Review on Neural Question Generation for Education Purposes

IF 4.7 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Said Al Faraby, Adiwijaya Adiwijaya, Ade Romadhony
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Abstract

Abstract Questioning plays a vital role in education, directing knowledge construction and assessing students’ understanding. However, creating high-level questions requires significant creativity and effort. Automatic question generation is expected to facilitate the generation of not only fluent and relevant but also educationally valuable questions. While rule-based methods are intuitive for short inputs, they struggle with longer and more complex inputs. Neural question generation (NQG) has shown better results in this regard. This review summarizes the advancements in NQG between 2016 and early 2022. The focus is on the development of NQG for educational purposes, including challenges and research opportunities. We found that although NQG can generate fluent and relevant factoid-type questions, few studies focus on education. Specifically, there is limited literature using context in the form of multi-paragraphs, which due to the input limitation of the current deep learning techniques, require key content identification. The desirable key content should be important to specific topics or learning objectives and be able to generate certain types of questions. A further research opportunity is controllable NQG systems, which can be customized by taking into account factors like difficulty level, desired answer type, and other individualized needs. Equally important, the results of our review also suggest that it is necessary to create datasets specific to the question generation tasks with annotations that support better learning for neural-based methods.
面向教育的神经问题生成研究综述
摘要提问在教育中起着指导知识建构和评价学生理解的重要作用。然而,创造高水平的问题需要大量的创造力和努力。预计自动问题生成不仅有助于生成流利和相关的问题,而且还具有教育价值。虽然基于规则的方法对于短输入是直观的,但它们很难处理更长的、更复杂的输入。神经问题生成(NQG)在这方面表现出较好的效果。本综述总结了2016年至2022年初NQG的进展。重点是发展NQG的教育目的,包括挑战和研究机会。我们发现,虽然NQG可以生成流畅和相关的factoid型问题,但很少有研究关注教育。具体来说,使用多段落形式的上下文的文献有限,由于当前深度学习技术的输入限制,需要识别关键内容。理想的关键内容应该对特定的主题或学习目标很重要,并且能够产生特定类型的问题。一个进一步的研究机会是可控的NQG系统,它可以通过考虑难度等级、期望的答案类型和其他个性化需求等因素来定制。同样重要的是,我们的综述结果还表明,有必要创建特定于问题生成任务的数据集,并添加注释,以支持基于神经的方法更好地学习。
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来源期刊
International Journal of Artificial Intelligence in Education
International Journal of Artificial Intelligence in Education COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
11.10
自引率
6.10%
发文量
32
期刊介绍: IJAIED publishes papers concerned with the application of AI to education. It aims to help the development of principles for the design of computer-based learning systems. Its premise is that such principles involve the modelling and representation of relevant aspects of knowledge, before implementation or during execution, and hence require the application of AI techniques and concepts. IJAIED has a very broad notion of the scope of AI and of a ''computer-based learning system'', as indicated by the following list of topics considered to be within the scope of IJAIED: adaptive and intelligent multimedia and hypermedia systemsagent-based learning environmentsAIED and teacher educationarchitectures for AIED systemsassessment and testing of learning outcomesauthoring systems and shells for AIED systemsbayesian and statistical methodscase-based systemscognitive developmentcognitive models of problem-solvingcognitive tools for learningcomputer-assisted language learningcomputer-supported collaborative learningdialogue (argumentation, explanation, negotiation, etc.) discovery environments and microworldsdistributed learning environmentseducational roboticsembedded training systemsempirical studies to inform the design of learning environmentsenvironments to support the learning of programmingevaluation of AIED systemsformal models of components of AIED systemshelp and advice systemshuman factors and interface designinstructional design principlesinstructional planningintelligent agents on the internetintelligent courseware for computer-based trainingintelligent tutoring systemsknowledge and skill acquisitionknowledge representation for instructionmodelling metacognitive skillsmodelling pedagogical interactionsmotivationnatural language interfaces for instructional systemsnetworked learning and teaching systemsneural models applied to AIED systemsperformance support systemspractical, real-world applications of AIED systemsqualitative reasoning in simulationssituated learning and cognitive apprenticeshipsocial and cultural aspects of learningstudent modelling and cognitive diagnosissupport for knowledge building communitiessupport for networked communicationtheories of learning and conceptual changetools for administration and curriculum integrationtools for the guided exploration of information resources
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