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2023 | OriginalPaper | Chapter

Generation of Course Prerequisites and Learning Outcomes Using Machine Learning Methods

Authors : Polina Shnaider, Anastasiia Chernysheva, Maksim Khlopotov, Carina Babayants

Published in: Artificial Intelligence in Education Technologies: New Development and Innovative Practices

Publisher: Springer Nature Singapore

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Abstract

The paper addresses the problem of academic course prerequisites and learning outcomes generation in learning analytics systems. For prerequisites generation, collaborative filtering, i.e., ALS algorithm for Matrix Factorization, is used. For learning outcomes generation, the study discusses an approach based on Computational Linguistics data extraction methods and content-based filtering to recommend potential outcomes. The recommendation mechanisms are designed to be implemented in the Educational Program Maker service for working with education process elements. The study's primary goal is to simplify, formalize and speed up the course development process. Implementation of the approach will make it possible to build unambiguous interdisciplinary connections, identify the closest intersections of the curriculum courses, and build individual learning pathways.

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previous chapter Identification and Hierarchical Analysis of Risk Factors in Primary and Secondary Schools: A Novel GT-DEMATEL-ISM Approach
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Metadata
Title
Generation of Course Prerequisites and Learning Outcomes Using Machine Learning Methods
Authors
Polina Shnaider
Anastasiia Chernysheva
Maksim Khlopotov
Carina Babayants
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Artificial Intelligence in Education Technologies: New Development and Innovative Practices

Print ISBN: 978-981-19-8039-8

Electronic ISBN: 978-981-19-8040-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-8040-4_3

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