1) The regression analysis found that increased lecture preparation time, up to 5.127 hours, has a statistically significant positive effect on professors' CAPE scores. 2) Higher average expected grades, as a proxy for how well exams match lectures, also have a statistically significant positive effect on CAPE scores. 3) While other variables like years teaching and class size were not statistically significant, they are important to control for to avoid omitted variable bias.

1. The Effect of Pre-Lecture Preparation Time on Professors’ CAPE Score
Irvin Lan*
University of California, San Diego
Econ 120BH
March 2016
Keywords: CAPE score, Lecture preparation time, Average grade expected, Cape evaluations.
*I wish to thank Professor Berman for his helpful advice and guidance throughout the entire process of writing this
paper and to Pablo Ruiz Junco and Ying Jenny Feng for their meaningful comments. Additional thanks to the professors of
UCSD for their participation in my survey, without whom this paper would not have been possible.

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1 Introduction
In a University as large as UCSD, students learn from professors from all varieties of teaching styles.
Some professors convey knowledge through PowerPoint while others illustrate concepts with chalk and
the traditional lecture format. Some of these lectures are a thrill and students feel stimulated while other
lectures are monotonous and leave students wondering if attending was worth the opportunity-cost.
Upon quarter’s end UCSD students review their professors through CAPE and professors receive
student recommendation ratings generated by CAPE evaluation results. Seemingly these ratings are at
the discretion of students enrolled in a class, but I wonder if in fact professors are able to influence the
scores which they receive. I hypothesize that student perception and feedback is only one side of the
coin, that professor characteristics play a significant role and therefore is essential to paint the complete
picture behind each CAPE score. In this paper I look at statistics from 127 courses taught by 85 UCSD
undergraduate professors during fall quarter 2015 to see if the time that a professor spends preparing
before lecture has a statistically significant effect on the CAPE scores that they receive.
2 Theory
CAPE Score = β0 + β1LecturePrepTime + β2PrepTimeSquared + β3AvgGradeExpected + β4CapeEval +
β5StudyHours +β6YearsTeachingUCSD + β7AssocProfessor + β8Professor + ε
In this paper, a best linear predictor is used to illustrate the relationship between the dependent
variable CAPE Score and professor characteristics. LecturePrepTime is an independent variable for
the amount of time that it took for a professor to prepare prior to giving a lecture during Fall Quarter
2015. In addition, to account for possible diminishing effects of lecture preparation time the variable
PrepTimeSquared is included. The variable AvgGradeExpected is the average grade that students
expect to receive from a professor and is used as a proxy variable for how closely exams and
assignments are written to complement lecture materials, implying a professor’s ability to gauge student

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learning. CapeEval is a variable for the number of CAPE evaluations made in each class and is used as
a proxy to control for class size. StudyHours is a variable for student study hours per week that is
related to the amount of prep time involved in a lecture and also is associated with the dependent
variable CAPE Score. An important characteristic of UCSD professors is the number of years that they
have taught at UCSD, YearsTeachingUCSD. AssocProfessor is a binary variable that will take on
value 1 if the professor holds the academic rank of associate professor or 0 if they hold the rank of
lecturer. Similarly, Professor is a binary variable that will take on value 1 if the professor holds the rank
of professor or 0 if they hold the rank of lecturer.
On the assumption that more time spent preparing for a lecture is likely to yield better results, β1
is likely positive. β2 is negative since there may be diminishing marginal benefits to additional hours of
preparation time. β3 is likely positive since higher grade expectation suggests better assessment and
understanding of student learning ability, and therefore a higher CAPE Score. We can also expect β4 to
be positive since professors who lecture larger classes tend also to prepare more before lecture, and we
can expect the two variables to move in the same direction with a positive effect on CAPE Score. β5 is
likely to be negative since students have to spend more time studying materials on their own if
professors are not well prepared. β6 is expected to be positive on the assumption that professors who
have taught many years at UCSD require less lecture preparation time and are more adept at leading a
positive lecture. Because of the possible effect of reputation, one would expect β7 and β8 to be positive. It
is also possible that the signs and magnitudes of the coefficients on professor title are not statistically
significant.

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3 Data
The data on the 127 classes taught by 85 professors are from UCSD Fall Quarter 2015. To collect data
on professors, I created an online survey using Google Forms to extract two key professor
characteristics, Experience Teaching at UCSD and Lecture Preparation Time. The results from the
survey were then transferred to an Excel spreadsheet. The following is a link to my survey:
https://docs.google.com/forms/d/1JTJ2wdeSPLGW0cJVWNPK7YRi6YWyhvJNaeFDXho0oUg/viewfo
rm?usp=send_form.
This link was sent via email to professors who taught at UCSD during fall 2015. The professor
names provided in the survey made it possible to collect additional corresponding CAPE data from the
CAPE website https://cape.ucsd.edu/responses/Results.aspx. The website provided data on CAPE Score
(instructor recommendation), CAPE Evaluations Made, Study Hours/Week, and Average Grade
Expected. I then used blink.ucsd.edu to search for professor titles and referenced department websites
when a professor could not be located on the UCSD employee database.
Below is the table of means:
Table 1. Summary Statistics
Variable Observations Mean Std. Dev. Min. Max.
CAPE Score 127 88.58606 13.23671 33.3 100
Lecture Preparation Time (Hrs) 127 3.45248 2.480102 0.25 13
Average Grade Expected 127 3.386425 0.279747 2.5 4
Experience Teaching at UCSD (Yrs) 127 10.06031 10.45134 0.33 50
CAPE Evaluations Made 127 72.66929 73.15064 2 318
Study Hours/Wk 127 6.271811 1.827642 2.5 12.9
Associate Professor 127 0.314961 0.466340 0 1
Professor 127 0.338583 0.475102 0 1
Lecturer 127 0.354331 0.480204 0 1

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4 Results
Table 2. Results of Regression of CAPE Score on Classroom and Professor Characteristics
Dependent variable: CAPE Score
Regressor (1) (2) (3) (4) (5)
LecturePrepTime 0.138 4.940** 4.310** 4.039** 4.245**
(0.795) (2.064) (1.928) (1.906) (1.852)
PrepTimeSquared -0.472** -0.418** -0.399* -0.414**
(0.226) (0.209) (0.206) (0.198)
AvgGradeExpected 17.82*** 17.00*** 17.80***
(3.408) (3.913) (3.841)
CapeEval 0.011 0.012
(0.013) (0.013)
StudyHours -0.511 -0.377
(0.677) (0.655)
YearsTeachingUCSD 0.021
(0.114)
AssocProfessor -1.949
(2.687)
Professor 2.226
(2.769)
Constant 88.11*** 80.22*** 20.90* 26.70* 22.28
(2.732) (3.682) (11.70) (16.10) (15.78)
Summary Statistics
Observations 127 127 127 127 127
R-Squared 0.001 0.090 0.230 0.238 0.256
RMSE 13.285 12.731 11.757 11.789 11.797
Robust standard errors in parenthesis
***p<0.01, **p<0.05, *0.1
From each of the regressions performed on the data, the results show that by increasing
preparation time, it is possible to increase CAPE score, keeping all other factors constant. In addition,
there are diminishing marginal returns to preparation. The first order derivative of the long regression
indicates that 5.127 hours is the amount of time predicted to maximize CAPE Score and increasing

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preparation time beyond that will likely not yield significant benefits. The downward sloping portion of
the quadratic curve is not used for prediction since it is not covered by much actual observed data. This
treatment seems reasonable since a professor who prepares 6.127 hours is expected to present a lecture
at least as well as a professor who prepares an hour less, all else constant. Notice also that the
coefficients from the short regression (2) are positively biased as they include the effect that variables
like Average Grade Expected have on improving CAPE Score. Thus in the long regression (5), by
including endogenous variables, the coefficient on Lecture Preparation Time decreases from 4.940 to
4.245 at 5% significance and we are able to relieve some of the issues that arise from omitted variable
bias.

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Another noteworthy result is the dependence of CAPE Score on the average grade expected in a
class. On average, a 0.25 increase in expected grade distribution is associated with a 4.45 point increase
in CAPE Score, keeping all other variables constant. The results show that we reject the null that
average grade expected has no effect on CAPE Score at the 1% level of significance. Classes with
higher grade distributions are more likely to have higher CAPE Scores. The following scatterplot
illustrates the positive relationship between CAPE Score and Average Grade Expected.
From the results we see that the variables Lecture Preparation Time and Average Grade
Expected are highly significant. Though the other variables are not jointly significant with an
F-statistic of 1.03 they are still important to keep in the model as they are associated with both lecture
preparation time and CAPE Score. One reason for including years taught at UCSD is that professors

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with more experience might have been teaching long enough such that they don’t need to prepare as
much as professors who are new, so it is important to control to the extent that experience affects CAPE
Scores. Furthermore, note the change in the significance of the constant term from the short regression
to long regression. In the short regression the constant term is significant at the 1% level which suggests
that there are endogenous variables in the error term that have not been identified and in the long
regression the constant term becomes insignificant with a t-statistic of just 1.41, as relevant variables are
added. In addition the standard error of regression (RMSE) decreases from 13.285 to 11.797 which
indicates that the typical deviation from the predicted value of each CAPE score is about 11.797 and is a
reasonably good fit for the model.
5 Conclusion
The results support my hypotheses and shows that in fact preparation time and the CAPE Score received
by professors are related. At 5% significance level, increasing preparation time before a lecture, up to
5.127 hours, is associated with having a positive effect on CAPE Score. This makes sense since by
preparing more, holding all other variables constant, professors are putting more thought into the lesson
which yields better results.
Another important factor that influences CAPE Score is the grade that students expect to receive
from their professors. At 1% significance level, we can expect high CAPE Scores to be associated with
high average expected grades. A possible explanation is that difficult tests do not resemble material
presented in lecture and homework assignments and may lead students to turn against the professor
during CAPE evaluations. Conversely, a professor who tests on material that they allow students to
practice in homework and during lecture are more likely to receive high CAPE Scores.
I would like to estimate the linear causal effect that Lecture Preparation Time has on CAPE
Score but a weakness of this model is that it does not account for endogenous variables in the error term

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that are difficult to observe such as professor ability, resulting in omitted variable bias and a poor
estimation of the coefficient of interest. Ability includes factors such as how well professors are able to
communicate with students and their motivation to help students learn. For example some professors
who are less stimulated by the course material may spend less effort providing thoughtful intuition to
students. Some professors could have more energy in the way they speak that affects how well students
are able to connect and learn from them. In a future project, panel data can be used which will allow for
the addition of professor fixed effects to control for time-invariant professor characteristics such as
innate teaching ability or motivation. Professor fixed effects may help to explain the data from the
professors who prepare less but still get high CAPE Scores.
The findings from my analysis are interesting as they indicate that professors likely have some
control over CAPE scores they receive. Though each professor has their own unique teaching style,
preparedness is universal and indeed better prepared instructors are recognized by students for their
dedication to providing quality education.