Objective
The objective of this experiment is to compare and contrast the relative effectiveness of various methods of encouraging secondary school students to pursue a career path in the science, technology, engineering, and mathematics (STEM) disciplines. Two methods are suggested: a general assembly that presents to the entire student body a set of statistics concerning the high salaries that are enjoyed by graduates who have matriculated in these disciplines; and a presentation by several visiting scholars in which they briefly share their own career experiences as a means of motivating the students. The experiment must necessarily be conducted in parallel throughout a range of secondary schools. Note that the approach is quite different from that seen in the published literature that addresses this question, including studies by Hossain & Robinson and Reinhold et al.
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Variables
The independent variable is the type of stimulus program engaged in at a given school, either general assembly or visiting scholars’ colloquium. The dependent variable is the percentage of graduates from the school who formally indicate an intention to pursue undergraduate studies in the STEM fields.
Data Collection
The study must be conducted in parallel across a variety of schools that evidence similar demographics so that cultural or other biases for or against STEM careers do not creep into the data or its evaluation. It is anticipated that four schools will engage in the general assembly format; four schools will avail themselves of the visiting scholars format; and four schools, designated as controls, will take no particular action either to encourage or discourage students vis-à-vis their pursuit of STEM studies. The general assemblies or colloquia will be presented to students entering their senior year of high school. The data concerning which graduating seniors have decided to pursue STEM fields versus other courses of undergraduate study are to be collected late in the spring semester, after colleges have forwarded their formal acceptance letters.
Sampling
The question of sampling pertains to the identification of schools, not to the collection of data that pertain to those schools chosen to participate. Random samples of schools can be chosen across a variety of demographics. The sampling technique is probability-based (Foley), with the chosen candidate schools organized into three groups. For each of the three experimental scenarios—general assembly, scholars’ colloquium, or no interventive measures—four schools will be chosen: one in a poor neighborhood, two in lower-middle-class neighborhoods, and one in an upper-middle-class neighborhood. The decision to weight the selections in this manner is based upon the assumption that more Americans are classifiable as lower middle class than as either poor or upper middle class.
Data Analysis
The data are analyzed from the perspective, not of what percentage of the senior class ultimately decides to pursue degrees in the STEM fields, but of what percentage of the senior class that has chosen to attend college has elected to study STEM. This difference is critical because poorer neighborhoods, for example, will naturally evidence a lower rate of college attendance, and the question to be answered by the study is how successful the intervention was at steering college-bound students toward STEM. Clearly, those students who do not evidence an early predilection to attend college will not even consider pursuing STEM studies, since they are universally regarded as more intellectually challenging and academically burdensome than alternative courses.
The dependent variable measured, which is the rate of STEM declaration as a percentage of the rate of formally accepted college attendance, is clearly metric (“Non-Metric Data”). While it cannot be forecast a priori whether the statistical approach or the colloquium approach will prove more successful, it is likely that both approaches will be markedly more effective than the null approach of offering no interventive measures that encourage students to pursue STEM curricula.
Works Cited
Foley, Ben. “The Methods of Probability Sampling.” Survey Gizmo, 19 March 2018. Retrieved from https://www.surveygizmo.com/resources/blog/probability-sampling/.
Hossain, M. Mokter, & Robinson, Michael G. “How to Motivate US Students to Pursue STEM Careers.” US-China Education Review 4 (2012). Retrieved from https://files.eric.ed.gov/fulltext/ED533548.pdf.
“Non-Metric Data.” Insights Association. Retrieved from https://www.insightsassociation.org/issues-policies/glossary/non-metric-data.
Reinhold, Sarah, Holzberger, Doris, & Seidel, Tina. “Encouraging a Career in Science: A Research Review of Secondary Schools’ Effects on Students’ STEM Orientation.” Studies in Science Education 54.1 (2018). Retrieved from https://www.tandfonline.com/doi/abs/10.1080/03057267.2018.1442900.