Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums

Yu, Jialin; Alrajhi, Laila; Harit, Anoushka; Sun, Zhongtian; Cristea, Alexandra I.; Shi, Lei

doi:10.1007/978-3-030-80421-3_10

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Uncertainty estimation for NNs has been investigated in different domains of OR: predictive maintenance (Kraus & Feuerriegel, 2019), recommender systems (Nahta et al, 2021), finance (Ghahtarani, 2021), stress-level prediction (Oh et al, 2021), transportation (Zhang & Mahadevan, 2020;Feng et al, 2022), predictive process monitoring (Weytjens & De Weerdt, 2022), and educational data mining (Yu et al, 2021). In the available work, however, uncertainty estimates are merely monitored as an additional metric, not used in combination with a human expert such as in classification with rejection (i.e., shortcoming 1).…”

Section: Uncertainty and Neural Network In Operations Researchmentioning

confidence: 99%

Explainability through uncertainty: Trustworthy decision-making with neural networks

Thuy,

Benoit

2024

European Journal of Operational Research

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Section: Uncertainty and Neural Network In Operations Researchmentioning

confidence: 99%

Explainability through uncertainty: Trustworthy decision-making with neural networks

Thuy,

Benoit

2024

European Journal of Operational Research

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“…Historically, research on such intelligent tutors has envisioned selecting a teachable moment and influencing the learner at that moment (Shute & Psotka, 1994). Recently, improvements in AI interactions can also flag the urgency of instructional interventions with promise for automated tutors (Yu et al, 2021). Other pedagogical interventions support study success (Ifenthaler et al, 2019) and provide data and evidence from virtual practicums, games and simulations for learning (Gibson & Jakl, 2015).…”

Section: Ai As a Partner In Individual Exploration Learning And Expre...mentioning

confidence: 99%

Learning theories for artificial intelligence promoting learning processes

Gibson

Kovanović

Dexter

et al. 2023

Brit J Educational Tech

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This paper discusses a three‐level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities. Practitioner notesWhat is already known about this topic Numerous learning theories exist with significant cross‐over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning. Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified. Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices. What this paper adds A three‐level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education. A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels. The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for practice and policy Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems. Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

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“…The posterior distribution can then be approximated via multiple runs of the same model with Dropout applied, using the same input data. This practical tool for epistemic uncertainty estimation has been successfully used on a wide range of applications, such as semantic segmentation [23], language modelling [24], diabetic retinopathy [25], transport data analysis [26], magnetic resonance imaging (MRI) segmentation [27], text classification [28] and learning analytics [29].…”

Section: Related Workmentioning

confidence: 99%

Efficient Uncertainty Quantification for Multilabel Text Classification

Cristea

Harit

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty quantification methods. Existing approaches to uncertainty quantification in natural language processing (NLP) take a Bayesian Deep Learning approach. However, the latter is known to not be computationally efficient in testing time, thus hindering its applicability in real-life scenarios. This paper proposes a new focus on the efficiency of uncertainty quantification methods, evaluating them on four multi-label text classification tasks. Our novel methods of representing epistemic and aleatoric uncertainties enable efficient uncertainty quantification (around 13 to 45 times faster than existing approaches, depending on architecture) with posterior analysis in the (approximated) latent-and data space. We conduct extensive experiments and studies on diverse neural network architectures (LSTM, CNN and Transformer) to analyse their power. Our results prove the benefits of explicitly modelling uncertainty in neural networks.

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Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums

Cited by 7 publications

References 22 publications

Explainability through uncertainty: Trustworthy decision-making with neural networks

Explainability through uncertainty: Trustworthy decision-making with neural networks

Learning theories for artificial intelligence promoting learning processes

Efficient Uncertainty Quantification for Multilabel Text Classification

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