Predicting Learner’s Deductive Reasoning Skills Using a Bayesian Network

Tato, Ange; Nkambou, Roger; Robert, Serge

doi:10.1007/978-3-319-61425-0_32

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…They have allowed notable results in the field of behavioral modeling, particularly in knowledge tracing. Some authors have used Bayesian networks for modeling the knowledge acquisition process (Tato et al, 2017a;Kantharaju et al, 2018;Zhang and Yao, 2018). Several other authors have recently focused on knowledge tracing using deep learning architectures including recurrent neural networks (RNNs) and long and short-term memories (LSTMs) (Piech et al, 2015;Wang et al, 2017;Zhang et al, 2017b;Montero et al, 2018;Song et al, 2021;Xing et al, 2021) and other have focused on user modeling using the same techniques with some improvements (Yu et al, 2019;Nur, 2021;Yuan et al, 2021).…”

Section: Machine Learning and Deep Learning/ai Approaches For User Mo...mentioning

confidence: 99%

“…In the educational domain, both a priori and a posteriori knowledge are generally available. For example, in Tato et al (2017a), expert knowledge was used via a Bayesian network, designed entirely by domain experts to predict learners' logical reasoning skills. This knowledge is available in most cases in the form of written material such as books, experimental results, etc.…”

Section: A Priori and A Posteriori Knowledgementioning

confidence: 99%

“…The cognitive state is generated from the learner behavior during his interactions with the system, that is, it is inferred by the system from the information available. It is supported by a bayesian network (Tato et al, 2017a) based on domain knowledge, where influence relationships between nodes (reasoning skills) as well as prior probabilities are provided by the experts. Some nodes are directly connected to the reasoning activities (items).…”

Section: Logic-musementioning

confidence: 99%

See 2 more Smart Citations

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Tato

Nkambou

2022

Front. Artif. Intell.

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Machine learning models are biased toward data seen during the training steps. The models will tend to give good results in classes where there are many examples and poor results in those with few examples. This problem generally occurs when the classes to predict are imbalanced and this is frequent in educational data where for example, there are skills that are very difficult or very easy to master. There will be less data on students that correctly answered questions related to difficult skills and who incorrectly answered those related to skills easy to master. In this paper, we tackled this problem by proposing a hybrid architecture combining Deep Neural Network architectures— especially Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)—with expert knowledge for user modeling. The proposed solution uses attention mechanism to infuse expert knowledge into the Deep Neural Network. It has been tested in two contexts: knowledge tracing in an intelligent tutoring system (ITS) called Logic-Muse and prediction of socio-moral reasoning in a serious game called MorALERT. The proposed solution is compared to state-of-the-art machine learning solutions and experiments show that the resulting model can accurately predict the current student's knowledge state (in Logic-Muse) and thus enable an accurate personalization of the learning process. Other experiments show that the model can also be used to predict the level of socio-moral reasoning skills (in MorALERT). Our findings suggest the need for hybrid neural networks that integrate prior expert knowledge (especially when it is necessary to compensate for the strong dependency—of deep learning methods—on data size or the possible unbalanced datasets). Many domains can benefit from such an approach to building models that allow generalization even when there are small training data.

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Section: Machine Learning and Deep Learning/ai Approaches For User Mo...mentioning

confidence: 99%

Section: A Priori and A Posteriori Knowledgementioning

confidence: 99%

Section: Logic-musementioning

confidence: 99%

See 1 more Smart Citation

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Tato

Nkambou

2022

Front. Artif. Intell.

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“…Some works tend to hand-code user features, based on experts' theory (Nkambou et al 2016) while others explicitly define rules on how different behaviors can appear (Ramesh et al 2013). In those conditions, Bayesian networks are sometimes used to represent the current user state (Tato et al 2017a).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Towards a multi-modal Deep Learning Architecture for User Modeling

Tato

Nkambou

2023

FLAIRS

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Deep learning has succeeded in various applications, including image classification and feature learning. However, there needs to be more research on its use in Intelligent Tutoring Systems or Serious Games, particularly in modeling user behavior during learning or gaming sessions using multi-modal data. Creating an effective user model is crucial for developing a highly adaptive system. To achieve this, it is necessary to consider all available data sources to inform the user’s current state. This study proposes a user-sensitive deep multi-modal architecture that leverages deep learning and user data to extract a rich latent representation of the user. The architecture combines a Long Short-Term Memory, a Convolutional Neural Network, and multiple Deep Neu-ral Networks to handle the multi-modality of data. The resulting model was evaluated on a public multi-modal dataset, achieving better results than state-of-the-art algorithms for a similar task: opinion polarity detection. These findings suggest that the latent representation learned from the data is useful in discriminating behaviors. This proposed solution can be applied in various contexts where user modeling using multi-modal data is critical for improving the user experience.

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