Title

Automatic Chinese Multiple Choice Question Generation Using Mixed Similarity Strategy

Abstract

Automatic question generation can help teachers to save the time necessary for constructing examination papers. Several approaches were proposed to automatically generate multiple-choice questions for vocalbuary assessment or grammar exercises. However, most of these studies focused on generating questions in English with a certain similarity strategy. This paper presents a mixed similarity strategy which generates Chinese multiple choice distractors with a statistical regression model including orthographic, phonological and semantic features, i.e., features that were shown in previous psycholinguistics studies to contribute to character recognition. In a first experiment, we evaluated the predictive power of the proposed features in measuring Chinese character similarity. One of the significant experimental results showed that the combination of the four proposed categories of features (structure, semantic radical, stroke and meaning) accounts for 62.5 percent of the variance in the human judgments of character similarity. In the second experiment, a user study was conducted to evaluate the quality of system-generated questions using a test item analysis method. Two hundred ninety-six Chinese primary school students (10-11-year-old) participated in this study. We have compared the mixed strategy with another three common distractor generation strategies, orthographic strategy, semantic strategy, and phonological strategy. One of important findings suggested that the mixed strategy significantly outperformed other three strategies in terms of the distractor usefulness and has a highest discrimination power among four strategies.

Publication Title

IEEE Transactions on Learning Technologies

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