View PDF VersionPrevious ArticleNext Article
 
DOI: 10.1039/C7RP00110J (Paper) Chem. Educ. Res. Pract., 2018, 19, 60-67

The relationship between chemistry self-efficacy of South African first year university students and their academic performance†

Umesh Ramnarain * and Sam Ramaila
University of Johannesburg, South Africa. E-mail: uramnarain@uj.ac.za

Received 13th June 2017 , Accepted 5th October 2017

First published on 5th October 2017

Abstract

This study investigated the self-efficacy of first-year Chemistry students at a South African university. The research involved a quantitative survey of 333 students using the College Chemistry Self-Efficacy Scale (CCSS) developed by Uzuntiryaki and Capa Aydin (2009). Descriptive statistics on data for the CCSS scales suggested that students have positive beliefs in their capability to accomplish chemistry tasks. The students scored more strongly on the self-efficacy constructs of cognitive and psychomotor skills than on everyday application. There was a significant difference between students of different professional orientations for cognitive skills and everyday applications, with students enrolled for Chemical Engineering having the highest mean scores for these constructs. A multiple regression analysis was run in order to explore the relationship between chemistry self-efficacy and performance in a chemistry examination. The analysis indicated that cognitive skills significantly predicted chemistry performance, while psychomotor skills and everyday applications had no significant impact. The implications for research and instruction are discussed in terms of the relationship between chemistry self-efficacy and performance.

Background

Historically, the number of South African university graduates, especially Black graduates in science and engineering, was small as a direct consequence of a small proportion of students pursuing studies in these fields. This can be largely ascribed to the Apartheid policies of the country that provided limited opportunities for Black learners to study science at the secondary school level. The education system was racially divided into separate departments for Blacks, Whites, Mixed-race, and Indians with discriminatory funding policies. This was evidenced in the capitation for Black and White students, where the per capita expenditure for a White student was five times that for a Black student (Foundation for Research Development, 1993). According to Naidoo and Lewin (1998), “the apartheid policies inflicted on the African student a curriculum which many be perceived as largely irrelevant to their needs, difficult to the point of impossible given the learning context, and taught by a large number of unqualified science teachers in schools with few or no laboratories and science equipment.” These inequalities resulted in only 1 in 1000 Black children achieving a school leaving qualification with mathematics and science as subjects (Foundation for Research Development, 1993). The economic development necessary to redress the racial inequities of the Apartheid system in South Africa depends upon a strong emphasis being placed on human resources in the fields of science and technology (Taylor, 2007).

Today, more Black students, especially from disadvantaged communities, are pursuing physical sciences at secondary school and, subsequently, studies in science, engineering, and technology at universities (Department of Education, 2010). This is the prevailing trend at all universities in South Africa, but more especially at the particular South African city university that forms the primary focus of this study, where the student headcount in the Faculty of Science was 2382 in 2008 and 3410 in 2017. Chemistry 1 is a key module as it is not only an elective for students enrolled for a Bachelor of Science degree, but is also a crucial requirement for studies in other degree programs such as the Bachelor of Engineering and the Bachelor of Optometry. Over the past ten years there has been a rapid increase in the number of students registered for first-year chemistry (Chemistry 1) with the enrolment being 472 in 2017. However, despite the larger number of students pursuing studies in the sciences, the number of graduates does not correlate well with this trend. This is partly due to the high drop-out rate which is a function of failure in certain first-year modules.

Research in science education has focused largely on cognition with little consideration of affective constructs and its relationship with academic performance (Koballa and Glynn, 2007; Fortus and Vedder-Weiss, 2014) Hence, there is a need to turn our attention to affective constructs such as student motivation, goal orientation, and self-efficacy (Schunk et al., 2008) with a view to addressing a dearth of research investigating affective factors related to science education in the literature (Gungor et al., 2007; Fortus and Daphna, 2017). Certainly, in South Africa, student affective constructs such as self-efficacy have been under-researched (Harry and Coetzee, 2011; Sofowora, 2014).

Accordingly, the research was guided by the following questions.

(1) How do first-year students perceive their self-efficacy in chemistry?

(2) Is there a significant difference in chemistry self-efficacy for students of various professional orientations?

(3) How well do students' perception of their chemistry self-efficacy predict and explain their performance in chemistry?

Self-efficacy and its characteristics

Self-efficacy refers to individuals' beliefs in their capability to accomplish a specific task (Bandura, 1986). At the heart of self-efficacy is the theoretical position that maintains that individuals are self-regulating and will monitor and regulate their behaviour (Bandura, 1982). It is a part of an individual's belief system and is influenced by prior experiences, successes, and failures (Uzuntiryaki; 2008; Jones and Leagon, 2014). In a similar vein, self-efficacy increases with positive experiences and decreases with negative experiences (Dalgety and Coll, 2006). For instance, if students successfully complete a given task, they will feel confident and will be more willing to try the next task. This can be ascribed to their perception of their ability that increases with positive experiences. Self-efficacy therefore concerns one's judgment about his/her own capability rather than one's intention to perform a task (Uzuntiryaki and Çapa Aydın, 2007). Self-efficacy beliefs may be determinants of outcome expectancies, and highly efficacious students tend to expect high grades in examinations and tests. When science teachers use the term, they refer to the evaluation that a student makes about his or her personal competence to succeed in a field of science (Koballa and Glynn, 2007). In this way, self-efficacy is not necessarily related to the skills individuals actually possess, but how they perceive their own capabilities for a specific task (Mataka and Kowalske, 2015). This suggests that people with higher self-efficacy beliefs are more likely to attempt difficult tasks than their counterparts with low self-efficacy beliefs (Fairbrother, 2000).

Bandura (1986, 1997) postulates that there are four sources of self-efficacy. According to him, a student's sense of self-efficacy emanates from mastery experiences, vicarious experiences, social persuasion as well as emotional and psychological states. Mastery experiences have the greatest impact on student self-efficacy. As students become successful, their self-efficacy is enhanced, while failure lowers self-efficacy. Vicarious experiences are related to the observation of role models such as teachers, parents, peers or characters in films with whom students can identify. Social persuasion can influence students positively and make them work harder towards achieving desired outcomes in science. The last source, emotional and psychological states, refers to the anxiety and stress that a person faces when performing a given task. If a student experiences high anxiety or stress when performing a given task, it can be interpreted by students as a lack of skill or ability to complete the task and will likely have a negative impact on students' self-efficacy (Usher and Pajares, 2008).

Self-efficacy is a construct that is context and task dependent (Pajares, 1996; Bong, 2006). Self-efficacy is therefore domain-specific. For example, a student may have a high self-efficacy with respect to knowledge and skills in chemistry and a low self-efficacy with respect to knowledge and skills in biology. The research reported in this paper recognises this domain-specificity in self-efficacy and is focussed on the self-efficacy and achievement in chemistry.

Self-efficacy and achievement

Although it is widely recognised that cognitive factors such as spatial skills (Oke and Alam, 2010; Stieff, 2011a, 2011b), problem solving ability (Cartrette and Bodner, 2010; Kraft et al., 2010), mathematical ability (Spencer et al. 1999; Lewis and Lewis, 2007), and concept development (Grove et al., 2012a, 2012b) are all linked to chemistry achievement, student success in chemistry is also tied to affective factors (Ferrell et al., 2016). Self-efficacy is the most widely studied of these affective constructs in view of its link with academic achievement. The relationship between self-efficacy and achievement is well documented. Research shows that self-efficacy consistently displays a positive relationship with academic performance (e.g., Lightsey, 1999; Multon et al., 1991; Nicolidau and Philippou, 2004; Robbins et al., 2004; Chowdhury and Shahabuddin, 2007; Jungert and Rosander, 2010). In addition, a review of research by Honicke and Broadbent (2016) that investigated the direct, mediated and moderated relationship between self-efficacy and academic performance in 59 recent studies points out that a moderate positive relationship exists between these constructs. Self-efficacy is perceived to influence cognitive processing and problem solving (Evans, 2011).

In particular, self-efficacy has been found to predict achievement amongst college students and high school students doing science (Zusho et al., 2003; Cavallo et al., 2004; Lalich et al., 2006; Jansen et al., 2015; Uzuntiryaki-Kondakci and Senay, 2015; Villafañe et al., 2016). Jansen et al. (2015) found that self-efficacy had a stronger predictive impact on the achievement of high school students in general science than self-concept. Cavallo et al. (2004) showed that self-efficacy significantly predicted physics achievement in college physics students in the United States. Villafañe et al. (2016) found a significant positive relationship between organic chemistry self-efficacy and chemistry performance. Lalich et al. (2006) found a correlation between self-efficacy and final achievement in a general chemistry course. In college chemistry, Zusho et al. (2003) conducted research with United States students enrolled in introductory college chemistry, and found self-efficacy to be the best predictor of grades, even controlling for prior achievement. They found that students' ratings of their levels of self-efficacy were better predictors of final course performance than the SAT-mathematics scores. A study reported by Uzuntiryaki-Kondakci and Senay (2015) with Turkish grade 11 students revealed that chemistry self-efficacy for cognitive skills was a significant positive predictor of chemistry achievement.

The research reported in this article purports to build upon the emerging knowledge domain of self-efficacy of college students studying science and the relationship of this construct with achievement. This research takes on particular significance in light of the increasing enrolment in science at South African universities and the disturbing trend of high failure in science courses.

Research design and methodology

This study adopted a cohort design as it involved participants who are united by some commonality or similarity (Healy and Devane, 2011). The term ‘cohort’ refers to a set of people in a population sharing a common attribute or who have experienced a common event (Burns and Grove, 2001). In the case of this study, the cohort was constituted of first year students at a South African university taking chemistry. Cohort studies are generally concerned with information regarding the prevalence, distribution and inter-relationship of variables in a population. In this design, provision is made for statistical occurrence within a particular subgroup united by the same or similar characteristics that were relevant to the research problem being investigated. The subgroups were students pursuing qualifications in Optometry, Mechanical Engineering, Chemical Engineering, Analytical Chemistry, Chiropractic, Homoeopathy and Food Technology.

The instrument

The chemistry self-efficacy data were collected by administering the College Chemistry Self-Efficacy Scale (CCSS) developed by Uzuntiryaki and Çapa Aydın (2009). In this questionnaire, three dimensions for self-efficacy were used: self-efficacy for cognitive skills, self-efficacy for psychomotor skills, and self-efficacy for everyday applications. The items were statements to which students had to respond on a 5-point rating scale that ranged from 1 (very poorly) to 5 (very well) (see Appendix A).

Self-efficacy for cognitive skills (SCS) is a construct on students' beliefs in their ability to deal with intellectual operations in chemistry. This includes both lower and higher levels of understanding in the cognitive domain. An example of an item from this dimension is, “How well can you interpret chemical equations?” Self-efficacy for psychomotor skills (SPS) relates to students' beliefs in their ability to apply psychomotor skills. A sample item is, “How well can you construct laboratory apparatus?” Self-efficacy for everyday applications (SEA) describes students' beliefs in their ability to use the learned chemistry concepts in daily life situations. For example, such beliefs can be elicited by the item, “How well can you understand the news/documentary you watched on television related to chemistry?”

Data collection and sampling

The questionnaire was administered to 333 first-year university students who were studying chemistry in 2016. The sample is composed of 160 female students and 173 male students. The age distributions of students are as follows: 18–19 years (281 students); 20–21 years (19 students); 22–23 years (33 students). Chemistry 1 is a first-year year module that is offered by the Faculty of Science. The content of this module includes atomic and molecular structure; quantitative calculations; types of chemical reactions; periodicity; acid–base reactions; dynamic equilibrium; and performing laboratory experiments. The instrument was administered to all students at the end of the first semester, a week before the commencement of the examination. This cohort, which constituted a purposive sample, included students who were registered for qualifications in Optometry, Mechanical Engineering, Chemical Engineering, Analytical Chemistry, Chiropractic, Homoeopathy and Food Technology. Table 1 below shows the distribution of the sample according to professional orientations.
Table 1 Profile of sample
Professional orientation N Percent
Chiropractic 57 17.1
Optometry 38 11.4
Mechanical Engineering 30 9.0
Chemical Engineering 31 9.3
Analytical Chemistry 93 27.9
Food Technology 84 25.2
Total 333 100.0

Data analysis

Items of the questionnaire were subjected to exploratory factor analysis using SPSS Version 24 in order to identify the underlying factor structure of the questionnaire data, and the relationship between the measured variables for these data. Factor analysis operates on the premise that measurable variables can be reduced to fewer latent variables that share a common variance and are unobservable, which is known as reducing dimensionality (Bartholomew et al., 2011). Prior to performing the factor analysis, the suitability of the data for factor analysis was assessed. The Kaiser–Meyer–Oklin value was 0.89, exceeding the recommended value of 0.6 (Kaiser, 1974). The Bartlett's Test for Sphericity (Bartlett, 1954) reached statistical significance, supporting the factorability of the correlation matrix.

The factor analysis revealed the presence of three components with eigenvalues exceeding 1, explaining 52.37% of the variance. The interpretation of the three factors was consistent with previous research on the College Chemistry Self-Efficacy Scale (CCSS). The internal reliabilities of scales were evaluated by calculating Cronbach's alpha for each scale. Cronbach's alpha is used in this study as an indicator of scale reliability or internal consistency (Taber, 2017). This is the degree to which the items that made up the scale are all measuring the same underlying attribute (Pallant, 2007), which in this case is dimensions of self-efficacy. Table 2 presents the constructs assessed by the questionnaire used in this study, the items clustered in each construct, and Cronbach's alpha for each construct. The Cronbach's alpha of the scales compared favourably with the statistics obtained when the instrument was developed by Uzuntiryaki and Çapa Aydın (2009). This suggested satisfactory reliability. The Kolmogorov–Smirnov statistic assessed the normality of distribution. A non-significant result of more than 0.05 indicated normality.

Table 2 Constructs assessed by the questionnaire
Dimension Cronbach alpha Number of items Items
Self-efficacy for cognitive skills (SCS) 0.68 5 To what extent can you explain chemical laws and theories?

How well can you describe the structure of an atom?

How well can you interpret chemical equations?

How well can you interpret data during the laboratory sessions?

How well can you solve chemistry problems?
Self-efficacy for psychomotor skills (SPS) 0.76 4 How well can you work with chemicals?

How well can you construct laboratory apparatus?

How well can you use the equipment in the chemistry laboratory?

How well can you carry out experimental procedures in the chemistry laboratory?
Self-efficacy for everyday applications (SEA) 0.69 3 To what extent can you propose solutions to everyday problems by using chemistry?

To what extent can you explain everyday life by using chemical theories?

How well can you understand the news/documentary you watched on television related to chemistry?

The first research question was addressed by computing the mean, standard deviation and bivariate correlations for the three self-efficacy constructs under consideration. The second research question was investigated by conducting a one-way analysis of variance (ANOVA). The third research question was investigated by constructing a hypothesized model describing the relationship between students' self-efficacy constructs and their achievement in chemistry. Based on previous research findings already reported in this article, we hypothesized that students' self-efficacy for cognitive skills, psychomotor skills and everyday applications will predict their achievement in chemistry. A multiple regression analysis was run in order to explore the relationship between self-efficacy constructs and chemistry achievement. For chemistry achievement, we used the students' semester examination mark in chemistry because the semester examination is considered a high stakes assessment. The examination is composed of questions that test conceptual understanding and application of chemistry concepts.

Results

Students' self-efficacy in chemistry

Table 3 summarizes the means, standard deviations and bivariate correlations for the measured variables.
Table 3 Summary of means, standard deviations and bivariate correlations for the measured variables
Self-efficacy constructs
Cognitive skills Psychomotor skills Everyday application
Cognitive skills Pearson correlation 1 0.575 0.538
Sig. (2-tailed) 0.000 0.000
N 333 333 333
Psychomotor skills Pearson correlation 0.575 1 0.437
Sig. (2-tailed) 0.000 0.000
N 333 333 333
Everyday application Pearson correlation 0.538 0.437 1
Sig. (2-tailed) 0.000 0.000
N 333 333 333
Mean 3.4469 3.5766 3.0255
Standard deviation 0.54850 0.62194 0.73229

The results shown in Table 3 indicate that students scored more strongly on the self-efficacy constructs of cognitive (M = 3.45, SD = 0.54) and psychomotor skills (M = 3.58, SD = 0.62) than on everyday application (M = 3.02, SD = 0.73). The overall mean was 3.35. From this score, it can be interpreted that the students' rating of their self-efficacy is slightly above neutral, with students exhibiting a favourable rating of self-efficacy. Correlation analyses revealed that students' perceptions of self-efficacy for cognitive skills and psychomotor skills were moderately related (r = 0.58, n = 333, p < 0.01). There was also a moderate correlation between everyday application and cognitive skills (r = 0.54, n = 333, p < 0.01) and a weak correlation between everyday skills and psychomotor skills (r = 0.44, n = 333, p < 0.01). The results per qualification are shown in Table 4 below.

Table 4 Descriptive statistics for self-efficacy scores
N Mean Std deviation Minimum Maximum
Cognitive skills Chiropractic/homoeopathy 57 3.27 0.55 2.25 4.50
Optometry 38 3.41 0.6 2.25 4.75
Mechanical Engineering 30 3.37 0.57 2.25 4.50
Chemical Engineering 31 3.66 0.58 2.75 4.75
Analytical Chemistry 93 3.53 0.53 2.00 4.50
Food Technology 84 3.43 0.49 1.67 4.50
Overall 333 3.44 0.55 1.67 4.75
Psychomotor skills Chiropractic/homoeopathy 57 3.56 0.57 2.00 5.00
Optometry 38 3.53 0.65 2.00 5.00
Mechanical Engineering 30 3.42 0.62 2.25 4.75
Chemical Engineering 31 3.83 0.68 2.50 5.00
Analytical Chemistry 93 3.53 0.63 1.50 5.00
Food Technology 84 3.63 0.60 2.50 5.00
Overall 333 3.58 0.62 1.50 5.00
Everyday application Chiropractic/homoeopathy 57 2.8 0.73 1.00 5.00
Optometry 38 2.84 0.75 1.50 4.50
Mechanical Engineering 30 2.92 0.76 1.50 4.50
Chemical Engineering 31 3.21 0.91 1.00 5.00
Analytical Chemistry 93 3.11 0.61 1.50 5.00
Food Technology 84 3.14 0.73 1.50 5.00
Overall 333 3.03 0.73 1.00 5.00

The differences between the qualification groups were investigated by conducting the analysis of variance (ANOVA) test. The ANOVA showed that the effect of the qualification type on self-efficacy was significant for cognitive skills and everyday applications. There was a statistically significant difference at the p < 0.05 level in cognitive skills: F(5, 330) = 2.844, p = 0.016 and everyday applications: F(5, 330) = 2.706, p = 0.021. The students enrolled for Chemical Engineering had the highest mean score for both cognitive skills and everyday applications. The effect size for cognitive skills, calculated using eta squared, was 0.04, and for everyday applications, it was 0.05. This suggests that despite reaching statistical significance, the actual difference in mean scores between the groups was quite small.

Relationship between chemistry self-efficacy and performance

The hypothesized model as illustrated in Fig. 1 below served to describe the relationship between students' performance in chemistry and their self-efficacy in cognitive skills, psychomotor skills and everyday applications. The strength of the model would be reflected by the positive associations between each of the self-efficacy constructs and performance. The multiple regression model took the form: Y = a + b1X1 + b2X2 + b3X3 + c + e, where Y is the value of the Dependent variable (Y), which is being predicted or explained, c is the constant or intercept, and e is the error term reflected in the residuals. In this study, Y refers to chemistry performance. The b's are the regression coefficients for the corresponding X (independent) terms, where c is the constant or intercept, and e is the error term reflected in the residuals. The Xs in this study refer to the three self-efficacy constructs.
image file: c7rp00110j-f1.tif
Fig. 1 Hypothesised model for chemistry performance.

An analysis of standard residuals was carried out, which showed that the data contained no outliers. The histogram of standardised residuals indicated that the data contained approximately normally distributed errors, as did the normal P–P plot of standardised residuals, which showed points that were not completely on the line, but close. The scatter plot of standardised residuals showed that the data met the assumptions of homogeneity of variance and linearity. Correlation analyses revealed that students' perceptions of their self-efficacy for cognitive skills, psychomotor skills and everyday applications were weakly to moderately related to each other (0.44 ≤ r ≤ 0.58; p < 0.01) (Table 3). As correlations between most exogenous variables (independent variables) were lower than 0.6, the possibility of multicollinearity between these variables was excluded (Grewal et al., 2004) and all variables were retained for multiple regression analysis.

The R2 value indicates how much of the variance in student performance (dependent variable) can be explained by the model. In this case a value of 0.32 was attained and that means this model explains 32% of variance in the performance of students. The statistical significance of this result was then established through the ANOVA, F(3, 354) = 43.56, p < 0.001. The regression weight (beta) of the cognitive skills (0.35) was greater than 0.1 and significant (p < 0.05). This suggested that this variable positively predicted the performance. Both psychomotor skills (beta = 0.21) and everyday application (beta = 0.23) had no significant impact on student performance with p greater than 0.05.

Discussion

In this study, we investigated the self-efficacy in chemistry of first-year chemistry students at a South African university as well as the impact of self-efficacy in cognitive skills, psychomotor skills and everyday applications on their performance in chemistry. The findings of this study indicate that students scored more strongly on the self-efficacy constructs of cognitive and psychomotor skills than on everyday application. An exploratory factor analysis revealed a three-factor structure that was consistent with the dimensions of cognitive skills, psychomotor skills and everyday applications of College Chemistry Self-Efficacy Scale (CCSS) developed by Uzuntiryaki and Çapa Aydin (2009). The descriptive statistics (mean and standard deviation) that students scored are in line with results from other studies where either the same or similar constructs were measured (Uzuntiryaki and Çapa Aydın, 2007; Uzuntiryaki, 2008; Ferrell et al., 2016). The mean for the CCSS scales for all qualification groups ranged from 2.81 to 3.83, and this suggested that students have positive belief in their capability to accomplish chemistry tasks. The students scored more strongly on the self-efficacy constructs of cognitive and psychomotor skills than on everyday application. The low self-efficacy score on everyday application is suggestive of students having limited experiences where the learned chemistry concepts are applied in daily life situations. In addressing this low self-efficacy, university lecturers can encourage students to become involved in chemistry projects that require them to apply their knowledge to real world situations.

There was a significant difference between students of different professional orientations for cognitive skills and everyday applications, with students enrolled for Chemical Engineering having the highest mean scores for these constructs. This finding may be attributed to students who register for Chemical Engineering needing to meet a higher pre-requisite score for this qualification than students registered for other qualifications. This is suggestive of a causative relationship where prior chemistry performance influences self-efficacy constructs. This can be explained in terms of the reciprocal relationship between chemistry self-efficacy and achievement that was revealed in the study by Villafañe et al. (2016).

A hypothesised model tested the effects of chemistry self-efficacy constructs on performance in a chemistry semester examination. In testing the model, multiple regression analysis indicated that cognitive skills predicted chemistry performance, while psychomotor skills and everyday applications had no significant impact. This result adds to the growing literature on psychological constructs within the chemistry education domain by identifying variables related to motivation that have a significant relationship to chemistry performance (Zusho et al., 2003; Cavallo et al., 2004; Lalich et al., 2006; Jansen et al., 2015; Uzuntiryaki-Kondakci and Senay, 2015; Villafañe et al., 2016).

The findings of this research highlight the significance of self-efficacy as an affective factor in the classroom for chemistry performance in particular. While other studies have investigated the role of various psycho-social and study skill variables in academic performance, self-efficacy has been identified as a strong predictor of performance (Robbins et al., 2004). The implication of this for chemistry teaching is that learning experiences ought to be tailored to improve the self-efficacy of students. For example, Vishnumolakala et al. (2016) reported that first-year undergraduate chemistry process-oriented guided inquiry learning (POGIL) classes positively influenced students' self-efficacy. Mataka and Kowalske (2015) found that students experiencing problem-based learning (PBL) in chemistry classes had enhanced self-efficacy scores. Nbina (2012) concluded that instruction in meta-cognitive self-assessment strategies has a significant effect on the Chemistry self-efficacy of secondary school students. In addition, a metacognitive self-assessment strategy and self-explanation have been shown to enhance students' chemistry self-efficacy and achievement at the levels of senior secondary school and college (Crippen and Earl, 2007). The findings of this study take on a particular significance within the South African education landscape where previously disadvantaged communities have under-performed in science, especially at the tertiary level. The significant relationship between the cognitive skills component of self-efficacy and chemistry performance suggests that cognitive skills are a good predictor of achievement, and are something that should be further promoted. However, no such relationship could be established between the other two self-efficacy constructs and chemistry achievement. Students appeared to overestimate their psychomotor skills. These findings are consistent with the claim that the overestimation of performance abilities is a result of not knowing one's limitations (Lawson et al., 2007). Thus, when students lack competencies, their judgment of what they can do is impaired, and they will often overestimate their competence.

Further research may address some of the limitations of this study. This research was conducted with students at a single university, and so future research may be carried out with more students at other universities in order to improve the generalizability of the findings. The research focussed primarily on the relationship between self-efficacy and achievement. Future studies may examine how student characteristics such as age, gender, race or ethnicity shape students' self-efficacy. This model can be tested with other factors that have been hypothesized to affect performance such as motivation, interest, and effort beliefs.

Future research can also investigate the interconnected reciprocal causation relationship between self-efficacy and performance chemistry. A recent study by Villafañe et al. (2016) was the first to introduce a reciprocal causation model in examining the interrelated relationship between students' chemistry self-efficacy in chemistry and performance. The findings of that study lent support to the hypothesis that self-efficacy has a consistent effect on examination performance and vice versa. There is a need for confirmation of such findings that examine the effect of chemistry performance on self-efficacy.

Conflicts of interest

There are no conflicts to declare.

References

  1. Bandura A., (1982), Self-efficacy mechanism in human agency, Am. Psychol., 37(2), 122–147.
  2. Bandura A., (1986), Social Foundations of Thought and Action: A Social Cognitive Theory, Englewood Cliffs, NJ: Prentice Hall.
  3. Bandura A., (1997), Self-Efficacy: The Exercise of Control, New York: W. H. Freeman and Company.
  4. Bartholomew D., Knotts M. and Moustaki I., (2011), Latent variable models and factor analysis: a unified approach, 3rd edn, West Sussex, UK: John Wiley & Sons.
  5. Bartlett M. S., (1954), A note on the multiplying factors for various chi square approximations, J. R. Stat. Soc., 16, 296–298.
  6. Bong M., (2006), Asking the right question: How confident are you that you could successfully perform these tasks? in Pajares F. and Urdan T. (ed.), Self-Efficacy Beliefs of Adolescents, Greenwich, CT: Information Age Publishing, pp. 287–305.
  7. Burns N. and Grove S., (2001), The practice of nursing research: conduct, critique and utilization, 4th edn, Philadelphia, Pennsylvania, USA: W.B. Saunders.
  8. Cartrette D. P. and Bodner G. M., (2010), Non-Mathematical Problem Solving in Organic Chemistry, J. Res. Sci. Teach., 47(6), 643–660.
  9. Cavallo A. M. L., Potter W. H. and Rozman M., (2004), Gender differences in learning constructs, shifts in learning constructs, and their relationship to course achievement in a structured inquiry, yearlong college physics course for life science majors, Sch. Sci. Math., 104(6), 288–300.
  10. Chowdhury M. and Shahabuddin A., (2007), Self-Efficacy, motivation and their relationship to academic performance of Bangladesh college students, Coll. Quart., 10(1), 1–9.
  11. Crippen K. J. and Earl B. L., (2007), The impact of web-based worked examples and self-explanation on performance, problem solving, and self-efficacy, Comput. Educ., 49(3), 809–821.
  12. Dalgety J. and Coll R. K., (2006), The influence of first-year chemistry students' learning experiences on their educational choices, Assess. Eval. High. Educ., 31, 303–328.
  13. Department of Education, (2010), Education Statistics in South Africa, Pretoria: Government Printer.
  14. Evans B. T., (2011), Dual-process theories of reasoning: Contemporary issues and developmental applications, Dev. Rev., 31(2), 86–102.
  15. Fairbrother R., (2000), Strategies for learning, in Monk M. and Osborne J. (ed.), Good Practice in Science Teaching. What Research Has To Say, Philadelphia, PA: Open University Press, pp. 7–24.
  16. Ferrell B, Phillips M. M. and Barbera J., (2016), Connecting achievement motivation to performance in general chemistry, Chem. Educ. Res. Pract., 17(4), 1054–1066.
  17. Fortus D. and Daphna L., (2017), Adolescents' goal orientations for science in single-gender Israeli religious schools, Int. J. Sci. Educ., 39, 86–103.
  18. Fortus D. and Vedder-Weiss D., (2014), Measuring Students' Continuing Motivation for Science Learning, J. Res. Sci. Teach., 51, 497–522.
  19. Foundation for Research Development, (1993), South African science and technology indicators: foundations for research development, Pretoria: Government Printer.
  20. Grewal R., Cote J. and Baumgartner H., (2004), Multicollinearity and Measurement Error in Structural Equation Models: Implications for Theory Testing, Market. Sci., 23(4), 519–529.
  21. Grove N. P., Cooper M. M. and Cox E. L., (2012a), Does Mechanistic Thinking Improve Student Success in Organic Chemistry? J. Chem. Educ., 89, 850–853.
  22. Grove N. P., Cooper M. M. and Rush K. M., (2012b), Decorating with Arrows: Toward the Development of Representational Competence in Organic Chemistry, J. Chem. Educ., 89, 844–849.
  23. Gungor A., Eryilmaz A. and Fakioglu T., (2007), The relationship of freshmen's physics achievement and their related affective characteristics, J. Res. Sci. Teach., 44(8), 1036–1056.
  24. Harry N. and Coetzee M., (2011), Sense of coherence, affective wellbeing and burnout in a South African higher education institution call centre, South African Journal of Labour Relations, 35(2), 26–46.
  25. Healy P. and Devane D., (2011), Methodological considerations in cohort study designs, Nurse Res., 18, 32–36.
  26. Honicke T. and Broadbent J., (2016), The influence of academic self-efficacy on academic performance: a systematic review, Educ. Res. Rev., 17(1), 63–84.
  27. Jansen M., Scherer R. and Schroeders U., (2015), Students' self-concept and self-efficacy in the sciences: differential relations to antecedents and educational outcomes, Contemp. Educ. Psychol., 41, 13–24.
  28. Jones M. G. and Leagon M., (2014), Science Teacher Attitudes and Beliefs, in Lederman N. G. and Abell S. K. (ed.), Handbook of Research on Science Education, New York, NY: Routledge, vol. II, pp. 830–847.
  29. Jungert T. and Rosander M., (2010), Self-efficacy and strategies to influence the study environment, Teach. High. Educ., 15(6), 647–659.
  30. Kaiser H., (1974), An index of factor simplicity, Psychometrika, 39, 31–36.
  31. Koballa T. R. and Glynn S. M., (2007), Attitudinal and motivational constructs in science learning, in Abell S. K. and Lederman N. G. (ed.), Handbook of Research on Science Education, Mahwah, NJ: Lawrence Erlbaum Associates, pp. 379–382.
  32. Kraft A., Strickland A. M. and Bhattacharyya G., (2010), Reasonable reasoning: multi-variate problem-solving in organic chemistry, Chem. Educ. Res. Pract., 11, 281–292.
  33. Lalich I. J., Taylor M. J. and Pribyl J. R., (2006), Identification of the Correlation Between Student Self-Efficacy and Final Course Percentage in a General Chemistry Course, Mankato: Minnesota State University.
  34. Lawson A. E., Banks D. L. and Logvin M., (2007), Self-efficacy, reasoning ability, and achievement in college biology, J. Res. Sci. Teach., 44(5), 706–724.
  35. Lewis S. E. and Lewis J. E., (2007), Predicting at-risk students in general chemistry: comparing formal thought to a general achievement measure, Chem. Educ. Res. Pract., 8, 32–51.
  36. Lightsey R., (1999), Albert Bandura and the exercise of self-efficacy [Review of the book Self-Efficacy: The Exercise of Control], J. Cognit. Psychother., 13(2), 158–166.
  37. Mataka L. M. and Kowalske M. G., (2015), The influence of PBL on students' self-efficacy beliefs in chemistry, Chem. Educ. Res. Pract., 16(4), 929–938.
  38. Multon K. D., Brown S. D., Lent R. W., (1991), Relation of self-efficacy beliefs to academic outcomes: a meta-analytic investigation, J. Couns. Psychol., 38, 30–38.
  39. Naidoo P. and Lewin J., (1998), Policy and planning of physical science education in South Africa: myths and realities, J. Res. Sci. Teach., 35(7), 729–744.
  40. Nbina J. B., (2012), The effect of instruction in metacognitive self-assessment strategy on chemistry self-efficacy and achievement of senior secondary school students in Rivers State, Nigeria, J. Res. Educ., 3(2), 83–94.
  41. Nicolidau M. and Philippou G., (2004), Attitudes towards mathematics, self-efficacy and achievement in problem-solving, European Research in Mathematics Education III, Thematic Group 2, 1–11.
  42. Oke O. K. and Alam G. M., (2010), Comparative evaluation of the effectiveness of 2 and 3D visualizations in students' understanding of structures of organic molecules, Int. J. Phys. Sci., 5(5), 605–611.
  43. Pajares F., (1996), Self-efficacy Beliefs in Academic Settings, Rev. Educ. Res., 66(4), 543–578.
  44. Pallant J., (2007), SPSS Survival Manual, 3rd edn, Crows West, New South Wales.
  45. Robbins S. B., Lauver K., Le H., Davis D., Langley R. and Carlstrom A., (2004), Do psychosocial and study skill factors predict college outcomes? A meta-analysis, Psychol. Bull., 130, 261–288.
  46. Schunk D. H., Pintrich P. R. and Meece J. L., (2008), Motivation in Education: Theory, Research and Application, Upper Saddle River, New Jersey and Columbus, Ohio: Pearson.
  47. Sofowora S. O., (2014), Anxiety and lack of motivation as factors affecting success rates in bridging mathematics, Unpublished dissertation, Pretoria: University of South Africa.
  48. Spencer S. J., Steele C. M. and Quinn D., (1999), Stereotype threat and women's math performance, J. Exp. Soc. Psychol., 35, 4–28.
  49. Stieff M., (2011a), Improving representational competence using molecular simulations embedded in inquiry activities, J. Res. Sci. Teach., 48(10), 1137–1158.
  50. Stieff M., (2011b), When is a molecule three-dimensional? A task-specific role for imagistic reasoning in advanced chemistry, Sci. Educ., 95(2), 310–336.
  51. Taber, K., (2017), The use of Cronbach's Alpha when developing and reporting research in instruments in science education, Res. Sci. Educ.,  DOI:10.1007/s11165-016-9602-2.
  52. Taylor N., (2007), Equity, efficiency and the development of South African schools, in Townsend T. (ed.), International Handbook of School Effectiveness and Improvement, New York, NY: Springer, pp. 523–540.
  53. Usher E. L. and Pajares F., (2008), Self-efficacy for self-regulated learning: a validation study, Educ. Psychol. Meas., 68(3), 443–463.
  54. Uzuntiryaki E., (2008), Exploring the Sources of Turkish Pre-service Chemistry Teachers' Chemistry Self-efficacy Beliefs, Aust. J. Teach. Educ., 33(6), 12–28.
  55. Uzuntiryaki E. and Çapa Aydın Y., (2007), The relationship between high school students' chemistry self-efficacy and chemistry achievement, Proceedings 2nd European Variety in Chemistry Education, Prague, 81–83, Charles University, Prague.
  56. Uzuntiryaki E. and Çapa Aydın Y., (2009), Development and validation of chemistry self-efficacy scale for college students, Res. Sci. Educ., 39, 539–551.
  57. Uzuntiryaki-Kondakci E. and Senay A., (2015), Predicting chemistry achievement through task value, goal orientations, and self-efficacy: a structural model, Croat. J. Educ., 17(3), 725–753.
  58. Villafañe S. M., Xu X. and Raker J. R., (2016), Self-efficacy and academic performance in first-semester organic chemistry: testing a model of reciprocal causation, Chem. Educ. Res. Pract., 17(4), 973–984.
  59. Vishnumolakala V. R., Southam D. C., Treagust D. F. and Mocerino M., (2016), Latent constructs of the students' assessment of their learning gains instrument following instruction in stereochemistry, Chem. Educ. Res. Pract., 17(2), 309–319.
  60. Zusho A., Pintrich P. R. and Coppola B., (2003), Skill and will: the role of motivation and cognition in the learning of college chemistry, Int. J. Sci. Educ., 25, 1081–1094.

Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c7rp00110j
This journal is © The Royal Society of Chemistry 2018