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DOI: 10.1039/D2RP00165A (Paper) Chem. Educ. Res. Pract., 2023, 24, 478-493

Social support and continuing motivation in chemistry: the mediating roles of interest in chemistry and chemistry self-efficacy†

Qian Huangfu *, Nana Wei , Ruli Zhang , Yuefan Tang and Guixu Luo
Southwest University, Chongqing, 430079, China. E-mail: chemqian16@swu.edu.cn

Received 7th June 2022 , Accepted 10th November 2022

First published on 12th November 2022

Abstract

Continuing motivation in science can promote science literacy, identity, and lifelong learning, which has received considerable attention. As a crucial part of the science discipline, the study on continuing motivation in chemistry has also become a research hotspot. Yet, we have little knowledge about how to improve students’ continuing motivation in chemistry. Due to this situation, the present study was designed to explore the mechanisms of students’ perceived social support (parents support, teacher support and peer support), interest and self-efficacy in continuing motivation in the context of chemistry, further offering suggestions to the progress of students’ continuing motivation in chemistry. Measures were collected from 1260 Chinese high school students aged 15 to 17 years. Structural equation modelling (SEM) tested the hypothesized direct and mediated relations between these variables. The results showed that (1) parents support significantly negatively predicted students’ continuing motivation in chemistry; teacher support, peer support, interest in chemistry and chemistry self-efficacy significantly positively predicted students’ continuing motivation in chemistry; (2) both interest in chemistry and chemistry self-efficacy played mediating roles in the relationship between social support and continuing motivation in chemistry, respectively. We concluded by discussing the main findings of this study, highlighting their educational implications, acknowledging their limitations, and proposing lines of future research on chemistry education.

Introduction

Chemistry is not only one of the subjects required in The Outline of Curriculum Standards for The New School System in China, but also a necessary quality for students to enter universities, integrate into society and become modern citizens. The chemistry curriculum standard of senior high school (Ministry of Education, P. R. China, 2017) also emphasizes that chemistry curriculum is an important basis for students' lifelong learning and development, and plays an irreplaceable role in the inheritance of science, culture and the cultivation of high-quality talents. However, high school students generally have some problems, such as low enthusiasm for chemistry learning, lack of driving force and decline of learning interest. Chemistry as a required introductory subject within the STEM subject area (Gonzalez and Paoloni, 2015), the same report points out that one of the three aspects of a student's learning that affects persistence in STEM is motivation, which is a complex construct and is often accessed from different (or multiple) theoretical perspectives (Koballa and Glynn, 2007), such as social-cognitive theory (Pintrich and DeGroot, 1990; Glynn et al., 2009, 2011), expectancy-value theory (Wigfield, 1994; Wigfield and Eccles, 2000), and self-determination theory (Deci and Ryan, 2000, 2008). Motivation has been linked to student's continuous learning (Chiu and Chow, 2010; Yen et al., 2011; Gonzalez and Paoloni, 2015). Motivation for continuous learning has been identified as one of the factors that can affect students’ scientific literacy (Glynn et al., 2011; Vaino et al., 2012), and the need to enhance students’ scientific literacy has been well-established (American Association for the Advancement of Science (AAAS), 1993; National Research Council (NRC), 1996; OECD, 2009; Eurydice, 2011). Continuing motivation is the type of intrinsic motivation most directly concerned with education, and it reflects an individual's willingness to learn (Sun et al., 2021). While the growth of continuing motivation is identified as essential for encouraging students’ science learning, current studies on continuing motivation focus principally on science learning environments (Pascarella et al., 1981; Fortus and Vedder-Weiss, 2014); not much is known about how and which influencing factors could influence students’ continuing motivation in chemistry. Therefore, researches on the influencing factors of continuing motivation should be promoted to help us better understand how to improve students’ continuing motivation in chemistry areas as well as scientific literacy. Based on the fact, the present study sets a goal in exploring the influential underlying factors influencing chemistry continuing motivation in high school students, providing further suggestions for the advancement of students’ continuing motivation.

According to relevant research, high-quality social support is vital for students’ academic motivation and achievement (King et al., 2012). Vedder-Weiss and Fortus (2013) also showed that students' perceived parental and teacher support could be a good predictor of students continuing motivation to engage in STEAM-related activities. Thus, we are interested in examining the relationship between social support (including parents, teacher, and peer support) and continuing motivation in chemistry. Learning interest and self-efficacy have been reported to be both affected by social support and influence continuing motivation (Herndon, 1987; Garcia and Pintrich, 1996; Sha et al., 2016). Therefore, this report explores whether learning interest and self-efficacy play a separate mediating role in the relationship between social support and continuing motivation. Based on this, exploring the impact and mechanism of social support on the continuing motivation in chemistry has certain practical significance for improving the effectiveness of chemistry learning, optimizing chemistry learning experience and promoting teaching reform.

Social support

Social support refers to a material or psychological resources from individuals’ social networks that help them cope with challenges (Taylor et al., 2015) and mainly includes support received from family, teachers, and peers (Wang and Eccles, 2012; Wentzel et al., 2016; Cai and Lian, 2022). According to Bronfenbrenner's (1979) bio-ecological theory of human development, proximal processes such as interpersonal relationships and social support can either promote or hinder adolescents’ ability to achieve desired outcomes. Previous researchers have yielded evidences suggesting that social support has a positive effect on continuing motivation and multiple forms of social support have different effects on students’ continuing motivation (Pascarella et al., 1981; Wentzel, 1998; Brewster and Bowen, 2004).

Continuing motivation in chemistry

Continuing motivation is a term coined by Maehr (1976), who conceptualized it as a “behaviour in which the individual, relatively free from external constraints, returns to a task or task area and works on it on his own” (p. 448). Maehr's definition of continuing motivation fits a contemporary conceptualization of motivation “as action” (Schunk et al., 2008). In term of this definition, continuing motivation relates to the behaviour, and it emphasizes behaviours such as activity choice and persistence rather than motives, attitudes, or interest driving it, so continuing motivation is different from other motivational constructs such as intrinsic motivation and interest (Maehr, 1976; Fortus and Vedder-Weiss, 2014). In addition, continuing motivation emphasizes that an individual's behaviour is not influenced by external pressures and constraints, which has similarities with intrinsic motivation. In most cases, it belongs to intrinsic motivation (Fortus and Vedder-Weiss, 2014). For a behaviour to be considered continuing motivation for chemistry, it should be perceived by the students as related to chemistry. Still it does not necessarily need to be perceived as a learning activity (Maehr, 1976). Hence, continuing motivation in chemistry may be manifested through extracurricular engagement in activities such as reading chemistry-related books, watching chemistry-related TV programs, attending chemistry lectures, developing chemistry-related hobbies, etc., as long as these activities are not the result of school or other requirements (Fortus and Vedder-Weiss, 2014). According to Fortus and Vedder-Weiss (2014), our definition of continuing motivation in chemistry is: an engagement with chemical content or practices, which is free from external incentives and is manifested across varying contexts when other alternatives are available.

Interest in chemistry

Hidi and Renninger have focused on the role of interest in learning and development; they defined interest as a psychological state of engaging or having the tendency to reengage in a particular content over time. It was categorized into individual interest and situational interest (Hidi and Renninger, 2006). Situational interest refers to an interest triggered spontaneously through interaction with the environment (Harackiewicz et al., 2008). Individual interest refers to a relatively stable interest that develops over time and is associated with an enduring predisposition for the students to reengage with specific topics, subject areas, or activities (Schiefele, 1991; Hidi and Renninger, 2006). In this study, we primarily consider the role of students’ individual interest in chemistry learning.

Chemistry self-efficacy

One of the essential elements of social cognitive theory, as proposed by Bandura, is perceived self-efficacy, which is defined as an individual's belief in their ability to complete a specific task in a field (Bandura, 1986). Mainly, it concerns one's judgment about their capability rather than one's intention to perform a task (Uzuntiryaki and Çapa Aydın, 2009). For students to be successful in chemistry, a strong sense of self-efficacy is essential. Chemistry self-efficacy can be defined as students’ beliefs about the extent to which they are capable of performing specific chemistry tasks (Cheung, 2015).

Relationships between variables

According to self-determination (SDT), students’ motivation flourishes when their basic needs (autonomy, competence, and relatedness) are satisfied (Ryan and Deci, 2000). Social support from parents, teachers and peers can satisfy students’ basic needs, which promote student motivation (Ahn et al., 2018; Reeve, 2002; Cai and Lian, 2022). Concerning parents’ support, it is defined as how parents are involved in and promote their children's education (Brewster and Bowen, 2004). Families may be referred to as “learning institutions” where parents could take an active role as science educators (Ellenbogen et al., 2004; Rennie, 2007) and, as such, affect their children's in and out-of-school science learning motivation (Vedder-Weiss and Fortus, 2013). Students who are highly supported and encouraged by their parents tend to be positively engaged in school activities (Morrison et al., 2002) and demonstrate more persistence during complex activities (Hokoda and Fincham, 1995; Wentzel, 1998). Adolescents’ perceived family support for learning is associated with their choices for and engagement in science learning (Sha et al., 2016). Teacher support, it is defined as the degree to which teachers listen to, encourage, and respect students (Ginorio and Huston, 2001). Teachers may influence their students’ continuing motivation through the encouragement and support they offer (Wang and Eccles, 2012). And a measure of teacher enthusiasm and encouragement of students was significantly related to continuing motivation (Pascarella et al., 1981). Moreover, research by Vedder-Weiss and Fortus (2013) highlighted the role teachers play in influencing their students’ motivation for science learning, especially out of school. Regarding peer support, it refers to the expectations, help, safety, and emotional nurturing teenagers feel when interacting with their peers (Wentzel et al., 2010). Peers play an important role during adolescence, and many studies have documented the functional role of peers in providing support, academic guidance, companionship, choices and engagement (Garcia-Reid, 2007; Olitsky et al., 2010). Students with positive peer relationships at school are more behaviourally and emotionally participating in extracurricular activities (Wang and Eccles, 2012). Furthermore, the study by Patrick et al. (1999) indicated peer support was essential for students’ continuing motivation in sports and arts. Taken all together, we can infer that social support from parents, teachers and peers may positively predict students’ continuing motivation in chemistry.

The potential for interest is in the person, but the content and the environment define the direction of interest and contribute to its development. There are many aspects of interpersonal relationships that have the potential to influence academic interest (Juvonen and Wentzel, 1996). As argued by Deci and Ryan (1991), interpersonal relationships that provide students with a sense of belongingness can be powerful motivators of students’ interest in school. Social support is the vehicle that propels students’ development forward and is the foundation upon which all students’ development rests (Varga and Zaff, 2017). It has been demonstrated that situational interest has a mediating role in the relation between social support and learning persistence. Regarding the relationship between the subordinate dimension of social support and learning interest, a survey study by Christensen et al. (2015) found that parental and family member support significantly predicted students’ interest in STEAM learning. Further, Sha et al. (2016) conducted a study of students’ perceived parental support and interest in science learning, and found that perceived parental support significantly and positively predicted students’ interest in science learning. The previous study found that teacher support can stimulate students’ interest in science learning (Anderhag et al., 2015), and Rennie (2007) argued that science teachers’ support of students’ autonomy may have an impact on students’ autonomous motivation towards science and even have an impact on their interest in science-related fields. As to the link between peer support and learning interest, empirical results have shown that the association between perceived peer support and achievement was jointly mediated by competence, interest and enjoyment (Battistich et al. 1995). In summary, social support and the subordinate dimensions are positive predictors of learning interest.

Eccles and Wigfield expectancy-value model indicates expectancy and value are the basic constructs of motivation (Wigfield and Eccles, 2000; Eccles and Wigfield, 2002; Eccles, 2009; Wigfield et al., 2009; Eccles and Wang, 2012). Interest, as the inherent pleasure and enjoyment one gets from performing the activity, or the subjective interest the individual has in the subject, is one of the task value components and can influence motivation (Gonzalez and Paoloni, 2015). Regarding the relationship between learning interest and continuing motivation, they are always closely linked (Renninger, 1992; Krapp, 1999; Naceur and Schiefele, 2005). Continuing motivation, as defined by Maehr (1976), reflects an ongoing willingness to learn. Individuals display continuing motivation when they return to a learning activity later without the external pressure to do so (Kinzie, 1990). This return is presumably occasioned by a continuing interest in the task and not by external pressure (Maehr, 1976). Several previous studies have pointed out that interest in a certain field is actively related to continuing motivation. For example, Harackiewicz et al. found that task interest correlates positively with a return to the task; in other words, task interest can positively predict continuing motivation (Ryan et al., 1983; Harackiewicz et al., 2008). Herndon (1987) investigated the relationships among learner interest, achievement and continuing motivation in instruction. The founding showed that when students were motivated to learn during instruction, the intensity of their sustained motivation was significantly higher than that of students who were not motivated (Herndon, 1987). Besides, Sha et al. study of 6th graders found that student's interest in science learning was a significant positive predictor of their ongoing motivation and engagement (Sha et al., 2016).

According to social cognition theory, the support of others, such as emotional encouragement, material help, and supportive information, which are individual feels, can enhance the individual's sense of self-efficacy (Bandura, 1997). Self-efficacy has been found in various domains to be influenced by parents, teachers, and peers (Schunk et al., 2008). Sha et al. (2016) showed that perceived parents’ support in science has an enormous impact on students’ self-efficacy in science. Numerous studies have confirmed that perceived teacher support could positively predict students’ science self-efficacy (Scott and Walczak, 2009; Jungert and Koestner, 2015; Fredricks et al., 2016; Liu et al., 2018; Kim et al., 2018). According to the conclusion of Zhao and Qin (2021), when students perceive more peer support, it is easier for them to carry out deep learning, and thereby students will improve their self-efficacy. In addition, previous studies have confirmed that individuals who feel more social support (from parents, peers and teachers) have a higher level of a personal growth initiative, and their self-efficacy is stronger (Cai and Lian, 2022).

Bandura's self-efficacy theory points out that self-efficacy is closely related to students' motivation, quality and ability (Bandura, 1997). As for the link between self-efficacy and continuing motivation, studies have shown that self-efficacy has a positive effect on students’ continuing motivation (Garcia and Pintrich, 1996; Ferrell et al., 2016). As Çapa-Aydın and Uzuntiryaki (2009) outline, students’ choices of chemistry-related activities, their efforts to perform them, and their persistence and resilience, when faced with obstacles are all affected by their self-efficacy beliefs. And it is continuing motivation that focuses on students’ choice and persistence in learning activities (Maehr, 1976). Several studies have proven that self-efficacy is positively related to continuing motivation. For example, Artino (2009) explored the effect of college students’ motivational beliefs (self-efficacy, etc.) on their continuing motivation to take future online courses and found that self-efficacy beliefs were moderately strong positive predictors of continuing motivation. Sha et al. (2016) found that students’ science self-efficacy was a significant positive predictive effect on their choice and participation in science-related activities, and the predictive effect of self-efficacy on activity choice was more substantial than that on activity participation. According to Mataka and Kowalske (2015), students with high chemistry self-efficacy beliefs have higher continuing motivation in chemistry and put forth more effort than students with lower chemistry self-efficacy beliefs.

The present study

Based on the above literature review, each of the three indicators of social support: parents support, teacher support and peer support will affect continuing motivation, respectively. However, the research on social support and continuing motivation is mainly limited to univariate or two-variable research. There is a lack of research on the combination of parents’ support, teacher support and peer support. Therefore, the present study aims to discuss the relationship between social support and the subordinate dimensions it contains and continuing motivation in chemistry of students in high school at first.

Further, given that the enhancement of interest can improve students’ continuing motivation and each of the lower dimensions of social support can influence students’ interest in learning, it could be hypothesized that parents support, teacher support and peer support can influence students’ continuing motivation through learning interest. Since both learning interest and continuing motivation are domain-specific, the present study assumes that the impact of social support on continuing motivation is mediated by interest in chemistry.

In addition, social support could promote self-efficacy, which is one of the crucial conditions for promoting continuing motivation. Since self-efficacy is domain-specific, we assume that social support would affect continuing motivation in chemistry through chemistry self-efficacy.

In conclusion, the hypotheses of the present study are elaborated as follows (Fig. 1):


image file: d2rp00165a-f1.tif
Fig. 1 Hypothesized relationships among social support, interest in chemistry, chemistry self-efficacy, and continuing motivation in chemistry.

Hypothesis 1: There is a positive relationship between parents support and continuing motivation in chemistry.

Hypothesis 2: There is a positive relationship between teacher support and continuing motivation in chemistry.

Hypothesis 3: There is a positive relationship between peer support and continuing motivation in chemistry.

Hypothesis 4: Interest in chemistry has a mediating effect between parents support and continuing motivation in chemistry.

Hypothesis 5: Interest in chemistry has a mediating effect between teacher support and continuing motivation in chemistry.

Hypothesis 6: Interest in chemistry has a mediating effect between peer support and continuing motivation in chemistry.

Hypothesis 7: Chemistry self-efficacy acts as a mediating role between parents support and continuing motivation in chemistry.

Hypothesis 8: Chemistry self-efficacy acts as a mediating role between teacher support and continuing motivation in chemistry.

Hypothesis 9: Chemistry self-efficacy acts as a mediating role between peer support and continuing motivation in chemistry.

Method

Participants

The present study selected 1260 students (51% males; 49% females) in Grade 10 from 12 high schools in China by the stratified purposive sampling method. At first, we selected four cities from China's first-tier cities and second-tier cities, respectively, and each two cities from third-tier cities and fourth-tier cities, which is based on the economic development situation, making a total of 12 cities (first-tier cities have the best economic development) (Xu et al., 2021). Then, we chose an ordinary high school in each of the 12 cities. Finally, we divided the students in each school into three levels, the high, medium, and low based on their academic performance, and 35 students from each level were selected to participant in this experiment. The age range of the participants was 15 to 17 years old, and the average age was 16.3 years old. All participants have completed six years of elementary school science courses (which includes chemistry) and have already experienced chemistry classes for at least one and a half years.

Survey administration

The survey was completed in computer class through an electronic questionnaire, which was presided over by well-trained researchers. As the survey was implemented in 12 different schools, it took a total of one week to collect the questionnaires from all 12 schools. Permission has been obtained from all appropriate authorities and students’ parents before proceeding with the data gathered. In addition, before the investigation began, the researchers explained to all participants the purpose of the study and the use of the data, as well as promised to keep participants’ personal information strictly confidential. All participants voluntarily took part in the study, and no incentives or extra credits were given for their participation. The Academic Ethics Committee of Southwestern University approved this study.

Survey instrument

We designed a questionnaire based on measures that had previously been employed in large-scale studies, that are established in their respective fields, and that are available in English. The original measurement tools in English were translated into Chinese by the authors who have a good command of the English language. Then, we adapted existing scales according to the context of high school chemistry education in China and, in some cases, deleted or added items in order to limit the students’ time on tasks to a reasonable amount and help students better understand the task and express themselves. Next, 15 students from the 10th grade were supposed to read and comment on the questionnaire. After that, the researchers spent some time interviewing them. Researchers asked every student to express his/her understanding of each item, and then expressions of some questions in the questionnaire were modified, combining with the interview results, to make sure that the meaning understood by students was completely consistent with the contents expressed in the questionnaire.
Parents support. A 6-item scale adapted from the subscale of the Multidimensional Scale of Perceived Social Support (MSPSS) developed by Zimet et al. (Zimet et al., 1988; Ye et al., 2014; Cai and Lian, 2022) was used to measure students’ perceived parents support. 6 items in the scale were divided into two sub-dimensions, which were substantial support and emotional support. 3 items were associated with substantial support (e.g., “Parents often give me financial support for my learning, such as buying reference books and learning stationery, etc.”), and the remaining 3 items were related to emotional support (e.g., “From my parents' behaviour I can feel they really love me”). Students scored each item on a five-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicated more perceived parents support.
Teacher support. The Teacher as Social Context (TaSC) questionnaire proposed by Belmont et al. (Belmont et al., 1992; Vansteenkiste et al., 2012; Gonzalez and Paoloni, 2015) is the most widely used measure instrument for students’ perception of teacher support, which includes 8 five-point items. Based on the TaSC, we got an adapted perceived teacher support scale suitable, taking into account the characteristics of Chinese teachers' help and care for students, which consisted of two sub-dimensions: instrumental support and emotional support. Instrumental support included 3 items (e.g., “My chemistry teacher will take the initiative to care about my study and give guidance”) and emotional support contained 2 items (e.g., “When I encounter difficulties or setbacks, my chemistry teacher will encourage me”). There were totally arranging from 1 “strongly disagree” to 5 “strongly agree”. Higher scores reflected students perceived more teacher support in their studies and life.
Peer support. The students' perception of peer support was evaluated using an adapted version of the peer support subscale of the Multidimensional Scale of Perceived Social Support (MSPSS) developed by Zimet et al. (Zimet et al., 1988; Ye et al., 2014; Cai and Lian, 2022), which consisted of 6 items. In this scale, 2 items evaluated the degree to which students perceived emotional support from their peers (e.g., “Peers make me feel important”). Another 4 items measured the degree to which students perceived information sharing and decision support from peers (e.g., “Peers provide me with relevant knowledge or useful information”). Participants responded on a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree); the higher the score, the stronger the sense of peer support.
Continuing motivation in chemistry. The measurement instrument for continuing motivation in chemistry was adapted based on Continuing Motivation in Science for Adolescents, which was an instrument developed by Pascarella et al. (Pascarella et al., 1981; Fortus and Vedder-Weiss, 2014; Luo et al., 2019). The original instrument (8 items) addressed the frequency of student participation in non-school science activities when activities are not required for science class (chemistry is one part of science), which is close to the research content of the present study. Therefore, we modified the wording of these items slightly to fit the context of a chemistry course, mostly by just replacing the word “science” with “chemistry”, and lastly used 5 items to measure students' continuing interest and participation in chemistry activities outside of class. (e.g., “Outside of chemistry class, I talked about chemistry topics with my friends”) All items were measured on a five-point Likert scale (1 = Never, 2 = Seldom, 3 = Sometimes, 4 = Often, 5 = Always).
Interest in chemistry. Students’ interest in chemistry was assessed with a 4-item scale ranging from 1 (strongly disagree) to 5 (strongly agree), which was adapted from an established scale used by Ferrell and Barbera (2015) and originally developed by Harackiewicz et al. (2008). The scale captured the feeling-related (2 items) and value-related (2 items) facets of interest. Example items were “I’ve always been fascinated by chemistry.” and, “I think what we will study in chemistry will be important for me to know.” The higher scores were, the more interested in chemistry students were.
Chemistry self-efficacy. The self-efficacy scale was taken from the High School Chemistry Self-Efficacy Scale (HCSS) (Çapa-Aydın and Uzuntiryaki, 2009). These items are designed to assess high school students’ self-efficacy beliefs in chemistry-related tasks. The original instrument (16 items) has two dimensions of chemistry self-efficacy: Chemistry self-efficacy for cognitive skills (10 items) and self-efficacy for chemistry laboratory (6 items). Chemistry self-efficacy for cognitive skills means students’ beliefs in their ability to use intellectual skills in chemistry and self-efficacy for chemistry laboratory refers to students’ beliefs in their ability to accomplish laboratory tasks. Based on the psychological characteristics of Chinese high school students' chemistry learning, 8 items from two dimensions were selected and adapted for assessment in this study, chemistry self-efficacy for cognitive skills (5 items), an example of an item from this dimension is, “I can choose an appropriate formula to solve a chemistry problem”, and self-efficacy for chemistry laboratory (3 items), a sample item is, “I can use chemical instruments proficiently”. All items are on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Data analysis procedures and tools

Specifically, SPSS 25.0 and AMOS 25.0 were used to conduct statistical analyses in this study. The main uses of the tools and the research procedures are shown below:
(1) Inspection of measuring tools. The questionnaires used in the current study were adapted from the well-established questionnaires via analysing the 435 sample from the pretest. The process of adapting the questionnaires was as follows:

Firstly, we performed an item analysis to ensure the appropriateness and reliability of the items in each scale (Chen, 2004). The item analysis can explore the difference between high and low scoring subjects on each question item or perform a test of homogeneity between items, and the result of it can be used as a basis for screening or modifying individual items (Wu, 2010). In this study, we deleted items that had the lowest correlations with each scale because they represented poorer measures, which tended to lower the scale's overall reliability. In fact, there were very few such items.

Then, the exploratory factor analysis (EFA) and the confirmatory factor analysis (CFA) were used to revise the six scales together. In this study, all samples were randomly divided into two groups. EFA was first implemented to sample 1 (n = 216) to test if the factor structure of each measurement instrument was in relation to the original predefined (Lee et al., 2008). Next, we administrated a CFA on sample 2 (n = 217) to verify the validity of the potential variables (Velayutham and Aldridge, 2012). Previous studies showed that MacDonald's omega is more appropriate to reflect the reliability of congeneric scales, and they have less risk of overestimating or underestimating reliability to Cronbach's alpha (Dunn et al., 2013). Hence, omega coefficient was performed using SPSS 25.0 to detect the internal consistency of each questionnaire in this study (McNeish, 2018). For EFA result, the number of factors extracted was determined based on principal component analysis, and the factor matrix was analysed using the maximum variance method. Items that had at least one loading value greater than or equal to 0.4, commonality less than 0.3 were retained (Li et al., 2020). We identified items of each scale which not load on any factor (no loadings ≥0.4) as well as items that loaded on more than one factor (cross-loading; more than one factor loading ≥0.4) (Table 4). If the items which did not load on any factor or which cross-loaded on multiple factors, it will be removed (Costello and Osborne, 2005). The CFA results indicate an acceptable model fit when the following indicators are shown: χ2/df < 5 (Opperman et al., 2013), RMSEA < 0.08, SRMR < 0.08 (Hair et al., 2006), AGFI > 0.8 (Marsh et al., 1988), as well as GFI, TLI, CFI, IFI >0.9 (Hair et al., 2006; Opperman et al., 2013). An excellent model fit is indicated when χ2/df < 3, RMSEA and SRMR values are below 0.05, and GFI, CFI, TFI and IFI are greater than 0.95 (Hu and Bentler, 1999; Brown and Cudeck, 1992). Finally, the omega coefficient for each scale should be higher than 0.7, indicating a high reliability of the scale (Li et al., 2019).

(2) Validation of model hypotheses. Before testing the hypotheses model, the Harman single factor test was employed to test the questionnaires to ensure that there is no common method bias issue (Harris and Mossholder, 1996). Then, we used the composite reliability (CR) and the average variance extracted (AVE) to examine the internal reliability and validity of the entire questionnaire respectively, and the square roots of AVEs for all constructs were greater than their corresponding inter-construct correlations coefficients, indicating good discriminant validity (Bagozzi, 1982; Bagozzi and Yi, 1988). After that, the direct and mediating effects in the hypothesized model were tested using structural equation model (SEM) with AMOS 25.0. Mediation analysis is a statistical method that helps researchers understand the mechanisms underlying the phenomena they study (Mackinnon and Fairchild, 2009), in a simple mediational model, mediation can be complete or partial, if both mediating and direct effects are significant, the effect of independent variable on the dependent variable is incompletely mediated; when the mediating effects is significant while the direct effect is insignificant, it means that the relationship between variables is completely mediated (Gonzalez and Paoloni, 2015). Previous studies have shown that among many mediation analysis methods, the confidence intervals obtained by the bias-corrected nonparametric percentile Bootstrap method are more accurate than those obtained by the Sobel method and with higher power of the test (Taylor et al., 2008). Therefore, in this study, we used bootstrapping and an estimated bias-corrected 95% confidence interval to test the significance of the hypotheses model. 1000 bootstrap samples along with 95% confidence interval were used to determine the significance of the effect. If the 95% confidence intervals don’t include 0, the effect is significant (Hayes, 2013). The fit of the entire hypothetical model was evaluated by the χ2/df, RMSEA, SRMR, GFI, AGFI, TLI, CFI, IFI, Hoelter's N (CN), the cutoff criteria for fit indexes of the model are presented in the Table 2 (Hoelter, 1983; Marsh et al., 1988; Bollen, 1989; Hair et al., 2006; Opperman et al., 2013).

Suitability for measurement instruments

Parents support. General data from the formal administration of the test were randomly selected for exploratory factor analysis. Through the analysis, a KMO coefficient of 0.761 and a Bartlett's spherical test coefficient of 1351.466 (p < 0.001) were obtained, indicating the presence of common factors between the correlation matrices of the data clusters and suitable for EFA. Factors were extracted using principal component analysis, and the factor matrix was analyzed using the maximum variance method to eliminate 2 items in the scale with item loading values less than 0.4, commonality less than 0.3, and factor loadings that were similar on more than two factors and did not conform to the theoretical logic, resulting in 4 remaining question items. The resulting question items were subjected to EFA again. The total explained variance was 60.624%, and the factor loadings of each item ranged from 0.518 to 0.820. The CFA results of the other half of the data revealed that the fit indices of the model met the statistical requirements: χ2/df = 1.286; SRMR = 0.027; GFI = 0.971; CFI = 0.988; TLI = 0.965; RMSEA = 0.080, indicating that the model structure was reasonably set up and conceived. The omega of the whole scale was 0.789, indicating that the questionnaire had good reliability.
Teacher support. Consistent with what was previously mentioned, 5 items in total were initially used to measure teacher support. After EFA analysis, all question items were retained (none was removed), and their factor loadings were above 0.4. The Kaiser–Meyer–Olking (KMO = 0.870) and the Bartlett's test of sphericity (χ2 =3409.029, p < 0.001) showed the comprehensive data set to conduct the factor analysis; the factors were extracted and explained 71.129% of the total variance. The factor loadings for all items were in the range of 0.727 to 0.876. The results of the CFA analysis showed that the teacher support model fitted well: χ2/df = 1.872; SRMR = 0.031; GFI = 0.931; CFI = 0.972; TLI= 0.943; RMSEA = 0.042. In addition, the omega for the whole questionnaire was 0.899, demonstrating that the questionnaire for chemistry identity had high reliability.
Peer support. An EFA analysis of the six-question items for peer support revealed a KMO coefficient of 0.899, and Bartlett's test of sphericity indicated χ2 = 3778.914 (p < 0.001), the total explained variance of all items was 66.159%, and the factor loadings were in the range of 0.649 to 0.818. The second-order CFA analysis of the whole scale revealed that χ2/df = 1.295; SRMR = 0.036; GFI = 0.932; CFI = 0.979; TLI = 0.966; RMSEA = 0.073. This experimental result indicates that the model has a good fit. In addition, the omega of the whole questionnaire was 0.898, which indicates that the questionnaire had high reliability.
Continuing motivation in chemistry. The data obtained from the continuing motivation in chemistry were divided into two parts according to a random sampling method, and EFA analysis was conducted on sample 1, and there are no question items that did not meet the statistical requirements were found. Therefore, all items are retained. And after the test of the maximum variance method, the KMO coefficient of this scale was found to be 0.893, the Bartlett's spherical test coefficient was 2252.807 (p < 0.001), and the total variance explained was 61.936, and the factor loadings were ranged from 0.583 to 0.824. CFA analysis using sample 2 showed that χ2/df = 2.314; SRMR = 0.060; GFI = 0.913; CFI = 0.957; TLI = 0.913; RMSEA = 0.055. This experimental result indicates that the model has a good fit. Further examination of the reliability of the scale revealed that the omega of the entire scale was 0.847, indicating that the scale had high reliability.
Interest in chemistry. EFA analysis of the four questions of the adapted interest in chemistry scale showed a KMO value of 0.846, and Bartlett's test of sphericity revealed 1450.792 (p < 0.001). The cumulative variance was 62.341, and the factor loadings ranged from 0.621 to 0.842 for each question item after rotation. The CFA results of the scale showed a highly fit for the interest in chemistry measure model with χ2/df = 2.376; SRMR = 0.027; GFI = 0.930; CFI = 0.918; TLI = 0.931; and RMSEA = 0.025. In addition, the omega of the scale was 0.802.
Chemistry self-efficacy. Consistent with the expectations, the EFA extracted two factors for Self-efficacy. After two rounds of EFA analysis, 2 items were sequentially removed because one item had high loadings on two factors, while one item's factor loading was below 0.4. The third round of EFA was performed on the remaining six items with a KMO value of 0.893. Bartlett's spherical test coefficient of 3149.700 (p < 0.001). The cumulative variance explained by the two dimensions was 62.112, and the factor loadings of all 6 items were within the range of 0.634 to 0.801. Based on the results of the EFA analysis, a CFA analysis of the additional data revealed that all fit indices of the second-order model met the statistical requirements: χ2/df = 1.390; SRMR = 0.044; GFI = 0.930; CFI = 0.976; TLI = 0.976; RMSEA = 0.064. The omega of the whole scale was 0.878, indicating that the adapted Chemistry self-efficacy scale had high-reliability indicators.

Results

Common method deviation test

In this study, Harman's single factor was used to estimate the common source variance (Podsakoff et al., 2003) and test the results of the unrotated factor analysis. The results showed that the initial eigenvalues of 6 factors’ exceed 1, and the explanatory rate of the first common factor was 38.314%, which was no more than 40%. And the cumulative interpretation total variance was 65.639%. The first factor certificated that the variance was less than half of the cumulative total variance. Therefore, no common method bias effect was observed between the main variables (Mo et al., 2019).

Descriptive statistics and bivariate correlations

The mean and standard deviation of each variable were calculated and presented in Table 1. In addition to this, the skewness and kurtosis of each scale were presented in Table 1 to assure the normality of the variables. According to Kline (2005), the kurtosis and skewness of the variables within ±3 are consistent with normal. As demonstrated in Table 1, the skewness of the variables in the present study ranged from −1.44 to −0.05, and the kurtosis was in the range of −0.13 to 2.39, all of which were inside the threshold value. Table 1 also displayed the bivariate correlations between variables in this study. The results suggested that the correlations between all variables were significant (Fig. 2) (Guo et al., 2022).
Table 1 Descriptive statistics and bivariate correlations
Mean SD Skewness Kurtosis Confirmatory factor analysis Bivariate correlations
CR > 0.7 AVE > 0.5 1 2 3 4 5 6
Notes: * p < 0.05. ** p < 0.01. *** p < 0.001. 1, 2, 3, 4, 5, 6 represent parents support, teacher support, peer support, interest in chemistry, chemistry self-efficacy and continuing motivation in chemistry, respectively. CR = composite reliability; AVE = average variance extracted.
Parents support 4.53 0.56 −1.02 0.54 0.79 0.49 0.77
Teacher support 4.35 0.69 −0.71 0.23 0.90 0.64 0.48*** 0.80
Peer support 4.23 0.67 −1.44 2.39 0.90 0.60 0.52*** 0.63*** 0.77
Interest in chemistry 4.05 0.69 −0.66 0.46 0.80 0.51 0.49*** 0.59*** 0.56*** 0.71
Chemistry self-efficacy 3.80 0.74 −0.38 0.01 0.88 0.55 0.31*** 0.43*** 0.47*** 0.57*** 0.74
Continuing motivation in chemistry 3.13 0.76 −0.05 −0.13 0.85 0.53 0.40*** 0.60*** 0.58*** 0.63*** 0.51*** 0.73


image file: d2rp00165a-f2.tif
Fig. 2 Structural relations between social support, interest in chemistry, chemistry self-efficacy, and continuing motivation in chemistry (standardized regressions weights). Notes: *p < 0.05; **p < 0.01; ***p < 0.001.

Structural model analysis

Given that the measurement model fitted the data well, several SEM analyses were performed to test the structural mode proposed in Table 2. The hypothesized model fit adequately the data, ML χ2 = 1635.72, p < 0.001, df = 391, GFI = 4.18, CFI = 0.93, TLI = 0.93, IFI = 0.93, RMSEA = 0.05, SRMR = 0.05, and Hoelter's N (CN) = 308. Fig. 2 displayed the overall structural model with standardized regression weights.
Table 2 Model and its criteria
Fitting index Criteria Fitted index indices Good or bad indicators
MLχ2 The smaller, the better 1635.72 —
df The bigger, the better 391 —
χ 2/df <5 4.18 Pass
GFI >0.9 0.91 Pass
AGFI >0.8 0.89 Pass
RMSEA <0.08 0.05 Pass
SRMR <0.08 0.05 Pass
TLI >0.9 0.93 Pass
CFI >0.9 0.93 Pass
IFI >0.9 0.93 Pass
Hoelter's N (CN) >200 308 Pass

Test for mediation

We tested whether the indirect effects (i.e., mediating effects) of parental support, peer support and teacher support on continuing motivation in chemistry were significant. Table 3 mainly focused on the results of mediation analysis, including the direct effects, indirect effects and total effects. Similarly, if the 95% confidence interval (95% CI) of the coefficients of each pathway did not include 0, it indicated that the indirect pathway had a significant mediating effect.
Table 3 The indirect effect of interest in chemistry and chemistry self-efficacy
Paths Effect value SE value Bootstrap 1000 times 95% confidence interval P value
Lower Upper
Notes: all variables used in this table have been standardized.
Direct effects
Parents support → Continuing motivation in chemistry −0.154 0.041 −0.232 −0.074 p < 0.001
Teacher support → Continuing motivation in chemistry 0.264 0.041 0.182 0.343 p < 0.001
Peer support → Continuing motivation in chemistry 0.200 0.042 0.116 0.279 p < 0.001
Indirect effects
Parents support → Interest in chemistry → Continuing motivation in chemistry 0.100 0.025 0.058 0.154 0.002
Parents support → Chemistry self-efficacy → Continuing motivation in chemistry 0.019 0.011 0.002 0.043 0.035
Teacher support → Interest in chemistry → Continuing motivation in chemistry 0.126 0.025 0.082 0.185 0.001
Teacher support → Chemistry self-efficacy → Continuing motivation in chemistry 0.036 0.011 0.018 0.062 0.001
Peer support → Interest in chemistry → Continuing motivation in chemistry 0.097 0.022 0.059 0.149 0.001
Peer support → Chemistry self-efficacy → Continuing motivation in chemistry 0.054 0.014 0.032 0.090 0.001
Total effects
Parents support → Continuing motivation in chemistry −0.035 0.039 −0.114 0.040 0.376
Teacher support → Continuing motivation in chemistry 0.426 0.036 0.356 0.495 0.001
Peer support → Continuing motivation in chemistry 0.351 0.041 0.274 0.435 0.002

As can be seen in Table 3, the 95% confidence intervals for the six indirect pathways of three supports on continuing motivation in chemistry did not include 0. Firstly, the indirect effect of parents support → interest in chemistry → continuing motivation in chemistry was 0.100 (95% CI was 0.058–0.154), supporting hypothesis 4; the indirect effect of parents support → chemistry self-efficacy → continuing motivation in chemistry was 0.019 (95% CI was 0.002–0.043), supporting hypothesis 7. It was noteworthy that both the direct and indirect effects of parents support on continuing motivation in chemistry were significant and had opposite signs, suggesting that it was a competitive mediation (Zhao et al., 2010). Secondly, teacher support influenced continuing motivation in chemistry through two indirect pathways: the indirect effect of teacher support → interest in chemistry → continuing motivation in chemistry was 0.126 (95% CI was 0.082–0.185); the indirect effect of teacher support → chemistry self-efficacy → continuing motivation in chemistry was 0.036 (95% CI was 0.018–0.062), supporting hypothesis 5 and hypothesis 8. Thirdly, peer support influenced continuing motivation in chemistry through two indirect pathways: the indirect effect of peer support → interest in chemistry → continuing motivation in chemistry was 0.097 (95% CI was 0.059–0.149); the indirect effect of peer support → chemistry self-efficacy → continuing motivation in chemistry was 0.054 (95% CI was 0.032–0.090), supporting hypothesis 6 and hypothesis 9.

Discussion and conclusion

As a type of intrinsic motivation directly related to education (Kinzie, 1990), chemistry continuing motivation is vital for students’ ability to comprehensive practical skills in chemistry, real-world problem-solving skills, etc. (Schunk et al., 2014; Rosenzweig and Wigfield, 2016). And as Bronfenbrenner's socio-ecological theory (1979) emphasizes, individual continuing motivation for science can be influenced by the external environment (Fortus and Vedder-Weiss, 2014), resulting in a science continuing motivation which is higher or lower than a person's continuing motivation for other domains. To determine teaching interventions can effectively improve students’ continuing motivation in chemistry, this research investigated how chemistry continuing motivation developed in high school students is associated with social support, interest in chemistry, and chemistry efficacy.

The relationship between parents support, teacher support peer support and continuing motivation in chemistry

The first set of findings showed that teacher support and peer support could positively, directly affect the chemistry continuing motivation of Chinese high school students, which was consistent with the hypothesis H2, H3 and previous research findings (Fyans, 1981; Wentzel and Brophy, 2014), suggesting that both teachers and peers were perceived to play an essential role in these students’ chemistry continuing motivation. The most exciting finding was that, although there was a significant relationship between parents’ support and students’ continuing motivation in chemistry, this relationship was negative. This finding is inconsistent with previous hypotheses and studies.

The negative relationship between parents support and students’ chemistry continuing motivation suggests that the stronger the support from parents as perceived by students, the worse they tend to perform. This pattern seems counter-intuitive at first. A closer examination, however, leads to some meaningful interpretations. In China, parents generally have high expectations for their children's education and hope they will have better academic achievement (Guo, 2022). They believe that “knowledge changes fate” is an important way to obtain a superior material life and achieve class transformation (Dello, 2009). However, if parents’ expectations are too high, students tend to feel anxious and thus retreat in their studies (Raufelder et al., 2015). Overly harsh, stressful, and non-supportive negative parental behaviour reduces students’ continuing motivation (Froiland, 2011; Raufelder et al., 2015), which may cause students to focus more on whether their external performance is satisfying to their parents and may try to escape from competitive situations out of fear. Based on the self-determination, the satisfaction of students’ needs-autonomy, competence and relatedness is fundamental to continuing motivation (Deci and Ryan, 2000). Thus, an alternative interpretation is that perceived higher levels of parents’ support may not be welcomed because they conflict with the developmental needs of the student in adolescence to seek autonomy and detachment from their parents. The study by McNeal (1999) found students perceived higher levels of parents involvement as interfering with their needs to be independent, to the extent of evoking adverse reactions, and Maehr (1976) also suggested that the relative effect of family on students’ continuing motivation changes with age, and students’ continuing motivation to learning science often declines around the transition to high school (Vedder-Weiss and Fortus, 2013, 2014). Therefore, it is reasonable that students at this stage showed a negative correlation between chemistry continuing motivation and parents support and the lowest chemistry continuing motivation scores (Score = 3.2).

In addition, the present study showed that both teacher and peer support had significant predictive effects on students’ continuing motivation in chemistry, which is consistent with previous studies (Wentzel et al., 2010). And it is worth noting that the standardized predictive coefficient of teacher support for chemistry continuing motivation was higher than those of peer support and parents support, regardless of whether chemistry learning interest and self-efficacy were included. The results of this study suggest that the contribution of teacher support to continuing motivation may be more significant than the contribution of peer and parents support to continuing motivation among Chinese high school students, and similar results were obtained by Wu et al. (2022) study of 2211 Chinese students. What's more, according to the Ecosystem Theory (Siegler et al., 2010), the school is the micro-system that has the closest influence on the development of students besides the family environment. As an important part of school micro-system, the interaction between teachers and students will affect the chemistry continuing motivation of the students (Pascarella et al., 1981; Goodenow, 1993; Cox and Williams, 2008; Wentzel and Brophy, 2014). The support of the teacher satisfies the students’ psychological needs and stimulates students’ continuing motivation. They are more willing to invest time and energy in learning tasks, and the more likely they are to achieve academic success. And considering the high prestige and authority ascribed to teachers in Chinese culture (Chen, 2004), it is not surprising that self-perceived teacher support, among all support systems, is found to relate the most to high school students’ continuing motivation in chemistry.

The mediating roles of interest in chemistry and chemistry self-efficacy

Another contribution of this study concerned the study of the mediating effects of interests in chemistry and chemistry self-efficacy between social support and continuing motivation in chemistry. Firstly, the study found that high school students’ interest in chemistry and chemistry self-efficacy mediated the relationship between parents’ support and continuing motivation in chemistry, and hypothesis 4 and hypothesis 7 were supported. However, it was interesting that the direct and indirect effects of parents support on continuing motivation in chemistry were both significant and had opposite signs, indicating that this was a competitive mediation (Zhao et al., 2010). This showed that parents support could positively affect continuing motivation in chemistry through interest in chemistry and chemistry self-efficacy, and the positive effects of interest in chemistry and chemistry self-efficacy suppressed the negative effects of parents support. That was, when parents support stimulated students’ chemistry interest and chemical self-efficacy, the negative effect of parents support on continuing motivation in chemistry would be counteracted to some extent. Besides, consistent with the results of previous studies (Cox and Williams, 2008), the test results show that interest in chemistry and chemistry self-efficacy play an intermediary role in the teacher support, peer support and continuing motivation in chemistry of Chinese high school students, respectively. Hypothesis 5, 6, 8 and 9 are verified. In summary, this finding suggests that receiving higher quality support (especially teacher support) can encourage Chinese high school students to demonstrate higher levels of interest in chemistry and chemistry self-efficacy, which may contribute to their improved continuing motivation.

In the motivation literature, interest is defined as both cognitive and affect-oriented, whereas self-efficacy is primarily cognitive (Schunk et al., 2014). Bandura's social cognitive theory suggests that cognition, society, and environment can interact with each other (Bandura, 1997); that means, environmental factors (e.g., teacher support, etc.) can influence cognitive factors (e.g., interest and self-efficacy). Expected Value Theory suggests that perceived self-efficacy plays an influential role in motivation through outcome expectations (Bandura, 1997). The study of Froiland (2011) also demonstrated that increased self-efficacy may have further elevated intrinsic motivation. In terms of the relationship between interest and motivation, Herndon's study (1987) found that learning interest has a positive predictive effect on continuing motivation. After that, it was also confirmed by Shernof and Hoogstra (2001) and Sha et al. (2016). Therefore, learning interest and self-efficacy can play a positive mediating role between social support and continuing motivation in the present study.

Implications for practice

On the basis of the results of the present study, it's practicable to develop students’ continuing motivation in chemistry by promoting social support. At the same time, teacher and peer support were more effective in promoting levels of continuing motivation in chemistry than parents’ support. Based on Self-Determination Theory, contextual factors which help fulfil learners’ basic psychological needs (i.e., autonomy, competence, and relatedness) can considerably foster internalization and integration of behavioural regulations, which may result in increased continuing motivation, learning, and performance (Ryan and Deci, 2017). Teachers play a crucial role in building a motivating, supportive, secure, and friendly environment as an essential factor in learning situations that can establish a positive rapport with learners to maximize their self-motivation as well as persistence and engagement in learning activities (Reeve and Tseng, 2011) and to stimulate students' autonomous motivation (Liu et al., 2021). With the purpose of enhancing the students' continuing motivation in chemistry, as Vedder-Weiss and Fortus (2018) recommend, teachers should try: to assign “challenge chemistry tasks” as incentives to encourage students to invest, make an effort, and think; encourage students to ask chemistry questions and seek help from different sources, such as the internet, experts and important others; be enthusiastic about and enjoying about the chemistry subject matter; and giving different chemistry assignments to different students. Peers play a crucial role in personal development as another important factor in learning situations (Martin and Dowson, 2009; Parker et al., 2015), and peer support can influence students' learning interests and learning motivation (Nelson and DeBacker, 2008; Wentzel et al., 2010). The results of this study also demonstrate the positive impact of students' perceived peer support on their continuing motivation, with adolescents who enjoy positive support from peers tend to be motivated and actively engaged in academic activities and have higher levels of continuing motivation for chemistry learning. Therefore, schools can create a supportive and inclusive learning environment by adapting the curriculum to promote peer collaboration and positive interactions, while teachers and parents should also encourage students to care for each other and help each other in life and learning, strengthen organizational exchanges and mutual assistance and cooperation of students, so that students can perceive more autonomy support, and further enhance students' continuing motivation in chemistry. Of course, the role of parents cannot be overlooked. Parents should fully understand children's current stage of development, chemistry learning interests and learning difficulties, and on this basis, reasonable expectations and requirements are presented to child, while maintaining full trust and confidence in students’ chemistry learning and meeting their need for independent development. Besides, Parents should show reasonable concern and support for their children, providing them with support in choosing their own chemistry-related activities after school, both emotionally and materially (Ricard and Pelletier, 2016; Zhang et al., 2019). In addition to this, parents need to work closely with teachers to enhance harmonious and effective communication between them so that students are consistently motivated to participate in chemistry activities actively.

Furthermore, the results of this study show that social support also have an indirect impact on continuing motivation in chemistry through interest in chemistry and chemistry self-efficacy. And students' interest in chemistry was a more significant predictor of their continuing motivation in chemistry than perceived teacher support, suggesting that students' own factors are also important for their continuing motivation in learning chemistry. According to Self-Determination Theory, for external stimuli (e.g. parents, teachers, peers) has an effect on an individual's final behaviour, they need to undergo internal cognitive processing, through which cognitive processing forms perceptions of the external stimuli on the basis of which the individual makes a decision whether to produce a behaviour (Ryan and Deci, 2000). It is evident that an individual own psychological traits and thoughts play a very critical role in their learning behaviour. Project-based learning (PBL), an important teaching method nowadays, which is based on real-life situations, to create authentic driving questions and outcomes, and to translate learning literacy into continuing learning practices (Blumenfeld et al., 1991; Xia, 2018). PBL helps students improve their ability to learn effectively, increases their interest and enjoyment of learning chemistry, strengthens their sense of self-efficacy in chemistry, and motivates them to learn both inside and outside the classroom (Ngereja et al., 2020). In this way, students can perceive and give practical meaning to chemical content and learning processes, not only learning chemistry in the classroom but also continuing to explore and apply what they have learned in their daily lives outside the classroom, thus enhancing their interest and self-efficacy in chemistry learning (Mou, 2019), and further enhancing their continuing motivation.

Limitations and future directions

The present study was conducted with high school students and mainly based on the underlying effect mechanisms between social support and continuing motivation in chemistry. Nevertheless, there are some limitations of this study that need further research in the future.

All variables in this study used self-reported data, which has limitations. Therefore, to enhance the validity of the study in the future, surveys of students, parents and teachers need to be conducted and combined interviews and observation simultaneously. Besides, the present study was cross-sectional, which can only illustrate the correlations between several variables, as well as possible causal relationships. So, in the future, longitudinal studies are needed to confirm the causal relationships between several variables. Moreover, this study only explored the simple mechanism of how social support affects the continuing motivation in chemistry of high school students. However, in the process there are other complex forms where multiple variables simultaneously act as mediation effects. Hence, in subsequent studies, more mediating variables should be considered in the model to reveal the complex process by which parents support, teacher support and peer support influence motivation in chemistry in high school students.

Conflicts of interest

There are no conflicts to declare.

Appendix. Std. loading of items

 
Table 4 Std. loading of items
Item Std. loading
Parents support Q1 0.630
Q2 0.820
Q3 0.793
Q4 0.518
Teacher support Q1 0.727
Q2 0.787
Q3 0.876
Q4 0.849
Q5 0.755
Peer Support Q1 0.649
Q2 0.801
Q3 0.746
Q4 0.813
Q5 0.792
Q6 0.818
Continuing motivation in chemistry Q1 0.707
Q2 0.773
Q3 0.727
Q4 0.824
Q5 0.583
Interest in chemistry Q1 0.692
Q2 0.671
Q3 0.842
Q4 0.621
Chemistry self-efficacy Q1 0.727
Q2 0.634
Q3 0.778
Q4 0.801
Q5 0.753
Q6 0.733

Acknowledgements

This study was financially supported by the Funds of the Ministry of Education of Humanities and Social Science Project (21YJA880018). We express our sincere thanks to the teachers and students who participated in this study.

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Footnote

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