Psychol. Rev., 6(1), 49–78.
  65. Wigfield A. and Eccles J. S., (2000), Expectancy-value theory of achievement motivation, Contemp. Educ. Psychol., 25(1), 68–81 DOI:10.1006/ceps.1999.1015.
  66. Wigfield A. and Eccles J. S., (2020), 35 years of research on students’ subjective task values and motivation: a look back and a look forward, Advances in Motivation Science, Elsevier Inc, 1st edn, vol. 7 DOI:10.1016/bs.adms.2019.05.002.
  67. Wigfield A., Tonks, S. and Klauda S. T., (2009), Expectancy-value theory, Handbook of motivation at school, Wentzel K. R. and Wigfield K. A. (ed.), New York, NY: Routledge, pp. 55–75.
  68. Woods P. R. and Lamond D. A., (2011), What would Confucius do? Confucian ethics and self-regulation in management, J. Bus. Ethics, 102(4), 669–683.
  69. Zuo Y., (2021), Why students decline to choose chemistry under “3+1+2” mode, Teach. Manag. Educ. Res., 6(16), 94–95.

Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d2rp00169a
This journal is © The Royal Society of Chemistry 2023