Time saving students’ formative assessment: Algorithm to balance number of tasks and result reliability

Abstract

Feedback is a crucial component of effective, personalized learning, and is usually provided through formative assessment. Introducing formative assessment into a classroom can be challenging because of test creation complexity and the need to provide time for assessment. The newly proposed formative assessment algorithm uses multivariate Elo rating and multi-armed bandit approaches to solve these challenges. In the case study involving 106 students of the Cloud Computing course, the algorithm shows double learning path recommendation precision compared to classical test theory based assessment methods. The algorithm usage approaches item response theory benchmark precision with greatly reduced quiz length without the need for item difficulty calibration.