Automatic question generation for intelligent tutoring systems

Shah, Riken; Shah, Deesha; Kurup, Lakshmi

doi:10.1109/cscita.2017.8066538

Cited by 24 publications

(12 citation statements)

References 5 publications

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“…Other approaches selected sentences by checking the occurrence of, or measuring the similarity to, a reference set of patterns under the assumption that these sentences convey similar information to sentences used to extract patterns (Majumder and Saha 2015;Das and Majumder 2017). Others (Shah et al 2017;Zhang and Takuma 2015) filtered sentences that are insufficient on their own to make valid questions, such as sentences starting with discourse connectives (e.g. thus, also, so, etc.)…”

Section: Generation Tasksmentioning

confidence: 99%

“…A dominant approach is the selection of distractors based on their similarity to the key, using different notions of similarity, such as syntax-based similarity (i.e. similar POS, similar letters); Satria and Tokunaga 2017a, b; Jiang and Lee 2017), feature-based similarity(Wita et al 2018;Majumder and Saha 2015; Patra and Saha 2018a, b;Alsubait et al 2016;Leo et al 2019), or contextual similarity(Afzal 2015; Kumar et al 2015a, b;Yaneva and et al 2018;Shah et al 2017;Jiang and Lee 2017). Some studies(Lopetegui et al 2015;Faizan and Lohmann 2018;Faizan et al 2017;…”

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confidence: 99%

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A Systematic Review of Automatic Question Generation for Educational Purposes

Kurdi

Leo

Parsia

et al. 2019

Int J Artif Intell Educ

251

111

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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.

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Section: Generation Tasksmentioning

confidence: 99%

mentioning

confidence: 99%

A Systematic Review of Automatic Question Generation for Educational Purposes

Kurdi

Leo

Parsia

et al. 2019

Int J Artif Intell Educ

251

111

View full text Add to dashboard Cite

show abstract

“…In Table 1 , an overview of different systems is presented. The accuracy of the systems is not compared due to different approaches and the unavailability of a benchmark dataset ( Shah, Shah & Kurup, 2017 ; Susanti et al, 2017 ).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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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“…Of particular relevance here is the line of related work, where researchers have investigated how machine learning and large-scale, open-access resources such as Wikipedia can be utilized to generate various types of educational content and interactions with the aim of scaling up computer-based learning systems and addressing the needs of their students (Brunskill et al, 2018;Dinan et al, 2018;Guo et al, 2016;Willis et al, 2019). In particular, it has been shown that the use of NLP techniques in application to Wikipedia may be helpful in generating pedagogically motivated concept maps to be used within an ITS (Lahti, 2009); identifying pre-requisite relations and sequencing among learning concepts to better model the learning path of the student and assess gaps in student's understanding of the subject (De Medio et al, 2016;Ramírez-Noriega et al, 2018;Talukdar & Cohen, 2012); and generating a variety of pedagogical interventions ranging from open questions Shah et al, 2017) to multiple-choice quizzes (Guo et al, 2016;Tamura et al, 2015) across a number of subject domains.…”

Section: Intelligent Tutoring Systemsmentioning

confidence: 99%

Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

Kochmar

Belfer³

et al. 2021

Int J Artif Intell Educ

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Intelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

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Automatic question generation for intelligent tutoring systems

Cited by 24 publications

References 5 publications

A Systematic Review of Automatic Question Generation for Educational Purposes

A Systematic Review of Automatic Question Generation for Educational Purposes

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

Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

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