Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations

Liu, Tianqiao; Qian, Fang; Ding, Wenbiao; Liu, Zitao

doi:10.18653/v1/2021.emnlp-main.348

Cited by 17 publications

(3 citation statements)

References 37 publications

(27 reference statements)

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“…Following the same shift as in AQG, Zhou and Huang (2019) proposed an approach using Recurrent Neural Networks (RNNs) that encodes math expressions and topic words to automatically generate such problems. Subsequent research along this direction has focused on improving topic relevance, expression relevance, language coherence, as well as completeness and validity of the generated problems using a spectrum of approaches (Liu et al, 2021;Wang et al, 2021;Wu et al, 2022).…”

Section: Content Generationmentioning

confidence: 99%

Proactive and reactive engagement of artificial intelligence methods for education: a review

Mallik

Gangopadhyay

2023

Front. Artif. Intell.

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The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.

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Section: Content Generationmentioning

confidence: 99%

Proactive and reactive engagement of artificial intelligence methods for education: a review

Mallik

Gangopadhyay

2023

Front. Artif. Intell.

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“…Figure 1 gives an illustrative example of the KT task. Such predictive capabilities can potentially help students learn better and faster when paired with high-quality learning materials and instructions and the KT models have been widely used to support intelligent tutoring systems and MOOC platforms (Käser et al 2017;Cen, Koedinger, and Junker 2006;Lavoué et al 2018;Liu et al 2021a).…”

Section: Introductionmentioning

confidence: 99%

Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Liu¹,

Huang²,

Liu³

et al. 2023

Preprint

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Knowledge tracing (KT) is a crucial technique to predict students' future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students' knowledge state variations at a fine-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at https://pykt.org/.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Liu¹,

Huang²,

Liu³

et al. 2023

AAAI

View full text Add to dashboard Cite

Knowledge tracing (KT) is a crucial technique to predict students’ future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students’ knowledge state variations at a ﬁne-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at https://pykt.org/.

show abstract

Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations

Cited by 17 publications

References 37 publications

Proactive and reactive engagement of artificial intelligence methods for education: a review

Proactive and reactive engagement of artificial intelligence methods for education: a review

Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

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