A Systematic Review of Automatic Question Generation for Educational Purposes

Abstract

While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience, and resources. This, in turn, hinders and slows down the use of educational activities (e.g. providing practice questions) and new advances (e.g. adaptive testing) that require a large pool of questions. To reduce the expenses associated with manual construction of questions and to satisfy the need for a continuous supply of new questions, automatic question generation (AQG) techniques were introduced. This review extends a previous review on AQG literature that has been published up to late 2014. It includes 93 papers that were between 2015 and early 2019 and tackle the automatic generation of questions for educational purposes. The aims of this review are to: provide an overview of the AQG community and its activities, summarise the current trends and advances in AQG, highlight the changes that the area has undergone in the recent years, and suggest areas for improvement and future opportunities for AQG. Similar to what was found previously, there is little focus in the current literature on generating questions of controlled difficulty, enriching question forms and structures, automating template construction, improving presentation, and generating feedback. Our findings also suggest the need to further improve experimental reporting, harmonise evaluation metrics, and investigate other evaluation methods that are more feasible.

Towards development of a system for automatic assessment of the quality of a question paper

“…Automatic question generation (AQG) is a related research area where a lot of works have been carried out to develop systems for the automatic generation of questions from the text. An overview of the literature on automatic question generation specifically for educational purposes may be found in Kurdi et al (2019). Automatic multiple-choice question generation is a subproblem of AQG; a survey of the literature on automatic MCQ generation can be found in Rao and Saha (2020).…”

“…Various types of questions (e.g., objective or subjective, open-ended or cloze questions, recall or synthesis) are used in educational assessment depending on the level or purpose of assessment. Although a lot of research is being carried out on automatic question generation (Kurdi, Leo, Parsia, Sattler, & Al-Emari, 2019;Rao & Saha, 2020) and automatic answer grading (Burrows, Gurevych, & Stein, 2015;Mohler, Bunesc, & Mihalcea, 2011;Zhang, Huang, Yang, Yu, & Zhuang, 2020), available systems are unable to generate/evaluate all types of questions correctly. Therefore, a fully automatic educational assessment is still not widely used.…”

Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation

“…Other transformations have been in the past defined in terms of templates Nielsen, 2014, 2015;Mazidi and Tarau, 2016;Flor and Riordan, 2018), or explicitly performed (Heilman and Smith, 2009) by searching tree patterns via Tregex, followed by their manipulation using Tsurgeon (Levy and Andrew, 2006). Kurdi et al (2020) provide a comprehensive summary of QG, analysing and comparing approaches before and after 2014.…”

AQG: Arabic Question Generator

“…The review provides a classification based on technological as well as on pedagogical aspects of the works presented. According to recent research [5,6], the majority of QG systems focus on generating questions for the purpose of assessment. The template-based approach was the most common method employed in the reviewed literature.…”