Does Space Matter? Impact of Classroom Space on Student Learning

Nov 26, 2012 - Given that the field of learning space design is still in its infancy,(16) we sought to study whether space matters by examining studen...
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Does Space Matter? Impact of Classroom Space on Student Learning in an Organic-First Curriculum Rajeev S. Muthyala*,† and Wei Wei†,‡ †

Center for Learning Innovation, University of Minnesota Rochester, Rochester, Minnesota 55904, United States ABSTRACT: A number of studies have reported on the positive impact of social constructivist approaches on learning in introductory chemistry courses. However, the widespread use of such approaches is being hampered to a certain degree by uncertainty as to whether one needs a special type of classroom. In this study, we investigated student learning in two different classroom environments, the Node and the Spoke, in two first-year organic chemistry courses. Using, for the first time, formative assessment (in the form of clickers, or personal response systems) together with summative evaluations, we found no significant difference in student performance between the two classrooms. There was, however, a consistent trend of higher average grades among students in the Spoke environment compared to those in the Node classroom. KEYWORDS: First-Year Undergraduate/General, Collaborative/Cooperative Learning, Constructivism, Student-Centered Learning FEATURE: Chemical Education Research

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INTRODUCTION In recent years a number of student-centered pedagogies, including POGIL (process-oriented, guided-inquiry learning), PBL (problem-based learning), and PLTL (peer-led, team learning) have come to the fore in chemistry.1−7 These “pedagogies of engagement” rely to a large extent on social constructivism, in which the learners construct knowledge through cooperative social interactions.8,9 Despite a number of studies showing the positive impact on student learning,10−13 student-centered pedagogies are not as widely adopted. Traditional lecture remains a dominant mode of content delivery in many introductory chemistry courses. While there are many reasons for the lack of widespread adoption of active learning, one of them is the uncertainty of whether its implementation is contingent on having an active learning classroom (ALC) that consists of a “restaurant style” layout14,10,12 in which students interact with one another. In studies that have reported the positive impact of studentcentered pedagogies in chemistry, it is unclear whether active learning can be implemented regardless of the type of classroom. For example, in an important study, Oliver-Hoyo et al.12 compared student learning in a SCALE-UP (studentcentered active learning environments for undergraduate programs) environment to that in a traditional lecture setting and found that students in the former type of classroom outperformed those in a traditional lecture setting. However, it is not clear to what extent the physical environment in the technology-enhanced SCALE-UP classrooms, and not just the new pedagogy, contributed to the positive impact on student learning. Similarly, Paulson, in a longitudinal study, incorporated discussion and group activities into a second-year organic © XXXX American Chemical Society and Division of Chemical Education, Inc.

chemistry course and reported higher retention rates and better overall performance compared to traditional lecture.6 Again, because the group activities were conducted in a nontraditional classroom (the chairs were arranged in semicircular groups of four to facilitate discussion, compared to the traditional classroom layout with the students seated in rows and the instructor in the front), it is unclear whether the different classroom space contributed significantly to the overall better learning outcomes. For more widespread adoption of active learning strategies, the question of whether classroom space impacts student learning in chemistry needs to be addressed. In a recent study, Brooks compared student performance in a traditional lecture classroom and a technology-enhanced ALC and showed that the students in the latter classroom performed better than their peers in the traditional classroom15 in a Principles of Biology course. However, this study does not address the question of whether a specific type of ALC is more conducive to social constructivist learning or whether any ALC would suffice. Given that the field of learning space design is still in its infancy,16 we sought to study whether space matters by examining student learning in two different types of ALCs over the course of two semesters in an “organic-first” curriculum. In our study, we used formative assessments, such as clicker (personal response systems) responses, along with summative evaluations, such as exam grades. The former provides information, in real time, of student learning within the confines of the classroom. Therefore, we reasoned that analysis of clicker responses in a peer-instruction format would allow us to gauge the impact of classroom space on

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Figure 1. Two types of active learning classroom configurations: A, Spoke classroom; B, Node classroom. In the Spoke configuration, note the radial layout of the tables in the room resembling spokes in a wheel.

and ample white board space. The total area of the Spoke classroom was 950 ft2 (∼88 m2), while that of the Node ALC was 1226 ft2 (∼114 m2). Electronic content was delivered through a learning management system using PowerPoint files. All students in this study were in a single major: Bachelors of Science in Health Sciences (BSHS). Students in the BSHS major go through an “organic-first” curriculum in which organic chemistry is taught in the first year instead of general chemistry. The two-semester organic chemistry sequence consists of CHEM 1231 (taught in fall), which is mandatory for all students, and CHEM 2231 (taught in spring), which is optional. The organic-first curriculum at UMR is similar in many respects to that described earlier by Reingold17,20 and Coppola,21,22 except for a heavy emphasis on contextualized and interdisciplinary learning. A modular format was used to organize the course content and a modified Hendrickson Cram and Hammond (HCH) approach23 was used. There was a pronounced emphasis on spectroscopic evidence, mechanisms, and their relevance to biochemical phenomena. The textbook used for both semesters was McMurry’s Organic Chemistry with Biological Applications (2nd ed.).24 The average composite ACT score for the first-year class participating in this study was 24; female students comprised 73% of the class. In CHEM 1231, 76 students out of a total of 103 consented to the study and were distributed into three separate sections: a Node class starting at 9:00 a.m. (27 students, 72% female); a Spoke class starting at 10:00 a.m. (28 students, 72% female); and a second Spoke class starting at 11:00 a.m. (21 students, 69% female). In CHEM 2231, 50 students consented to this study (out of a total of 58) and were divided between a Node class starting at 9:00 a.m. (26 students; 69% female) and a Spoke class starting at 10:00 a.m. (24 students; 63% female). Classroom assignments in both the fall and spring courses were not random but were based on a system in which the students are assigned a “lecture” section based on the laboratory section in which they were enrolled. Despite the section assignment not being random, we believe that all sections in both CHEM 1231 and CHEM 2231 were comparable in terms of student background preparation because of the following: (i) the content of an organic-first curriculum is radically different from high school chemistry and consequently has a leveling effect on variations in a student’s precollege preparation;7 and (ii) it is known that measures of high school academic ability, such as SAT or ACT scores, are not good predictors of performance in organic chemistry courses.25 All sections were taught by the same instructor (RSM), who designed the courses and the student-centered activities. Each class period was 50 min long and consisted of a

constructivist learning. Also, by examining the impact of classroom space on student learning in an organic-first curriculum, we sought to reduce or eliminate variations in student preparation. As others have noted before, an organicfirst curriculum “levels the playing field”,17 owing to its radically different content compared to high school chemistry.

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EXPERIMENTAL DESIGN At the time when this study was conducted, there were two types of ALCs at our institution (University of Minnesota Rochester, UMR): the Spoke and the Node classrooms. In the Spoke classroom, the seating arrangement (restaurant style) was similar to that described by Oliver-Hoyo and others,12,14,18 except that at UMR, rectangular tables were used instead of round tables (Figure 1A). Because the layout of the tables in the room resembled spokes in a wheel, the classroom was referred to as the Spoke ALC. Students appeared to be initially unsettled in the Spoke classroom, seemingly because of their unfamiliarity with the layout of the learning space. In the Node classroom,19 there were no tables for the students to sit around but the chairs were equipped with a work surface large enough to accommodate a notebook or a laptop (Figure 1B). Additionally, space at the bottom of the chair allowed the students to store their personal belongings. The Node classroom layout appeared to be more familiar to the students and resembled the learning space described earlier in Paulson’s paper.6 The Spoke and the Node classrooms each had their own pros and cons: In the Spoke type of ALC, the biggest advantage was that groups of students were tethered to a table that readily fostered group work and discussion. The tables also provided a surface for the students to lay out their work for group discussion and problem solving. This tethering to a table, however, was also a disadvantage, particularly if a “minilecture” was needed or something had to be written on the whiteboard for the entire class to see. Invariably, at least one group of students would be unable to see the instructor or what was being written on the whiteboards. A major advantage of the Node classroom was that the casters on the chairs allowed for mobility. Students could swivel around and the transition from group work to minilecture was easier in the Node than in the Spoke classroom. However, in the Node classroom, it was challenging, without a table, for the students to show their individual work to other members of their team during in-class group work or discussion. Additionally, the use of large “huddle” boards (portable marker boards for drawing chemical structures) was problematic in group problem-solving sessions. Both the Node and the Spoke ALCs were equipped with identical classroom amenities such as a smart board, projectors, B

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class time on average grades existed, as well as interaction between average midterm exam grades and class time. Because the final exam and overall grade distribution was not normal, we performed the Kruskal−Wallis test to compare the distributions of the grades from the 9 a.m., 10 a.m., and 11 a.m. sections by ranking the grades. The two-way ANOVA revealed no interaction between time and exam (p = 0.8088) and no significant effects of time on student grades (p = 0.0521), meaning that the average midterm exam grades for any two sections (9 a.m. vs 10 a.m., 10 a.m. vs 11 a.m., or 9 a.m. vs 11 a.m.) are not significantly different based on an α value (significance level) of 0.05 (Figure 2).

10-min minilecture followed by student-centered activities focused on a specific concept or topic. Students worked in groups of three or four in both the Node and the Spoke ALCs. In the Spoke classroom, each table had either two groups (one was a four-person group and another a three-person group) or just one group. Table assignment for each group was random in the Spoke ALC. In CHEM 1231, students were randomly selected to form a group, whereas in CHEM 2231, the group composition was based on student performance in the first and the second midterm exams. Both in CHEM 1231 and CHEM 2231 students were rotated among different groups in each section approximately every five weeks in a semester. In each group students were assigned roles as in POGIL.26 Class participation accounted for 5% of the total grade in both CHEM 1231 and CHEM 2231. Individual accountability in the group was monitored based on the following: (i) student responses to a clicker question that required discussion within their respective groups and (ii) students’ ability to present to the entire class when called upon. All students, regardless of the section, were exposed to the same content. All exams in CHEM 1231 and CHEM 2231 were common to all students, regardless of whether they were in the Node or the Spoke classroom. In CHEM 1231, all students, regardless of the section, took each exam at the same time; there was no variation in the number of students taking the exams throughout the semester. Students in the CHEM 2231 course took their midterm exams during their respective class times but took the final exam at the same time. All exams were graded by the same two individuals. Analyses of significant differences in student performance between the Spoke and Node classrooms were performed using D’Agostino−Pearson27 normality test, t-test, nonparametric Wilcoxon rank-sum test, nonparametric Kruskal−Wallis test, and analysis of variance (ANOVA). All analyses were conducted using the Statistical Analysis Systems (SAS 9.2) software (SAS Institute Inc., Cary, NC).

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Figure 2. Grade comparison among three sections (represented by time of day) for three midterm exams and the final exam in CHEM 1231. The error bar represents one standard error of the mean.

Due to the low p-value of the effects of time (p = 0.0521), we also performed one-way ANOVA for each midterm exam and found no significant differences among the three sections. The p values for midterm exam one, two, and three were 0.2126, 0.1754, and 0.4070, respectively. The Kruskal−Wallis test showed no significant grade difference among the three sections for the final exam (p = 0.5057) (Figure 2) or for the overall grade (p = 0.6629). Therefore, despite the apparent trend with the average grades increasing in going from the 9 a.m. section to the 11 a.m. section, one can conclude that there is no significant effect of time on student performance. Consequently, in all further analyses of student performance in CHEM 1231, we combined the data for the 10 and 11 a.m. Spoke sections into one set.

RESULTS AND DISCUSSION

Statistical Analysis of Effects of Class Time on Student Learning

Because the three different sections of CHEM 1231 met at different times (9 a.m., Node; 10 a.m., Spoke; and 11 a.m., Spoke), we first investigated whether time had any impact on student performance. Summative evaluations of student performance in the firstyear chemistry course (CHEM 1231) were based on three midterm exam grades, one final exam grade, and one overall grade. The last grade type was the cumulative grade for the entire semester and included evaluation of student work in the laboratory, homework, and the three midterm grades. The null hypothesis is H0:μ1 = μ2 = μ3 and the alternative hypothesis is H1:μi ≠ μj, i, j = 1, 2, or 3, where μ1, μ2, and μ3 are the average grades for the 9 a.m., 10 a.m., and 11 a.m. sections, respectively. To determine the appropriate statistical analysis approach, we first conducted a normality test to examine the distributions of student grades. The D’Agostino−Pearson normality test showed, on the basis of an examination of the skewness and kurtosis of the grade distributions, that the distribution of the midterm exam grades was normal, while the final exam grade and the overall grade distributions were not. Given the normal distribution of midterm exam grades, we performed a two-way ANOVA analysis to examine whether any significant effect of

Measurements of Centrality and Dispersion

Statistical measurements of centrality and dispersion, including the mean and standard deviation of grades, were calculated for the three midterm exams, the final exam, and the overall grade for both the Node and the Spoke ALC. The average and standard deviation of grades for each exam in both types ALCs for CHEM 1231 are shown in Table 1, while in Table 2, the mean and standard deviation of the same five types of grades for CHEM 2231 are shown. Statistical analyses are shown in Tables 3 and 4. Without detailed statistical analysis, two trends are apparent from the data shown in Table 1. First, it is evident that, in CHEM 1231, the average grades for students in the Spoke C

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Statistical Analysis of Grade Differences between the Node and Spoke Classrooms

Table 1. Comparison of CHEM 1231 Course Grade Averages from Node and Spoke Classrooms

For CHEM 1231, the final exam was cumulative and the overall course grade reflected the overall student performance throughout the whole course. The final exam and overall course grade were both highly correlated with the three midterm exams. Therefore, we first tested whether the average final exam and overall grades for the Spoke classroom were significantly different from those for the Node classroom. Because the D’Agostino−Pearson normality test25 showed that both the final exam and overall grade distributions were not normal, we conducted a nonparametric Wilcoxon rank-sum test by comparing the grade distributions from the two ALCs. No significant difference was observed in the grades between the Node and Spoke classrooms for the final exam (p = 0.1987) as well as the overall grades (p = 0.1741) at the significance level of 0.05. We then performed a two-way analysis of variance, including the factors of exam and classroom, to assess midterm exam grade differences in the two different types of ALCs for CHEM1231. Three assumptions have to be valid to perform a two-way ANOVA. Applied to this study, the assumptions are as follows: 1. Exam scores across individuals are independent. 2. Exam scores for each individual are independent. 3. Exam grades are normally distributed. All of the three assumptions were satisfied in our CHEM 1231 and CHEM 2231 courses for the following reasons: (i) one student’s exam performance gave us no information about another student’s performance. (ii) for any student, his or her score in each exam is independent, as each midterm exam had a different set of questions and the exams were not cumulative; and (iii) the D’Agostino−Pearson normality test showed that the three midterm grades were normally distributed. On the basis of the results of the two-way ANOVA, we found no interaction between exam and classroom (p = 0.5942) and no statistically significant effects from classrooms (p = 0.3919) at a significance level of 0.05, which means that the average grades of the midterm exams are not significantly different between the Node and Spoke classrooms. For CHEM 2231, we performed similar analyses to CHEM 1231 to compare the grades between the Spoke and Node classroom for the final exam, overall, and three midterm exam grades. The D’Agostino−Pearson normality tests revealed that the final, overall, and midterm exam grades all followed normal distributions. We then conducted an unpaired two-sample t-test to investigate the average grade differences between the Node and Spoke classrooms for the final and overall grades individually because the assumption of normality was satisfied for the t-test. Our null hypothesis is H0:μ1 = μ2, and our alternative hypothesis is H1:μ1 < μ2, where μ1 and μ2 are the average grades from the Node and Spoke classroom. The results showed that the grade difference between the Spoke and Node classroom is not significant for either the final exam (p = 0.2751) or the overall grades (p = 0.0828). We performed the two-way ANOVA to evaluate the difference in average midterm grades between the two types of ALCs because the same assumptions of the two-way ANOVA held for the three CHEM 2231 midterm exams. We found no interaction between exam and classroom (p = 0.2313) and no significant effects from classrooms (p = 0.0550) at a significance level of 0.05, which means that the average grades

Avg Grades (SD) in Chem 1231 Coursea Summative Eval Type Exam 1 Exam 2 Exam 3 Final exam Overall grade a

Node Config 82.52 88.30 71.86 74.87 83.19

(±13.28) (±17.94) (±17.41) (±16.39) (±11.42)

Spoke Config 81.88 89.76 76.36 79.34 86.35

(±13.39) (±14.42) (±14.71) (±13.15) (±8.46)

Grades could range from 0 to 100.

Table 2. Comparison of CHEM 2231 Course Grade Means from Node and Spoke Classrooms Mean Grades (SD) in Chem 2231 Coursea

a

Summative Eval Type

Node Config

Spoke Config

Exam 1 Exam 2 Exam 3 Final exam Overall grade

60.0 (±15.42) 75.42 (±16.23) 52.50 (±19.47) 68.33 (±14.17) 80.32 (±10.73)

60.0 (±19.15) 85.00 (±10.83) 61.33 (±16.99) 73.24 (±15.58) 85.77 (±9.70)

Grades could range from 0 to 100.

Table 3. Statistical Analysis of Grade Differences between Spoke and Node ALCs in CHEM 1231 Assessment Type

a

Analysis Method

Midterm exams

Two-way ANOVA

Final exam Overall grade

Wilcoxon rank-sum test Wilcoxon rank-sum test

p Values 0.3919a 0.5942b 0.1987 0.1741

Classroom. bExam × classroom.

Table 4. Statistical Analysis of Grade Differences between Spoke and Node ALCs in CHEM 2231

a

Assessment Type

Analysis Method

p Values

Midterm exams

Two-way ANOVA

Final exam Overall grade

Unpaired t test Unpaired t test

0.0550a 0.2313b 0.2751 0.0828

Classroom. bExam × classroom.

classroom were lower in Exam 1 compared to the Node ALC but slightly higher for all the subsequent exams. The initial lower grades in Exam 1 for students in the Spoke ALC in CHEM 1231 are likely attributable to an adjustment, in the first few weeks, to a new classroom environment, as well as the organic chemistry course content in CHEM 1231, which represents a radical departure from high school chemistry.17 The Node ALC, perhaps, was not that different from what the students were used to in high school classrooms, and therefore, students in that ALC required less adjustment. Second, the standard deviations for most of the exams (particularly in CHEM 1231) from the Spoke classroom were smaller than those from the Node classroom. This suggests that the gap between individual performances may have been reduced in the Spoke classroom, possibly because group work and discussion were better facilitated in this ALC. D

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of the midterm exams are not significantly different between the Node and Spoke classrooms. Given the small p-value for the effects of classrooms (p = 0.0550), we further performed unpaired t-tests and verified that there was no significant difference in student performance between the Node and Spoke classrooms in exam one (p = 1.0000) and exam three (p = 0.1147). There was, however, a significant difference between the Node and the Spoke ALCs in exam two (p = 0.0462). The reason for this difference is unclear. Overall, analysis of the average grades from the two ALCs in both CHEM1231 and CHEM2231 shows that, with the exception of exam two in CHEM 2231, there was no statistically significant difference in student performance between the Spoke and the Node ALC. Formative Assessment: Statistical Analysis of Clicker Question Responses

Because summative evaluations are a reflection of both in-class and out-of-class learning, it may not be possible to isolate the impact, if any, of classroom space on student learning. Therefore, we sought to use formative assessment using student response systems (commonly known as clickers) as a tool to analyze the impact of classroom space on cooperative learning. While the clicker responses alone do not capture the nature of in-class group work in each type of ALC, studies have shown that they are a good measure of in-class cooperative learning.11,29 Accordingly, in CHEM 1231 commercially available RF clickers28 were used in the Node and Spoke ALCs, while in CHEM 2231 students used their laptops as clickers.29 As mentioned in the Introduction, this study constitutes the first example of the use of formative assessment to study the impact of classroom space on student learning. Clicker questions (20 total) were used at various points during the fall and spring semesters, and the percentage correct responses for each question were aggregated according to classroom type. The clicker questions differed from the midterm exam questions, although they were related in content. Variation in class attendance was minimal, as the students were given participation points for responding to the clicker questions. The clicker responses in the Node and Spoke classrooms were analyzed based on the percentage of correct responses to a given question in the two types of ALCs. Our decision to aggregate the clicker responses for each ALC and analyze according to question type was based on the following two reasons: First, students were specifically asked to discuss within their assigned groups prior to responding to clicker questions individually.11 We reasoned that if social constructivist learning were impaired, this would be reflected in a significant difference in the of percentage correct responses to a given question between the two types of ALCs. Second, the clicker questions served as an internal check to ensure that there was no variation in content coverage between the two types of ALCs. Initial analysis of the raw data from the clicker responses indicated similar percentages of correct responses in both the Node and Spoke ALC (Figure 3) in both courses. The student responses for each clicker question in CHEM 1231 and CHEM 2231 were separately aggregated and then analyzed by a paired two-sample t-test to compare the percentage of correct clicker question responses in the Node classroom and the Spoke classroom. The paired t-test showed a statistically nonsignificant difference between the proportion of correct clicker responses

Figure 3. Fraction of correct clicker question responses by participants of two courses, each course with a classroom arranged in either a Node or a Spoke configuration. The error bars represent one standard error of the mean.

from the Node and those from the Spoke classrooms (p = 0.9480) for CHEM 1231. The average for the percentage of correct clicker responses from the Node classroom was slightly higher than that from the Spoke classroom for CHEM 2231 (Figure 2). However, the difference is again not statistically significant (p = 0.9140). This confirms the absence of any variation in instruction or content in the different classrooms. More importantly, the near-identical nature of the clicker responses clearly suggests that classroom space has no significant impact on constructivist learning as measured by the percentage of correct responses in the two types of ALCs.

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SUMMARY Our study augments the work done earlier by Brooks15 and, therefore, adds to the growing body of research on the impact of formal learning spaces on student learning. Brooks compared student performance in a traditional lecture classroom (presumably with tiered seating, a chalkboard, and the instructor in front) and a technology-enhanced ALC. In contrast, this study examined student learning in two different ALCs, both equipped with the same technological features. We used formative assessment, in the form of clickers, together with summative evaluations. Statistical analysis of formative assessment from clicker responses shows that there was no significant difference between the Node and the Spoke ALCs. Similarly, with the exception of exam two in CHEM 2231, no significant difference was found in the summative evaluations between the Node and Spoke classrooms. Further, class time had no significant effect.30 Our findings suggest that constructivist learning can be facilitated in any learning space that is conducive to class discussions. Therefore, even if a Spoke or SCALE-UP14 type of classroom is unavailable, instructors could use a Node type of classroom, which is similar to the learning space described by Paulson6 for the implementation of social constructivist pedagogies. The latter is likely more readily available given its widespread use in discussion-based courses. E

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(17) Reingold, I. D. Bioorganic First: A New Model for the College Chemistry Curriculum. J. Chem. Educ. 2001, 78, 869−871. (18) Dori, Y. J.; Belcher, J. How Does Technology-Enabled Active Learning Affect Undergraduate Students’ Understanding of Electromagnetism Concepts? J. Learn. Sci. 2005, 14, 243−279. (19) Steelcase, Inc. product Web page for the Node chair. http:// www.steelcase.com/en/products/category/educational/seating/node/ pages/node.aspx (accessed Nov 2012). (20) Reingold, I. D. Inverting Organic and Biochemistry: A Curriculum Tweak That Benefits All. J. Chem. Educ. 2004, 81, 470− 474. (21) Coppola, B. P.; Ege, S. N.; Lawton, R. G. The University of Michigan Undergraduate Chemistry Curriculum 2: Instructional Strategies and Assessment. J. Chem. Educ. 1997, 74, 84−94. (22) Ege, S. N.; Coppola, B. P.; Lawton, R. G. The University of Michigan Undergraduate Chemistry Curriculum 1: Philosophy, Curriculum, and the Nature of Change. J. Chem. Educ. 1997, 74, 74−83. (23) Bowman, B. G.; Karty, J. M.; Gooch, G. Teaching a Modified Hendrickson, Cram, and Hammond Curriculum in Organic Chemistry. J. Chem. Educ. 2007, 84, 1209−1216. (24) McMurry, J. Organic Chemistry with Biological Applications, 2nd ed.; Brooks Cole: Belmont, CA, 2010. (25) Pursell, D. P. Predicted versus Actual Performance in Undergraduate Organic Chemistry and Implications for Student Advising. J. Chem. Educ. 2007, 84, 1448−1452. (26) POGIL home page. http://www.pogil.org/ (accessed Nov 2012). (27) D’Agostino, R.; Pearson, E. S. Tests for Departure from Normality. Empirical Results for the Distributions of b2 and √b1. Biometrika 1973, 60 (3), 613−622. (28) Towns, M. H. Crossing the Chasm with Classroom Response Systems. J. Chem. Educ. 2010, 87, 1317−1319. (29) MacArthur, J. R.; Jones, L. L. A Review of Literature Reports of Clickers Applicable to College Chemistry Classrooms. Chem. Educ. Res. Pract. 2008, 9, 187−195. (30) In the absence of a 9 a.m. Spoke section, which would have been an ideal control group, we had to rely solely on statistical analysis to decipher the effect of time, if any, on student performance.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Present Address ‡

Metropolitan State University, Saint Paul, MN 55106.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Peter Larsen for help with grading and Zhi Qiao for help with the SAS programming. We wish to thank Gillian Roehrig and Bijaya Aryal for their helpful comments during the preparation of this manuscript. This study was approved by the Internal Review Board, University of Minnesota. The title of the IRB protocol is: “Investigating the efficacy of learning activities in an integrated curriculum” (exempt status, Protocol No. 1008E87333).

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