About this paper

Appears in:
Pages: 10986-10993
Publication year: 2019
ISBN: 978-84-09-14755-7
ISSN: 2340-1095
doi: 10.21125/iceri.2019.2703

Conference name: 12th annual International Conference of Education, Research and Innovation
Dates: 11-13 November, 2019
Location: Seville, Spain

CONTENT RECOMMENDER FOR THE COLLECTIVE TEXT EDITOR: A CASE STUDY

J. Mayumi Akazaki1, C. Zank1, A. Lorandi Macedo1, L. Rocha Machado2, K.K. Araújo da Silva1, P. Alejandra Behar1

1Universidade Federal do Rio Grande do Sul (BRAZIL)
2Federal University of Rio Grande do Sul / Federal University of Santa Catarina (BRAZIL)
The volume of authored material available on the Internet, especially collective texts, has been increasing at an exorbitant rate. In this sense, there is a growing need to provide appropriate content according to the interest of each user. To remedy these difficulties, a content recommendation tool for Digital Collective Writing (DCW), called Content Recommender for the Collective Text Editor (CRCTE), was implemented in 2017. DCW is understood as a textual production performed collectively and remotely through a Collective Text Editor (CTE). The editor has tools that support interaction, communication and collective work activities to support collaborative learning. CRCTE is a content advisor that enables users to perform inter-individual exchanges and build knowledge. Based on the textual production of each user, the recommender indicates materials in a personalized manner and in different formats, such as texts, images and videos. In this context, CRCTE aims to expand the possibilities of search in one place, creating conditions for better quality of written production. The main objective of this research is to analyze potentialities and limitations in content recommendation for digital collective writing. The method was a qualitative case study approach, and the data collection instrument was a questionnaire with subjective and objective questions. CRCTE was applied in a discipline in a Postgraduate course at a Brazilian University, with a sample of 12 participants, 2 male and 10 female. The results showed that the contents recommended were updated, contributing to qualifying the writing process through the offering of bibliographic references that served as research sources. The data also pointed out that there was an increase in the number of materials that were consulted for digital writing. However, the participants mentioned that the CRCTE was not sufficient to help the development of a theoretical framework of writing, as it presented few materials from diverse sources. Thus, it is possible to realize that further studies should be performed, as well as an evaluation of the recommendations made in the development of DCW.
@InProceedings{MAYUMIAKAZAKI2019CON,
author = {Mayumi Akazaki, J. and Zank, C. and Lorandi Macedo, A. and Rocha Machado, L. and Ara{\'{u}}jo da Silva, K.K. and Alejandra Behar, P.},
title = {CONTENT RECOMMENDER FOR THE COLLECTIVE TEXT EDITOR: A CASE STUDY},
series = {12th annual International Conference of Education, Research and Innovation},
booktitle = {ICERI2019 Proceedings},
isbn = {978-84-09-14755-7},
issn = {2340-1095},
doi = {10.21125/iceri.2019.2703},
url = {http://dx.doi.org/10.21125/iceri.2019.2703},
publisher = {IATED},
location = {Seville, Spain},
month = {11-13 November, 2019},
year = {2019},
pages = {10986-10993}}
TY - CONF
AU - J. Mayumi Akazaki AU - C. Zank AU - A. Lorandi Macedo AU - L. Rocha Machado AU - K.K. Araújo da Silva AU - P. Alejandra Behar
TI - CONTENT RECOMMENDER FOR THE COLLECTIVE TEXT EDITOR: A CASE STUDY
SN - 978-84-09-14755-7/2340-1095
DO - 10.21125/iceri.2019.2703
PY - 2019
Y1 - 11-13 November, 2019
CI - Seville, Spain
JO - 12th annual International Conference of Education, Research and Innovation
JA - ICERI2019 Proceedings
SP - 10986
EP - 10993
ER -
J. Mayumi Akazaki, C. Zank, A. Lorandi Macedo, L. Rocha Machado, K.K. Araújo da Silva, P. Alejandra Behar (2019) CONTENT RECOMMENDER FOR THE COLLECTIVE TEXT EDITOR: A CASE STUDY, ICERI2019 Proceedings, pp. 10986-10993.
User:
Pass: