MLASL: ML-Assisted Student Learning

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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
Date created May 5, 2020

Creators/Contributors

Author Celis, Diego
Degree granting institution Stanford University, Department of Computer Science
Primary advisor Piech, Chris
Advisor Kochenderfer, Mykel

Subjects

Subject machine learning
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

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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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