Towards AI-powered personalization in MOOC learning

Yu, Han; Chen, Miao; Leung, Cyril; White, Timothy John

doi:10.1038/s41539-017-0016-3

Cited by 93 publications

(64 citation statements)

References 36 publications

(29 reference statements)

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“…The transition has begun, but it will call for deeper trans-disciplinary research and a substantial technological innovation in a variety of research areas. Education will also play a key role in changing lifestyle habits and the way health is perceived, communicated, and delivered [82]. From a clinical standpoint, AI will support a plethora of different tasks from medical check up to personalised intervention strategies to contrast ripple effects or to promote healthy habits.…”

Section: Discussionmentioning

confidence: 99%

Graph representation forecasting of patient’s medical conditions: towards a digital twin

Barbiero

Viñas

Lió

2020

Preprint

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Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalised, systemic and precise treatment plans to patients. The aim of this work is to present how the integration of machine learning approaches with mechanistic computational modelling could yield a reliable infrastructure to run probabilistic simulations where the entire organism is considered as a whole. Methods: We propose a general framework that composes advanced AI approaches and integrates mathematical modelling in order to provide a panoramic view over current and future physiological conditions. The proposed architecture is based on a graph neural network (GNNs) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GANs) providing a proof of concept of transcriptomic integrability. Results: We show the results of the investigation of pathological effects of overexpression of ACE2 across different signalling pathways in multiple tissues on cardiovascular functions. We provide a proof of concept of integrating a large set of composable clinical models using molecular data to drive local and global clinical parameters and derive future trajectories representing the evolution of the physiological state of the patient. Significance: We argue that the graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modelling with AI. We believe that this work represents a step forward towards a healthcare digital twin.

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Section: Discussionmentioning

confidence: 99%

Graph representation forecasting of patient’s medical conditions: towards a digital twin

Barbiero

Viñas

Lió

2020

Preprint

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“…The original data is exported from the student's profile. Individual educational trajectories are built, taking into account information about the graduates of the course [13].…”

Section: Related Workmentioning

confidence: 99%

“…"Instrumentation Engineering, Electronics and Telecommunications -2018" Proceedings of the IV International Forum (Izhevsk, Russia, December,[12][13][14] 2018) …”

mentioning

confidence: 99%

Implementation of genetic algorithm for forming of individual educational trajectories for listeners of online courses

Parfenov¹,

Zaporozhko²

2018

Instrumentation Engineering, Electronics and Telecommunications

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The widespread use of massive open online courses (MOOCs) ensures the further globalization of open education and the sustainable development of a single modern digital educational environment. However, it is certain that in the context of the implementation of mass education, the maximum consideration of the individual characteristics of each student is an urgent necessity. In this regard, the construction of an individual educational trajectory of the MOOC student becomes particularly important. Achieving this goal is quite feasible when training students on individual learning routes. In the present work, a genetic algorithm is proposed that allows you to form an optimal learning route, designed to meet the personal educational needs and individual capabilities of each MOOC listener. The results of a computational experiment and examples of individual trajectories formed on the basis of the proposed algorithm are presented.

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“…Integrating CURATE.AI‐based dynamic optimization with learning/training regimens could have broad impact toward enhancing the spectrum of digital therapeutics that are being explored for depression and anxiety, Parkinson's disease, dementia, and many others. CURATE.AI also has the potential to be applied to improving the administration of pedagogical techniques, optimizing learning trajectories and outcomes, enhancing personnel training, and advancing other areas related to cognitive performance.…”

Section: Introductionmentioning

confidence: 99%

Harnessing CURATE.AI as a Digital Therapeutics Platform by Identifying N‐of‐1 Learning Trajectory Profiles

Kee

Weiyan

Blasiak

et al. 2019

Advanced Therapeutics

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Conventional therapeutic interventions, which range from drug treatment to learning and training regimens, are often given at a fixed dose/intensity. This often leads to sub-optimal responses, or even none at all. Similarly, fixed intensity training can lead to plateaus in learning trajectories and training outcomes. This barrier will impact the field of digital therapeutics, where drug-based therapies may be complemented or replaced by learning and training platforms. A potential solution is to optimize training by identifying N-of-1 (single subject) training profiles that can then enhance learning trajectories through individualized training regimens. In this study, CURATE.AI, a mechanism-independent and indication-agnostic artificial intelligence (AI) platform, is used to identify N-of-1 learning trajectory profiles for healthy volunteers trained on the Multi-Attribute Task Battery (MATB), a flight deck simulator developed by the National Aeronautics and Space Administration and United States Air Force. By leveraging modulated MATB training intensity in a prospective study, CURATE.AI successfully develops N-of-1 learning trajectory profiles that may actionably mediate training optimization on the single-subject level, by dynamically identifying training inputs that drive the best possible scoring outcome. Therefore, CURATE.AI-guided training may serve as a powerful optimization platform for digital therapy, student learning, cognitive decline prevention, and other indications. and non-pharmacologic, often involve fixed or static magnitudes of treatment. [18][19][20][21][22][23][24][25][26] These treatments may be represented by dosage in the pharmacologic setting, or training intensities in learning-related domains. While fixed-dose drug treatment and fixed-intensity training are often used as a standard of care,

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Towards AI-powered personalization in MOOC learning

Cited by 93 publications

References 36 publications

Graph representation forecasting of patient’s medical conditions: towards a digital twin

Graph representation forecasting of patient’s medical conditions: towards a digital twin

Implementation of genetic algorithm for forming of individual educational trajectories for listeners of online courses

Harnessing CURATE.AI as a Digital Therapeutics Platform by Identifying N‐of‐1 Learning Trajectory Profiles

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