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DOI: 10.1039/C9RP00073A (Paper) Chem. Educ. Res. Pract., 2019, 20, 618-632

Motivational pathways towards academic achievement in physics & chemistry: a comparison between students who opt out and those who persist

Diego Ardura *a and Alberto Pérez-Bitrián b
aDepartamento de Métodos de Investigación y Diagnóstico en Educación I (MIDE I), Facultad de Educación, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 14, ES-28040, Madrid, Spain. E-mail: dardura@edu.uned.es
bInstituto de Síntesis Química y Catálisis Homogénea (iSQCH), CSIC–Universidad de Zaragoza, C/Pedro Cerbuna 12, ES-50009, Zaragoza, Spain

Received 12th March 2019 , Accepted 27th May 2019

First published on 29th May 2019

Abstract

The main goal of the present investigation was to analyze the effect of motivation towards physics & chemistry on achievement in secondary school students. We focused our interest on the comparison between students who chose the subject when becoming optional in the Spanish educational system and those who opted out. Our analyses uncovered the existence of three different motivational profiles towards achievement in physics & chemistry, depending on the students’ decisions to persist or to leave. Regardless of their choice, self-efficacy played a key role, not only as a direct predictor of academic achievement but also as a facilitator of the indirect effects of other motivational variables on academic performance. However, our models showed that, for students who opt out physics and chemistry, grade motivation and self-determination are more important predictors of performance than for those who choose the subject. Gender effects and educational implications are also addressed.

Introduction

In the last few decades, the significant decrease in the number of STEM (Science, Technology, Engineering, and Mathematics) students has become an international concern for both researchers and practitioners (Lyons, 2006a; Rocard et al., 2007; Ulriksen et al., 2010; Bøe, 2012). This problem slightly depends on the particular field of science, with chemistry and physics being among the most affected areas (Osborne et al., 2003; Solbes et al., 2007; Ulriksen et al., 2010; Solbes, 2011). Additionally, it does not appear exclusively in the context of college education (Oon and Subramaniam, 2010), but also in secondary education (Lyons, 2006b; Smyth and Hannan, 2006; Solbes et al., 2007; Solbes, 2011; Bennett et al., 2013). For this reason, there is increasing interest in the variables influencing the choice of science subjects when they are not compulsory any more. In light of official reports, a third of secondary school students leave chemistry at the age of 15, when it first becomes optional in the Spanish educational system (Consejería de Educación y Cultura del Principado de Asturias, 2017). Extensive research has been carried out in the last few years to determine the factors responsible for this decision (see, for example, Cerinsek et al., 2013; Potvin and Hasni, 2014; Shirazi, 2017), and it seems that a handful of variables, sometimes related to each other, are involved in it (Cleaves, 2005; Bennett et al., 2013).

Factors influencing persistence in STEM studies are as a matter of fact really different and include, among others, social and cultural aspects (see, for example, Lyons, 2006b; Gorard and See, 2009; Anderhag et al., 2013; Mujtaba et al., 2018), the role of teachers and their teaching styles (see, for example, Lyons, 2006a; Gorard, 2010; Cerinsek et al., 2013; Mujtaba and Reiss, 2014; Broman and Simon, 2015; Juuti and Lavonen, 2016; Sheldrake et al., 2017; Shirazi, 2017; Meli et al., 2018), students’ perceptions of the subjects (see, for example, Stokking, 2000; Smyth and Hannan, 2006; Palmer et al., 2017; Shirazi, 2017), their future intentions of pursuing scientific studies (Stokking, 2000; Bøe et al., 2011; Bøe, 2012; Bennett et al., 2013; Broman and Simon, 2015; Palmer et al., 2017; Ardura and Pérez-Bitrián, 2018) and their gender (see, for example, Jacobs, 2005; Smyth and Hannan, 2006). In addition, prior achievement in the subject can also make a difference (see, for example, Stokking, 2000; Anderhag et al., 2013; Gill and Bell, 2013; Palmer et al., 2017). Both general academic performance and grades obtained in scientific subjects have an impact on students’ choice, but the latter seem to be more important when making the decision (Anderhag et al., 2013). Additionally, students who have chosen physics & chemistry outperform those who have opted out the subject in both general academic performance and grades in physics & chemistry in the year before they make the decision (Ardura and Pérez-Bitrián, 2018). Finally, another factor influencing the choice, sometimes even more important than achievement, is motivation (Salta et al., 2012; Cerinsek et al., 2013; Mujtaba and Reiss, 2014; Aeschlimann et al., 2016; Sheldrake, 2016; Palmer et al., 2017; Sheldrake et al., 2017; Ardura and Pérez-Bitrián, 2018).

Theoretical framework

Motivation towards science and its relationship with the choice of physics and chemistry

According to the Social Cognitive Theory developed by Bandura (1986, 2001, 2012), motivation to learn science can be defined as the “internal state that arouses, directs, and sustains science-learning behavior” (Glynn et al., 2011, p. 1160). In this theory, motivation is conceptualized as a multidimensional trait (Glynn et al., 2011). In particular, the main factors that have been identified as influencing the self-regulated learning described in the Social Cognitive Theory are intrinsic motivation, extrinsic motivation, self-determination and self-efficacy (Glynn and Koballa Jr., 2006; Glynn et al., 2011). Intrinsic motivation involves the inherent satisfaction in learning science because it is found to be interesting, challenging or enjoyable (Ryan and Deci, 2000). Extrinsic motivation describes the learning of science because it drives to a tangible end or an external reward, such as a career or achieving a grade (Ryan and Deci, 2000). Self-determination accounts for the control that students believe they have over their learning of science (Black and Deci, 2000). Finally, self-efficacy refers to students’ confidence that they will succeed in science (Lawson et al., 2007; Ferrell and Barbera, 2015).

The relationship between students’ motivation towards learning science and their persistence in STEM has been widely studied at both secondary (see, for example, Mujtaba and Reiss, 2014; Palmer et al., 2017; Ardura and Pérez-Bitrián, 2018) and tertiary levels (see, for example, Shedlosky-Shoemaker and Fautch, 2015; Hinds and Shultz, 2018). In particular, it has been found that motivation towards science is higher in students who chose science subjects both at secondary school (Ardura and Pérez-Bitrián, 2018) and at university (Glynn et al., 2011). In addition, Lau and Roeser (2002) observed that motivational factors were the strongest predictors of students’ future choice of persistence in science.

Regarding the different components of motivation, extrinsic motivation seems to be the most important one associated with future preferences for science subjects. In particular, Ardura and Pérez-Bitrián (2018) found that career motivation was the best predictor of this decision. Moreover, self-efficacy beliefs also have an effect on adolescents’ career decisions (Bandura et al., 2001). In fact, self-efficacy seems to be a mediating variable between learning experiences and career interests in the model formulated by Lent et al. (1994). Also, the lack of science self-efficacy seems to support the choice of opting out physics and chemistry in high-achieving students who are motivated towards a future career in science (Ardura and Pérez-Bitrián, 2018). The key role of self-efficacy was also recently observed by van Aalderen-Smeets et al. (2018). These authors found that implicit STEM ability beliefs can predict the intention to enter a STEM degree, with this relation being partly mediated by self-efficacy beliefs.

Factors influencing performance in physics and chemistry

There is huge interest in understanding the variables that have an effect on students’ achievement in physics and chemistry courses since, as stated above, this is an important factor influencing their future participation in them. In the same vein, these variables allow predicting at-risk students in chemistry and help in identifying those who may have some difficulties with the subject (Wagner et al., 2002; Lewis and Lewis, 2007; Chan and Bauer, 2014). Among these variables, we can find individual factors, background characteristics, gender, and school-related factors. In turn, individual factors can be divided into cognitive and non-cognitive factors, both influencing students’ involvement in their learning and, consequently, their performance (Pintrich, 2000).

Factors belonging to the cognitive domain mainly include math ability (Spencer, 1996; Wagner et al., 2002; Hahn and Polik, 2004; Tai et al., 2005; Lewis and Lewis, 2007; Cooper and Pearson, 2012; Scott, 2012; Xu et al., 2013; Villafañe and Lewis, 2016), spatial skills (Carter et al., 1987), scientific and logical reasoning ability (Nicoll and Francisco, 2001; Lewis and Lewis, 2007; Bird, 2010; Cracolice and Busby, 2015; Vilia et al., 2017) and prior conceptual knowledge (Wagner et al., 2002; Seery, 2009; Xu et al., 2013; Vahedi and Yari, 2014). It is important to mention that the way learners acquire and represent concepts also has an impact on their success in chemistry courses (Frey et al., 2017). In particular, it must be taken into account that previous incorrect ideas or misconceptions can render the understanding of new concepts difficult (Nakhleh, 1992; Nicoll, 2001), which also has an impact on students’ achievement.

Among the non-cognitive factors, we can include attitude towards science (House, 1995; Cukrowska et al., 1999; Kan and Akbaş, 2006; Xu and Lewis, 2011; Xu et al., 2013; Awodun et al., 2014; Chan and Bauer, 2014; Vahedi and Yari, 2014; Villafañe and Lewis, 2016; Vilia et al., 2017), self-concept (Britner, 2008; Lewis et al., 2009; Chan and Bauer, 2014), utility value (González and Paoloni, 2015) and motivation or any of its components (Black and Deci, 2000; Zusho et al., 2003; Cavallo et al., 2004; Kan and Akbaş, 2006; Akbaş and Kan, 2007; Gungor et al., 2007; Britner, 2008; Chan and Bauer, 2014; González and Paoloni, 2015; Uzuntiryaki-Kondakci and Senay, 2015; Ferrell et al., 2016; Villafañe et al., 2016; González et al., 2017; Ramnarain and Ramaila, 2018). Interestingly, in the case of motivation, Zusho et al. (2003) found that self-efficacy and task value were the motivational components which best predict performance in a sample of college chemistry students, even after controlling for prior achievement. Regarding interest, Ferrell et al. (2016) also found a significant correlation between the final grade in college general chemistry and personal interest.

Additionally, other factors such as students’ background characteristics have an impact on their grades (Tai et al., 2005). In particular, Areepattamannil and Kaur (2013) found that home language, family wealth, and socio-economic status could only significantly predict physics success in the case of non-immigrant students in Canada. In addition, Pyburn et al. (2013) found that language comprehension contributes to performance in college general chemistry and actually the comprehension skill partially compensates for insufficient prior knowledge.

Gender differences regarding performance in science have also been extensively studied (Eddy and Brownell, 2016). Since females do not outrank males in general intelligence (Halpern, 2000), many efforts have been devoted towards investigating why females generally outperform males in science. For example, Turner and Lindsay (2003) observed the importance of non-cognitive factors in explaining the distinct grades in organic chemistry of males and females. In particular, in the model for Turkish students developed by Acar et al. (2015), girls’ utility value of science was higher than boys’, explaining girls’ higher science achievement. Besides, Fischer et al. (2013) rationalized females’ advantage in terms of their higher achievement motivation.

Remarkably, some school-related factors have an important effect on students’ achievement in physics and chemistry. In general, a combination of teacher quality and curriculum quality influences students’ achievement, with both the expertise and academic merits of the teacher being important (Darling-Hammond, 2007). The role of teachers and the curriculum can also have an important effect on students’ achievement in physics and chemistry (Lawrenz et al., 2009). Moreover, teachers’ pedagogical choices and practices also have a great impact on it (Tai et al., 2005).

Relation between motivation and performance in science

According to the Social Cognitive Theory (Bandura, 1986, 2001, 2012), students’ functioning is based on the interactions among personal traits, environmental contexts, and behaviors. Consequently, students’ self-regulation of learning is more effective when they understand, monitor and control their behavior and motivation. Several models like the Self-Determination Theory (Ryan and Deci, 2017), the Expectancy-Value Theory (Wigfield and Eccles, 2000) or the Goal-Setting Theory (Locke and Latham, 2002) have been proposed in the context of achievement motivation. These approaches must be understood as complementary to each other as some of the motivational traits which arise from them refer to similar constructs. The Self-Determination Theory (SDT) suggests that motivation rests on a continuum which ranges from controlled to autonomous (Ryan and Deci, 2017), distinguishing between intrinsic motivation, which is related to autonomous behaviors by definition, and extrinsic motivation, which covers a wide range of behaviors from autonomous to controlled (Ryan et al., 1985). The expectancy-value model of achievement motivation suggests three motivational determinants of achievement (Wigfield and Eccles, 2000): (a) different components of subjective task values (usefulness, importance, and interest), (b) success expectancies, and (c) ability beliefs. Finally, the Goal-Setting Theory reveals that high goals are related to high levels of effort and achievement (Locke and Latham, 2002). In the context of this theory, goals can be oriented to learning (mastery) or performance.

Motivation and performance have been found to be closely related (Denissen et al., 2007). As stated in the previous section, both cognitive and non-cognitive factors influence academic achievement in science. Whereas cognitive factors have been more widely studied in educational research than affective variables, the latter are increasingly under investigation (Koballa Jr. and Glynn, 2007; Fortus and Vedder-Weiss, 2014). In particular, cognitive factors seem to have a stronger effect than non-cognitive ones (see, for example, Turner and Lindsay, 2003). However, Lau and Roeser (2002) claimed that, although cognitive variables are the strongest predictors of achievement in science, taking simultaneously into account both types of factors provides better models to explain science performance. In the case of organic chemistry, Villafañe et al. (2016) found that motivation and performance were connected by a reciprocal relationship, which means that previous achievement influences motivation and motivation also has an impact on future achievement.

Each of the different motivational components has been found to influence achievement in physics and chemistry. Both intrinsic and extrinsic motivations seem to play a simultaneous key role in general academic achievement (Cerasoli et al., 2014), and in science in particular (Mujtaba et al., 2018). Intrinsic motivation played a relevant part in science academic achievement and made a difference in the four learning profiles found for high school students in Singapore (Ng et al., 2016). By their part, Gungor et al. (2007) observed that achievement motivation was the non-cognitive characteristic that had the greatest influence on physics achievement. In the case of Austin et al. (2018), even though students were highly motivated towards earning a high grade in organic chemistry, grade motivation was only weakly correlated with performance. Extrinsic motivation can be related to instrumentality (Husman and Lens, 1999) and utility value (Wigfield and Eccles, 2000). Evidence of an association of both constructs with performance has been previously found (Hulleman et al., 2016; Peteranetz et al., 2018). Besides, the performance goal orientation, which could also be related to extrinsic motivation, also showed a positive relationship with achievement (Elliot and McGregor, 2001). Interestingly, the same study found that self-determination was uncorrelated with academic performance. In other contexts, self-determination is next to what other authors call cognitive engagement, which has been related to performance (Christenson et al., 2012).

Self-efficacy was already claimed by Bandura (1986, 1994, 1997) to have an effect on students’ success through their persistence and effort. In particular, in the context of science education, there has been special interest in understanding the role that self-efficacy plays in academic outcomes. Thus, a huge number of studies describing the interconnection between this motivational component and science performance have been published (Andrew, 1998; Zusho et al., 2003; Cavallo et al., 2004; Kan and Akbaş, 2006; Lawson et al., 2007; Britner, 2008; Bryan et al., 2011; Jansen et al., 2015; Uzuntiryaki-Kondakci and Senay, 2015; Boz et al., 2016; Ng et al., 2016; Villafañe et al., 2016; Ramnarain and Ramaila, 2018). Moreover, although Austin et al. (2018) found that all four motivation factors of the Organic Chemistry Motivation Survey (relevance, self-determination, self-efficacy, and grade motivation) correlated positively with students’ performance, the strongest correlation was found for self-efficacy.

The moment when self-efficacy is assessed is also important. In fact, Zusho et al. (2003) observed an overall decline in students’ motivation over time but, interestingly, the level of self-efficacy in the high-achieving group of students was higher at the end of the semester than at the beginning. Ferrell et al. (2016) found that self-efficacy measured at the end of the semester was the strongest predictor of the grades in a general chemistry course, whereas that measured at the start of the semester was only a weak one. In a more recent study, Young et al. (2018) observed that there was a decline in four motivational factors with a rebound between semesters. These motivational factors, which are linked to grades, included self-efficacy, intrinsic motivation and grade motivation, for which they observed a total recovery, and career motivation, which was only partially recovered. However, no change was observed in self-determination, for which differences among academic fields were found.

The present study

As stated above, motivation towards science and previous performance are key to understanding students’ future choices within the STEM track. In the context of the Social Cognitive Theory (Bandura, 1986, 2001, 2012), motivation is a multidimensional construct, including 5 components according to Glynn et al. (2011). These components are self-determination (SD), self-efficacy (SE), intrinsic motivation (IM), and, connected to extrinsic motivation, career motivation (CM) and grade motivation (GM). Besides, previous studies have reported the existence of different learning profiles for high school science students (Ng et al., 2016; Ferguson and Hull, 2018), and gender effects on academic achievement have also been reported (Ardura and Pérez-Bitrián, 2018). In light of these findings, the main goal of the present investigation was to unravel the role that the aforementioned motivational traits play in secondary school chemistry students’ performance. In particular, we focused on studying how the interplay of these traits varies from students who choose physics & chemistry when it becomes optional in the Spanish educational system to those who opt out the subject at that point. To this aim, three research questions served as a guide for our investigation.

RQ1. Are motivational profiles towards physics & chemistry academic achievement different for the students who choose and for those who leave the subject when it becomes optional?

RQ2. How do the different components of motivation towards physics & chemistry explain academic achievement for the students who choose and for those who leave the subject when it becomes optional?

RQ3. Are there any gender differences in secondary school physics & chemistry students’ motivational profiles?

A structural equation model approach was selected to answer these research questions. Our proposed models were based both on previous theoretical knowledge and on a preliminary multiple regression analysis. First, previous studies suggest, as stated above, that motivation and performance are highly related. Besides, the motivational traits involved in our investigation correlate among each other. For instance, intrinsic motivation has been related to self-efficacy (Bandura, 1997; Prat-Sala and Redford, 2010). Moreover, according to the Cognitive Evaluation Theory (CET) developed in the context of the SDT, self-determination must be accompanied by feelings of competence (self-efficacy) (Ryan and Deci, 2017). Besides, as stated by Locke and Latham (1990, p. 58), “intrinsic motivation rarely operates in isolation from other types of motivation”. In fact, previous studies showed that several types of extrinsic motivation, such as career and grade motivations, can have an impact on the effect of intrinsic motivation as a predictor of performance (Cerasoli et al., 2014). Second, given the plausible complexity of the relationships between the motivational traits, preliminary multiple regression analyses were carried out in each sub-sample (PC students and non-PC students) in order to suggest a good starting point for the search of our models. These analyses showed that, for PC students, only self-efficacy (β = 0.341, p < 0.001) and grade motivation (β = 0.149, p = 0.001) were significant predictors of academic achievement in physics & chemistry. However, in the case of non-PC students, career motivation (β = −0.112, p = 0.043) was also a significant predictor in addition to self-efficacy (β = 0.317, p = 0.017) and grade motivation (β = 0.294, p < 0.001). Taking these results into account, SE and GM were taken in our model for PC students as direct predictors and our model explored the remaining motivational traits (IM, SD, and CM) as indirect predictors of PCPA through SE and GM (see Fig. 1). Consequently, SE and GM would ease indirect effects. In the case of the model proposed for the non-PC students, SE, GM, and CM were selected as variables with direct effects on PCPA, whereas IM and SD were considered initially as indirect predictors of PCPA (see Fig. 2). Following the Social Cognitive Theory (Bandura, 1986, 2001, 2012), a central role of the SE in the models is proposed as the main facilitator of indirect effects of the remaining motivational traits.


image file: c9rp00073a-f1.tif
Fig. 1 Proposed model for PC students.

image file: c9rp00073a-f2.tif
Fig. 2 Proposed model for non-PC students.

Methods

Context and sample

In the Spanish educational system, chemistry is taught together with physics in a subject named physics & chemistry, except for the last year before starting university, when they are separate subjects. This subject becomes optional when students are 15 years old, one year before the end of compulsory education. A convenience sampling based on the accessibility of the schools was used in this study. After presenting the project to the schools, 15 Spanish secondary schools (10 public and 5 private) participated in the data gathering. A total of 1060 students (50.8% girls and 49.2% boys), with an average age of 15.03, comprised the sample for this investigation. This sample was naturally divided into two groups: the first one (PC group) included students who decided to choose physics & chemistry (n = 695, 65.6%) in the present academic year and the second one (non-PC group) consisted of students who decided to leave the subject (n = 365, 34.4%). In the PC group, the number of males (n = 359, 51.7%) was almost the same as the number of females (n = 336, 48.3%). However, the non-PC group included 163 males (44.7%) and 202 females (55.3%). All the students of the sample gave their consent to participate in our investigation.

Data collection

The data gathering took place at the start of the 2017–2018 academic year. The schools were contacted in September 2017 and the authors of this study explained the goals and the procedure that was meant to be used to the principals and teachers. Data were gathered in the classrooms right after the students made the decision of choosing the subject of physics & chemistry or not for the starting academic year. Two different modalities were offered to the schools: students could complete either an online or a paper-based survey, depending on the school resources, but always under the supervision of their teachers. The students were asked to complete the survey sincerely as the researchers guaranteed their anonymity both during the course of the research and when results were to be presented.

Variables and instruments

To accomplish the goals of the present investigation, physics & chemistry academic achievement was taken as the outcome variable and five components of motivation towards physics & chemistry, which will be introduced below, were the predictor variables.
Academic achievement in physics & chemistry. No standardized instrument is available to measure students’ achievement in the context of physics & chemistry in Spain. Since 2013, the Spanish educational system has been based on a set of assessment learning standards, which teachers are bound to use when planning their lessons and assessing students’ outcomes (Jefatura del Estado, 2013). Consequently, in the context of the present investigation, academic achievement was measured using the physics & chemistry point average (PCPA) based on the students’ final grade in the frame of the aforementioned standards, and which ranges from 1 to 10 in the Spanish educational system. The final grade on a subject has been repeatedly used as an indicator of academic performance in previous research in science teaching (see, for example, Gungor et al., 2007; Britner, 2008; González and Paoloni, 2015; Ferrell et al., 2016; González et al., 2017).
Motivation towards physics & chemistry. Motivation towards physics & chemistry was measured with the Spanish translation and adaptation (Ardura and Pérez-Bitrián, 2018) of the Science Motivation Questionnaire II (SMQII) (Glynn et al., 2011), which has also been previously translated into other languages to evaluate science motivation levels at high school (Salta and Koulougliotis, 2015; Schumm and Bogner, 2016).

This instrument comprises 25 items and it is based on the Social Cognitive Theory (Bandura, 1986, 2001, 2012). It measures five components of motivation: self-efficacy, self-determination, intrinsic motivation, career motivation and grade motivation, with the last two being related to extrinsic motivation. SE invokes students’ beliefs in their ability to succeed in physics & chemistry and it is measured by items like ‘I am confident I will do well on physics & chemistry tests’. SD is related to the degree of control the students believe they have when they learn physics & chemistry and items like ‘I spend a lot of time learning physics & chemistry’ are meant to measure this construct with the instrument. IM involves students’ interest in learning about the topic and it is measured by items like ‘I am curious about discoveries in physics & chemistry’. Items like ‘Understanding physics & chemistry will benefit me in my career’ measure CM, which refers to the students’ motivation to a future career related to physics & chemistry. Finally, GM is related to extrinsic motivation of achieving a good grade in the subject. This subscale comprises items like ‘Scoring high on physics & chemistry tests and labs matters to me’. The score in each of these components can vary from 0 to 20. The factor analysis of the Spanish translation and adaptation rendered the same factor structure of the original instrument (Ardura and Pérez-Bitrián, 2018). This structure was verified by confirmatory factor analysis (GFI = 0.89, CFI = 0.92, RMSEA = 0.07, SRMR = 0.05). Reliability analysis was undertaken by computing the Cronbach's alpha for each subscale. The values obtained for our sample were: 0.86 for self-efficacy, 0.87 for self-determination, 0.88 for intrinsic motivation, 0.92 for career motivation and 0.82 for grade motivation. Based on this previous validation work with the same sample of students, in the present study, we used the latent variables extracted from the Spanish adaptation of the SMQII.

Data analyses

Our investigation was carried out using a structural equation model (SEM) approach. A preliminary exploration of the data helped us find a SEM for the non-PC students’ subsample starting with our proposed model for these students (see Fig. 2). However, we were unable to find a single model to explain the PC students’ PCPA using the proposed model for these students (see Fig. 1). Therefore, we hypothesized that at least two different models were needed to explain PCPA in terms of the motivational variables for the PC students. For this reason, the PC group was divided into two subsamples by means of a hierarchical cluster analysis using the motivational variables (PC/highly-motivated students and PC/low-motivated students), using the squared Euclidean distance as the proximity measure and Ward's method as the grouping procedure. The non-PC students constituted the third group considered in our investigation. Thus, the SEM analyses were tackled using the three different groups of students independently. One-way ANOVA was employed to examine mean differences. Several indices have been proposed in the literature to examine the model fit to the data (Blunch, 2013). Among them, we will present first the statistical significance (p), which tests the difference between the data and the model. Thus, p values larger than 0.05 indicate that the model fits the data. Besides, other fit indices like the Goodness of Fit Index (GFI), the Adjusted GFI (AGFI), and the Root Mean Square Error Approximation (RMSEA) will be discussed to assess the three models. When the whole sample was considered, the motivation variables showed a non-normal behavior. However, when each of the three groups used in the present investigation was analyzed, the Kolmogorov–Smirnov test showed the normality of the motivational variables. A multigroup analysis was used to assess gender differences in the models. To this aim, model parameters were constrained to be equal for boys and girls. Then, using a χ2 difference test, this constrained model was compared with another one in which the parameters for boys and girls were set free. All the computations were undertaken using SPSS and AMOS (Arbuckle, 2010).

Results

Cluster analysis

Two different groups of students were initially taken into consideration in the present investigation. The first group (N = 695) included students who decided to choose physics & chemistry when it first becomes optional in the Spanish educational system (PC students), whereas those students who opted out this subject (N = 365) constituted the second group (non-PC students). However, our SEM procedure was unable to identify a single model for which the PC students’ data would fit. For this reason, we undertook a cluster analysis within this group, which revealed the presence of two subgroups with respect to their motivation towards physics & chemistry (see Table 1). The first subgroup (n = 276) comprised students who opted for this subject and had a high motivation towards it. The second subgroup (n = 419) included students who chose physics & chemistry and whose motivational levels were in between the first subgroup and those who opted out the subject (see Table 1). As expected, ANOVA and post hoc analyses revealed significant differences in PCPA among these three groups of students (F(2,1057) = 128.31, p < 0.001, η2 = 0.195). The highest PCPA was found in the group of highly-motivated physics & chemistry students (7.82), followed by those students who chose physics & chemistry but had a lower motivation (6.95). Finally, those who opted out the subject were the lowest-achieving students (5.92). It is interesting to note that, in view of our ANOVA results, all motivational variables were significantly different in the three groups. The largest size effect was found in the case of career motivation (0.636), whereas self-determination (0.180) presented the lowest (see Table 1). Post hoc analyses revealed significant differences between the three groups in all the pairs of variables considered in the present investigation.
Table 1 Descriptive statistics from the cluster analysis for the PC group, descriptive statistics for the non-PC group, and ANOVA group comparison
PC/highly-motivated PC/low-motivated Non-PC Mean comparison
M SD M SD M SD F(2,1057) η 2
SE: self-efficacy; GM: grade motivation; IM: intrinsic motivation; SD: self-determination; CM: career motivation; PCPA: physics & chemistry point average.
SE 16.27 2.58 12.45 3.79 9.05 4.64 277.42 0.344
GM 17.86 1.86 13.81 3.56 10.32 4.80 323.26 0.380
IM 14.28 2.85 9.57 3.75 5.84 4.13 412.52 0.438
SD 15.11 2.83 11.69 3.67 10.35 4.98 116.00 0.180
CM 16.60 2.29 9.51 3.94 4.71 3.63 921.66 0.636
PCPA 7.82 1.50 6.95 1.52 5.92 1.45 128.31 0.195

Descriptive and correlational analyses

Table 2 collects the results of the descriptive and inferential analyses by gender in each of the three groups of students taken into consideration after the cluster analysis. The mean differences in PCPA were nonsignificant in the case of the non-PC students. However, for both groups of PC students, the differences were statistically significant as girls outperformed boys. The largest effect size was found in the PC/low-motivated students. SD displayed the largest effect size among all the motivational variables in the three groups (see Table 2) and it was the only one that rendered significant differences in all of them, with the girls being more self-determined than the boys. In turn, in the PC groups, boys had higher levels of self-efficacy than girls, whereas in the non-PC group both genders were statistically even. Finally, our analyses show that girls were significantly less career-motivated than boys only among the non-PC students. In Table 2, the Pearson correlation coefficients by gender are presented. Overall, the highest correlations among the variables involved in the present investigation were found in the non-PC group and the lowest in the PC/highly-motivated students. The strengths of the relationships between the variables in the PC/low-motivated students were closer to those found in the non-PC students than in the PC/highly-motivated students.
Table 2 Correlations for girls (upper part) and boys (lower part), descriptive statistics, and gender mean comparisons
Correlations Mean Mean comparison
PCPA SE GM IM SD CM Boys Girls t p d
PCPA: physics & chemistry point average; SE: self-efficacy; GM: grade motivation; IM: intrinsic motivation; SD: self-determination; CM: career motivation; d: effect size (Cohen's statistics). *p < 0.05; **p < 0.01.
PC/highly-motivated
PCPA 1 0.290** 0.151 0.110 0.105 0.034 7.59 8.01 −2.37 0.019 −0.28
SE 0.476** 1 0.169 0.366** 0.111 0.366** 16.57 16.01 1.82 0.070 0.22
GM 0.172 0.132 1 0.135 0.040 0.104 17.72 17.99 −1.20 0.233 −0.14
IM 0.141* 0.254** 0.018 1 0.087 0.227** 14.69 13.92 2.23 0.027 0.27
SD −0.160 −0.185* −0.052 0.129 1 −0.029 14.36 15.78 −4.27 <0.001 −0.51
CM 0.267** 0.315** 0.114 0.287** 0.069 1 16.69 16.53 0.58 0.564 0.07
PC/low-motivated
PCPA 1 0.373** 0.252** 0.146* 0.216** −0.004 6.66 7.31 −4.48 <0.001 −0.43
SE 0.366** 1 0.274** 0.386** 0.221** 0.080 12.92 11.88 2.82 0.005 0.28
GM 0.241** 0.396** 1 0.153** 0.414** 0.309** 13.52 14.15 −1.81 0.071 −0.18
IM 0.129 0.440** 0.251** 1 0.199** 0.153* 9.98 9.06 2.51 0.012 0.25
SD 0.118 0.227** 0.367** 0.080 1 0.139 10.90 12.64 −4.98 <0.001 −0.49
CM 0.099* 0.257** 0.236** 0.392** 0.099 1 9.71 9.28 1.09 0.274 0.11
Non-PC
PCPA 1 0.482** 0.471** 0.177* 0.284** 0.032 5.83 6.00 −1.09 0.277 −0.12
SE 0.509** 1 0.695** 0.467** 0.396** 0.262** 9.23 8.90 0.69 0.690 0.07
GM 0.503** 0.635** 1 0.456** 0.633** 0.286** 10.17 10.45 −0.55 0.551 −0.06
IM 0.401** 0.598** 0.475** 1 0.412** 0.636** 5.71 5.96 −0.56 0.575 −0.06
SD 0.360** 0.461** 0.513** 0.455** 1 0.226** 9.48 11.05 −3.02 0.003 −0.32
CM 0.197** 0.356** 0.351** 0.592** 0.333** 1 5.21 4.32 2.34 0.020 0.25

Structural equation models

The main goal of the present investigation was to disentangle the different relationships among the students’ motivational traits and academic achievement in physics & chemistry for the PC and non-PC students. Our SEM analysis rendered a different model for each of the groups of students described in the previous section. The models sought to explain the effect of the five motivational traits (SE, GM, IM, SD, and CM) on PCPA. The model for each group of students is described in the following paragraphs.

The model which best fitted the data for the group of PC/highly-motivated students is presented in Fig. 3 and Table 3. A value of p = 0.502 indicated that there was no significant difference between the model and the data. Additionally, other fit indices displayed a very good adjustment (χ2/df = 0.916, GFI = 0.991, AGFI = 0.977, and RMSEA < 0.001), confirming the agreement between our theoretical model and the data. The model explained 15% of the PCPA variance. Only two variables of the model, SE and GM, exhibited direct relationships with PCPA (see Fig. 3). Besides, our model predicted four indirect effects of IM, SD, CM, and GM on PCPA through SE, with the strongest of these effects (β = 0.12**) being the one between IM and PCPA (see Table 3). All correlations found were positive except for the one between SD and SE (β = −0.16*). It is interesting to note the presence of a positive direct relationship (β = 0.26**) between IM and CM (see Fig. 3).


image file: c9rp00073a-f3.tif
Fig. 3 Structural model of motivational variables and PCPA for PC/highly-motivated students. Significant correlations for the whole sample, boys (brackets) and girls (square brackets) are shown. *p < 0.05; **p < 0.01.
Table 3 Direct, indirect and total effects on PCPA in the sample
PC/Highly-motivated PC/low-motivated Non-PC
Direct Indirect Total Direct Indirect Total Direct Indirect Total
SE: self-efficacy; GM: grade motivation; IM: intrinsic motivation; SD: self-determination; CM: career motivation. *p < 0.05; **p < 0.01.
SE 0.34** — 0.34** 0.27** — 0.27** 0.33** — 0.33**
GM 0.12* 0.04* 0.16** 0.17* 0.07* 0.24* 0.31** 0.16** 0.47**
IM — 0.12** 0.12** — 0.05** 0.05** — 0.16** 0.16**
SD — −0.05** −0.05** — 0.09** 0.09** — 0.22** 0.22**
CM — 0.06* 0.06* — 0.05** 0.05** −0.10* — −0.10*

The model for the PC/low-motivated students showed slightly worse fit indices than the previous group (p = 0.061, χ2/df = 1.927, GFI = 0.996, AGFI = 0.969, and RMSEA = 0.047), but still within the acceptable threshold for a good model. This model explained 13% of the PCPA variance. The description of this model is presented in Fig. 4 and Table 3. It is interesting to note that the relationship between SE and PCPA is slightly weaker in this group than in the first one. Besides, the indirect effect of SD on PCPA increased, compared to the case of the PC/highly-motivated students, and it is positive instead of negative (see Table 3). Interestingly, a change was found in how the indirect effects take place: unlike in the case of the PC/highly-motivated students, the relationship between SD and PCPA takes place through GM instead of SE (see Fig. 4). Finally, our model revealed the presence of a covariance between IM and SD, which was not present in the case of the PC/highly-motivated students’ model.


image file: c9rp00073a-f4.tif
Fig. 4 Structural model of motivational variables and PCPA for PC/low-motivated students. Significant correlations for the whole sample, boys (brackets) and girls (square brackets) are shown. *p < 0.05; **p < 0.01.

The model for the non-PC students showed a very good fit to the data (p = 0.860, χ2/df = 0.512, GFI = 0.998, AGFI = 0.990, and RMSEA < 0.001). This model explained 30% of the PCPA variance. The indirect effects of IM and GM on PCPA through SE remained qualitatively similar to those found in the first two models (see Fig. 5 and Table 3). It is interesting to note that the correlation coefficient between GM and SE increased compared to those found in the previous models (see Fig. 5). A significant relationship between IM and GM—not present in the previous models—was found in this one and led to a higher indirect effect of IM on PCPA (β = 0.16**). Besides, the indirect effect of SD on PCPA was remarkably higher in the case of this model (β = 0.22**), in comparison with the two models corresponding to the PC students. It is worth noting that the model predicts a significant direct negative relationship between CM and PCPA (see Fig. 5 and Table 3), which was not found in the models for the other two types of students described above. Interestingly, due to this last relationship, IM is indirectly connected to PCPA through CM. Finally, the covariance between IM and SD is stronger in the case of this model than in the previous one.


image file: c9rp00073a-f5.tif
Fig. 5 Structural model of motivational variables and PCPA for non-PC students. Significant correlations for the whole sample, boys (brackets) and girls (square brackets) are shown. *p < 0.05; **p < 0.01.

Gender effects

In order to study the effect of gender, a multigroup SEM analysis was tackled in each of the three models described above. The model for the PC/highly-motivated students accounted for 24% and 9% of the PCPA variance for boys and girls, respectively, despite the fact that the model was statistically identical for both subsamples (χ2 = 10.358, p = 0.169). The multigroup analysis revealed that the correlation coefficients between both SD and CM with SE were significant in the case of boys but nonsignificant in the case of girls (see Fig. 3). In fact, a pair-wise comparison showed significant differences in the correlation coefficient between CM and SE, which was higher for the boys than for the girls in the sample (χ2 = 3.926, p = 0.045). None of the other comparisons rendered statistical differences. It is worth noting the lack of significant indirect effects of SD and CM on PCPA in the case of girls (see Table 4). Besides, in spite of being significant for the whole group of students, the correlations between GM and PCPA were nonsignificant for both boys and girls, when considered separately (see Table 4 and Fig. 3).
Table 4 Direct, indirect and total effects on PCPA in the models by gender
Direct Indirect Total
Boys Girls Boys Girls Boys Girls
SE: self-efficacy; GM: grade motivation; IM: intrinsic motivation; SD: self-determination; CM: career motivation. *p < 0.05; **p < 0.01.
PC/highly-motivated
SE 0.46** 0.27* — — 0.46** 0.27*
GM 0.11 0.11 — — 0.11 0.11
IM — — 0.13* 0.10* 0.13* 0.10*
SD — — −0.10* −0.02 −0.10* −0.02
CM — — 0.12* < 0.01 0.12* < 0.01
PC/low-motivated
SE 0.32** 0.33** — — 0.32** 0.33**
GM 0.11 0.16* 0.10** 0.07** 0.21** 0.23**
IM — — 0.13** 0.14** 0.13** 0.14**
SD — — 0.07** 0.09** 0.07** 0.09**
CM — — 0.04** 0.06** 0.04** 0.06**
Non-PC
SE 0.32** 0.33** — — 0.32** 0.33**
GM 0.30** 0.31** 0.15* 0.17** 0.45** 0.48**
IM — — 0.25* 0.10* 0.25* 0.10*
SD — — 0.17* 0.26* 0.17* 0.26*
CM −0.03 −0.14* — — −0.03 −0.14*

The multigroup analysis in the case of the PC/low-motivated students’ model confirmed the adjustment to the data for both boys and girls (χ2 = 2.320, p = 0.940). Consequently, the model held for both genders. The percentages of PCPA variance explained for boys (14%) and girls (16%) were similar. Two pair-wise differences were found in the model for both genders. First, the direct relationship between GM and PCPA was significant in the case of girls, but nonsignificant for their gender counterparts (see Table 4 and Fig. 4). Second, the same fact was found in the mutual relationship between IM and SD (see Fig. 4). Indirect effects displayed similar coefficients for both genders (see Table 4).

Finally, the model for the non-PC students also retained its structure for both genders (χ2 = 7.887, p = 0.445). The percentage of the PCPA variance explained by the model was 31% for boys and 30% for girls. As shown in Fig. 5 and Table 4, the direct correlation between CM and PCPA was significant only for girls. Two distinct behaviors of the indirect effects on PCPA are worth mentioning: while the indirect effects of IM were higher for boys, the indirect effects of SD were higher for girls. No gender effect was found in the remaining direct and indirect relationships between variables (see Table 4).

Discussion and conclusions

The present investigation aimed to disentangle the role of motivational constructs in physics & chemistry academic achievement and to study how these findings could affect students’ choice of the subject when it becomes optional in the Spanish educational system. This problem was approached by means of structural equation models.

The first research question we addressed in the present work was: are motivational profiles towards physics & chemistry academic achievement different for the students who choose and for those who leave the subject when it becomes optional? From our proposed models, we were able to find a SEM for the students who opted out physics & chemistry. However, our search for a single model from the proposal for the students who chose the subject was unsuccessful. This fact would imply the existence of different motivational profiles towards academic achievement in physics & chemistry among the participants. A subsequent cluster analysis rendered two different sets of students in the latter group, in which two different models were found. Consequently, in line with Ng et al. (2016) and Ferguson and Hull (2018), our analyses revealed the presence of different motivational profiles for secondary school physics & chemistry students. It is interesting to note that, as previously stated by these authors, the present investigation uncovered that the PC/highly-motivated students performed significantly better in physics & chemistry than the PC/low-motivated students. Moreover, as found in a previous work (Ardura and Pérez-Bitrián, 2018), the non-PC students were the least motivated of the total sample and showed the lowest level of academic achievement. This positive effect of motivation on students’ outcomes has been previously reported in the context of science education (Singh et al., 2002; González and Paoloni, 2015; Uzuntiryaki-Kondakci and Senay, 2015). It is interesting to note that our analyses uncovered a subgroup of students among those who decided to choose the subject, whose levels of motivation towards physics & chemistry and academic achievement were average. Given the relevance of both variables when explaining students’ opt out, these students could be identified as at risk of abandonment students.

As stated above, our analyses uncovered the existence of three different motivational profiles among the participants. Given the aforementioned importance of academic achievement for students’ persistence, the second research question we addressed in the present work was: how do the different components of motivation towards physics & chemistry explain academic achievement for the students who choose and for those who leave the subject when it becomes optional? Our results showed that motivational constructs partially explain the variance in academic achievement, in line with previously reported findings (Spinath et al., 2006; Steinmayr and Spinath, 2009). Interestingly, the motivational variables included in our investigation explained an average of 14% of the PCPA variance in the two PC subsamples but this percentage increased to 30% in the case of the non-PC students. This fact would imply that motivation could be more important when explaining physics & chemistry academic achievement for those students who decide to opt out than for those who persist when the subject becomes optional.

In a similar fashion to previous research in the context of science education and in line with the Social Cognitive Theory (Bandura, 1986, 2001, 2012), self-efficacy played a relevant role in our three models as it was a good predictor of physics & chemistry academic achievement (Zusho et al., 2003; Kan and Akbaş, 2006; Uzuntiryaki-Kondakci and Senay, 2015; Ferrell et al., 2016; Ng et al., 2016; Ramnarain and Ramaila, 2018). Besides, as stated by Schunk (1991), self-efficacy is not the only relevant variable in achievement settings in which students’ skills, perceived value outcomes and outcome expectations are also very important for explaining academic success. In this vein, our analyses uncovered that the second direct predictor of academic achievement was grade motivation. These direct effects were common to the three models, but it is interesting to note that while the strength of the relationship between self-efficacy and PCPA was stable along the three models, the relationship between grade motivation and PCPA increased its strength from the PC/highly-motivated students to the non-PC students. This fact reflects how, as the overall motivation decreases, grade motivation becomes more important regarding academic achievement. Besides, in the case of the non-PC students, grade motivation is strongly related to self-efficacy and this fact leads to a remarkable indirect effect of students’ motivation towards grades on PCPA. As a consequence of direct and indirect effects, self-efficacy is overcome by grade motivation as the best predictor of PCPA for these students who opted out physics & chemistry. On the other hand, our analysis found that these students were the lowest achievers in the sample. These findings suggest that grades are a sensitive issue for those students who decide to opt out the subject.

Interestingly, both self-efficacy and grade motivation ease indirect effects between the remaining motivational variables and academic achievement. Our results for self-efficacy are in agreement with previous investigations that have already highlighted the relevance of this construct as a mediating variable (Stajkovic et al., 2018). Our models showed how grade motivation may also be key to understanding the indirect effects of the other motivational traits, such as intrinsic motivation, self-determination and career motivation, on academic achievement in physics & chemistry. However, it is worth noting that these indirect effects are different depending on the type of student. On the one hand, SE assists the indirect effects of IM, SD, GM and CM for the PC/highly-motivated students, IM, GM and CM for the PC/low-motivated students, and only IM and GM for the non-PC students. On the other hand, although GM does not ease any indirect effect in the case of the PC/highly-motivated students, it eases the indirect effects of SD for the PC/low-motivated students and IM and SD in the case of the non-PC students. Therefore, with regard to indirect effects, SE seems to be more crucial for the highly-motivated students and GM for the low-motivated students. This different relative importance of SE and GM depending on the type of student could be explained invoking the fact that highly-motivated students perceive learning as controllable and, consequently, would base their achievement on their self-efficacy (Komarraju and Nadler, 2013). On the other hand, students who opt out physics & chemistry have the lowest motivation levels but, in light of our models, their achievement could be more driven by external motivational traits such us grade motivation.

Intrinsic motivation showed, in all our models, a direct relation to self-efficacy. This finding is in line with the Social Cognitive Theory (Bandura, 1986, 2001, 2012) as, in the case of intrinsic motivation, the sheer learning acts as a “reward”. In effect, intrinsically-motivated students would perceive their progression while they learn and, as stated by Bandura (1986, 1994, 1997), self-efficacy would be enhanced. In light of our models, this relationship is stable across the three groups of students taken into consideration. However, it is important to bear in mind that the students who opted out the subject presented the lowest levels of IM and SE. Self-determination played a differential role if the model for the PC/highly-motivated students is compared with the models for the PC/low-motivated and non-PC students. SD is a predictor of SE only in the first model, whereas in the second and third models SD is related to GM instead. In light of the SDT, this could lead to a different role of autonomy depending on the students’ motivational profiles: autonomy would increase highly-motivated students’ self-efficacy but low-motivated students’ grade motivation. Career motivation, which could be related to long term goal settings, is a significant predictor of self-efficacy for the PC students. This fact could be explained by the Social Cognitive Theory (Bandura, 1986, 2001, 2012), as goal setting has been linked to “an initial sense of self-efficacy for attaining the goals” (Schunk, 1991, p. 213).

As a consequence of the relationships described in the previous paragraph, the contributions of intrinsic motivation, grade motivation and career motivation to academic achievement are present in the three groups of students. This finding supports the fact that the coexistence of both intrinsic and extrinsic motivations may be critical to performance (Cerasoli et al., 2014; Mujtaba et al., 2018). However, in the case of the students who opt out physics & chemistry, the contribution of self-determination to academic achievement is remarkably important. In accordance with the Self-Determination Theory, this construct is related to autonomy (Zuckerman et al., 1978). Consequently, in the case of these students, a fully-guided instruction could result in a decrease in their autonomy, which, in turn, would damage their intrinsic motivation (Assor et al., 2005). Besides, this fact could have been particularly relevant for these students, who may have perceived the subject as an obligation, since they would not have had the choice to opt out until the next academic year (Zuckerman et al., 1978).

Our third research question was: are there any gender differences in secondary school physics & chemistry students’ motivational profiles? Our multigroup analyses revealed that the three models fit the data for both male and female students. It is interesting to note that the explained variance of PCPA is almost three times larger in the case of boys in the PC/highly-motivated group. Thus, for this group of students, motivation seems to be more important for boys than for girls when explaining PCPA. Our analysis revealed that female PC students outperformed male PC students in physics & chemistry. Besides, girls were more self-determined than boys. Bearing these facts in mind, our model for the PC/low-motivated students may contribute to explaining these differences since, for them, SD is a predictor of PCPA. However, we found no significant gender differences in PCPA in the case of the students who opted out physics & chemistry. Our study also revealed that girls were significantly more self-determined and less career-motivated than boys among the students who left the STEM track, but both genders were statistically even in the remaining motivational traits. In view of our model for this profile of students, this fact could help narrow gender differences in PCPA since, for girls, SD is a better predictor of PCPA and, moreover, CM is a significant negative predictor of PCPA for boys.

Educational implications

Previous research reported that students’ motivation is susceptible to being modified so that teachers can help students improve it (Hulleman et al., 2016). Since one of the postulated reasons for school failure is the students’ lack of motivation, the identification of students’ individual differences could help with diversity outreach. In light of our findings, two main educational implications can be highlighted.

First, as mentioned above, academic achievement in science (Anderhag et al., 2013; Palmer et al., 2017) and motivation (Salta et al., 2012; Mujtaba and Reiss, 2014; Sheldrake, 2016; Palmer et al., 2017; Sheldrake et al., 2017; Ardura and Pérez-Bitrián, 2018) are among the most influencing variables on students’ future choices. Consequently, interventions to improve motivation could result in an improvement in students’ achievement and they would also help with retention in the case of those students who are more likely to opt out the subject. Among all the different components of motivation, our analyses uncovered that self-efficacy is not only a good physics & chemistry academic achievement predictor, but it also eases the indirect effects of the remaining variables in students’ performance in the subject. Therefore, teachers could incorporate the work on students’ self-efficacy in their instructional designs. A possible pathway to boost this motivational trait in students would be prompting them to work on their own misconceptions, which in turn would allow them to self-regulate their learning, as previously suggested (Chen, 2011; Zamora and Ardura, 2014; Zamora et al., 2018a, 2018b).

Second, the role of motivation in secondary school physics & chemistry students depends on the type of student. Our investigation identified three types of students for whom the interplay of motivational traits is different. It could be interesting for teachers to take this into account when they seek to improve the motivation and academic performance of their students. In particular, grade motivation is the best predictor of academic achievement in students who abandon the subject. In light of our findings, for these students, interventions and teaching should be intended to boost all the motivational variables, with grade motivation and self-efficacy being particularly important given their potential to ease the effects of intrinsic motivation and self-determination on academic achievement. It is interesting to take into account that self-determination is especially relevant for girls. However, self-determination is not usually taught in primary or secondary schools (Wehmeyer et al., 2000) and, in light of our findings and some previous results, it would be a good decision for schools to incorporate this aspect into their curricula, especially with disadvantaged students (Carter et al., 2008; Gaumer Erickson et al., 2015). One way of working on students’ self-determination could be a shift from a teacher-centered to a student-oriented approach, since providing support for students’ autonomy would increase their self-determination (Black and Deci, 2000; León et al., 2015). In agreement with our models, this would be particularly important in the case of the students who are more likely to opt out physics & chemistry, as our research revealed that academic achievement of these students relies more on motivation than in the case of the students who choose the subject once it becomes optional. Finally, it is interesting to note that our SEM analyses uncovered a profile of students who may be at risk of abandonment as they display average levels of achievement and motivation towards physics & chemistry (PC/low-motivated students). In view of our model for these students, the aforementioned effects of grade motivation and self-determination are incipient. Thus, our suggestions for students who leave the subject could also be important in this case in order to enhance the future persistence of this particular profile of students.

Limitations and prospects

Several limitations of our work must be addressed. First, even though the instrument used in this investigation to measure students’ motivation towards physics & chemistry has been validated for the same sample, the information provided is self-reported. Therefore, students’ scores in each subscale may be somehow biased by their own understanding of the questions or their perceptions. Second, in the context of the Spanish educational system, there is no standardized instrument to measure students’ outcomes. For this reason, we used grades as a measure of academic achievement. Although our educational system provides a well-defined standardized assessment framework, a certain bias in the grades given to the students may be present. Third, since our investigation is a cross-sectional study, no causal relationships can be directly inferred. Instead, our analyses provide a framework in which future experimental research can be undertaken. These prospective studies should be planned to investigate, by means of a quasi-experimental approach, if interventions in the relevant motivational variables uncovered by our work would have any effect on academic achievement and students’ future choices regarding their school trajectories.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

The authors would like to acknowledge the students, teachers, and schools involved in the data gathering for their help. We are also thankful to the Colegio Oficial de Químicos de Asturias y León and the Asociación de Químicos del Principado de Asturias for their help in our first contact with the schools. A. P.-B. thanks the Spanish Ministerio de Educación, Cultura y Deporte for a grant (FPU15/03940).

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