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DOI: 10.1039/D3RP00332A (Paper) Chem. Educ. Res. Pract., 2024, 25, 920-933

The relationship between self-handicapping in chemistry and chemistry academic engagement: a moderated mediation model investigation

Qian Huangfu *a, Weilin Huang a, Qianmei He a, Sisi Luo a and Qimei Chen b
aSouthwest University, Chongqing, 400715, China. E-mail: chemqian16@swu.edu.cn
bLiangjiang Experimental Middle School of Southwest University, Chongqing, 401121, China

Received 1st December 2023 , Accepted 24th April 2024

First published on 7th May 2024

Abstract

Chemistry academic engagement has received considerable attention for its role in enhancing students’ learning and overall development. As a significant factor influencing students’ chemistry improvement, research on chemistry academic engagement has emerged as a focal point. However, the methods to improve students’ chemistry academic engagement remain limited until now. Therefore, this study aims to investigate the mechanisms of chemistry academic engagement, self-handicapping, chemistry academic buoyancy and teacher support, and offer suggestions to improve students’ academic engagement. We assumed a moderated mediation model and evaluated all variables for twelve different schools in China (N = 3344, Grade 10), then analyzed the data with structural equation models (SEM). The results suggested that (1) self-handicapping in chemistry negatively predicted chemistry academic engagement; (2) chemistry academic buoyancy moderated the relationship between chemistry academic engagement and self-handicapping to a certain extent, and this indirect effect was significant irrespective of teacher support levels; (3) instrumental support moderated the mediating process in the academic buoyancy to academic engagement pathway; (4) teacher emotional support moderated the direct and indirect pathways of the mediating process from self-handicapping in chemistry to chemistry academic engagement. Finally, we analyzed the results of this research, highlighted its educational significance, recognized the limitations and made recommendations for further research.

Introduction

Chemistry is not only an introductory course required for STEM courses, but also one of the subjects in The Outline of Curriculum Standards for The New School System in China. The chemistry curriculum standard of senior high school (Ministry of Education, P. R. China, 2017) emphasized the vital role of chemistry as a foundation for lifelong learning and development of students. It is necessary to preserve scientific knowledge and cultural heritage, and nurture high-quality talents. However, chemistry has been regarded as a formidable and challenging subject by students in high school (Papaphotis and Tsaparlis, 2008; Tsaparlis and Papaphotis, 2008; David Agwu and Nmadu, 2023). Along the same lines, high school students often display difficulties such as lack of motivation, less enthusiasm for studying and reduced engagement in the subject. In high school students’ chemistry learning process, struggling in challenges and meeting failure is unavoidable. In the event of adversities, some students may ascribe their failures to certain factors beyond their control, such as ability, effort, and luck (Weiner, 2010). Self-handicapping was defined as adapting maladaptive strategies to maintain or improve one's self-esteem from academic defeat. According to Berglas and Jones (1978a, 1978b), the existence of an obstacle offers students a chance to alter explanations of poor performance, attributing it from limited ability to the impediment factors. Previous research has showed that self-handicapping has a negative influence on students’ academic performance (Martin et al., 2003; Putwain, 2019; Barutçu Yıldırım and Demir, 2020), and regular adoption of self-handicapping typically resulted in notable harm to students’ learning, including dropping out, poor performance and reduced academic engagement (Núez et al., 2021). Until now, studies about self-handicapping and academic engagement focus on mathematics/physical education (Martin et al., 2003; Collie et al., 2019), and there is a lack of proof proving the relationship between self-handicapping in chemistry and students’ chemistry academic engagement. Thus, the initial objective of the present research is to investigate these correlations, so that we can propose practical suggestions to prompt students’ chemistry academic engagement and improve their chemistry learning by reducing their self-handicapping.

In addition, according to relevant studies, self-handicapping, academic buoyancy, teacher support and academic engagement are related closely (Martin et al., 2003; Martin, 2013; Yu and McLellan, 2019; Zhao and Yang, 2022). Behavior like self-handicapping would contribute to students becoming a failure-avoidant student (Martin, 2011), which contributes to a negative orientation to their learning, and thereby regulates their academic buoyancy, resulting in poor academic engagement (Granziera et al., 2022). Studies also revealed that multiple forms of teacher support including instrumental support and emotional support are bound up with self-handicapping, academic buoyancy and teacher support (Turner et al., 2002). However, no study has been found that tested their correlation in a model of a composite variable. Consequently, we hypothesized and examined a moderated mediation model to address this gap. In this model, self-handicapping in chemistry serves as the independent variable, chemistry academic buoyancy is the mediating variable, teacher support is a moderating variable, and chemistry academic engagement is the dependent variable. Our aim is to explore whether chemistry academic buoyancy mediates the correlation between self-handicapping in chemistry and chemistry academic engagement, and whether teacher support moderates the relationships between self-handicapping in chemistry, chemistry academic buoyancy and chemistry academic engagement. Finally, this research aims to generate educational interventions that can improve students’ chemistry academic engagement, and thereby prompt students’ chemistry learning.

Review of the literature

Self-handicapping in chemistry and chemistry academic engagement

For the term self-handicapping, “we define self-handicapping as any action or choice of performance setting that enhances the opportunity to externalize failure and internalize success”. It was firstly introduced by Berglas and Jones (1978a, 1978b, Vol. 36, No. 4, 406). After this, Martin et al. (2003) defined self-handicapping as the deliberate choice of obstacles or excuses to deflect failure away from one's competence, protecting self-image and avoiding disconfirmation. Self-handicapping, according to Török et al. (2018), is a strategy to safeguard or enhance students’ positive self-image and the positive opinions others hold of them. Then, self-handicapping was defined by Núez et al. (2021) as a strategy in which students intentionally create obstacles that impede their chances of achieving success. In this study, self-handicapping in chemistry refers to when students intentionally create obstacles that hinder their learning to protect their self-image and self-esteem. A typical example of self-handicapping in chemistry includes procrastination in the process of chemistry academic tasks (Nieberding and Heckler, 2021).

Theoretical and empirical evidence shows that self-handicapping may affect students' academic engagement in chemistry. For one thing, according to Covington's self-worth theory (Covington, 1992), students' need for value and acceptance is linked to their perceived achievements and competence. In demanding environments such as chemical academia, the high level of effort combined with failure is evidence to judge others as personally incompetent (Martin et al., 2016). In this context, self-handicapping offers a good alternative to protect students' competence and self-esteem. In addition, according to achievement motivation theory (Elliot, 1999; Wigfield and Cambria, 2010), students who are afraid of failure tend to be anxious because of their fear of failure, which can lead to procrastination, cheating, and reduced engagement (Martin, 2011; Ferrell et al., 2016). Self-handicapping refers to a strategy where students create obstacles to protect their self-image and self-esteem. Thus, it could therefore be expected that self-referencing has a negative impact on learning engagement. For another thing, previous research has also demonstrated their relationship. Rhodewalt and Fairfield (1991) reported that high self-handicapping students have a low likelihood of personal success and therefore are reluctant to work hard, and then are not good at completing tasks. Cano et al. (2018) observed that self-handicapping negatively affects deeper approaches to learning, which in turn negatively affects students' engagement in learning. Núez et al. (2021) investigated whether self-handicapping could predict students' behavioral engagement and their results showed that students' engagement with homework was greatly diminished by self-handicapping.

The potential mediating role of chemistry academic buoyancy

Academic buoyancy was defined as a motivational construction of the ability to successfully overcome challenges, adversities and setbacks (e.g., low grades, challenging deadlines, difficult coursework, testing pressures) in daily school learning (Martin and Marsh, 2009; Granziera et al., 2022). In this research, we focus on academic buoyancy, delving into the interrelationships between self-defeat, academic buoyancy, and academic engagement. As highlighted by achievement goal theory, both the goal orientation type and the goal structure can have a profound effect on student's learning (Elliot et al., 2011; Santos-Diaz et al., 2019). Learning mastery and performance goals come in two flavors: avoidance goals and approach goals (Linnenbrink, 2005). Students who set avoidance goals endeavour to avoid poorer grades than others and incomplete knowledge acquisition, whereas students who pursue proximity goals focus on improving grades and self-development. Along with this, students following avoidance goals tend to have negative strategies and poor performance, and thus, lack of capacity to overcome challenges. As mentioned above, self-handicapping was defined as creating obstacles that hinder students’ success, while academic buoyancy refers to the capacity to overcome obstacles and setbacks. Therefore, it is reasonable to expect a negative relationship between academic buoyancy and self-handicapping. Martin and Marsh (2008a, 2008b) explored a model in which coping self-handicapping utilizes academic buoyancy as a mediating factor to predict individuals’ well-being, and in this model, self-handicapping negatively predicts academic buoyancy. Alarcon et al. (2011) suggested that students with less self-handicapping usually show academic persistence, positive attitudes, and less academic procrastination in the face of adversities, which results in successfully coping with setbacks (Skinner and Pitzer, 2012). Yu and Martin (2014) suggested that students with low self-handicapping tend to hold a progressive view of ability and obtain the improvement of ability, which can positively predict the student's academic buoyancy.

In the past two decades, Martin and Marsh have proposed a series of models to investigate relationships between academic buoyancy and students’ learning. According to Martin (2006), academic buoyancy strongly predicted both class participation and enjoyment of school, serving as a predictive variable for academic engagement. They discovered that academic buoyancy could predict engagement outcomes in a follow-up study, for example, academic buoyancy negatively predicts absenteeism but positively predicts task completion and academic intension (Martin, 2008). The studies above have provided a theoretical basis for the relationships between academic engagement and academic buoyancy. Recent studies have empirically validated these findings as well. Martin et al. (2016) investigated the correlation of engagement and academic buoyancy in a cross-cultural study, and the results showed that buoyancy significantly and positively correlated with engagement including enjoyment of school, positive academic intentions and class participation for both the UK, North America and China. It was shown that academic buoyancy was positively correlated with students' behavioral and emotional disaffection (Thomas and Allen, 2021). Recently, a mediation model was created by Putwain and Wood (2023) to test the correlation of academic achievement and academic buoyancy in mathematics, and the findings indicated that buoyancy indirectly influenced achievement through academic buoyancy, and concurrent engagement and engagement were consistently correlated throughout the study.

The potential moderating role of teacher support

According to the theory of emergent motivation (Csikszentmihalyi, 1990), motivation appears as a function of the environment and influences students' interaction in the academic environment. Thus, the academic engagement of students can be seen as malleable and variable, rather than a characteristic personal trait (Urdan and Schoenfelder, 2006). The important role of the teachers is to create an atmosphere for learners’ engagement and learning in the school environment. This can be seen in the teacher's choice of teaching behavior (teacher support). As an integral part of context factors, teacher support builds a supportive relation between students and teachers, which may help students to cope better with academic demands (Hughes et al., 2008). Consistent with previous research, we also believe that teacher support includes both emotional and instrumental dimensions (Semmer et al., 2008; Granziera et al., 2022), and is multidimensional (Anderman et al., 2011) and malleable (Gehlbach et al., 2012).

This study focuses on two typical dimensions of teacher support. Firstly, instrumental support was defined as a construction of practical support and instrumental resources which is helpful for learners to complete difficult tasks (Semmer et al., 2008; Wong et al., 2018). In the chemistry learning context, instrumental support includes teachers’ elaborating, clarifying, and guiding in the chemistry course, chemistry laboratory experiments and chemistry extracurricular activities (Smith and Alonso, 2020; Reid et al., 2022). Social cognitive theory (Bandura, 1986) emphasizes the important role of communication in promoting motivation and performance outcomes. That is, students who believe that the teacher will demonstrate methods tend to increase their belief that they can solve the problem themselves (González and Paoloni, 2015).

The definition of chemistry academic buoyancy includes the ability to overcome adversity and meet challenges. Accordingly, we can expect that instrumental support has a moderating effect on the correlation of chemistry academic buoyancy and chemistry academic engagement, and empirical studies have reported evidence. Instrumental support positively affected students’ academic beliefs (Suldo et al., 2009), academic achievement (Tennant et al., 2015), and perceived utility value (Federici and Skaalvik, 2014). The latest empirical research has indicated the positive association between instrumental support from teachers and student academic buoyancy (Granziera et al., 2022). In general, instrumental support may be a factor that improves and facilitates the positive effects of academic buoyancy on students’ engagement (Strati et al., 2017).

Secondly, emotional support of teacher was defined as the extent to which the teacher accepts, respects, trusts and encourages students (Malecki and Demaray, 2003), which was characterized by esteem, friendliness, caring and empathy (Semmer et al., 2008). In chemistry learning, emotional support is usually demonstrated by showing acceptance in response to concerns raised by students, and encouraging students’ interactions in classroom practices. Self-determination theory (Deci et al., 1991; Southam and Lewis, 2013) argues that satisfying the three needs of autonomy, competence, and relatedness is an important factor in promoting student engagement and motivation. When people are able to act according to their natural inclinations (i.e., autonomy), they are motivated to learn and articulate their personal interests by pursuing goals that are personally valued to them (McAlpin et al., 2023). Teachers can show their participation by showing positive attention to students, encouraging empathy and prosocial behavior in the classroom, and providing emotional support to students in the classroom. The findings of Juriševič et al. (2012) suggest that hands-on laboratory work with autonomy-supportive teachers can create a motivating learning environment for students to learn in understanding and collaborate with each other in chemistry academic tasks of higher cognitive complexity. The self-worth theory holds that the most important need of individuals is the need of self-worth, and this is the most important pursuit (Covington, 1992). Therefore, once self-worth is threatened, individuals will strive to maintain and defend themselves in order to establish a positive self-image and accept themselves. Therefore, we can expect that emotional support has a moderating effect in the mediation model of self-handicapping, chemistry academic buoyancy and chemistry academic engagement. But little empirical evidence has been provided to test this moderating effect in prior studies until now.

Turner found that students may be less prone to take defensive measures such as maladaptive strategies including self-handicapping under circumstances where they perceive that teachers are supporting their learning (Turner et al., 2002; Weyns et al., 2017). Therefore, we can expect that teacher emotional support may have a moderating role between students’ self-handicapping and academic engagement. Additionally, Ruzek et al. (2016), who explored the relationship between students’ learning and emotional support, reported that emotional support satisfies their need for a sense of community and strengthens self-confidence, and thus improves their abilities and confidence to deal with adversities and setbacks (Pianta and Hamre, 2009).

The present study

The existing study suggests that self-handicapping has a very important predictive effect on academic engagement (Holliman et al., 2018; Schwinger et al., 2022). While the relationship is domain specific, the relationship between the two concepts has not been well studied. Therefore, we aim to first explore the correlation between self-handicapping in chemistry and chemistry academic engagement.

Furthermore, self-handicapping can reduce academic buoyancy, and academic buoyancy can influence academic engagement (Putwain and Wood, 2023). Thus, we assume that chemistry academic buoyancy is a mediating variable between self-handicapping in chemistry and chemistry academic engagement. Meanwhile, instrumental support positively influences academic buoyancy; and the influence of emotional support on self-handicapping is negative, but the influence on chemistry academic buoyancy is positive. So, we assume that the two forms of teacher support have different moderating effects in the mediation model of self-handicapping in chemistry, chemistry academic buoyancy and chemistry academic engagement.

Finally, since academic buoyancy mediates the correlation between self-handicapping and academic engagement, and teacher support (including instrumental support and emotional support) is closely related with self-handicapping, academic buoyancy and academic engagement, we assume that self-handicapping in chemistry affects chemistry academic engagement through a moderated mediation model, in which chemistry academic buoyancy has a mediating effect and teacher support (one is instrumental support and the other is emotional support) has moderating effects.

Taking theoretical knowledge and empirical findings together, the following hypotheses can be proposed: (hypothesized relationships see Fig. 1).


image file: d3rp00332a-f1.tif
Fig. 1 Hypothesized relationships among the variables.

Hypothesis 1: self-handicapping in chemistry is negatively correlated with chemistry academic engagement.

Hypothesis 2: chemistry academic buoyancy has a mediating effect between self-handicapping and chemistry academic engagement.

Hypothesis 3: instrumental support has a moderating effect between self-handicapping in chemistry, chemistry academic buoyancy, and chemistry academic engagement.

Hypothesis 4: emotional support has a moderating effect between self-handicapping in chemistry, chemistry academic buoyancy, and chemistry academic engagement.

Methods

Research design

A cross-sectional non-experimental study design was used in this study, in which interventions such as student interviews and questionnaires were used to investigate the correlation of the variables involved in the study.

Participants

A stratified purposive sampling method was used in the study to choose 3344 grade 10 students (46% males; 54% females) from 12 high schools in China. Firstly, based on the main division criteria of economic status, we classify Chinese cities into first, second and third-tier cities and other cities (Tier 1 cities have the highest economic status) (Xu et al., 2022). Second, 12 schools were chosen in all, with three each from Tier 1, Tier 2, Tier 3 and other cities respectively. In the study, about 278 students were selected from each grade level in each school. Among the sample, all participants have spent two years studying chemistry and they now have a good understanding of it. In addition, the 10th-grade students were selected for the study because they had to choose whether to carry on with the Chemistry course in the future, and students' engagement would, to some extent, affect their choice.

Survey administration

It took one week to complete the data collection process across twelve different schools. Prior to data collection, the Academic Ethics Committee at Southwestern University in China, along with other relevant authorities and the parents of the students, approved the research. Furthermore, prior to the investigation, all participants were informed of the study's goal and the intended use of the data. They were guaranteed that their personal information would be treated with the utmost confidentiality. Not a single student received any additional rewards for their voluntary participation.

Survey instrument

Measurement tools that are well known in their respective disciplines, have been used in large-scale studies in the past, and are available in English were used to create the questionnaire (Urdan et al., 1998; Schaufeli et al., 2002; Martin and Marsh, 2008a, 2008b; Federici and Skaalvik, 2013). The existing scales were in English, but our subjects were Chinese students. Therefore, in order to make it easier for students to understand the exact meaning of the questions, we used Brislin's (1970) reverse translation method to translate all English questions into Chinese (Liu et al., 2016). First, four authors majoring in pedagogy who were native Chinese, and proficient in English, independently translated all the projects, and then discussed together to determine the final version. Next, we translated the Chinese questions back into English through translation software and compared them with the original English text. Finally, Fifteen Year 10 students then read the questionnaire and provided feedback. They were then interviewed by the researchers over a period of time. Every student was asked to explain their understanding of each item by the researchers, who then used the interview results and the expressions of some questionnaire questions to make sure that the meaning that the students understood matched exactly what was stated in the questionnaire.
Self-handicapping in chemistry. We used a measurement tool developed by Urdan et al. (1998) to assess self-handicapping in chemistry learning. Since the concept of chemistry self-handicapping is comparable to that of maths/physical education self-handicapping, we modified the item formulation by replacing “maths/physical education” with “chemistry”. The scale comprises 6 items (e.g., “Some students wait until the last minute to finish their chemistry homework in order to justify themselves in the face of failure. Is this true in your case?”). The students were given a five-point scale, ranging from 1 (completely disagree) to 5 (completely agree), to indicate how much they agreed with each statement. Higher scores indicate higher degrees of self-handicapping.
Chemistry academic buoyancy. The questionnaire measuring mathematical academic buoyancy was adapted to chemical academic buoyancy due to the similarity of the definitions and characteristics of mathematical academic buoyancy and chemical academic buoyancy (Martin and Marsh, 2008a, 2008b). The questionnaire consisted of four items (e.g., “ I'm not going to let a bad grade affect my confidence”), requiring students to choose from a five-point Likert scale which ranges from 1 (completely disagree) to 5 (completely agree). Greater academic buoyancy is indicated by higher scores.
Instrumental support. A scale adapted from the Teacher Support Scale was used to assess instrumental support in chemistry (Federici and Skaalvik, 2013). Since the definitions of instrumental support in mathematics learning and instrumental support in chemistry learning are similar, the items were modified by replacing “my maths teacher” with “my chemistry teacher”. A total of six items (e.g., “My chemistry teacher gives me good help and guidance when I have problems in chemistry.”), asking students to choose an option from a five-point Likert scale that ranges from 1 (disagree) to 5 (agree) that best matches their feelings. Higher scores indicate that students perceive more instrumental support in their daily chemistry studies.
Emotional support. Students' perception of the emotional support provided by chemistry teachers was assessed by a modified teacher support scale (Federici and Skaalvik, 2013). In addition to assessing instrumental support, the items were modified by replacing “my maths teacher” with “my chemistry teacher”. To measure the six items (e.g., “My chemistry teacher cares about me”), a five-point Likert scale was used, rated from 1 (totally disagree) to 5 (totally agree). In this case, a high score indicated more emotional support from the chemistry teacher.
Chemistry academic engagement. Chemistry academic engagement was assessed with a scale that was adapted from the “Engagement Scale” (Schaufeli et al., 2002). The scale comprises 17 items (e.g., “Every morning I wake up and look forward to going to my chemistry class”) and is rated using a five-point scale, ranging from 1 (totally disagree) to 5 (totally agree). The study used the combined mean score of the items as an indicator of academic engagement, with higher scores indicating more engagement.

Data analysis procedures and tools

All data analysis was carried out in Mplus 8.3 and SPSS 25.0 in the current study.

Preliminary analysis assessed the descriptive and distributional properties of the subscales and computed means, standard deviations, skewness, and kurtosis for each variable. Following this, we used confirmatory-factor-analysis (CFA) and exploratory-factor-analysis (EFA) to test for 5 scales. In the pretest, we randomly selected data (n = 540) and divided these data equally into two groups (n1 = n2 = 270). In this study, items having the lowest correlations with each scale were eliminated because they were considered to be lower-quality measures and thus had a tendency to reduce the overall reliability of the scale. In actuality, there weren't many of these things.

First, we used EFA for Group 1 in SPSS 25.0 to explore the factor structure of the scale (Lee et al., 2008). The number of extracted factors was determined based on principal component analysis, and the factor matrix was analyzed by the maximum variance method. Based on instrumental and theoretical considerations (Watkins, 2018; Schreiber, 2021), when a specific number of factors was selected, factors with eigenvalues greater than one are retained and those with eigenvalues less than one were discarded. Then, the scale was modified based on the results of the EFA and CFA was administered to group 2 using Mplus 8.3 to test the reliability of the fitness scale (Arjoon et al., 2013). Finally, in the scale internal consistency test, the McDonald coefficient (ω) and alpha coefficient (α) are of equal importance. Thus, we tested the omega coefficient (ω) of every scale of the present study in SPSS 25.0.

The Kaiser–Meyer–Olkin and Bartlett's test of sphericity showed the comprehensive data set to conduct the factor analysis. For the EFA result, the Kaiser–Meyer–Olkin (KMO) is supposed to be greater than 0.6, and the p value of Bartlett's sphericity test should be less than 0.05 (Guo et al., 2022). If the cross-loading score is less than 0.32, and the factor-loading score is greater than 0.4, these items are retained (Wei et al., 2021). Recommendations for CFA fitting index values are: χ!/df < 5, RMSEA < 0.08, SRMR < 0.08, CFI > 0.9 and TLI > 0.9 (Hu and Bentler, 1999; Lee et al., 2008; Opperman et al., 2013). And omega coefficients (ω) greater than 0.7 per scale are acceptable (Li et al., 2019).

Before testing hypothetical models, we performed a Harman-one-way test to identify and resolve any potential common methodological biases in the data. Mo et al. (2019) showed that there was no common method bias when the first factor did not exceed 40% of the explanatory variance, or less than half of the cumulative total explanatory variance. The explanatory variance of the first factor in this study was 30.59%, which met the criteria of no common method bias. We then did not report how well the full model fit the data, but only considered the path coefficient (Guo et al., 2022) because our model was a saturation model (Nurkhaidarov and Shochat, 2011; Zhang et al., 2019; Huangfu et al., 2023). Subsequently, we used the Mplus 8.3 Structural Equation Model (SEM) to test this hypothetical model. More specifically, since all the variables in this study were regarded as observable variables, we chose the path analysis to explore the associations among variables and the mediating effects (Jonnada and Fegley, 1974; Granziera et al., 2022). At the same time, we used the average score of each instrument in the correlation and mediation analysis process (Soncini et al., 2022; Xiong et al., 2023). Consistent with González and Paoloni's (2015) study, there is a complete mediating relationship between the independent variable and the dependent variable when the indirect effect is significant and the direct effect is not, and the effect of the independent variable on the dependent variable is partially mediated when the direct effect and indirect effect are significant.

Because bias-corrected percentile Bootstrap methods do not require large samples or normality assumptions, nor do they require standard error to estimate mediating effect intervals, they prove to be one of the best methods for using mediation analysis methods (Jie et al., 2014). The latent variable approach requires a latent change model of the dependent variable, so we test for moderated mediating effects by analyzing indirect effects and interactions by an unconstrained approach. In addition, we estimated bias-based 95% confidence intervals (CIs) using 5000 bootstrap samples. If zeros are not included in the CIs, the null hypothesis that there are no interaction or indirect effects is excluded. For consistency, we reported the level of significance of the CI rather than all effects.

Instruments

Self-handicapping in chemistry. After EFA analysis, the factor load of all items in our scale was above 0.4, and all items were retained. The KMO statistic was 0.870 [>0.6] and the Bartlett's spherical test coefficient was 1285.908 (p < 0.001), the factor-explained variance was 78.238%, and the factor load of all items was between 0.586–0.813. The experimental results show that the fitting effect of the teacher-supported model is better: χ!/df = 1.145 [<5]; RMSEA = 0.023 [<0.08]; CFI = 0.998 [>0.9]; TLI = 0.997 [>0.9]; SRMR = 0.019 [<0.08]. In addition, the omega for the whole questionnaire was 0.841 [>0.7], demonstrating the high reliability of the questionnaire for self-handicapping in chemistry.
Chemistry academic buoyancy. After EFA analysis, all question items were retained (none was removed), and their factor loadings were above 0.4; the factors were extracted and explained 67.875% of the total variance; the KMO statistic was 0.812 [>0.6], the Bartlett's spherical test coefficient was 848.846 (p < 0.001), and the factor load was 0.797 to 0.803 [>0.4]. The results of the CFA provide a good fit model: χ!/df = 2.223 [<5]; RMSEA = 0.027 [<0.08]; CFI = 0.958 [>0.9]; TLI = 0.996 [>0.9] and SRMR = 0.009 [<0.08]. The omega coefficient of the scale was 0.832 [>0.7].
Instrumental support. Consistent with what was mentioned earlier, a total of 6 items were tested. All items were retained after EFA analysis, which were based on the KMO statistic of 0.886 [>0.6], the Bartlett's spherical test coefficient of 1120.372 (p < 0.001) and the factor loading of 0.696–0.826 [>0.4], and factor interpretation variance was 76.439%. The CFA results revealed that χ!/df = 1.255 [<5]; RMSEA = 0.041 [<0.08]; CFI = 0.922 [>0.9]; TLI = 0.931 [>0.9] and SRMR = 0.007 [<0.08]. The omega coefficient of the scale was 0.845 [>0.7].
Emotional support. By EFA analysis, the KMO statistic was 0.900 [>0.6] and Bartlett's spherical test coefficient was 1397.682 (p < 0.001), factor interpretation variance was 61.461%, and the factor load was 0.762 to 0.836 [>0.4]. The CFA results showed: χ!/df = 1.379 [<5]; RMSEA = 0.014 [<0.08]; CFI = 0.920 [>0.9]; TLI = 0.932 [>0.9] and SRMR = 0.007 [<0.08]. The omega coefficient of the scale was 0.840 [>0.7].
Chemistry academic engagement. Consistent with what I mentioned earlier, a total of 17 items were tested. All items were loaded onto one factor. After the EFA, we deleted three items which have strong loading on the factor. The second round of EFAs for the 14 retained items showed a KMO statistic of 0.957 [>0.6], and the Bartlett's spherical test coefficient of 4983.969 (p < 0.001), the factor-explained variance of 61.806%, and the factorial load was 0.701 to 0.753 [>0.4]. We then performed a CFA analysis, and the results of CFA provided a well-fitting model: χ!/df = 2.347 [<5]; RMSEA = 0.068 [<0.08]; CFI = 0.935 [>0.9]; TLI = 0.930 [>0.9] and SRMR = 0.034 [<0.08]. The omega coefficient was 0.945 [>0.7], indicating high reliability (see Table 1).
Table 1 Descriptive statistics and correlations (N = 3344)
Variables Mean SD Skew Kurt 1 2 3 4
Note: *p < 0.05, **p < 0.01; SD = standard deviation. 1,2,3 and 4 represent self-handicapping in chemistry, chemistry academic buoyancy, emotional support, and instrumental support.
Self-handicapping in chemistry 2.18 0.85 0.46 −0.77 —
Chemistry academic buoyancy 3.42 0.83 −0.01 −0.82 −0.418** —
Emotional support 3.83 0.78 −0.27 −0.96 −0.168** 0.162** —
Instrumental support 3.93 0.75 −0.32 −0.97 −0.220** 0.116** 0.194** —
Chemistry academic engagement 3.29 0.74 0.01 −1.11 −0.443** 0.550** 0.207** 0.364**

Results

In our study, Harman's one-factor test was used to test for common method biases and it was found that the eigenvalues of all five factors were greater than 1. As a result, no common method deviations were observed.

Descriptive statistics and correlation analysis

Descriptive statistics (including standard deviation (SD) and mean) and correlation analyses were performed for each variable (see Table 1). As shown in Table 1, the skewness range of each variable is −0.32 to 0.46, and the kurtosis range is −1.11 to −0.77. Since the skewness above 2.0 and the kurtosis above 7.0 were considered non-normal distributions (Lorenzo Muthen and Kaplan, 1992), the present results indicated a normal distribution.

The correlation between all variables in this study is also presented in Table 1. Specifically, self-handicapping in chemistry was negatively related with chemistry academic buoyancy (r = −0.418, p < 0.01), emotional support (r = −0.168, p < 0.01), instrumental support (r = −0.220, p < 0.01), and chemistry academic engagement (r = −0.443, p < 0.01). Chemistry academic buoyancy was positively related with emotional support (r = 0.162, p < 0.01), instrumental support (r = 0.116, p < 0.01), and chemistry academic engagement (r = 0.550, p < 0.01). Furthermore, emotional support was positively related with chemistry academic engagement (r = 0.207, p < 0.01) and instrumental support was positively related with chemistry academic engagement (r = 0.364, p < 0.01).

The moderated mediating effects analyses

The general hypothetical model (shown in Fig. 1) is tested using a structural-equation-model, and the model fits well: χ!/df = 4.784, p < 0.001, RMSEA = 0.034, SRMR = 0.013, CFI = 0.996, and TLI = 0.976. Firstly, the results indicated that self-handicapping in chemistry and chemistry academic engagement were negatively and significantly associated (β = −0.486, p < 0.001), confirming hypothesis 1. Then, the influence of self-handicapping in chemistry on chemistry academic buoyancy was found to be a significant negative effect (β = −0.489, p < 0.001). Then, we used the Bootstrap method to examine the mediating effect of chemistry academic buoyancy (as shown in Table 2). Hayes (2015) showed that when the 95% confidence interval for all pathways was not 0, it indicated a significant mediating effect, and this study met the case of a significant mediating effect. When chemical academic buoyancy was used as a mediator, it positively predicted chemical academic input (β = 0.492, p < 0.001), while the direct effect between self-handicapping in chemistry and chemistry academic engagement was substantially reduced (β = −0.246, p < 0.001). Bootstrapping analysis indicated that the indirect effect of self-handicapping on chemistry academic engagement (β = −0.240, 95% CI = [0.266, −0.217]) through chemistry academic buoyancy was significant, showing that a mediation model had been established. Therefore, the results supported Hypothesis 2.
Table 2 The mediating effect of chemistry academic buoyancy
Path Effect SE CI95 P
LL UL
Note: SE = standard error, CI95 = 95% confidence interval, LL = lower level, UL = upper level. confidence intervals were using 5000 bootstraps, all variables used in this table have been standardized.
Self-handicapping in chemistry → chemistry academic engagement
Direct effect −0.246 0.013 −0.242 −0.192 P < 0.001
Self-handicapping in chemistry → chemistry academic buoyancy → chemistry academic engagement
Indirect effect −0.240 0.013 −0.266 −0.217 P < 0.001
Self-handicapping in chemistry → chemistry academic engagement
Total mediation effect −0.486 0.016 −0.516 −0.455 P < 0.001

To further clarify the mediating role of teacher support in obtaining self-handicapping and chemistry academic engagement, emotional support (ES) and instrumental support (IS) were subdivided by adding or subtracting a standard deviation from the mean, and a simple slope analysis was performed to estimate the relationship on self-handicapping, chemistry academic buoyancy, and chemistry academic engagement at different levels of self-esteem (Table 3). The data indicated that the indirect effects of self-handicapping on chemistry emotional support, chemistry academic buoyancy, and chemistry academic engagement were significant regardless of whether emotional support was high (indirect effect = −0.096, 95% CI = [−0.118 to −0.076]) or low (indirect effect = −0.14, 95% CI = [−0.166 to −0.117]); the indirect effects of affective support on self-handicapping in chemistry, chemistry academic buoyancy, and chemistry academic engagement were also significant, and when emotional support was high, the indirect effect = −0.121, 95% CI = [−0.154, −0.090], and when emotional support was low, indirect effects = −0.319, 95% CI = −0.359 to −0.280, partially supporting hypothesis 4. Research showed that instrumental support had a significant indirect effect on self-handicapping in chemistry, chemistry academic buoyancy, and chemistry academic engagement regardless of whether instrumental support was high (indirect effect = −0.197, 95% CI = [−0.216 to −0.178]) or low instrumental (indirect effect = −0.095, 95% CI = [−0.111 to −0.079]).

Table 3 The moderated effects of emotional support and instrumental support
Path Indirect effect CI95 P
High ES/IS Low ES/IS High ES/IS Low ES/IS
Note: CI95 = 95% confidence interval, high ES/IS = high emotional support or instrumental support, low ES/IS = low emotional support or instrumental support. confidence intervals were using 5000 bootstraps, all variables used in this table have been standardized.
Self-handicapping in chemistry × emotional support → chemistry academic buoyancy → chemistry academic engagement −0.096 −0.14 [−0.118, −0.076] [−0.166, −0.117] P < 0.001
Self-handicapping in chemistry → chemistry academic buoyancy × emotional support → chemistry academic engagement −0.121 −0.319 [−0.154, −0.090] [−0.359, −0.280] P < 0.001
Self-handicapping in chemistry → chemistry academic buoyancy × instrumental support → chemistry academic engagement −0.197 −0.095 [−0.216, −0.178] [−0.111, −0.079] P < 0.001

We performed a simple slope analysis in order to further explore the pattern of moderating effects. Fig. 2–4 present emotional support (M ± SD) and instrumental support (M ± SD), respectively. First, Fig. 2 shows that the chemistry academic buoyancy of both students with higher emotional support and those with lower emotional support decreases with the increase of self-handicapping in chemistry, both of which show that self-handicapping in chemistry is negatively correlated with chemistry academic buoyancy. Secondly, Fig. 3 shows that both the students who received higher emotional support and those who received lower emotional support increased their chemistry academic buoyancy, indicating that the chemistry academic buoyancy in both cases was positively correlated with the chemistry academic engagement. Thirdly, there was a positive correlation between chemistry academic buoyancy and chemistry academic engagement for both students with higher and lower instrumental support (as shown in Fig. 4). Taken together, instrumental support has a moderating role in the correlation between chemistry academic buoyancy and chemistry academic engagement, supporting hypothesis 3, and these results suggest that emotional support has a moderating effect in the correlations between self-handicapping in chemistry, chemistry academic buoyancy and chemistry academic engagement, supporting hypothesis 4.


image file: d3rp00332a-f2.tif
Fig. 2 The moderating role of emotional support between self-handicapping in chemistry and chemistry academic buoyancy. Notes. SH = self-handicapping in chemistry, ES = emotional support, CAB = chemistry academic buoyancy.

image file: d3rp00332a-f3.tif
Fig. 3 The moderating role of emotional support between chemistry academic buoyancy and chemistry academic engagement. Notes. ES = emotional support, CAB = chemistry academic buoyancy, CAE = chemistry academic engagement.

image file: d3rp00332a-f4.tif
Fig. 4 The moderating role of instrumental support between chemistry academic buoyancy and chemistry academic engagement. Notes. TS = instrumental support, CAB = chemistry academic buoyancy, CAE = chemistry academic engagement.

Discussion

The relationship between self-handicapping in chemistry and chemistry academic engagement

The initial contribution of our study is the finding that self-handicapping has a negative influence on students’ chemistry academic engagement (direct effect = −0.246). As with other studies (Urdan, 2004; Thomas and Gadbois, 2007; Núez et al., 2021), which showed a negative interaction between self-handicapping and performance in school, self-handicapping resulted in decreasing engagement over time. Covington (1984, 1992) argued that failure will affect students' self-worth on account of failure being regarded to be a sign of low ability, which also leads to a decrease in students' self-worth. As a result, many students try their utmost to avoid defeat or to change its meaning. Self-handicapping behaviour is thought to be due to a motivation to protect or enhance the self. Self-handicappers change the meaning of defeat by changing the ability to fail and elements such as lack of effort that are unlikely to hurt their self-esteem. Unfortunately, self-handicapping is likely to further weaken their academic performance, so they may move toward deeper academic disengagement and academic devaluation (Urdan et al., 1998). In reality, self-handicapping is connected with the use of poor learning habits (Zuckerman et al., 1998), especially leaving less time to study, and being more likely to procrastinate (Török et al., 2018; Barutçu Yıldırım and Demir, 2020). Some previous studies found a positive interaction between procrastination and self-handicapping (Ferrari, 1991; Van Eerde, 2003; Strunk and Steele, 2011). Students with high self-handicapping in chemistry tend to procrastinate in chemistry learning and reduce efforts or practices for a performance situation in front of possible failure. Then, they can replace attributions of incompetence with external attributions, which directly decreases engagement to learning. On the other hand, over time, self-handicapping caused a decline in self-esteem, which further increased self-handicapping. In addition, the more dissatisfied students are with their learning outcome and the poorer their abilities, the more likely they will adopt self-handicapping (Urdan et al. 1998). It must also be noted that self-handicapping is inversely associated with chemistry academic engagement partly because of students' incorrect use of learning strategies (Warner and Moore, 2004; Thomas and Gadbois, 2007; Gadbois and Sturgeon, 2011).

The mediating role of chemistry academic buoyancy

In this study, the second contribution is the result of the test of the mediation of chemistry academic buoyancy between handicapping in chemistry and chemistry academic engagement. Specifically, our findings showed that self-handicapping in chemistry negatively affected chemistry academic buoyancy (β = −0.489), while chemistry academic buoyancy exhibited a positive impact on chemistry academic engagement (β = 0.492), so hypothesis 2 was supported. The findings are consistent with earlier research that demonstrated that self-handicapping could negatively affect chemistry academic engagement through chemistry academic buoyancy, and the positive impact of chemistry academic buoyancy suppressed the negative effects of self-handicapping in chemistry (Thomas and Allen, 2021). According to achievement goal theory, avoidance-oriented students usually show poor performance and they can’t take proper strategies when faced with setbacks (Elliot and Church, 1997). Students with high self-handicapping often pursue avoidance goals, and self-handicapping is essentially avoidance-oriented (Santos-Diaz et al., 2019). This means that self-handicapping can influence the coping strategies of students in front of challenges (academic buoyancy). In academic learning, academic buoyancy helps students with low self-handicapping to recover from setbacks and failures encountered in their daily lives, allowing them to engage constructively in challenging academic tasks, enhancing their sense of academic identity and academic control (Collie et al., 2015), and reducing levels of anxiety. This, in turn, can increase academic engagement (Martin et al., 2010).

The moderating roles of emotional support and instrumental support

Instrumental support. An important fruit of our study is that instrumental support plays a moderating role in the correlations between chemistry academic buoyancy and chemistry academic engagement, supporting hypothesis 3. Consistent with previous studies (Federici and Skaalvik, 2014), teacher instrumental support was positively correlated with motivational outcomes. Self-determination theory (Deci et al., 1991) argues that satisfying the three needs of autonomy, competence, and relatedness is an important factor in promoting student engagement and motivation. Studies have shown that chemistry academic engagement increases when chemistry teachers support students' academic efforts in chemistry through the use of structured questions, learning materials, and learning feedback. This could be because when teachers provide instrumental support, students feel a sense of belonging to the school and thus increase motivation (Collie et al., 2016). At the same time, students who are supported by tools are able to use learning strategies to deal with challenges related to their current experiences; for example, when faced with a difficult chemistry problem, implementing problem-solving strategies learned from teachers.

This could also be because instrumental support promotes a focus on chemical tasks, and students may find it more relevant to use skills and strategies to accomplish them, although these tasks are difficult (Helena Granziera et al., 2022). Our findings are one of the few empirical studies that establish a link between teachers’ instrumental support and students’ chemistry academic engagement and chemistry academic buoyancy, demonstrating the importance of instrumental support when learners face academic difficulties.

Emotional support. The results of this study support hypothesis 4. As expected, teachers' affective support moderated the relationship between students’ self-handicapping in chemistry, chemistry academic buoyancy, and chemistry academic engagement. According to the stage-environment fit theory (Roeser and Eccles, 2015), adolescents’ academic engagement in school is influenced by the degree to which students' psychological needs are met. In addition, Covington's (2000) research suggests that self-handicapping is associated with the preservation of self-worth when performing poorly. Thus, when teachers provide emotional or emotional support, students' confidence in their academic ability in chemistry is strengthened and their ability to overcome challenges and adversities (i.e., chemistry academic buoyancy) is enhanced; as a result, they feel safer and more committed to learning (Kikas and Tang, 2019), thus increasing their chemistry academic engagement. This may be the reason why emotional support modulates the correlation between self-handicapping in chemistry, chemistry academic buoyancy, and chemistry academic engagement. Strati et al. (2017) demonstrated that the association between students’ engagement and emotional support is individualized, which suggested that emotional support may play an indirect role in academic engagement, and the present study revealed that this indirect role acts through academic buoyancy.

In sum, our study concerns the significant moderating effects of instrumental and emotional support. Our finding suggested that instrumental support has moderating roles in the correlations between chemistry academic buoyancy and chemistry academic engagement. And as well, our findings suggested that emotional support has moderating roles in the correlations between self-handicapping in chemistry, chemistry academic buoyancy and chemistry academic engagement. In addition, the results showed that the potential mediating role of chemistry academic buoyancy was greater for students perceiving high emotional support and high instrumental support.

Implication for practice

The fruits of our study suggested that self-handicapping has a negative influence on academic engagement in chemistry, and thus it is practicable to prompt students’ chemistry academic engagement by reducing their self-handicapping in chemistry. Based on attribution theory, what lies behind the self-handicapping is the maladaptive attribution schema, which is the cause of uncontrollability and stability (Yu and McLellan, 2019). Teachers can avoid the development of this dysfunctional interpretive thinking by working hard and using appropriate procedures and strategies (e.g., desired self-attribution and active self-talk) to encourage students to treat competence as a modifiable and improvable trait (Marsh and Craven, 2006). This intervention led students to understand and control their learning performance and process, to view failure as a learning chance (rather than a sign that diminishes self-worth) and set more appropriate achievement targets (Matteucci, 2017; Graham, 2020). Thus, teachers should focus on this attributional work since it would lead to lower self-handicapping and greater students’ motivational and behavioral involvement in academic work. In terms of prevention of self-handicapping, Midgley and Urdan, 2001 have pointed out the importance of learning goal structure. Teachers supporting this structure helps students understand instead of memorize chemistry knowledge in the classroom (Lewis and Neighbors, 2005; Török et al., 2018), with the purpose of making the process of acquiring knowledge enjoyable and appreciated efforts. To achieve these goals, students’ autonomy needs to be noted, because it can reduce the self-handicapping by itself.

In addition, our findings suggested that academic buoyancy in chemistry mediates the correlation between self-handicapping and academic engagement in chemistry. Martin et al. (2010) discovered a number of elements that influenced academic buoyancy, including control, persistence, and self-efficacy. Martin (2013) also suggested having students learn how to minimize the academic abilities they can control and maintain personal attributes to help them cope with these risks. Putwain (2019) suggested programs to enhancing academic buoyancy: programs to enhance cognitive and emotional regulation through cognitive-behavioral interventions, commitment, and the principles of access to therapy. To be specific, chemistry teachers should pay attention to some typical, long-term academic setbacks students encounter in chemistry learning tasks, and actively develop intervention programs that foster and improve chemistry academic buoyancy, reduce their self-handicapping in chemistry, and increase chemistry academic engagement.

At the same time, our study has verified teacher support (including instrumental and emotional support) as a moderating factor in the moderated mediation model of the current study. Our results showed that the implementation of teachers’ instrumental and emotional support should be considered in practice. In the basis of self-determination theory, contextual factors that fulfill one's basic psychological needs could greatly facilitate the integration and internalization of behavioral regulations, thereby increasing sustained learning, performance, and motivation (Ryan and Deci, 2017). As an irreplaceable factor in the learning context, teachers with emotional support could build a supportive, secure, friendly and motivating learning context that is beneficial for maximizing students’ persistence (Kikas and Tang, 2019), stimulating student's autonomous motivation, and enhancing academic engagement (Grazia, 2022). Taking emotional support as a form of affective-motivational support, chemistry teachers should provide guidance and direction without being overly controlling, and hearten students to be responsible for their own learning.

Furthermore, in the aim of enhancing students’ chemistry academic engagement, teachers could follow Vedder-Weiss and Fortus's (2018) suggestion: assigning “challenge chemistry tasks” to encourage students to identify problems in chemistry learning and ask for solutions; encourage students to enjoy and be enthusiastic about chemistry learning. Our study also revealed the moderating impact of instrumental support in the correlation of chemistry academic buoyancy and chemistry academic engagement. As Suldo et al. (2009) advised, the instrumental support's improvement can be further provided by understanding what each student needs. As David recommended, instrumental support of chemistry teachers could be achieved by adopting student-centered active learning teaching methods, discontinuing teacher-centered teaching methods, and attending teacher professional development courses.

Limitations and further directions

This study focused on 10th grade students and explored the relationship between self-handicapping in chemistry and chemistry academic engagement, but there are still some limitations. Firstly, all variables were statistically analyzed using data from students who self-reported their perceptions about themselves, which may lead to some methodological biases. Therefore, future studies should use measurement methods such as daily observations and interviews to obtain more objective results. Second, although the size of the study was sufficient and the sex ratio was the same, the sample used in this study was all Chinese middle school students, limiting the results of the sample. Therefore, future research can explore the potential relationship between the variables at different hierarchical and cultural contexts. Finally, the current study can only explain the correlation between several variables, which was a non-experimental cross-sectional study. Therefore, future research needs to be combined with longitudinal studies to expand and apply our findings.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This work was sponsored by the 14th Five-Year Plan Research Project of Chongqing Education Science (Grant No. K23YB2020027).

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