Automatic Generation of Fill-in-the-Blank Questions From History Books for School-Level Evaluation

Pannu, Sumeet; Krishna, Aishwarya; Shiwani, Kumari; Patra, Rakesh; Saha, Sujan Kumar

doi:10.1007/978-981-10-7871-2_44

Cited by 4 publications

(1 citation statement)

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“…Using human expert as much as 67 native English-speaking volunteers to give an opinion on the given question (Pannu et al, 2018) Sentence and blank position selection using NER Fill-in-theblank question English Using human expert with 3 assessment metrics namely validity, key quality, and sentence quality (Chen et al, 2006) Sentence selection and generate question using NLP techniques Error detection and fill-inthe-blank English Using human expert to assess the feasibility of the generated-question. (Agarwal et al, 2011) Sentence selection using summarization, NER…”

Section: Englishmentioning

confidence: 99%

Question Generator System of Sentence Completion in TOEFL Using NLP and K-Nearest Neighbor

Riza

Pertiwi

Rahman

et al. 2019

Indonesian J. Sci. Technol

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Test of English as a Foreign Language (TOEFL) is one of learning evaluation forms that requires excellent quality of questions. Preparing TOEFL questions using a conventional way certainly spends a lot of time. Computer technology can be used to solve the problem. Therefore, this research was conducted in order to solve the problem of making TOEFL questions with sentence completion type. The built system consists of several stages: (1) input data collection from foreign media news sites with excellent English grammar quality; (2) preprocessing with Natural Language Processing (NLP); (3) Part of Speech (POS) tagging; (4) question feature extraction; (5) separation and selection of news sentences; (6) determination and value collection of seven features; (7) conversion of categorical data value; (8) target classification of blank position word with K-Nearest Neighbor (KNN); (9) heuristic determination of rules from human experts; and (10) options selection or distraction based on heuristic rules. After conducting the experiment on 10 news, it is obtained that 20 questions based on the results of the evaluation showed that the generated questions had a very good quality with percentage of 81.93% (after the assessment by the human expert), and 70% was the same blank position from the historical data of TOEFL questions. So, it can be concluded that the generated question has the following characteristics: the quality of the result follows the data training from the historical TOEFL questions, and the quality of the distraction is very good because it is derived from the heuristics of human experts.

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Section: Englishmentioning

confidence: 99%