Bayesian Adaptation during Computer-Based Tests and Computer-Guided Practice Exercises

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Abstract
One of the potential advantages of computer-based instruction (CBI) is individualization of instruction. However, this goal has not been fully realized in practice, due largely to limitations of natural language understanding and to combinatorial explosion. It is nonetheless possible to develop CBI programs which can adapt to students, depending on their performance, by adjusting the length of computer-guided practice exercises and computer-based tests. The validity of this approach is supported empirically. The number of questions can be significantly reduced for many individuals, while mastery and nonmastery decisions remain highly accurate.