MLASL: ML-Assisted Student Learning
Abstract/Contents
- Abstract
With the invention of machine learning methods to learn any arbitrary function using the universal approximation theorem, there has been much curiosity around the application of such learning power to the human process of learning.
In this work, the main contributions are...
- a framework to completely define the student learning problem,
- a machine learning model that uses said framework to aid student learning,
- an in-depth analysis of the framework's challenges and potential solutions,
- discussion and development of data-efficient training methods, and
- discussion and development of practical applications of this theory.This paper calls such a framework ML-Assisted Student Learning (MLASL). Future work entails further research into the translation of information between vector spaces with large dimensionality differences, particularly in the large-to-small use case.
Description
Type of resource | text |
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Date created | May 5, 2020 |
Creators/Contributors
Author | Celis, Diego |
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Degree granting institution | Stanford University, Department of Computer Science |
Primary advisor | Piech, Chris |
Advisor | Kochenderfer, Mykel |
Subjects
Subject | machine learning |
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Subject | theory |
Subject | student learning |
Subject | human learning |
Subject | department of computer science |
Subject | stanford university |
Subject | artificial intelligence |
Subject | practical application |
Subject | honors thesis |
Genre | Thesis |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
- Celis, Diego. (2020). MLASL: ML-Assisted Student Learning. Stanford Digital Repository. Available at: https://purl.stanford.edu/dn650wy9737
Collection
Undergraduate Theses, School of Engineering
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- Contact
- dcelis@ai.stanford.edu
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