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Zhang, Lishan; VanLehn, Kurt – Interactive Learning Environments, 2021
Despite their drawback, multiple-choice questions are an enduring feature in instruction because they can be answered more rapidly than open response questions and they are easily scored. However, it can be difficult to generate good incorrect choices (called "distractors"). We designed an algorithm to generate distractors from a…
Descriptors: Semantics, Networks, Multiple Choice Tests, Teaching Methods
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Yun, Eunjeong – Research in Science & Technological Education, 2020
Background: We adopted a theoretical framework that the acquisition of a scientific concept comprises the development of connections among conceptual elements associated with a scientific term within a mental semantic network. Given this framework, the hypothesis that the surrounding words connected with a scientific term are relevant to the…
Descriptors: Correlation, Semantics, Scientific Concepts, Networks
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Zhang, Lishan; VanLehn, Kurt – Interactive Learning Environments, 2017
The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student's competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology…
Descriptors: Biology, Science Instruction, Intelligent Tutoring Systems, Probability
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Aldabe, Itziar; Maritxalar, Montse – IEEE Transactions on Learning Technologies, 2014
The work we present in this paper aims to help teachers create multiple-choice science tests. We focus on a scientific vocabulary-learning scenario taking place in a Basque-language educational environment. In this particular scenario, we explore the option of automatically generating Multiple-Choice Questions (MCQ) by means of Natural Language…
Descriptors: Science Tests, Test Construction, Computer Assisted Testing, Multiple Choice Tests