A Machine-Learning Based Approach to Validating Learning Materials

Frederick Ako-Nai, Enrique de la Cal Marin, Qing Tan

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

Abstract

In this paper, we propose a Machine Learning-based approach to validate suggested learning materials. Learning material validation is an essential part of the learning process, ensuring that learners have access to relevant and accurate information. However, the process of manual validation can be time-consuming and may not be scalable. Traditional learning contents are often only updated or changed in the yearly course revisions. This can be presented with some challenges, especially to courses on emerging subjects and catering to diversified learners, which includes the ability to provide adaptive and updated learning contents to the learners, and the opportunity to continually incorporate feedback. We present a solution and framework that utilizes machine learning algorithms to validate learning materials in an open learning content creation platform. Our approach involves pre-processing the data using Natural Language Processing techniques, creating vectors using TF-IDF and training a Machine Learning model to classify the subject of the learning material. We then calculate the similarity with existing materials for the given course to make sure there is not an existing mate-rial with same content and the new material will add new value. Using an augmented TF-IDF score, we check if the suggested learning materials satisfies the key phrases for the course. We evaluate our approach by comparing the Machine-Learning based approach to manual validation. Not only does the machine-learning based approach reduce the time and effort needed for validation, but it also achieves high accuracy in detecting duplicates and similarity matches.

Original languageEnglish
Title of host publicationInternational Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023) - Proceedings
EditorsPablo García Bringas, Hilde Pérez García, Francisco Javier Martínez de Pisón, Francisco Martínez Álvarez, Alicia Troncoso Lora, Álvaro Herrero, José Luis Calvo Rolle, Héctor Quintián, Emilio Corchado
Pages306-315
Number of pages10
DOIs
Publication statusPublished - 2023
Event16th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2023 and 14th International Conference on EUropean Transnational Education, ICEUTE 2023 - Salamanca, Spain
Duration: 5 Sep. 20237 Sep. 2023

Publication series

NameLecture Notes in Networks and Systems
Volume748 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference16th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2023 and 14th International Conference on EUropean Transnational Education, ICEUTE 2023
Country/TerritorySpain
CitySalamanca
Period5/09/237/09/23

Keywords

  • Learning materials
  • Machine Learning
  • Open learning
  • TF-IDF

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