Temporal learner modelling through integration of neural and symbolic architectures

Hooshyar, Danial

doi:10.1007/s10639-023-12334-y

Cited by 4 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This indicates the limitations of using traditional deep learning models without additional techniques or knowledge incorporation to address the challenges posed by education data. This finding confirms the argument put forward by Venugopal et al [83], Hooshyar and Yang [14], and Hooshyar [22], in that augmenting ANNs with symbolic knowledge can regularize them, improving their generalizability by achieving a higher accuracy and scalability by enabling them to learn from smaller datasets.…”

Section: Discussionsupporting

confidence: 90%

“…Moreover, Shakya et al [69] proposed a neural-symbolic approach combining the semantics of symbolic models with recurrent neural networks like LSTM, demonstrating superior prediction accuracy for KDD EDM challenge datasets compared to HMMs and pure LSTM methods, with a focus on a smaller training data fraction. Hooshyar [22] introduced a novel neural-symbolic AI approach for learner modeling across time. This approach tracks learners' knowledge in a temporal manner by combining probabilistic graphical models (i.e., dynamic Bayesian networks) with unsupervised neural networks enhanced with educational knowledge.…”

Section: Neural-symbolic Ai For Educationmentioning

confidence: 99%

“…To ensure fairness in AI applications in education, it is crucial to avoid algorithmic bias and prevent algorithms from being tuned to favor a majority group solely to achieve high performance accuracy [1,21]. While recent research provides evidence that incorporating educational knowledge can enhance deep neural networks' understanding of training examples and reduce biases, such applications in education are extremely limited [22].…”

Section: Introductionmentioning

confidence: 99%

“…One prospective means of addressing these challenges is a recently emerging paradigm in the AI research community that is called neural-symbolic AI or the third wave of AI [32,33]. By combining symbolic knowledge representation with deep neural networks, neural-symbolic AI offers a potential solution to enhance learner modeling in adaptive educational environments (e.g., [14,22,33,34]). This integration serves to leverage the interpretability and explanatory power of symbolic models while harnessing the scalability and predictive performance of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education

Hooshyar,

Azevedo,

Yang

2024

MAKE

Self Cite

View full text Add to dashboard Cite

Artificial neural networks (ANNs) have proven to be among the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due to challenges such as the following: (i) the difficulties in incorporating symbolic educational knowledge (e.g., causal relationships and practitioners’ knowledge) in their development, (ii) a propensity to learn and reflect biases, and (iii) a lack of interpretability. As education is classified as a ‘high-risk’ domain under recent regulatory frameworks like the EU AI Act—highlighting its influence on individual futures and discrimination risks—integrating educational insights into ANNs is essential. This ensures that AI applications adhere to essential educational restrictions and provide interpretable predictions. This research introduces NSAI, a neural-symbolic AI approach that integrates neural networks with knowledge representation and symbolic reasoning. It injects and extracts educational knowledge into and from deep neural networks to model learners’ computational thinking, aiming to enhance personalized learning and develop computational thinking skills. Our findings revealed that the NSAI approach demonstrates better generalizability compared to deep neural networks trained on both original training data and data enriched by SMOTE and autoencoder methods. More importantly, we found that, unlike traditional deep neural networks, which mainly relied on spurious correlations in their predictions, the NSAI approach prioritizes the development of robust representations that accurately capture causal relationships between inputs and outputs. This focus significantly reduces the reinforcement of biases and prevents misleading correlations in the models. Furthermore, our research showed that the NSAI approach enables the extraction of rules from the trained network, facilitating interpretation and reasoning during the path to predictions, as well as refining the initial educational knowledge. These findings imply that neural-symbolic AI not only overcomes the limitations of ANNs in education but also holds broader potential for transforming educational practices and outcomes through trustworthy and interpretable applications.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Neural-symbolic Ai For Educationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%