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Managing What We Can Measure: Quantifying the Susceptibility of Automated Scoring Systems to Gaming Behavior
Authors:Derrick Higgins  Michael Heilman
Institution:1. Civis Analytics;2. Educational Testing Service
Abstract:As methods for automated scoring of constructed‐response items become more widely adopted in state assessments, and are used in more consequential operational configurations, it is critical that their susceptibility to gaming behavior be investigated and managed. This article provides a review of research relevant to how construct‐irrelevant response behavior may affect automated constructed‐response scoring, and aims to address a gap in that literature: the need to assess the degree of risk before operational launch. A general framework is proposed for evaluating susceptibility to gaming, and an initial empirical demonstration is presented using the open‐source short‐answer scoring engines from the Automated Student Assessment Prize (ASAP) Challenge.
Keywords:artificial intelligence  automated scoring  constructed response  machine learning  simulation
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