View PDF VersionPrevious ArticleNext Article
 
DOI: 10.1039/C7RP00182G (Paper) Chem. Educ. Res. Pract., 2018, 19, 331-341

Relating motivation and student outcomes in general organic chemistry

Ara C. Austin a, Nicholas B. Hammond b, Nathan Barrows c, Deena L. Gould d and Ian R. Gould *a
aSchool of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, USA. E-mail: igould@asu.edu
bCenter for Excellence in Teaching and Learning, University of Rochester, Rochester, New York 14627, USA
cDepartment of Chemistry, Grand Valley State University, Allendale, MI 49401, USA
dMary Lou Fulton Teachers College, Arizona State University, Tempe, Arizona 85287, USA

Received 22nd September 2017 , Accepted 24th November 2017

First published on 25th November 2017

Abstract

A central tenet of self-regulated learning theories is that students are motivated towards learning in order to self-regulate. It is thus important to identify student motivations in order to inform efforts to improve instructional strategies that encourage self-regulation. Here we describe a study aimed at characterizing the important motivation factors for students taking general organic chemistry, and how they connect to, and correlate with student performance. A cross-sectional study was conducted involving 2648 undergraduate student participants at two institutions over five semesters and four instructors. Motivation was measured using the Organic Chemistry Motivation Survey (OCMS), a modified form of Glynn et al. (2011)'s Science Motivation Questionnaire II (SMQ-II). The results suggest that the students were highly motivated towards earning a high grade, but that this grade motivation correlated only weakly with performance. Other intrinsic and extrinsic motivation factors were found to be low, suggesting that the students perceived organic chemistry to have little relevance to their interests and careers. However, student performance was strongly correlated with self-efficacy, and, to a lesser extent, self-determination. This finding implies that high-performing students tended to be self-regulated learners who are not motivated primarily by the relevance of the course content. Alternate sources of motivation that can drive self-regulation are discussed.

Introduction

In the United States, over 70[thin space (1/6-em)]000 college students enroll in organic chemistry courses each year, but the majority of these students are not chemistry or biochemistry majors (Merritt, 2005). Although the role of organic chemistry in pre-professional preparation may be under review (Halford, 2016), many professional programs in the United States (e.g. medicine, dentistry, pharmacy) require a two-semester sequence in general organic chemistry as an admission prerequisite. Students often fear organic chemistry and attrition rates can be high, approaching 50% in some cases (Rowe, 1983; Grove, et al., 2008). Together with physics and calculus, organic chemistry is often described as a roadblock to success for many pre-professional students (Lovecchio and Dundes, 2002; Barr et al., 2010), particularly in underrepresented populations (Carmichael, et al., 1986, 1988; Barr et al., 2008). Understanding the student-centric factors that connect to learning in organic chemistry is essential to the design of instructional strategies to improve students’ perceptions of, and performance in, organic chemistry courses.

Several studies on student factors that are predictive of performance have been described in the literature. A range of different variables have been investigated, including student demographics (Steiner and Sullivan, 1984; Lopez et al., 2014), prior academic performance (Steiner and Sullivan, 1984; Turner and Lindsay, 2003; Szu et al., 2011), study habits (Szu et al., 2011), and attitude, anxiety and confidence (Steiner and Sullivan, 1984; Turner and Lindsay, 2003). However, understanding general predictive factors is not as useful as understanding those that directly connect to learning theories, since these can be used to better inform the development of new instructional strategies.

Several investigations of student performance in organic chemistry courses have been performed from the perspective of different learning theories. These studies have tended to emphasize cognitive aspects of learning over affective ones. Cognitive traits that have been investigated include working memory (Tsaparlis and Angelopoulos, 2000), spatial ability (Pribyl and Bodner, 1987), three-dimensional visualization (Ferk et al., 2003; Oke and Alam, 2010; Stieff, 2010; Lopez et al., 2014), problem-solving strategies (Bhattacharyya and Bodner, 2005; Cartrette and Bodner, 2010; Kraft et al., 2010) and conceptual understanding (Nash et al., 2000; Grove et al., 2012; Austin et al., 2015). In turn, suggestions for strategies to improve student learning and outcomes in organic chemistry are most often based on building student cognitive abilities and problem-solving strategies (see, for example: Grove and Bretz, 2010; Flynn and Biggs, 2012; Grove et al., 2012; Raker and Towns, 2012).

Although affective factors, such as motivation, are often considered important in science education in general (see, for example: Simpson et al., 1994; Schunk, 1995; Schunk and Pajares, 2001; Glynn and Koballa Jr., 2006; Lynch, 2006; Usher and Pajares, 2008; Taasoobshirazi and Glynn, 2009; Glynn et al., 2011; Schunk and Usher, 2012), only a few investigations of affective factors have been reported for general organic chemistry courses. Garcia et al. (1993) reported that in addition to prior achievement and learning strategies, students’ intrinsic and extrinsic motivation also correlated with performance in an organic chemistry course with small to moderate effect. A detailed study based on self-determination theory (Black and Deci, 2000) showed that student performance in a general organic chemistry course correlated both with the extent of self-regulation, and also the extent to which the course instructor encouraged self-regulation. Specifically, students’ perceived competence and interest/enjoyment of chemistry as variables were found to correlate with course grade with moderate effect size. Similar effect sizes were found for correlation of student grade with course instructor support in terms of perceived competence and interest/enjoyment of chemistry. Lynch and Trujillo (2011) reported that self-efficacy had a moderate to large effect on performance in organic chemistry and that intrinsic and extrinsic motivation factors were gender dependent, with males reporting higher levels of control and task value with moderate effect sizes. A recent study of a first semester general organic chemistry course showed that student performance correlated with self-efficacy, and also that performance was reciprocally connected to student self-efficacy. Self-efficacy as a variable was significantly correlated with exam score with small to moderate effect size (Villafañe et al., 2016). These studies are important not only because they highlight the role of affective factors in organic chemistry learning, but also because they can also be used to guide strategies to improve student success that reach beyond the traditional approaches that focus mainly on cognition (see: Villafañe et al., 2016). In particular, student motivation is a critical factor in social-cognitive theories of learning.

Motivation and social-cognitive learning theory

The fundamental principle underlying social-cognitive learning theory is that student performance can be understood in terms of self-regulating beliefs that are influenced by and also influence motivation. In turn, these beliefs control cognitive and affective learning behavior (Bandura, 2001; Schunk and Pajares, 2001). From this perspective, students learn when they have a source of motivation to self-regulate their cognitive development. A number of different motivation factors have been identified that can influence self-regulated learning (Pintrich, 2004; Glynn and Koballa Jr., 2006). These include intrinsic motivation, which describes student learning for its own sake, or because they find the subject interesting or internally rewarding; self-determination, which describes the amount of control students believe they have over their learning; self-efficacy, which describes students' beliefs that they can succeed in the subject; and extrinsic motivation, which accounts for the students' external influences on motivation, such as earning a high grade or advancing towards a career. Although these factors are somewhat interrelated, it is common to characterize them individually in order to build tractable models of student learning.

Social-cognitive theory suggests that understanding motivation (and other social factors) is as important as understanding cognitive factors (Black and Deci, 2000). Measuring motivation factors, however, is difficult since they are not directly observable. For instance, it might be possible to quantify a specific cognitive skill such as spatial ability by directly measuring student response to a specific task. But quantifying motivation can only be made indirectly, by measuring an empirical response that must then be related back to the specific motivation factor. Motivation instruments must, therefore, be carefully validated and checked for reliability.

In order to help students develop self-regulation skills in general organic chemistry, it is clearly important to understand their motivations for learning in the course since this can help to guide improvements in strategies for learning. For example, if they are motivated to learn due to extrinsic factors, such as a belief that the subject material might be relevant to their future careers, then a reasonable strategy might be to contextualize the course material, as has been attempted in general chemistry (see, for example, Middlecamp et al., 2014; Sjostrom and Talanquer, 2014). On the other hand, if students are more motivated by intrinsic factors, such as the desire to develop deeper understanding, then more discovery or problem-based based learning approaches might be explored.

Research goals

The overall goal of the present study is to contribute to an understanding of student motivation to learn in large organic chemistry college courses, in order to inform social-cognitive approaches to teaching and learning. A more specific goal is to develop a survey instrument to measure motivation factors in general organic chemistry in order to characterize the student populations in these courses. A further goal is to determine which of these motivation factors connect most with overall course performance.

Methods

Participants

The participants in the study were students taking general organic chemistry at two institutions. One is a large research university in the Southwestern United States (SWU), the other is a medium sized research university in the Northeastern United States (NEU). The two institutions were selected because they differ geographically, because they differ in type, since SWU is a public institution whereas NEU is private, and because both have large general organic chemistry classes. At both institutions, general organic chemistry is offered as a two-semester course sequence. The first course is taught in the fall semester and the second in the spring semester; in other words, each semester constitutes a separate course. Enrollments in the general organic chemistry courses was typically around 500 at SWU and around 300 at NEU. The course at SWU is taught primarily in traditional lecture style, while at NEU, peer-led team learning sessions are offered in addition to the traditional lecture. The same instructor taught all of the courses included in this study at SWU. At NEU, one instructor taught all of the first semester (fall) courses and two different instructors taught the second semester (spring) courses. Data at SWU were collected starting in the spring semester of 2014 through the spring semester of 2016, i.e. for five consecutive semesters. Data at NEU were collected over four consecutive semesters, from fall 2014 to spring 2016. The study was granted exempt status from the IRBs at both institutions. Although the two institutions differed both geographically and in type (public versus private), the demographics of the participating students were similar, Fig. 1 (see also Appendix 1), the major differences being somewhat larger Hispanic/Latino and Asian populations at SWU, and a larger percentage of students not identifying race at NEU.
image file: c7rp00182g-f1.tif
Fig. 1 Bar graph representation of demographic and gender data for the study participants. A larger percentage of the students reported “Race not identified” at NEU (19.7%), compared to SWU (1.8%). Detailed demographic information is provided in Appendix 1.

To be included as a participant in this study, students provided written consent, completed all course assessments, and completed the survey. Students who took the course but did not provide written consent, did not complete all course assessments, or did not complete the survey were not included in the study. The total number of individual participant responses from both institutions obtained over the course of the entire study was 2648. Many students took both the first and second semester courses and are therefore counted twice as participants in the study. On average, 85% of the general organic students who completed the courses at SWU during the time of the study were participants in the study, and 81% of the students who completed the courses at NEU during the time of the study were participants in the study.

Measuring motivation: instrument development

Student motivation factors in this study were assessed using the Organic Chemistry Motivation Survey (OCMS). The OCMS is based on the more general Science Motivation Questionnaire-II (SMQ-II), a validated and reliable instrument for measuring student motivation towards learning science described by Glynn et al. (2011).

SMQ-II is a 25-item instrument designed to measure student motivation in five areas (Table 1). The SMQ-II authors have also generated discipline-specific versions of the instrument for biology, chemistry, and physics by substituting the discipline for the word “science” in each question (see: http://https://coe.uga.edu/outreach/programs/science-motivation). For a recent example where the chemistry version of SMQ-II was used in a study of general chemistry students, see Hibbard et al. (2016). Similarly, we created the initial version of our survey instrument replacing the word “science” in each question with “organic chemistry.” As the SMQ-II was validated with general science and non-science majors, and our survey was for a specific group of students, we performed exploratory and confirmatory factor analysis to determine if the OCMS behaved similarly to the SMQ-II, or if further refinement was necessary.

Table 1 Questions included in the Science Motivation Questionnaire (SMQ-II), and the modification for use in an organic chemistry context, from Glynn et al., 2001.a The questions were randomly ordered in the surveys
a The questions in the original SMQ-II ask about and include the word “science”. For the present study, the word “science” was replaced by “organic chemistry”, as indicated by the italic text in parentheses.
Intrinsic motivation
1. The science (organic chemistry) I learn is relevant to my life.
2. Learning science (organic chemistry) is interesting.
3. Learning science (organic chemistry) makes my life more meaningful.
4. I enjoy learning science (organic chemistry).
5. I am curious about discoveries in science (organic chemistry).
Career motivation
6. Learning science (organic chemistry) will help me to get a good job.
7. Understanding science (organic chemistry) will benefit me in my career.
8. Knowing science (organic chemistry) will give me a career advantage.
9. My career will involve science (organic chemistry).
10. I will use science (organic chemistry) problem-solving skills in my career.
Self-determination
11. I put enough effort into learning science (organic chemistry).
12. I use strategies to learn science (organic chemistry) well.
13. I spend a lot of time learning science (organic chemistry).
14. I prepare well for science (organic chemistry) tests and labs.
15. I study hard to learn science (organic chemistry).
Self-efficacy
16. I am confident that I will do well on science (organic chemistry) tests.
17. I am confident that I will do well in science (organic chemistry) labs and projects.
18. I believe that I can master science (organic chemistry) knowledge and skills.
19. I believe I can earn an A grade in science (organic chemistry).
20. I’m sure I can understand science (organic chemistry).
Grade motivation
21. I like to do better than other students on science (organic chemistry) tests.
22. Getting a good science (organic chemistry) grade is important to me.
23. It is important that I get an A grade in science (organic chemistry).
24. I think about the grade I will get in science (organic chemistry).
25. Scoring high on science (organic chemistry) tests and labs matters to me.

Exploratory factor analysis was performed on the complete dataset by combining the student responses for all 25 questions from both SWU and NEU over all semesters studied. As indicated above, the total number of student participants was 2648. The principal axis factoring method with promax rotation was used because it accounts for the possibility of covariation among the variables and to accommodate the large nature of the dataset (Leech et al., 2011).

Four motivation factors emerged in the initial solution from this analysis rather than five factors as described in the original SMQ-II. The self-efficacy question “I am confident that I will do well in organic chemistry labs and projects” was not included in the initial solution because it did not meet the minimum factor loading criterion of 0.3. At both institutions, the organic chemistry lecture and laboratory courses are separate. Because the survey was administered to students enrolled in, and in the context of, the lecture course, it is understandable that students could respond differently to questions that are laboratory course specific. For the same reason, the self-determination question “I prepare well for organic chemistry tests and labs” and the grade motivation question “Scoring high on organic chemistry tests and labs matters to me” were removed from further analysis. In addition, the intrinsic motivation question “I am curious about discoveries in organic chemistry,” was removed due to low initial and extracted communalities values, 0.273 and 0.246 respectively.

In the second solution, the intrinsic motivation questions “Learning organic chemistry is interesting” and “I enjoy learning organic chemistry” cross-loaded under two factors with similar loading values and were thus also eliminated.

The final factor analysis solution contained 19 of the 25 original SMQ-II questions that loaded onto four factors, with none of the items loading below a value of 0.3 as show in Table 2. The remaining questions derived from the original career motivation and intrinsic motivation factors of SMQ-II were now combined under a single factor with a total of 7 questions. Attempts to force a five-factor solution at this point as in the original SMQ-II, resulted in an eigenvalue for the fifth factor of 0.706, i.e., lower than the minimum criterion value of 1. The Kaiser–Meyer–Olkin measure of sampling adequacy for the four-factor solution was 0.914, i.e., higher than the standard recommended value of 0.7 (Kaiser, 1974). Additionally, Bartlett's test of sphericity was found to be significant, χ2(171) = 27057.16, p ≤ 0.001.

Table 2 Exploratory and confirmatory factor analysis results of the modified SMQ-II questions. The four derived factors represent the Organic Chemistry Motivation Survey, OCMS. In the surveys the questions were randomly ordered
F1 F2 F3 F4
a For exploratory analysis of the full dataset. b For confirmatory analysis of the split datasets, see text.
Cronbach's alpha (α): exploratorya 0.891 0.868 0.864 0.742
Cronbach's alpha (α): SWU populationb 0.883 0.851 0.867 0.750
Cronbach's alpha (α): NEU populationb 0.894 0.886 0.856 0.750
Factor 1: relevance
Understanding organic chemistry will benefit me in my career. 0.899
Knowing organic chemistry will give me a career advantage. 0.836
My career will involve organic chemistry. 0.792
Learning organic chemistry will help me to get a good job. 0.714
The organic chemistry I learn is relevant to my life. 0.620
Learning organic chemistry makes my life more meaningful. 0.593
I will use organic chemistry problem-solving skills in my career. 0.574
Factor 2: self-efficacy
I believe I can earn an A grade in organic chemistry. 0.855
I am confident that I will do well on organic chemistry tests. 0.823
I believe that I can master organic chemistry knowledge and skills. 0.720
I’m sure I can understand organic chemistry. 0.680
Factor 3: self-determination
I study hard to learn organic chemistry. 0.827
I spend a lot of time learning organic chemistry. 0.811
I put enough effort into learning organic chemistry. 0.783
I use strategies to learn organic chemistry well. 0.595
Factor 4: grade motivation
Getting a good organic chemistry grade is important to me. 0.790
It is important that I get an A grade in organic chemistry. 0.684
I think about the grade that I will get in organic chemistry. 0.565
I like to do better than other students on organic chemistry tests. 0.487

This analysis shows that the intrinsic motivation questions from SMQ-II cannot be meaningfully separated from the extrinsic career motivation questions for this population of students. Together these represent a new factor we have called “relevance”, see Table 2. The term relevance has several specific meanings in the education research literature (see, for example; Stuckey et al., 2013). In the present context, we define relevance as the extent to which the students perceive the course content to be interesting and useful to them at the time they are taking their course and also to their future careers. The original self-efficacy, self-determination, and grade motivation factors remained, and each contained four items after dropping the questions that related to the laboratory course, Table 2. The Cronbach's alpha values for each of the four factors were all above the acceptable value of 0.7 (Tavakol and Dennick, 2011). The high Cronbach's alpha values indicate that the questions within each factor measure a similar construct, indicating acceptable internal reliability of the instrument. The questions and their factors as summarized in Table 2 together represent the new OCMS.

To test the external reliability of OCMS, confirmatory factor analysis was performed on the data from SWU and NEU populations separately. The confirmatory factor analysis results for NEU population, KMO = 0.891; χ2(171) = 8737.16, p ≤ 0.001, and for the SWU population, KMO = 0.917; χ2(171) = 17967.55, p ≤ 0.001, were similar. The similarity of these results for the two populations and also the similarity of the Cronbach's alpha values for the different factors (Table 2) provide strong support for external reliability. We conclude that OCMS provides a consistent and reliable measurement of the four motivation factors of Table 2 for the populations and experimental conditions included in the present study.

Measuring student performance and motivation factors

Student course performance was determined in the same way at both institutions, i.e., by summing the points on three midterm exams and one final exam. The total points for the four exams were then converted into a percent score.

Students’ self-reported levels of motivation were assessed using the OCMS. Student responses to the questions were scored using a five point Likert scale (0 = Never; 1 = Rarely; 2 = Sometimes; 3 = Often; 4 = Always). While the original SMQ-II had the same number of questions for each motivation factor, OCMS has different numbers of questions per factor. In order to compare the different factors on the same numerical scale, the raw point total was converted into a percent score based on the maximum possible point score for each factor (relevance = 28; self-determination = 16; self-efficacy = 16; grade motivation = 16).

OCMS was administered to the students one week prior to the final exam in each of the semesters included in the study. Students who completed the questionnaire received points that were equivalent to 0.5% of the total available points for the course as extra credit. Students could choose not to participate and still receive these incentive points by completing 4 short essay questions. The questionnaire was administered at the end of the semester based on prior work suggesting that the correlation between student motivation and performance increases as the semester progresses (Bong, 2001; Turner and Lindsay, 2003; Villafañe et al., 2016).

Results and discussion

OCMS motivation factors

The student motivation factor scores and the overall mean exam scores are summarized by semester and institution together with their standard deviations in Table 3. It is important to consider the context within which the OCMS data were collected. The surveys were distributed at the same time each semester at both institutions, which was one week before the end of the courses. Therefore, the data reflect student motivations after the students had been exposed to most of the course material. At this point the students had at least some idea of their ability to perform in the class, of their position in class relative to their peers, and how likely they were to attain the grade they felt they needed in the class.
Table 3 Mean scores and standard deviations of the various motivation factors,a and the mean overall percent performance scores and standard deviations by semester for both institutionsb
Motivation factor SWU NEU
Spring 2014 Fall 2014 Spring 2015 Fall 2015 Spring 2016 Fall 2014 Spring 2015 Fall 2015 Spring 2016
a The motivation score is determined as a percentage of the total points available per motivation factor. The standard deviation for each mean score is given in the parentheses. b The performance score for each student is determined as a percentage of the points available for the 3 midterm exams and one final exam each semester. The standard deviation for each mean score for the participating students in each course is given in the parentheses. c The sum of the n values over all of the semesters in this table is 2624, compared to the total number of study participants, 2648, since this data excludes students who were eliminated as outliers in the multiple linear regression analysis, see text.
Relevance 59.3 65.9 62.4 62.5 58.0 52.5 49.9 53.6 52.8
(20.4) (18.4) (18.9) (19.4) (20.8) (20.5) (19.5) (21.1) (20.7)
Self-determination 67.4 70.1 70.7 74.0 71.1 69.6 67.1 69.3 67.3
(21.2) (20.1) (19.7) (17.7) (19.4) (18.7) (18.6) (17.0) (19.0)
Self-efficacy 66.7 71.6 68.7 72.1 63.1 57.1 54.5 60.7 57.9
(23.7) (22.1) (23.2) (19.9) (24.9) (23.2) (23.5) (21.1) (25.2)
Grade 80.8 86.9 84.5 86.3 82.7 83.9 81.2 84.3 81.6
Motivation (19.0) (14.3) (15.9) (13.7) (17.7) (14.4) (14.7) (15.6) (18.1)
n 370 398 309 366 339 298 195 178 171
Exam 74.8 73.3 71.9 78.4 72.4 62.1 62.0 59.4 61.6
Performance (14.7) (15.7) (16.9) (11.2) (16.0) (15.8) (15.5) (13.7) (15.4)

Although not directly comparable due to differences in the questions, it is still interesting to compare the results from OCMS with those reported by Glynn et al. (2011) for SMQ-II. In SMQ-II, the extrinsic grade motivation was the highest scoring factor for both science majors (n = 367) and the non-science majors (n = 313). The same was true for organic chemistry students studied here, Table 3. This is readily understandable since the majority of the students in these courses are on pre-health tracks (Austin et al., 2015), and earning a good grade in organic chemistry is often considered important for entry into health-professional schools (Lovecchio and Dundes, 2002).

In SMQ-II the extrinsic career motivation factor was the second highest scoring factor for science majors, but was the second lowest scoring factor for non-science majors. In OCMS, the relevance factor combines elements from the SMQ-II intrinsic and extrinsic career motivation factors, and was the lowest scoring factor, Table 3. The relevance factor average score ranged from ca. 50–60%, depending upon semester and institution. The average scores for relevance for the organic chemistry students were thus similar to the scores for career and intrinsic motivation for the non-science majors in SMQ-II, which, when converted to percent scores, are 56% and 57%, respectively. For comparison, the percent career and intrinsic motivation scores for science majors in SMQ-II were much higher, at 80% and 71%, respectively. The OCMS survey questions are specifically focused on the organic chemistry course, whereas the SMQ-II questions in the Glynn et al. (2011) study refer to science in general. It is perhaps understandable that students would be more positive about science in general than a specific and critical course they are currently immersed in. We conclude that although the organic chemistry students were motivated towards earning a high grade, at the time in the course when OCMS is administered, they appeared to find the content not very relevant to their interests or careers.

The self-determination and self-efficacy mean scores for the organic chemistry students across semesters and institutions, ranged between ca. 60–70%. These scores were within the range of the corresponding scores for the science and non-science majors in SMQ-II, which, when converted to a percentage scale, range from 56–75%. Self-determination was generally higher than self-efficacy at both institutions, with self-efficacy being somewhat lower at NEU compared to SWU.

Correlation of motivation factors with overall course performance

Although student motivation data is of interest on its own, we are more interested in how the various motivation factors are connected to student performance. Here we measure student performance as a percentage of the total available exam points. Average performance scores from Spring 2014 to Spring 2016 at both institutions are shown in Table 3. Multiple linear regression analysis was performed for the students’ percent scores in the four different motivations factors of OCMS with their overall course percentage score. An analysis of standard residuals was first performed to remove any outliers above or below the standard residual range of ±3.0. On this basis, a total of 18 students were removed from the SWU dataset across the five semesters, and total of six students were removed from the NEU dataset across the four semesters. The assumptions for the normality of residuals were met based on the histogram and normality plot of residuals (Field, 2009). To test for collinearity in the factors, tolerance and variance inflation factor (VIF), values were examined for each semester at both institutions, as shown in Table 4. Acceptable values for each of these were obtained, and all tolerance values were greater than 0.25 and all VIF values were less than 2.0 (Keith, 2006).
Table 4 Tolerance (Tol) and variance inflation factor (VIF) values to test the assumption of collinearity for the prediction model including all motivation factors
SWU NEU
Spring 2014 Fall 2014 Spring 2015 Fall 2015 Spring 2016 Fall 2014 Spring 2015 Fall 2015 Spring 2016
Motivation factor Tol VIF Tol VIF Tol VIF Tol VIF Tol VIF Tol VIF Tol VIF Tol VIF Tol VIF
Relevance 0.68 1.47 0.79 1.26 0.80 1.25 0.86 1.17 0.74 1.36 0.80 1.25 0.71 1.40 0.80 1.24 0.68 1.46
Self-determination 0.57 1.69 0.60 1.66 0.67 1.49 0.66 1.51 0.70 1.44 0.89 1.12 0.86 1.16 0.84 1.20 0.71 1.42
Self-efficacy 0.59 1.80 0.58 1.73 0.64 1.56 0.62 1.63 0.60 1.68 0.86 1.17 0.65 1.55 0.80 1.26 0.66 1.52
Grade motivation 0.56 1.76 0.67 1.49 0.65 1.53 0.77 1.30 0.70 1.43 0.73 1.38 0.83 1.21 0.76 1.31 0.71 1.42

The results of the multiple linear regression analysis are summarized in Table 5. All four motivation factors correlate positively with student performance for each semester at both institutions, p ≤ 0.001. Self-efficacy was consistently found to be the factor with the strongest correlation with performance. Specifically, the effect sizes were all large for this factor (f2 ≥ 0.35, see Cohen, 1988), in several cases were greater than 0.7, Table 5. The factor with the second largest effect size tended to be self-determination, with moderate values at SWU, f2 = 0.11–0.30, and small to moderate values at NEU, f2 = 0.05–0.16, followed by grade motivation, which had small to moderate effect sizes at both institutions, f2 = 0.05–0.16. The relevance factor almost always had the smallest effect size, Table 5, especially at SWU.

Table 5 Correlation coefficients, R, the corresponding R2 and effect size, defined as Cohen's f2,a for multiple linear regression analysis of student performance by OCMS motivation factorb
Motivation factor SWU NEU
Spring 2014 Fall 2014 Spring 2015 Fall 2015 Spring 2016 Fall 2014 Spring 2015 Fall 2015 Spring 2016
a See Cohen (1988). b All coefficients were found to be significant (p ≤ 0.001).
Relevance R 0.25 0.25 0.27 0.27 0.27 0.24 0.24 0.20 0.39
R 2 0.06 0.06 0.07 0.07 0.07 0.06 0.06 0.04 0.15
f 2 0.06 0.06 0.08 0.08 0.08 0.06 0.06 0.04 0.18
Self-determination R 0.43 0.48 0.37 0.32 0.43 0.23 0.29 0.37 0.26
R 2 0.18 0.23 0.14 0.10 0.18 0.05 0.08 0.14 0.07
f 2 0.22 0.30 0.16 0.11 0.22 0.05 0.09 0.16 0.08
Self-efficacy R 0.59 0.72 0.66 0.65 0.65 0.64 0.66 0.52 0.64
R 2 0.35 0.52 0.44 0.43 0.42 0.41 0.43 0.27 0.41
f 2 0.54 1.08 0.79 0.75 0.72 0.69 0.75 0.37 0.69
Grade motivation R 0.37 0.38 0.34 0.30 0.34 0.22 0.27 0.29 0.35
R 2 0.14 0.14 0.12 0.09 0.12 0.05 0.07 0.08 0.12
f 2 0.16 0.16 0.14 0.10 0.14 0.05 0.08 0.09 0.14

R 2 values for the overall model, which included all of the factors, are compared to the R2 for self-efficacy alone in Table 6. It is clear that self-efficacy was the largest contributing factor to the multiple linear regression predictive model, and in some instances, it was the only significant variable in the overall model. For example, self-efficacy accounted for 52% of the overall variance in course performance for the fall semester of 2014 at SWU, F(4,393) = 113.78, p ≤ 0.001, R2 = 0.54. Including the self-determination and relevance factors each increased the predicted variance by only 0.7% compared to self-efficacy alone. For the same semester at NEU, self-efficacy explained 41% of the overall variance in course performance, F(4293) = 52.23, p ≤ 0.001, R2 = 0.416, and inclusion of the other factors did not increase the predicted variance at all. This result on its own is not particularly surprising, since strong correlations between student self-efficacy and performance have been observed many times previously, especially when measured towards the end of the semester when students are more aware of their confidence and capabilities (see, for example: Zusho et al., 2003; Ferrell et al., 2016). Nevertheless, it is still quite remarkable that the four self-efficacy questions alone accounted for such a large percentage of the variance in the total exam scores at both institutions, while the other factors only contributed on average an additional 1.6% at SWU and 2.8% at NEU to the overall model, Table 6. Although including self-determination does not improve the predictive model substantially, in general it has the second-highest correlation with performance. This is also not particularly surprising, since self-efficacy and self-determination are both closely connected to self-regulated learning, see further below.

Table 6 R 2 values from multiple regression models containing only self-efficacy compared to models with all motivation factorsa
Semester SWU ΔR2[thin space (1/6-em)]b NEU ΔR2[thin space (1/6-em)]b
Self-efficacy All factors Self-efficacy All factors
a All models were significant (p ≤ 0.001). b The improvement in R2 for the overall model that includes all factors over self-efficacy alone.
Spring 2014 0.35 0.37 0.02 — — —
Fall 2014 0.52 0.54 0.02 0.41 0.42 0.01
Spring 2015 0.44 0.45 0.01 0.43 0.46 0.03
Fall 2015 0.43 0.43 0.00 0.27 0.32 0.05
Spring 2016 0.42 0.45 0.03 0.41 0.43 0.02

The general trends in the motivation and performance data can be illustrated graphically, as shown in Fig. 2. Here, the mean scores for each of the motivation factors averaged over all of the semesters studied are summarized in the form of bar graphs. Also shown are the averages of the corresponding coefficients for correlation of each of the motivation factors with class performance over all semesters, in the form of line graphs (the data is given in Appendix 2). The trends in student motivation in the bar graphs compared to the trends in correlation with performance in the line graphs clearly show that there is no connection between the absolute values for the various motivation factors and whether they correlate with performance. At both institutions, the grade motivation scores were consistently the highest of the factors, Fig. 2 and Table 3, but grade motivation exhibited the second weakest correlation with performance, Fig. 2 and Table 5. A plausible explanation is that essentially all students were motivated towards earning a high grade, particularly those on pre-medical tracks, and indeed, the standard deviations for the grade motivation factor tended to be smaller than for the other factors, Table 3. Grade motivation is not as broadly distributed for these students, and is therefore not quite as discriminating as the other factors. In contrast to grade motivation, the relevance factor scores were almost always the lowest of the factors, Fig. 2 and Table 3, and the relevance factor also exhibited the weakest correlation with performance, Fig. 2 and Table 5.


image file: c7rp00182g-f2.tif
Fig. 2 Graphical representations of (bar graphs) the scores for the different motivation factors averaged over all students and all semesters at the two institutions, and (line graphs), the all students and all semesters at the two institutions. Detailed numerical data for these graphs are provided in Appendix 2.

Implications for learning

For the students in this study, self-efficacy correlates most strongly with course performance, followed by self-determination at SWU. Grade motivation is the highest scoring factor, but has a significantly weaker correlation with performance. The relevance factor is the lowest scoring factor, and also has the weakest correlation with performance, Fig. 2.

The finding that self-efficacy correlates strongly with course performance is perhaps not surprising, since it is frequently observed to be significant predictor of performance in college-level science courses (Schunk, 1995; Schunk and Pajares, 2001; Usher and Pajares, 2008; Ferrell et al., 2016). This result is also consistent with several previous studies of organic chemistry courses in particular (Zusho et al., 2003; Lynch and Trujillo, 2011; Villafañe et al., 2016). At both institutions, self-efficacy was the factor that most closely related to performance, and at SWU, self-determination was clearly the second most important closely related factor. This result suggests that students who performed well were also those who were more effective at self-regulation (see Zimmerman, 1989, 2000, 2001; Black and Deci, 2000). This in turn suggests that implementation of self-regulating learning approaches in general organic chemistry courses could be beneficial for all students (Black and Deci, 2000; Lynch and Trujillo, 2011). Methods for encouraging self-regulated learning in college-level classes have been extensively discussed in the literature (see, for example: Pintrich and Zusho, 2007; Nilson, 2013) and include:

1. Helping students to become goal-oriented and modelling systematic study behavior.

2. Helping students to understand effective strategies for mastering the knowledge and skills related to problem solving in the course, and providing multiple opportunities to practice new concepts in multiple contexts.

3. Helping students' metacognition (their self-awareness and control of their own thought processes) via timely and informative feedback.

4. Helping students to improve their confidence (self-efficacy) in their ability to learn, explaining that making mistakes is an important part of the overall learning process.

5. Aligning the course with the students' personal goals or career plans, so that they can be self-motivated to participate in the course rather than seeing it as something that happens to them.

Although most of these seem common sense, there are challenges to implementing such strategies in a large lecture course, where providing individual student feedback is difficult. Importantly, point 5 highlights a central tenet of self-regulated learning theory, i.e. that students should be intrinsically or extrinsically motivated to learn in order to self-regulate (see Zimmerman, 2000, 2001). An extrinsic factor that might be expected to motivate students is the course grade. Grade motivation was found to be a high scoring factor, however, it was also found to correlate only weakly with performance. The relevance factor contains questions that were part of the original “career” and “intrinsic” motivations of SMQ-II, i.e. it combines questions normally associated with conventional extrinsic and intrinsic motivation factors. However, the relevance factor also exhibited a weak correlation with performance and was also one of the lowest scoring factors. It is a significant finding that neither the course grade nor the relevance of the course seemed to be sufficient to drive student self-regulation. The obvious question, therefore, is what were the motivating factors that drove successful self-regulation?

The students may have found motivation in the learning process itself, although the present study cannot not provide direct evidence in support of this hypothesis. In a study on the factors that contributed to achievement across different college science subjects, Cavallo et al. (2003) reported that for biology majors, “motivation to learn for the sake of learning was most important for course achievement”. Achievement motivation can be defined as the need for accomplishment (McClelland et al., 1953), and the motivation towards attaining performance goals or learning goals (Ames and Archer, 1988). An important component of achievement motivation is intrinsic motivation. According to Rabideau (2005), “individuals perceive the achievement setting as a challenge, and this likely will create excitement, encourage cognitive functioning, increase concentration and task absorption, and direct the person toward success and mastery of information which facilitates intrinsic motivation”. This suggests that if self-regulation in organic chemistry students is not driven by the extrinsic and intrinsic factors measured in OCMS (grade and relevance), it may instead be driven by the intrinsic motivation connected to the learning process itself. If this is the case, then instructional strategies that enable students to attain learning and performance goals could be useful. Achievement motivation would therefore be an interesting subject for further research in general organic chemistry courses, similar to recently published research in general chemistry (Villafañe et al., 2014; Ferrell et al., 2016; Liu et al., 2017).

Summary and conclusions

An instrument for measuring student motivation in general organic chemistry courses, OCMS, has been adapted from the previously described SMQ-II. Its use in a multi-semester, multi-instructor, and two-institution study of motivation factors is described. Different motivation factors correlated quite differently with student course performance. Performance was found to be strongly correlated with student self-efficacy, and also with self-determination at SWU. This finding is in agreement with several previous studies that have demonstrated correlations of self-efficacy on performance in organic chemistry (Zusho et al., 2003; Lynch and Trujillo, 2011; Villafañe et al., 2016). This suggests that the successful students were self-regulating their learning. Although the students were highly motivated by grade, grade motivation did not correlate strongly with performance, and was unlikely to be the motivational factor that drove self-regulation. The relevance motivation factor was the lowest scoring factor and also exhibited the weakest correlation with performance. This suggests that the students found general organic chemistry to be only weakly relevant to their interests and careers, and that subject relevance was also unlikely to be the factor that drove self-regulation. Because the successful students had higher self-efficacy and self-determination, implementation of instructional strategies that promote self-regulation could be effective in improving student performance, especially for those with low self-efficacy. Although the students appear to have low intrinsic subject motivation, if they are motivated by the learning process itself then helping students to achieve performance and learning goals could be useful, although further research in this area is required.

Conflicts of interest

There are no conflicts of interest to declare.

Appendices

Appendix 1

Demographic data for the student participants, averaged over all semesters studied at SWU (spring 2014 to spring 2016: 5 semesters) and at NEU (fall 2014 to spring 2016: 4 semesters)
SWU NEU
White/Caucasian 55.9 49.0
Asian 21.1 17.1
Hispanic/Latino 14.0 4.8
Black/African American 3.0 4.5
Two or more races 3.6 4.8
Native American/American Indian 0.4 0
Race not identified 1.8 19.7
Male 46.8 43.9
Female 53.2 56.1

Appendix 2

Motivation and Correlation with Performance data, averaged over all semesters studied at SWU (spring 2014 to spring 2016: 5 semesters) and at NEU (fall 2014 to spring 2016: 4 semesters)
SWU NEU
Averaged scorea Correlation coefficientb Averaged scorec Correlation coefficientd
Relevance 61.6 0.26 52.2 0.27
Self-determination 70.7 0.41 68.3 0.29
Self-efficacy 68.4 0.65 57.6 0.62
Grade motivation 84.2 0.35 82.8 0.28

a [thin space (1/6-em)]Average of the mean scores for semesters Spring 2014–Spring 2016 from data in Table 3.

b [thin space (1/6-em)]Average of the correlation coefficients for semesters Spring 2014–Spring 2016 from data in Table 5.

c [thin space (1/6-em)]Average of the mean scores for semesters Fall 2014–Spring 2016 from data in Table 3.

d [thin space (1/6-em)]Average of the correlation coefficients for semesters Fall 2014–Spring 2016 from data in Table 5.

Acknowledgements

ACA and IRG acknowledge support from the National Science Foundation award DUE-1525197 and technical assistance from Mary Zhu of the School of Molecular Sciences at ASU. The authors thank an anonymous reviewer for useful suggestions.

References

  1. Ames C. and Archer, J., (1988), Achievement goals in the classroom: Students’ learning strategies and motivation processes. J. Educ. Psychol., 80, 260–267.
  2. Austin A. C., Ben-Daat H., Zhu M., Atkinson R., Barrows N. and Gould I. R., (2015), Measuring student performance in general organic chemistry, Chem. Educ. Res. Pract., 16, 168–178.
  3. Bandura A., (2001), Social cognitive theory: An agentive perspective, Annu. Rev. Psychol., 52, 1–26.
  4. Barr D. A., Gonzalez M. E. and Wanat S. F., (2008), The leaky pipeline: Factors associated with early decline in interest in premedical studies among underrepresented minority undergraduate students, Acad. Med., 83, 503–511.
  5. Barr D., Matsui J., Wanat S. F. and Gonzalez M., (2010), Chemistry courses as the turning point for premedical students, Adv. Health Sci. Educ., 15, 45–54.
  6. Bhattacharyya G. and Bodner G. M., (2005), “It gets me to the product”: how students propose organic mechanisms, J. Chem. Educ., 82, 1402–1407.
  7. Black A. E. and Deci E. L., (2000), The effects of instructors' autonomy support and students' autonomous motivation on learning organic chemistry: a self-determination theory perspective, Sci. Educ., 84, 740–756.
  8. Bong M., (2001), Role of self-efficacy and task-value in predicting college students’ course performance and future enrollment intentions, Contemp. Educ. Psychol., 26, 553–570.
  9. Carmichael J. W., Burke R. T., Hunter J. T., Labat D. D. and Sevenair, J. P., (1986), Predictors of success of Black Americans in a college-level pre-health professions program, Advisor, 6, 5–11.
  10. Carmichael J. W., Bauer J. D., Hunter J. R., Labat D. D. and Sevenair, J. P., (1988), An assessment of a premedical program in terms of its ability to serve Black Americans. J. Natl. Med. Assoc., 80, 1094–1104.
  11. Cartrette D. P. and Bodner G. M., (2010), Non-mathematical problem solving in organic chemistry, J. Res. Sci. Teach., 47, 643–660.
  12. Cavallo A. M., Rozman L. M., Blickenstaff J. and Walker N., (2003), Students’ learning approaches, reasoning abilities, motivational goals, and epistemological beliefs in differing college science courses, J. Coll. Sci. Teach., 33, 18–33.
  13. Cohen J., (1988), Statistical power analysis for the behavioral sciences, 2nd edn, Hillsdale, NJ: Erlbaum.
  14. Ferk V., Vrtacnik M., Blejec A. and Gril A., (2003), Students’ understanding of molecular structure representations, Int. J. Sci. Educ., 25, 1227–1245.
  15. Ferrell B., Phillips M. M. and Barbera J., (2016), Connecting achievement motivation to performance in general chemistry, Chem. Educ. Res. Pract., 17, 1054–1066.
  16. Field A., (2009), Discovering Statistics Using SPSS, Thousand Oaks, CA: Sage Publications.
  17. Flynn A. B. and Biggs R., (2012), The development and implementation of a problem-based learning format in a fourth-year undergraduate synthetic organic and medicinal chemistry laboratory course, J. Chem. Educ., 89, 52–57.
  18. Garcia T., Yu S. L. and Coppola B. P., (1993), Women and minorities in science: Motivational and cognitive correlates of achievement, Ann Arbor, Michigan: ERIC Document Reproduction Service No. ED359235.
  19. Glynn S. M. and Koballa Jr. T. R., (2006), Motivation to learn college science, in Handbook of college science teaching, (ed.) J. J. Mintzes and W. H. Leonard, Arlington, VA: National Science Teachers Association Press, pp. 25–32.
  20. Glynn S. M., Brickman P., Armstrong N. and Taasoobshirazi G., (2011), Science motivation questionnaire II: Validation with science majors and nonscience majors, J. Res. Sci. Teach, 48, 1159–1176.
  21. Grove N. P., Hershberger J. W. and Bretz S. L., (2008), Impact of a spiral organic curriculum on student attrition and learning, Chem. Educ. Res. Pract., 9, 157–162.
  22. Grove N. P. and Bretz S. L., (2010), Perry's scheme of intellectual and epistemological development as a framework for describing student difficulties in learning organic chemistry, Chem. Educ. Res. Pract., 11, 208–211.
  23. Grove N. P., Cooper M. M. and Cox E. L., (2012), Does mechanistic thinking improve student success in organic chemistry? J. Chem. Educ., 89, 850–853.
  24. Halford B., (2016), Is there a crisis in organic chemistry education? Chem. Eng. News, 94, 24–25.
  25. Hibbard L., Sung S. and Wells B., (2016), Examining the effectiveness of a semi-self-paced flipped learning format in a college general chemistry sequence, J. Chem. Educ., 93(1), 24–30.
  26. Kaiser H. F., (1974), An index of factorial simplicity, Psychometrika, 39, 31–36.
  27. Keith T., (2006), Multiple Regression and Beyond, Boston, MA: Pearson.
  28. 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.
  29. Leech N. L., Barrett K. C. and Morgan G. A., (2011), IBM SPSS for Intermediate Statistics: Use and Interpretation, 4th edn, New York, NY: Routledge.
  30. Liu Y., Ferrell B., Barbera J. and Lewis J. E., (2017), Development and evaluation of a chemistry-specific version of the academic motivation scale (AMS-Chemistry), Chem. Educ. Res. Pract., 18, 191–213.
  31. Lopez E. J., Shavelson R. J., Nandagopal K., Szu E. and Penn J., (2014), Factors contributing to problem-solving performance in first-semester organic chemistry, J. Chem. Educ., 91, 976–981.
  32. Lovecchio K. and Dundes L., (2002), Premed survival: Understanding the culling process in premedical undergraduate education, Acad. Med., 77, 719–724.
  33. Lynch D. J., (2006), Motivational factors, learning strategies, and resources management as predictors of course grades, Coll. Stud. J., 40, 423–428.
  34. Lynch D. J. and Trujillo H., (2011), Motivational beliefs and learning strategies in organic chemistry, Int. J. Sci. Math. Educ., 9 1351–1365.
  35. McClelland D. C., Atkinson J. W., Clark R. A. and Lowell E. L., (1953), The Achievement Motive, New York, NY: Appleton-Century-Crofts.
  36. Merritt M. V., (2005), Report on the CPT Survey of 2001–2004 Enrollments in Selected Chemistry Courses, C. O. P. Training, Washington, D.C.: American Chemical Society.
  37. Middlecamp C. H., Mury M. T., Anderson K. L., Bentley A. K., Cann M. C., Ellis, J. P. and Purvis-Roberts K. L., (2014), Chemistry in Context. Applying Chemistry to Society, 8th edn, New York, NY: McGraw Hill.
  38. Nash J. G., Liotta L. J. and Bravaco R. J., (2000), Measuring conceptual change in organic chemistry, J. Chem. Educ., 77, 333–337.
  39. Nilson L. B., (2013), Creating Self-Regulated Learners, Sterling, VA: Stylus Publishing.
  40. 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, 605–611.
  41. Pintrich P. R., (2004), A conceptual framework for assessing motivational and self-regulated learning in college students, Educ. Psychol. Rev., 16, 385–407.
  42. Pintrich P. R. and Zusho A., (2007), Student motivation and self-regulated learning in the college classroom, in The Scholarship of Teaching and Learning in Higher Education: An Evidence-Based Perspective, R. P. Perry and K. C. Smart (ed.), Dordrecht, The Netherlands: Springer, pp. 731–810.
  43. Pribyl, J. and Bodner, G. M., (1987), Spatial ability and its role in organic chemistry: A study of four organic courses, J. Res. Sci. Teach., 24(3), 229–240.
  44. Rabideau S. T., (2005), Effects of achievement motivation on behavior, retrieved July 12, 2017 from http://www.personalityresearch.org/papers/rabideau.html.
  45. Raker J. R. and Towns M. H., (2012), Problem types in synthetic organic chemistry research: implications for the development of curricular problems for second-year level organic chemistry instruction, Chem. Educ. Res. Pract., 13, 179–185.
  46. Rowe M. B., (1983), Getting chemistry off the killer course list, J. Chem. Educ., 60, 954–956.
  47. Schunk D. H., (1995), Self-efficacy, motivation, and performance, J. Appl. Sport Psychol., 7, 112–137.
  48. Schunk D. and Pajares F., (2001), The development of academic self-efficacy, in Development of Achievement Motivation, A. Wigfield and J. Eccles (ed.), San Diego: Academic Press.
  49. Schunk D. H. and Usher E. L., (2012), Social cognitive theory and motivation, in The Oxford Handbook of Human Motivation, R. Ryan (ed.), New York, NY: Oxford University Press, pp. 13–27.
  50. Simpson R. D., Koballa T. R., Oliver J. S. and Crawley F. E., (1994), Research on the affective dimension of science learning, in Handbook of Research in Science Teaching and Learning, D. Gabel (ed.), New York, NY, Macmillan.
  51. Sjostrom J. and Talanquer, V., (2014), Humanizing Chemistry Education: From Simple Contextualization to Multifaceted Problematization, J. Chem. Educ., 91, 1125–1131.
  52. Steiner R. and Sullivan J., (1984), Variables correlating with student success in organic chemistry, J. Chem. Educ., 61, 1072–1074.
  53. Stieff M., (2010), When is a molecule three dimensional? A task-specific role for imagistic reasoning in advanced chemistry, Sci. Educ., 95, 310–336.
  54. Stuckey M., Hofstein A., Mamlock-Naaman R. and Eilks I., (2013), The meaning of “relevance” in science education and its implications for the science curriculum, Stud. Sci. Educ., 49, 1–34.
  55. Szu E., Nandagopal K., Shavelson R. J., Lopez E. J., Penn J. H., Scharlberg M. and Hill G. W., (2011), Understanding academic performance in organic chemistry, J. Chem. Educ., 88, 1238–1242.
  56. Taasoobshirazi G. and Glynn S. M., (2009), College students solving chemistry problems: a theoretical model of expertise, J. Res. Sci. Teach., 46, 1070–1089.
  57. Tavakol M. and Dennick R., (2011), Making sense of Cronbach's alpha, Int. J. Med. Educ., 2, 53–55.
  58. Tsaparlis G. and Angelopoulos V., (2000), A model of problem solving: Its operation, validity, and usefulness in the case of organic-synthesis problems, Sci. Educ., 84(2), 131–153.
  59. Turner R. C. and Lindsay H. A., (2003), Gender differences in cognitive and noncognitive factors related to achievement in organic chemistry, J. Chem. Educ., 80, 563–568.
  60. Usher E. L. and Pajares F., (2008), Sources of self-efficacy in school: critical review of the literature and future directions, Rev. Educ. Res., 78, 751–796.
  61. Villafañe, S. M., Garcia, C. A. and Lewis, J. E., (2014), Exploring diverse students' trends in chemistry self-efficacy throughout a semester of college-level preparatory chemistry, Chem. Educ. Res. Pract., 15, 114–127.
  62. 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, 973–984.
  63. Zimmerman B. J., (1989), A social cognitive view of self-regulated academic learning, J. Educ. Psychol., 8, 329–339.
  64. Zimmerman B. J., (2000), Self-efficacy: An essential motive to learn, Contemp. Educ. Psychol., 25, 82–91.
  65. Zimmerman B. J., (2001), Theories of self-regulated learning and academic achievement: An overview and analysis, in Self-Regulated Learning and Academic Achievement: Theoretical Perspectives, Zimmerman, B. J. and Schunk, D. H. (ed.), Mahwah, NJ: Lawrence Erlbaum Associates, Publishers, pp. 1–65.
  66. 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.
This journal is © The Royal Society of Chemistry 2018