content: text: survey responses and outcomes from the experiments and games. This spreadsheet is linked to the
students' randomly assigned course ID (CID) numbers. The other spreadsheet, which is linked to their
university student ID numbers and their names, compiles their performances on quizzes, homework,
and exams assigned throughout the semester.

content: text: At the risk of sounding draconian, this is a course where it may make sense to base upwards of
50% of a student's grade upon their in-person attendance, which would entail carefully taking role at
the beginning of each class. If the class meets 30 times face-to-face during the semester, for example,
their grade attributable to attendance would then drop by 3.33 percentage points for each missed
class (excused absences withstanding). Granted, students who foresee having difficulty attending class
in-person throughout the semester would likely choose to drop the course immediately. For those
students who remain, the remaining 50% of their course grade would then be based upon their
quizzes, homework, and exam scores.

content: text: The issue of how best to convey written information to the student a priori (i.e., before conducting a
given experiment or game) also looms large in a participatory-learning setting such as this, especially
if the instructor desires to obtain unbiased responses from the students (or more practically, to
control for potential biases). For example, the first set of thought experiments presented in Section 1
is meant to demonstrate firsthand to the students the extent to which automatic, knee-jerk responses
from what Kahneman (2011) identifies as the System 1 portion of the brain can result in
miscalculations. Students who choose to read ahead (small in number though these types of students
may be) potentially skew the distribution of responses away from its otherwise true representation
of these miscalculations. Such skewness may be tolerable for strictly educational purposes, where the
goal is to demonstrate that at least a certain percentage of students are prone to miscalculation. But if
the instructor also hopes to compile student responses into a dataset amenable for statistical analysis,
2
then this type of potential bias draws into question the validity of the data.

content: text: To help control for potential biases associated with students having read ahead about the game or
experiment they are now participating in, I recommend including the following question on each
Response Card: "Did you read about this topic ahead of time?" (see Appendix A). Answers to this
question provide a control for the level of student foreknowledge, which is the potential bias of
concern.

content: text: I am personally unaware of any studies that have looked at how well students learn the lessons
of behavioral economics in a cumulative sense over a span of time (e.g., an entire semester) and
across a variety of experiments and games. In other words, I know of no studies that estimate the
extent to which individuals who begin a course in behavioral economics as bona fide Homo sapiens
evolve toward "Homo economism" in their individual and social choices. The pedagogy promoted in
this textbook-in particular, the data it generates-offers instructors the opportunity to empirically
test the hypothesis that students make this evolution.

content: text: 2. Note that this potential biasedness problem also extends to the laboratory experiments of Section 2 and of Section 3.
games

content: text: BEHAVIORAL ECONOMICS PRACTICUM XXV