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.
Highlights
Formative assessment is a crucial component of the learning process, because it measures student’s knowledge, maps the path towards learning goals, and tracks progress [1,2].This process quickens and makes learning more effective [3]
Formative assessment precision in establishing personalized suitable items for each synthetic student is presented in Figure 4 as measured against item response theory (IRT) Maximum Likelihood Estimation (MLE) values
The Elo-bandit algorithm performs significantly better than upper confidence bound (UCB) and classical test methods: random and enumerate
Summary
Formative assessment is a crucial component of the learning process, because it measures student’s knowledge, maps the path towards learning goals, and tracks progress [1,2]. This process quickens and makes learning more effective [3]. Instead of passing judgment on student’s knowledge level, the purpose of formative assessment is to provide constructive feedback in order to accelerate learning [6,7], frame learning goals, and monitor progress towards them [8]. Formative assessment is most effective when it is used to tailor student’s learning paths based on their current level of understanding
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