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We propose a new probabilistic graphical model that jointly models thedifficulties of questions, the abilities of participants and the correctanswers to questions in aptitude testing and crowdsourcing settings. We devisean active learning/adaptive testing scheme based on a greedy minimization ofexpected model entropy, which allows a more efficient resource allocation bydynamically choosing the next question to be asked based on the previousresponses. We present experimental results that confirm the ability of ourmodel to infer the required parameters and demonstrate that the adaptivetesting scheme requires fewer questions to obtain the same accuracy as a statictest scenario.

How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing