Personalized Computer-aided Question Generation for English Language Learning
Date Issued
2014
Date
2014
Author(s)
Huang, Yi-Ting
Abstract
In recent years, there has been increasing attention to computer-aided question generation in the field of computer assisted language learning and Natural Language Processing (NLP). However, the previous related work often provides examinees with an exhaustive amount of questions that are not designed for any specific testing pur-pose. In this study, we present a personalized automatic quiz generation that generates multiple–choice questions at various difficulty levels and categories, including grammar, vocabulary, and reading comprehension. We also design a reading difficulty estimation to predict the readability of a reading material, for learners taking English as a foreign language. The proposed reading difficulty estimation is based not only on the complex-ity of lexical and syntactic features, but also on several novel concepts, including the word and grammar acquisition grade distributions from several sources, word sense from WordNet, and the implicit relations between sentences. Moreover, we combine the proposed question generation with a quiz strategy for estimating a student’s ability and question selection. We develop a statistical and interpretable ability estimation. This method captures the succession of learning over time and provides an explainable interpretation of a statistical measurement, based on the quantiles of acquisition distri-butions and Item Response Theory (IRT). The concepts behind incorrectly answered questions are reincorporated into future tests in order to improve the weaknesses of examinees. The results showed that proposed second language reading difficulty esti-mation outperforms other first language reading difficulty estimations and the pro-posed ability estimation showed more accurate and robust than other ability estimations. In an empirical study, the results showed that the subjects with the personalized auto-matic quiz generation corrected their mistakes more frequently than ones only with computer–aided question generation. Moreover, subjects demonstrated the most pro-gress between the pre–test and post–test and correctly answered more difficult ques-tions.
Subjects
Computer-aided question generation
reading difficulty estimation
ability estimation
Item Response Theory
Computer assisted language learning
Type
thesis
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