Maintaining and monitoring quality of a continuously administered digital assessment

Abstract

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Digital-first assessments are a new generation of high-stakes assessments that can be taken anytime and anywhere in the world. The flexibility, complexity, and high-stakes nature of these assessments pose quality assurance challenges and require continuous data monitoring and the ability to promptly identify, interpret, and correct anomalous results. In this manuscript, we illustrate the development of a quality assurance system for anomaly detection for a new high-stakes digital-first assessment, for which the population of test takers is still in flux. Various control charts and models are applied to detect and flag any abnormal changes in the assessment statistics, which are then reviewed by experts. The procedure of determining the causes of a score anomaly is demonstrated with a real-world example. Several categories of statistics, including scores, test taker profiles, repeaters, item analysis and item exposure, are monitored to provide context and evidence for evaluating the score anomaly as well as assure the quality of the assessment. The monitoring results and alerts are programmed to be automatically updated and delivered via an interactive dashboard every day.

Keywords

• digital-first assessment
• quality assurance
• language assessment
• high-stakes assessment
• anomaly detection