Ben O. Smith

Bazinganomics: Economics of The Big Bang Theory

Bazinganomics.com is designed to provide instructors with clips, explanations, and lesson plans related to economics concepts from TV’s 2nd most-watched broadcast show of the 2014-2015 season, CBS’s The Big Bang Theory. The site contains approximately 100 clips. As the show continues to air (currently signed through the 2016-2017 season) the authors plan to increase the number of clips and lesson plans.

Multiplatform software tool to disaggregate and adjust value-added learning scores

In 2016, Walstad andWagner released an article that suggested practitioners should disaggregate valueadded learning scores into four categories: positive, negative, retained, and zero learning. Positive learning is said to occur when a student answers a question incorrectly on the pre-test and correctly on the post-test. Negative learning is said to occur when the student correctly answers the question on the pre-test but incorrectly on the post-test. Retained learning is said to occur when the student answers the question correctly on both exams and zero learning is said to occur when the student answers the question incorrectly on both exams. Smith and Wagner (2017) improved on this work by adjusting the learning categories for guessing. These distinct learning types provide the instructor with fundamentally different information and yet are intermixed in the traditional flow of knowledge measurement (post-test minus pre-test). Therefore, the interested instructor might disaggregate their class test results for pedagogical improvement reasons.While a procedure in Excel can be used to perform this disaggregation (Walstad 2016), it’s timeconsuming and does not scale when the disaggregation needs to be calculated for every class within a department or college. The Assessment Disaggregation software solves this problem. The program reads standard CSV Scantron output files usually produced by the institution’s testing center and provides both the original Walstad and Wagner (2016) learning values and adjusted estimates by Smith and Wagner (2017). Further, by supplying an assessment map file, the questions need not appear in the same order on the preand post-test. Because there is both a graphical and command line version of the software, the procedure can be incorporated into batch processing or folder actions when the disaggregation is part of a large assessment procedure. Both Windows and Mac versions of the tools are available for download on the Web site.

On Guessing: An Alternative Adjusted Positive Learning Estimator and Comparing Probability Misspecification with Monte Carlo Simulations

Instructors and researchers have used the ‘flow’ of knowledge (post-test score minus pre-test score) to measure learning in the classroom for the past fifty years. Walstad and Wagner (2016) and Smith and Wagner (2018) move this practice forward by disag- gregating the flow of knowledge and accounting for student guessing. These estimates are sensitive to misspecification of the probability of guessing correct. This work provides guidance to practitioners and researchers facing this problem. We introduce a transformed measure of true positive learning that under some knowable conditions performs better when students’ ability to guess correctly is misspecified. This measure converges to Hake’s (1998) under certain conditions. We then use simulations to compare the accuracy of two estimation techniques under various violations of the assumptions of those techniques. Using recursive partitioning trees fitted to our simulation results, we provide the practitioner concrete guidance based on a set of yes/no questions.

Create random assignments: A cloud-based tool to help implement alternative teaching materials

Research has shown that learning is enhanced by variety (Bransford, Brown, and Cocking 2000; Halpern and Hakel 2003) such as popular press books, podcasts and videos. However, these mediums do not contain question banks and further, while learning management systems (LMS) can be used to generate random quizzes, they are not designed for long-form open-ended responses thatmight be answered over the course of a few days to weeks; open-ended questions are often ideal in upper-level undergraduate andMBA courses where there are less definitive correct answers. We solve these problems by developing software to generate assignments with randomized open-ended questions. “Create RandomAssignments” is a free programdeveloped by the authors that runswithin theGoogle Drive environment. Using a Google Spreadsheet as a question bank, Create Random Assignments randomly generates an assignment for each student in the class. This assignment can be delivered in several ways: as physical printout for exams, as an emailed PDF, or as a shared Google Document. When using the latter of these features, the student completes the assignment within Google Documents and the instructor grades the assignment using Google Document’s comment feature. Because the assignment is a Google Document, the student can insert graphs (using Google’s drawing tool), equations, or any other media type. This allows for more nuanced answers and can fulfill a set of learning objectives that could not be achieved with traditional short-answer assignments. Further, it allows for multiple rounds of feedback between the instructor and student, creating additional learning opportunities. Our site features a link to Chrome App in the Google Apps Store as well as a direct link to the app for other browsers. Further, we provide software instructions and four sample question banks to test the software. An e-mail link for support is provided.

Adjusting for Guessing and Applying a Statistical Test to the Disaggregation of Value-Added Learning Scores

Abstract In 2016, Walstad and Wagner developed a procedure to split pre-test and post-test responses into four learning types: positive, negative, retained, and zero learning. This disaggregation is not only useful in academic studies; but also provides valuable insight to the practitioner: an instructor would take different mitigating actions in response to zero versus negative learning. However, the original disaggregation is sensitive to student guessing. This article extends the original work by accounting for guessing and provides adjusted estimators using the existing disaggregated values. Further, Monte Carlo simulations of the adjusted learning type estimates are provided. Under certain assumptions, an instructor can determine if a difference in positive (or negative) learning is the result of a true change in learning or “white noise.”

Network Externalities and Friendly Neighbors: When Firms Choose to Invite Competition

Economic theory on the subject of barriers to entry focuses almost exclusively on firms working to preserve market power and economic profits. In this paper, we propose that under certain circumstances firms may instead choose to reduce barriers to entry as a profit-maximizing mechanism. We model these conditions and predict that in some industries, an increase in the number of participating firms will induce enough growth in the industry to allow existing firms to increase profit by enticing other firms to enter the market. Using data on the National Football League, we demonstrate that firms (teams) do in fact engage in behavior to reduce barriers to entry for competitors and thereby increase their own profits. This model differs from the standard agglomeration models by proposing that firms deliberately lower fixed costs for their competitors as a rational act, instead of suggesting that fixed costs are incidentally reduced due to concentration of firms.

Improving Student Performance through Loss Aversion

As shown by Tversky and Kahneman (1991), framing an outcome as a loss causes individuals to expend extra effort to avoid that outcome. Since classroom performance is a function of student effort in search of a higher grade, we seek to use loss aversion to encourage student effort. This field experiment endows students with all of the points in the course upfront, then deducts points for every error throughout the semester. Students perform three to four percentage points better when controlling for student ability and domain knowledge. This result is significant at the 1% level in our most robust specification.

A Path To Consumer Surplus & Loyalty: How Path Dependent Products Result in Lower Prices and Order-Dependent Consumer Loyalty

In the technology industry one product category commonly builds on another. For instance, a smart television product enhances a smart phone. However, because firms can enhance the experience if they produce both products, the utility gained by owning both products from the same firm is greater than the sum of the two products’ utility if purchased from two separate firms. One would think this would increase the margins of the second product, as it does in aftermarkets. However, we show that in a duopoly environment the additional utility produced by the firms offset each other. This explains why we have not seen a dramatic increase in profit margins from hardware producers such as Apple, Google and Samsung.