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Optimally Discriminative Choice Sets in Discrete Choice Models: Application to Data-Driven Test Design
(2016)KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningAugust 2016Difficult multiple-choice (MC) questions can be made easy by providing a set of answer options of which most are obviously wrong. In the education literature, a plethora of instructional guides exist for crafting a suitable set of wrong choices (distractors) that enable the assessment of the students' understanding. The art of MC question design thus hinges on the question-maker's experience and knowledge of the potential misconceptions. ...Conference Paper -
Calibrated Self-Assessment
(2016)Proceedings of the International Conference on Educational Data Mining (EDM) (9th, Raleigh, North Carolina, June 29-July 2, 2016)Peer-grading is widely believed to be an inexpensive and scalable way to assess students in large classroom settings. In this paper, we propose calibrated self-grading as a more efficient alternative to peer grading. For self-grading, students assign themselves a grade that they think they deserve via an incentive-compatible mechanism that elicits maximally truthful judgements of performance. We show that the students' self-evaluation ...Conference Paper