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Found 12 papers, 4 papers with code
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling
We argue that this work can provide a valuable starting point for a framework of interpretable AI, and as such is of relevance to the application of both knowledge-based and machine learning systems in other high-stakes contexts, beyond education.
Predicting Confusion from Eye-Tracking Data with Recurrent Neural Networks
Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from eye-tracking data.
Toward Personalized XAI: A Case Study in Intelligent Tutoring Systems
In addition, we show that students' access of the explanation and learning gains are modulated by user characteristics, providing insights toward designing personalized Explainable AI (XAI) for ITS.
A Neural Architecture for Detecting Confusion in Eye-tracking Data
Encouraged by the success of deep learning in a variety of domains, we investigate a novel application of its methods on the effectiveness of detecting user confusion in eye-tracking data.
A Framework to Counteract Suboptimal User-Behaviors in Exploratory Learning Environments: an Application to MOOCs
While there is evidence that user-adaptive support can greatly enhance the effectiveness of educational systems, designing such support for exploratory learning environments (e. g., simulations) is still challenging due to the open-ended nature of their interaction.
Cascading Convolutional Temporal Colour Constancy
We extend this architecture with different models obtained by (i) substituting the TCCNet submodules with C4, the state-of-the-art method for CCC targeting images; (ii) adding a cascading strategy to perform an iterative improvement of the estimate of the illuminant.
Evaluating the Faithfulness of Saliency-based Explanations for Deep Learning Models for Temporal Colour Constancy
The opacity of deep learning models constrains their debugging and improvement.
A Theoretical Framework for AI Models Explainability with Application in Biomedicine
EXplainable Artificial Intelligence (XAI) is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains.
GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face.
Evaluating the overall sensitivity of saliency-based explanation methods
We address the need to generate faithful explanations of "black box" Deep Learning models.
Classification of Alzheimers Disease with Deep Learning on Eye-tracking Data
Existing research has shown the potential of classifying Alzheimers Disease (AD) from eye-tracking (ET) data with classifiers that rely on task-specific engineered features.
Personalizing explanations of AI-driven hints to users' cognitive abilities: an empirical evaluation
We investigate personalizing the explanations that an Intelligent Tutoring System generates to justify the hints it provides to students to foster their learning.
