IMASHEDU: Intelligent MAshups for EDUcation - Towards a Data Mining Approach

Priscila Cedillo, Priscila Cedillo, Pablo León, Marcos Orellana

2022

Abstract

Nowadays, technological tools greatly support the work of teaching-learning tasks. In this sense, there are various sources of information from which teachers and students rely on to complement their academic activities. Content is sought on the web, significantly updated and easy to understand, generally in the form of videos. As people progress in their learning, they face terms, concepts, and topics that they are not familiar with them. However, those topics are included in the video. In this context, a complex process is generated of alternating sections of the video with other sources of information that explain the related topics and contribute to the understanding of the topic discussed. In this regard, and considering the possibility of systematically consuming information from various sources, it is necessary to build a method and an application that orchestrates the contents of these sources in a convenient, fast and automatic way, according to the person's learning. This proposal contemplates the development of a Mashup. This mashup integrates different data sources in a single graphical interface. Also, it is considered the construction of a core software solution based on text mining techniques. This solution allows extracting the textual content from videos and identifying the terms that could support the knowledge of the topic. It would significantly contribute to the fact that related topics are presented unified in the same interface. At the same time, the learning experience is greatly improved, avoiding losing the common thread of the observed video. Therefore, this article presents a process of orchestrating various data sources in a Web Mashup application. It includes videos available on YouTube channels, with other sources (e.g., Wikipedia, Pinterest) that help understand the topic better, generating hypertext references based on the generation of terms through text mining techniques. A Mathematics Learning mashup has been built to show the proposal’s feasibility.

Paper Citation

in Harvard Style

Cedillo P., León P. and Orellana M. (2022). IMASHEDU: Intelligent MAshups for EDUcation - Towards a Data Mining Approach. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-562-3, pages 383-388. DOI: 10.5220/0011087500003182

in Bibtex Style

@conference{csedu22,
author={Priscila Cedillo and Pablo León and Marcos Orellana},
title={IMASHEDU: Intelligent MAshups for EDUcation - Towards a Data Mining Approach},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2022},
pages={383-388},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011087500003182},
isbn={978-989-758-562-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - IMASHEDU: Intelligent MAshups for EDUcation - Towards a Data Mining Approach
SN - 978-989-758-562-3
AU - Cedillo P.
AU - León P.
AU - Orellana M.
PY - 2022
SP - 383
EP - 388
DO - 10.5220/0011087500003182