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DOI: 10.1039/C1RP90055B (Paper) Chem. Educ. Res. Pract., 2012, 13, 47-52

Course structure, engagement, and the achievement of students in first-year chemistry

Rodney A. Clifton† *, W. George Baldwin‡ and Yichun Wei§
University of Manitoba, Winnipeg, Manitoba, Canada. E-mail: clifton@cc.umanitoba.ca

Received 27th January 2011 , Accepted 10th November 2011

First published on 6th December 2011

Abstract

In the 1998–99 academic year, the Department of Chemistry at the University of Manitoba reorganized its first-year course to include two types of sections: “regular sections” for students with high school chemistry grades above 70%, and “developmental sections” for students with grades between 50 and 70%. The regular sections had about 200 students with two or three lectures a week for 150 min and associated labs, and, in contrast, the developmental sections had between 60 and 100 students with five lectures a week for 250 min, weekly tests, and associated labs. By end of the 2006–07 academic year, 7890 students had completed the chemistry course, and 7045 (89%) of these students were included in this study: 5600 in regular sections and 1445 in developmental sections. We found that the students in the developmental sections did better than the students in the regular sections when a number of important variables were controlled. In addition, students' grades in high school chemistry and mathematics were strongly related to their grades in university chemistry, and their attendance in classes and labs, an indicator of their engagement, had strong effects on the developmental students’ chemistry grades.

Introduction

Approximately 15 years ago, both administrators and faculty members at the University of Manitoba (Canada) were becoming increasingly concerned about the academic success of undergraduate students. It had become noted that over 20% of first-year students were dropping out before enrolling in second year, and only about 55% were graduating within 6 years (University of Manitoba, 1998, 49, 51). In addition, an increasing number of students were not progressing directly from high school to university, and the impression of administrators and faculty members was that these students were even less successful than the students who came directly from high school to university. As a result, in the 1998–99 academic year, the University began a new program for first-year students, called University I, which was designed to better accommodate the interests and abilities of all the beginning students. Specifically, all faculties and schools at the University were asked to examine their first-year courses to ensure that they were meeting the needs of the diverse groups of first-year students so that more of them would successfully complete their courses. This study assesses the success of 7045 students in the regular and developmental sections of the first-year chemistry course between 1998–99 and 2006–07 and evaluates how the specially designed sections improve the students' achievement.

The first-year chemistry course

Chemistry is a required course for students who plan on studying Agriculture and Food Sciences, Dentistry, Engineering, Human Ecology, Medicine, Pharmacy, and Science. The redesigned first-year chemistry course, “University I Chemistry: Structure and Modeling in Chemistry,” is a 3 credit hour course that includes topics commonly found in many first-year college and university courses: atomic and molecular structures, solids, liquids and gaseous states, intermolecular attractions, nuclear chemistry, and polymers.

Before redesigning this course, two lecturers, Dr W. G. Baldwin and Dr N. R. Hunter, examined the admission records of approximately 5000 students who had enrolled in the first-year chemistry courses over the previous five years. They discovered that students with high school chemistry grades below 70% had only about a 50% chance of completing the first-year chemistry course with minimum grades of “C”, which was required for enrolling in advanced courses. As well, they found that the students' success in chemistry was related to their high school grades in mathematics, and that the students who had not come directly to university from high school had lower grades than the students who came directly from high school.

Not surprisingly, the research literature shows that success in university is strongly related to students' success in high school (McFate and Olmsted, 1999; McKenzie and Schweitzer, 2001; Kuh et al., 2005; Association of American Colleges and Universities, 2007), and a number of studies show that the students' prior academic achievement in mathematics also has a very strong effect on their performance in first-year chemistry (Wagner et al., 2002; Tai et al., 2005; Tai and Sadler, 2007; Potgieter et al., 2010; Seery, 2009). The research literature also suggests that males generally have higher grades in science courses than females (Jacobs, 1996; Clifton et al., 2008). But, we could not find any research in this literature that actually examined the effect of changing the structure of first-year courses or the effect of the gap students have in progressing from high school to university (see Potgieter et al., 2010). This study specifically focuses on the effects of these two variables on the grades students receive in a first-year chemistry course.

Seeing the difficulties that students who received relatively low grades in high school chemistry and mathematics and students who waited a year or more before enrolling in university had in first-year chemistry, the Department of Chemistry reorganized the first-year course so there were “developmental sections” for students with high school grades in chemistry below 70%, and “regular sections” for students with high school grades above 70%. Also, students who had been out of school for a year or more were encouraged to enrol in the developmental sections. Unfortunately, the choice of 70% and a one-year gap between high school and university were based on the two lecturers' impressions and not on rigorous statistical procedures that could more clearly identity students who were having difficulties with the course content.

Nevertheless, the developmental sections were organized to be more learner-centred; that is, the lecturers aligned their expectations more tightly with the actual knowledge and skills possessed by the less-prepared students in the developmental sections than in the regular sections. Specifically, the developmental sections met in classrooms of between 60 and 100 students five times a week for 50 minute periods, either from 8:30 to 9:20 am or from 1:30 to 2:20 pm, for 13 weeks. In addition, at the end of each week, the students were given a multiple choice test on the material covered in the previous class periods, and importantly, the students and lecturers discussed the answers. In this way, the lecturers helped the students understand their errors and how to correct them. These in-class tests counted for 10% of the students' final grades.

Essentially, the developmental sections were designed according to the principles of mastery-learning, a theory which assumes that for less-prepared students to succeed, they need to increase the time they spend on the course material (Lee and Pruitt, 1984; Guskey, 1985). As such, the developmental students spent 250 minutes in the classroom each week, and the lecturers organized their lectures into small discrete units. Following this, the lecturers had the students demonstrate their mastery of the subject matter at the end of each week in question and answer sessions (Reid, 2008). From the perspective of cognitive psychology, this organization of the course material helped the students transfer the subject matter from their instructors' lectures and the textbook into their working-memory in small “chunks” which, in turn, helped them transfer the information to their long-term memory, incrementally building their understanding of chemistry. In addition, the weekly tests and question and answer periods helped the students assess their understanding of the course content. Obviously, learning a complex and hierarchically organized subject, like chemistry, requires the integration of new information with information that the students already possess, and then assessing whether or not it has become integrated into the knowledge they have stored in their long-term memory (Ausubel et al., 1978; Cowan, 1995; Reid, 2008). Consequently, it was expected that the students in the developmental sections would become more actively engaged in the course material (Association of American Colleges and Universities, 2007; Eilks and Byers, 2010).

In contrast, the regular sections with about 200 students in a classroom met either three times a week for 50-minute periods or twice a week for 75-minute periods (150 minutes each week) for 13 weeks, which is the normal arrangement of lectures at the University of Manitoba. In these sections, lecturers were not as concerned about integrating the chunks of new information with information that the students already possessed nor did they give the students weekly feedback. Rather, these students were expected to be more independent than the students in the developmental sections. The students completed four take-home assignments during the semester, representing 10% of their final grades (which was the same weight as the weekly tests that the developmental students wrote). Of course, the students in both the developmental and regular sections used the same textbook, completed the same laboratory assignments, and wrote the same mid-term and final examinations which carried the same weight.

Methodology

Sample

The first-year chemistry course was offered three times during an academic year; normally, seven sections were offered in the first semester, September to December; two sections were offered in the second semester, January to April; and one section was offered during the summer session in July. Because most of the students who enrolled in the second semester and summer school were retaking the course, only students who registered in the first semester sections were included in this study. Thus, the data were collected from the students in the first semester (September to December) for nine academic years from 1998–99 to 2006–07. The sample included all students who appeared on the class lists in September. However, some students did not pay their tuition fees, some withdrew during the first two weeks before being penalized, and some were registered in more than one section. During the remaining 11 weeks in the semester, 1383 students voluntarily withdrew from the course, and 7890 first-year students completed the University I Chemistry course on their first attempt. However, 845 of these students were dropped from the data set because they were missing one or more variables, their high school chemistry grades, high school mathematics grades, the year they graduated from high school, or their gender. Consequently, the sample of students included in this study is 7045, representing about 89% of the 7890 students who completed the chemistry course during this time period. Of these, 5600 students (79.5%) were registered in regular sections (coded 1 in the analyses reported below), and 1445 (20.5%) were registered in developmental sections (coded 2).

Variables

University I Chemistry final grades. In this study, the dependent variable is the letter grades that the students received in the University I Chemistry course. Table 1 presents the distribution of grades (and the numerical coding), the means, and the standard deviations, for the students in the regular and developmental sections. These data show that the students in the regular sections had slightly higher mean grades (7.00 or B) than the students in the developmental sections (6.26 or slightly above C+); the distributions, however, overlapped considerably, and the standard deviations are very similar (1.71 and 1.69).
Table 1 The percentage distribution, means, and standard deviations of final grades in University I Chemistry
Grades F D C C+ B B+ A A+ Mean S.D.
Coding 3 4 5 6 7 8 9 10    
Regular 2.4 4.6 13.9 18.2 20.2 16.7 18.8 5.2 7.00 1.71
Developmental 5.5 8.2 22.8 19.4 20.2 12.2 9.7 1.9 6.26 1.69

High school chemistry grades. The students' high school chemistry percentage grades were obtained from their admission records. If students only had letter grades, or if they transferred from other provinces, states in the USA, or other countries they were dropped from the study. For the remaining students, the high school grades were grouped into intervals of 5 percentage points as indicated in Table 2. As expected, the students in the regular sections had much higher grades in high school chemistry (7.25 or about 82%) than the students in the developmental sections (4.13 or about 66%). Also, the distribution for the regular students is skewed to the right, while the distribution for the developmental students is skewed to the left, which was expected because of the way the students were recruited. Obviously, some better-prepared students enrolled in the developmental sections and some poorly-prepared students enrolled in the regular sections.
Table 2 The percentage distribution, means, and standard deviations of high school chemistry grades
Grades 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–89 90–94 ≥95 Mean S.D.
Coding 1 2 3 4 5 6 7 8 9 10    
Regular 0.9 1.0 2.0 3.0 10.3 13.5 20.8 20.5 19.4 8.8 7.25 1.84
Developmental 9.1 10.1 20.2 25.5 14.5 7.4 6.4 3.5 2.2 1.1 4.13 1.98

High school mathematics grades. The students' high school mathematics grades were coded in the same way as their high school chemistry grades as shown in Table 3. Not surprisingly, the students' high school mathematics grades were also higher in the regular sections (7.08 or about 80%) than in the developmental sections (4.83 or about 68%), and the distributions are similarly skewed as the high school chemistry grades.
Table 3 The percentage distribution, means, and standard deviations of high school mathematics grades
Grades 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–89 90–94 ≥95 Mean S.D.
Coding 1 2 3 4 5 6 7 8 9 10    
Regular 1.1 1.4 4.6 5.8 10.2 11.8 16.8 18.4 18.6 11.2 7.08 2.11
Developmental 6.7 8.1 16.8 14.3 17.0 12.5 11.6 7.8 3.9 1.4 4.83 2.21

Time from high school graduation. The month and year that the students graduated from high school was also recorded. The time between high school graduation and enrolling in university was calculated as a fraction of years, and students with scores of 0.8 or less were coded as direct entry students (coded 1) and students with higher scores were coded as having at least a one-year gap before enrolling in university (coded 2). About 81% of the students in the regular sections enrolled directly in university after high school and about 61% of the students in the developmental sections enrolled directly in university.
Gender. The students' gender was obtained from their names. Unfortunately, the names of some students did not easily identify their genders, at least not to the people working on this project, and these students were dropped from the data set. For the analyses, males were coded 1 and females were coded 2. 50% of the students in the regular sections were females, and 56% of the students in the developmental sections were females.

Data analyses

SPSS version 15.0 was the statistical package used to calculate the frequencies, means, standard deviations, Pearson's correlation coefficients, and linear regression coefficients. The dichotomous variables, time from high school graduation, gender, and program were used as dummy variables in the calculations of correlation and regression coefficients (see Tabachnick and Fidell, 2001, 112–113). The correlation coefficients represent the bivariate relationships between the variables, and the regression coefficients represent the relationships between the independent and dependent variables when the other independent variables have been statistically controlled. The p-values indicate the probability that the results were obtained by chance, and the coefficient of multiple determination (R2) represents the fraction of variance in the dependent variable that was explained by all the independent variables.

Results

The correlation matrix and the regression coefficients for the relationships between the six variables are reported in Table 4. The standardized coefficients are used for comparing the relative effects of the independent variables within samples, and the unstandardized coefficients are used for comparing the effects of the independent variables across sub-samples (see Tabachnick and Fidell, 2001, 111–176). Not surprisingly, the students' high school chemistry grades have the largest effect (0.426) on their University I Chemistry grades. In other words, a one standard deviation change in high school chemistry results in 42.6% of a standard deviation change in first-year university chemistry grades. Also, high school mathematics grades have a strong effect on university chemistry grades (0.301), probably because university chemistry requires mathematical calculations. Time since graduation from high school has a relatively small positive effect (0.063), indicating that students who had been out of school for at least one year do slightly better than students who transferred directly from high school to university when the other independent variables have been controlled. Gender has a small negative effect (−0.020) indicating that irrespective of program and the other independent variables, males do slightly better than females, a finding that is consistent with past research (Jacobs, 1996; Wagner et al., 2002; Tai et al., 2005). Most importantly, the Program (regular/developmental) has a relatively large effect (0.170), indicating that students in the developmental sections have higher grades in first-year university chemistry when the other independent variables are controlled than students in the regular sections. Overall, the five independent variables explained 33.4% of the variance in the chemistry grades.
Table 4 Correlation matrix and regression coefficients for the effects of the independent variables on University 1 Chemistry grades (N = 7045)
  Correlation coefficients Regression coefficients
1 2 3 4 5 Unstandardized Standardized
*p < 0.05; **p < 0.01; ***p < 0.001.
1. University I Chemistry              
2. High School Chemistry 0.512         0.327 0.426***
3. High School Mathematics 0.502 0.661       0.225 0.301***
4. Time from Graduation −0.092 −0.275 −0.232     0.257 0.063***
5. Gender −0.020 0.013 −0.046 0.008   −0.068 −0.020*
6. Program −0.174 −0.559 −0.391 0.188 0.045 0.730 0.170***
R 2           0.334  

The positive result for the developmental students, in comparison with the regular students, suggests that we should examine this program more closely. Analyses of the 1445 students in the developmental sections are reported in Table 5. Here, we see that the effect of high school chemistry and mathematics are slightly lower (an unstandardized coefficient of 0.278 vs. 0.327 in Table 4 for chemistry and 0.141 vs. 0.225 in Table 4 for mathematics), suggesting that the students in the developmental sections have been able to compensate to a considerable extent for their deficiencies in both chemistry and mathematics probably because of the increased time and attention they spent on the first-year chemistry course. Surprisingly, the time since high school graduation is considerably higher (0.536 vs. 0.257 in Table 4), indicating that for the students in the developmental sections, those who took time off between high school and university did much better than the students who progressed directly to university. Finally, the variance explained by the independent variables is much lower for the developmental students than for the total sample of students (20.3% vs. 33.4% in Table 4), which also suggests that the structure of the developmental course helped the students overcome their academic weaknesses. In other words, the effects that the independent variables have on the students' chemistry grades have decreased because of the combined effects of the structure of the course (the class size, five weekly lectures, tests, and assignments), and possibly because of the lecturers' concerns for the students in the developmental sections, and the students' own motivation and engagement improved as a result of the redesigned course structure.

Table 5 Correlation matrix and regression coefficients for the effects of the independent variables on University 1 Chemistry grades in the developmental sections (N = 1445)
  Correlation coefficients Regression coefficients
1 2 3 4 Unstandardized Standardized
***p < 0.001.
1. University I Chemistry            
2. High School Chemistry 0.394       0.278 0.327***
3. High School Mathematics 0.315 0.432     0.141 0.184***
4. Time from Graduation 0.118 −0.073 −0.071   0.536 0.155***
5. Gender 0.009 0.063 −0.011 0.024 −0.044 −0.013
R 2         0.203  

Obviously, lecturers can only do a few things to help students learn chemistry, and students need to become engaged in the course material (McKenzie and Schweitzer, 2001; Wagner et al., 2002; Kuh et al., 2005; Tai et al., 2005; Potgieter et al., 2010). For this reason, we hypothesized that students who attended more classes would have higher final grades than students who attended fewer classes. Fortunately, in one developmental section in each of the six academic years from 2001–02 to 2006–07, the students' attendance was collected from 427 students who had complete data on the other independent variables. 4% of these students attended less than 20% of the classes (coded 1); 9.1% attended between 20 and 49% of the classes (coded 2); 30.7% attended between 50 and 79% of the classes (coded 3); 29.0% attended between 80 and 94% of the classes (coded 4); and only 27.2% attended between 95 and 100% of the classes (coded 5).

Table 6 reports the correlation and regression coefficients for these 427 students in developmental sections in which their attendance was recorded. As expected, the strongest regression coefficient for the effects of the independent variables on the grades the students obtained in the first-year chemistry course is their attendance (0.439), in which a one standard deviation change results in a change of 43.9% of a standard deviation in chemistry grades. The other three variables have relatively similar effects; the effect of time from high school graduation is 0.213; the effect of high school chemistry is 0.191; and the effect of high school mathematics is 0.174. The inclusion of the attendance variable seems to decrease the effect of high school chemistry from 0.278 (the unstandardized coefficient reported in Table 5) to 0.147 (Table 6), indicating that students in the developmental sections can compensate for their weaknesses in high school chemistry, to a considerable extent, by enrolling in developmental sections and not skipping classes. Equally important, the variance explained has increased from 20.3% (reported in Table 5) to 33.4%, which supports the notion that attendance has an important effect on the developmental students' final grades in the first-year chemistry course. The effect of high school mathematics is about the same in both analyses (0.141 in Table 5 and 0.129 in Table 6), but the effect of time since high school graduation has increased from 0.536 (in Table 5) to 0.705, indicating that after controlling for the other independent variables, students who have been out of school for one or more years have higher final grades than students without a gap between high school and university.

Table 6 Correlation matrix and regression coefficients for the effects of the independent variables on University 1 Chemistry grades in the developmental sections with attendance information (N = 427)
  Correlation coefficients Regression coefficients
1 2 3 4 5 Unstandardized Standardized
***p < 0.001.
1. University I Chemistry              
2. High School Chemistry 0.381         0.147 0.191***
3. High School Mathematics 0.273 0.401       0.129 0.174***
4. Time from Graduation 0.080 −0.044 −0.124     0.705 0.213***
5. Gender 0.026 0.020 −0.002 −0.009   0.025 0.008
6. Attendance 0.465 0.295 0.111 −0.234 0.040 0.658 0.439***
R 2           0.334  

Discussion

Overall, four important conclusions can be derived from these results. First, the students in the developmental sections did considerably better in the first-year chemistry course than their peers in the regular sections when the other independent variables were controlled. Second, the students in the developmental sections who attended classes, and were probably more highly engaged in the course material, did much better than the students who attended fewer classes. Third, the academic achievement of students in high school had a strong effect on their university grades. Finally, success in university chemistry depended on the students' understanding of mathematics.

These findings, of course, may seem to be common sense, and not surprisingly, both the Association of American Colleges and Universities (2007) and the European Chemistry Thematic Network (Eilks and Byers, 2010) have recommended similar strategies for helping students become more engaged in course material. Specifically, these results demonstrate the importance of increasing the class time with more frequent and better monitored assessments for the less-prepared students. Considerable research shows that the time students spend on learning the course material in chemistry—as well as in other subjects—positively affects their grades (Wagner et al., 2002; Potgieter et al., 2010). Cognitive psychologists tell us that the amount of time students spend studying the subject and practicing the skills is vitally important in ensuring that the knowledge and skills become embedded in their long-term memories. And, only when the subject matter and skills have been embedded in the students’ memories can it be retrieved and used in solving complex problems (Ausubel et al., 1978; Cowan, 1995; Reid, 2008).

In creating the developmental sections, the lecturers in the Chemistry Department made three important assumptions about the first-year students. First, they assumed that almost all of the first-year students could master the course material even if they had relatively low chemistry grades in high school. The key, in the minds the chemistry lecturers, was to put the students in classes with fewer students and with empathetic instructors who assess their understanding of the subject week-by-week. Second, the chemistry lecturers assumed that the less-prepared students would need more time and smaller “chunks” of subject material than the better-prepared students. Third, the lecturers assumed that the less-prepared students would need more frequent assessment and feedback on their progress which was provided by the weekly tests. Not surprisingly, these assumptions are congruent with the theory of mastery learning, a theory that is particularly effective in explaining how less-prepared students learn subject matter, like chemistry, that is hierarchically organized (Lee and Pruitt, 1984; Guskey, 1985; Reid, 2008).

Of course, education is a cooperative enterprise, and lectures can only affect the opportunities for students to learn. The students themselves need to become actively engaged in learning the subject matter by attending classes and labs and actively working at understanding the course material. Thus, it is not surprising to find that in the developmental sections, attendance at classes has the strongest effect on the students' grades in the first-year chemistry course. In fact, attendance is more than twice as strong as the effect of prior achievement in chemistry. Nevertheless, not all students value the learning opportunities that the lecturers and department provided for them. In this study, about 15% of the students in the developmental sections attended less than 50% of the classes.

Overall, these results suggest some additional policies for universities to consider. First, admission tests could be used to assign students to developmental and regular sections of introductory courses (McFate and Olmsted, 1999). Second, the developmental sections, specifically, could have specially selected lecturers who closely monitor the progress of the less-prepared students. Third, at the beginning of courses, the lecturers could tell students that it is very important for them to attend classes and actively engage in learning the subject matter. This may seem obvious to lecturers and to most students, but unfortunately a small proportion of first-year students do not seem to realize the importance of attending classes, engaging in learning the course material, completing all assignments on time, and writing all examination.

This study has some serious limitations which may caution chemistry departments from implementing similar developmental programs. First, the study is limited because we cannot identify the effects of the various aspects of the intervention that account for the differences in the students' achievement. Further research is needed to determine how the smaller classes, increased instructional time, more student-centered curriculum, and more frequent assignments with prompt feedback independently affect the students' grades. This study only assessed the combined effect of the intervention in a first-year chemistry course at one university. Nevertheless, this package of strategies is consistent with the theory of mastery learning and with the recommendations of the Association of American Colleges and Universities (2007) and the European Chemistry Thematic Network (Eilks and Byers, 2010). Second, the evidence suggests that attendance, specifically, has a substantial effect on the academic achievement of the students in the developmental sections. This may suggest that the effect of attendance should be examined more carefully in other courses and at other universities. Third, even though the developmental sections were designed for the less-prepared students, there were better-prepared students in these sections, and there were less-prepared students in the regular sections. Of course, future research should compare the achievement of better-prepared students in the developmental sections with better-prepared students in the regular sections and less-prepared students in the developmental sections with less-prepared students in the regular sections. Finally, 1383 students, around 15% of the sample, withdrew voluntarily from the course. Unfortunately, this is a limitation because the students who withdrew are not random, and potentially this biases the results of the study.

Acknowledgements

We thank the University of Manitoba for providing a research development grant that supported this project, and we thank Dr N. R. Hunter and Ms Heather Patterson for their assistance in collecting and coding the data.

References

  1. Association of American Colleges and Universities, (2007), College learning for the new global century: A report from the National Leadership Council for Liberal Education and America's Promise, Washington, DC: Author.
  2. Ausubel D. P., Novak J. D. and Hanesian H., (1978), Educational psychology: A cognitive view (2nd ed.). New York: Holt, Rinehart and Winston.
  3. Clifton R. A., Perry R. P., Roberts L. W. and Peter T., (2008), Gender, psychosocial dispositions, and the academic achievement of college students, Res. High. Educ., 49, 684–703.
  4. Cowan N., (1995), Attention and memory: An integrated framework, New York: Oxford University.
  5. Eilks I. and Byers B., (2010), The need for innovative methods of teaching and learning chemistry in higher education—reflections from a project of the European Chemistry Thematic Network, Chem. Educ. Res. Pract., 11, 233–240.
  6. Guskey T. R., (1985), Implementing mastery learning, Belmont, CA: Wadsworth.
  7. Jacobs J. A., (1996), Gender inequality and higher education, Ann. Rev. Soc., 22, 153–185.
  8. Kuh G. D., Kinzie J., Schuh J. H., Whitt E. J. and Associates, (2005), Student success in college: Creating conditions that matter, San Francisco, CA: Jossey-Bass.
  9. Lee J. F. and Pruitt K. W., (1984), Providing for individual differences in student learning: A mastery learning approach, Springfield, IL: Charles C. Thomas.
  10. McFate C. and Olmsted J., (1999), Assessing student preparation through placement tests, J. Chem. Educ., 76, 562–565.
  11. McKenzie K. and Schweitzer R., (2001), Who succeeds at university? Factors predicting academic performance in first-year Australian university students, High. Educ. Res. Dev., 22, 21–33.
  12. Potgieter M., Ackermann M. and Fletcher L., (2010), Inaccuracy of self-evaluation as additional variable for prediction of students at risk of failing first-year chemistry, Chem. Educ. Res. Pract., 11, 17–24.
  13. Reid N., (2008), A scientific approach to the teaching of chemistry: What do we know about how students learn in the sciences, and how can we make our teaching match this to maximise performance? Chem. Educ. Res. Pract., 9, 51–59.
  14. Tabachnick B. G. and Fidell L. S., (2001), Using multivariate statistics, Boston, MA: Allyn and Bacon.
  15. Tai R. H. and Sadler P. M., (2007), High school chemistry instructional practices and their association with college chemistry grades, J. Chem. Educ., 84, 1040–1046.
  16. Tai R. H., Sadler P. M. and Loehr J. F., (2005), Factors influencing success in introductory college chemistry, J. Res. Sci. Teach., 42, 987–1012.
  17. Seery M. K., (2009), The role of prior knowledge and student aptitude in undergraduate performance in chemistry: A correlation-prediction study, Chem. Educ. Res. Pract., 10, 227–232.
  18. University of Manitoba, (1998), 1997–1998 institutional statistics, Winnipeg, MB: Author.
  19. Wagner E. P., Sasser H. and DiBiase W. J., (2002), Predicting students at risk in general chemistry using pre-semester assessments and demographic information, J. Chem. Educ., 79, 749–755.

Footnotes

† Rodney A. Clifton is a professor in the Department of Educational Administration, Foundations, and Psychology, Faculty of Education.
‡ W. George Baldwin is a visiting fellow in the Department of Chemistry, St. John's College.
§ Yichun Wei is a PhD candidate in the Department of Educational Administration, Foundations, and Psychology, Faculty of Education.
This journal is © The Royal Society of Chemistry 2012
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