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The inGEAR Program: Recruiting International Graduate Students through Undergraduate Research Internships
| Content Provider | Semantic Scholar |
|---|---|
| Author | Luchini-Colbry, Katy Walker, Mary Anne |
| Copyright Year | 2016 |
| Abstract | We describe a hands-on, team-based classroom activity designed to help engineering students understand the ethics of data collection, analysis and reporting processes. This lesson is presented to students as a “mini research competition” involving the collection of data to answer the question “what is the length of a toilet paper tube?” The lesson is purposely structured to both provide opportunities for ethical behavior and to offer temptations and rewards for cheating. The initial activity is followed by a substantial discussion and debriefing, and the lesson concludes with several related case studies for discussion within student teams. Discussions about this lesson focus on several questions: What are the ethical issues involved in collecting, analyzing and reporting data? How do documentation and reporting processes impact the ethical conduct of research? How does working in a team impact data collection, and what responsibilities do individuals have in ensuring that the team’s activities and outputs meet ethics standards? We also describe the evolution of this ethics lesson from an earlier classroom activity involving precision and accuracy in data measurement, which has been used in high school, college and continuing education settings for more than two decades. This paper describes the development of the curriculum; lessons learned from the classroom; and an analysis of student artifacts from the most recent offering as part of an engineering undergraduate research program at Michigan State University. The lesson materials are provided in appendices, in order to allow other educators to adapt these materials for their own classrooms. Background: Ethical Practices in Research Kenneth D. Pimple summarized the responsible conduct of research (RCR) as the search for “truth, fairness and wisdom.”1 This search for truth means considering whether the data are gathered and presented in a manner that is consistent with the physical world. Fairness considers the accompanying social relationships: is appropriate credit given where it is due? Are research subjects treated humanely? Are funding relationships acknowledged, and is funded research free from outside influences? Pimple’s final criterion, wisdom, “concerns the relationship between the research agenda and the broader social and physical world, present and future” and asks whether the proposed research is the best possible use of finite resources.1 Within the broader context of RCR are more specific questions about the ethics of collecting, analyzing and reporting data. While “the search for truth and its unbiased reporting are ultimate goals of conducting scientific research,”2 there are both implicit and explicit pressures that may lead to bias or fraud in data management. There may be valid reasons to exclude outlying data points, but researchers must carefully consider whether such exclusion criteria might change the interpretation of the remaining data. It is also important to include all pertinent data in its most authentic format; for instance, it can be tempting to report percentages rather than numerical values, or to downplay or exclude participant numbers for small experiments. However, these data should be clearly included in order to provide an appropriate context for interpreting the impact of the results.2 Research programs for undergraduates often include RCR training,3–6 and focused RCR instruction has been shown to increase students’ understanding of research ethics during a REU (Research Experience for Undergraduates) summer program.7–9 The ethics of data collection, analysis and reporting are an important topic for many REU programs, and can lead to broader conversations about RCR and the ethical challenges researchers may face: Advising [REU] students on how to write up their research seemed a good time to talk about what counts as data, how outliers should be handled, what must be included in a report, the reasons certain data may be omitted (and how that should be done), and even the importance (and effect) of putting some point first (or burying data in a table at the back of the report). Such a discussion might naturally open a wider discussion of the ethical aspects of the relationship a researcher has with funders and with those who may use the research (for example, what innovations may be published or what warnings should go into a report).10 Learning to collect accurate, precise data is also an important component of many engineering curricula. Past researchers have explored many aspects of data collection, analysis and reporting, such as error analysis,11 scientific measurement,12 and laboratory procedures.13 From Accuracy and Precision to Ethics: Evolution of the Curriculum The ethics exercise described here evolved from an earlier lesson on the difference between accuracy and precision in scientific measurements. While accuracy and precision are often used interchangeably, they have distinct meanings in the context of scientific measurements. An accurate measurement reflects the true value (possibly within an error range or degree of confidence), while a precise measurement is consistent and repeatable.14 It is possible for a measurement to be highly accurate but not precise (repeatable), or to be very precise but not accurate (reflecting the true value). Figure 1 depicts the difference between accuracy and precision in scientific measurement. Figure 1: Accuracy is the proximity of measurement results to the [reference] true value; precision, the repeatability, or reproducibility of the measurement. Figure and caption from Pekaje/Wikipedia.15 The initial lesson on accuracy versus precision was developed by Dr. John R. Luchini as part of a guest lecture for a high school science class. Students were asked to collect empty toilet paper tubes in advance, and worked in small groups to measure the tubes in various ways. Some groups focused on accuracy, measuring with calipers or rulers, while other groups focused on precision: obtaining consistent, repeatable results by dropping tubes onto graph paper (of various resolutions) and counting how many graph lines were covered. Individual results from each group were then combined and analyzed in various ways to explore the precision and accuracy of different measurement methods—and the impact of varying the size of the data set. The measurement and analysis process was complicated by the fact that individual manufacturers are free to determine the length of their own toilet paper rolls. While many tubes are about 4 inches long,16 there is enough variation in length to prompt interesting conversations among students about accuracy, precision, error rates and confidence intervals. This lesson was repeated and refined over more than two decades, both in high school classrooms and as part of professional development seminars and continuing education classes for college students and practicing engineers. Over time, the lesson expanded to include discussions about ethical issues related to data collection, analysis and reporting. Many of these expansions were inspired by questions raised by students; for instance, what should they do with the paper towel tube that someone brought to class? Some students argued for simply excluding the data point, since it did not match the desired input (toilet paper tubes). Others suggested modifying the data, by cutting the paper towel tube into one or more lengths that were similar to toilet paper tubes. These questions led to conversations about data falsification and fabrication, about when it is appropriate to exclude outlying data—and how to document and report such exclusions. As an alternate approach, Dr. Luchini suggested that students include the measurement from the (unmodified) paper towel tube in their data set and see what happened. Not surprisingly, the longer tube skewed the average length calculated within the small group that measured it; however, once their data were combined with results from other groups the overall average approached 4 inches. This led to a memorable conversation about the impact of data set sizes, and additional ethical considerations about collecting the “right” number of measurements: enough to allow confidence in the results, while not so many that resources are wasted unnecessarily. |
| File Format | PDF HTM / HTML |
| DOI | 10.18260/p.26195 |
| Alternate Webpage(s) | https://www.asee.org/public/conferences/64/papers/15171/download |
| Alternate Webpage(s) | https://doi.org/10.18260/p.26195 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
