Systematic Review of Recommendation Systems for Course Selection

Algarni, Shrooq; Sheldon, Frederick T.

doi:10.3390/make5020033

Cited by 5 publications

(2 citation statements)

References 56 publications

(80 reference statements)

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“…The predictive capabilities of ANNs extend to detecting undesirable student behavior, with contributions from Fei and Yeung [48], Teruel and Alemany [49], and Whitehill et al [50]. Furthermore, ANNs have been instrumental in generating recommendations, as evidenced by the studies of Abhinav et al [51], Algarni and Sheldon [11], Bhanuse and Mal [7], and Wong [52]. For instance, Abhinav et al [51] introduced a recommendation system that leverages ANNs for content-based filtering, alongside collaborative filtering techniques, to personalize learning opportunities.…”

Section: Neural Network In Educationmentioning

confidence: 99%

“…The key distinction lies in the explicit representation of knowledge in symbolic AI versus the more implicit, data-driven approach of sub-symbolic methods. As a result, sub-symbolic methods like deep neural networks, which belong to the family of artificial neural networks (ANNs), have gained considerable popularity in various educational tasks, including learner modeling (e.g., [7][8][9][10][11]). Despite their success and popularity, they face three primary challenges that limit their educational value.…”

Section: Introductionmentioning

confidence: 99%

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

Hooshyar,

Azevedo,

Yang

2024

MAKE

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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.

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