Date of Award

8-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational and Data Sciences

First Advisor

Stephen Rassenti

Second Advisor

David Porter

Third Advisor

Erik Linstead

Abstract

Science Olympiad (SO) is a team-based academic competition involving multiple subject areas (Events) with arcane rules governing the team composition. Add to the mix parental contention over which student(s) get on the “All-Star” team, and you have a potentially explosive situation. This project brings order and logic to school-based SO programs and defuses tense milestones through the implementation of an institutional structure that: assigns students to Events based on solicited student preferences for the Events, collects objective student performance data, composes competitive teams based on student performance (aka “Moneyball”), and brings transparency to the Team Selection process through crowdsourcing. The Event Assignment mechanism is simple, fast, easy to understand, and yields Pareto-optimal results based on student preferences, without the exchange of money or tokens, and with effectively no incentive to game the system. The Team Selection mechanism optimizes student performance data from teachers (Event Coaches) and competitions to compose a tiered series of teams with the greatest potential performance. And the Crowdsource Tool allows any stakeholder to compose a candidate team for advancing to the State competition, where the team with the highest potential performance score advances to State whether the team was composed with the Crowdsource Tool or by the Team Selection algorithm. The end result is that students get more of the Events that they want; Team Selection is transparent and far less contentious; teams are higher quality; and managing the SO program for a school takes considerably less time and effort.

Available for download on Friday, December 31, 2021

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