Automatic Generation of English Reference Question by Utilising Nonrestrictive Relative Clause

Satria, Arief Yudha; Tokunaga, Takenobu

doi:10.5220/0006320203790386

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…There has been a considerable number of studies on automatic question generation, particularly for the English test purposes (Brown et al 2005;Lin et al 2007;Smith et al 2010;Sakaguchi et al 2013;Susanti et al 2015;Satria and Tokunaga 2017). Multiple-choice question, in particular, has received extra attention because it appears in standardised English proficiency tests such as TOEFL, TOEIC and IELTS.…”

Section: Automatic Question Generation (Aqg)mentioning

confidence: 99%

Integrating automatic question generation with computerised adaptive test

Susanti

Tokunaga

Nishikawa

2020

RPTEL

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The present study focuses on the integration of an automatic question generation (AQG) system and a computerised adaptive test (CAT). We conducted two experiments. In the first experiment, we administered sets of questions to English learners to gather their responses. We further used their responses in the second experiment, which is a simulation-based experiment of the AQG and CAT integration. We proposed a method to integrate them with a predetermined item difficulty that enables to integrate AQG and CAT without administering the items in a pretesting. The result showed that all CAT simulations performed better than the baseline, a linear test, in estimating the test taker's true proficiency.

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Section: Automatic Question Generation (Aqg)mentioning

confidence: 99%

Integrating automatic question generation with computerised adaptive test

Susanti

Tokunaga

Nishikawa

2020

RPTEL

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“…For instance, Hoshino and Nakagawa (2005) developed a real-time system that generates multiple-choice questions on English grammar and vocabulary from online news articles. More recently, Susanti et al (2015) generated the multiple-choice English vocabulary questions currently used in TOEFL, while Satria and Tokunaga (2017) worked on the TOEFL English pronoun reference questions. Araki et al (2016) introduced a method to generate multiple-choice open-ended questions for reading comprehension.…”

Section: Automatic Question Generation Systemmentioning

confidence: 99%

Controlling item difficulty for automatic vocabulary question generation

Susanti

Tokunaga

Nishikawa

et al. 2017

RPTEL

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The present study investigates the best factor for controlling the item difficulty of multiple-choice English vocabulary questions generated by an automatic question generation system. Three factors are considered for controlling item difficulty: (1) reading passage difficulty, (2) semantic similarity between the correct answer and distractors, and (3) the distractor word difficulty level. An experiment was conducted by administering machine-generated items to three groups of English learners. The groups were determined based on their standardised English test scores. In total, 120 items, generated using combinations of the above three factors, were tested. The results reveal that the distractor word difficulty level had the greatest impact on item difficulty, but this tendency changed depending on the proficiency of the test takers. These results will be of use when implementing a fully automatic system for administrating tests.

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“…Since readily available systems for generating MCQs lack informative sentences, the quality of generated distractors is low. The proposed system overcomes such gaps in the prevalent works ( Malinova & Rahneva, 2016 ; Susanti et al, 2016 ; Satria & Tokunaga, 2017 ).…”

Section: Introductionmentioning

confidence: 98%

Automatic computer science domain multiple-choice questions generation based on informative sentences

Maheen

Asif

Ahmad

et al. 2022

PeerJ Computer Science

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Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a system must be present that can automatically generate MCQs from the given text. The automatic generation of MCQs can be carried out by following three sequential steps: extracting informative sentences from the textual data, identifying the key, and determining distractors. The dataset comprising of various topics from the 9th and 11th-grade computer science course books are used in this work. Moreover, TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers techniques are utilized for automatic MCQs generation. Domain experts validated the generated MCQs with 83%, 77%, and 80% accuracy, key generation, and distractor generation, respectively. The overall MCQ generation achieved 80% accuracy through this system by the experts. Finally, a desktop app was developed that takes the contents in textual form as input, processes it at the backend, and visualizes the generated MCQs on the interface. The presented solution may help teachers, students, and other stakeholders with automatic MCQ generation.

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Automatic Generation of English Reference Question by Utilising Nonrestrictive Relative Clause

Cited by 12 publications

References 7 publications

Integrating automatic question generation with computerised adaptive test

Integrating automatic question generation with computerised adaptive test

Controlling item difficulty for automatic vocabulary question generation

Automatic computer science domain multiple-choice questions generation based on informative sentences

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