Designing Pedagogic Conversational Agents through Data Analysis

Pedagogical Conversational Agents are systems or programs that represent a resource and a means of learning for students, making the teaching and learning process more enjoyable. The aim is to improve the teaching-learning process. Currently, there are many agents being implemented in multiple knowl...

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Main Authors: Diana Pérez-Marín, Silvia Tamayo-Moreno
Format: Article
Language:English
Published: Instituto Tecnológico Metropolitano 2020-01-01
Series:TecnoLógicas
Subjects:
Online Access:https://revistas.itm.edu.co/index.php/tecnologicas/article/view/1455
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spelling doaj-aee40a0947d94e44bffaf0b0687c2d1f2020-11-25T02:57:29ZengInstituto Tecnológico MetropolitanoTecnoLógicas0123-77992256-53372020-01-01234724325610.22430/22565337.14551455Designing Pedagogic Conversational Agents through Data AnalysisDiana Pérez-Marín0Silvia Tamayo-Moreno1Universidad Rey Juan Carlos, EspañaStratio, EspañaPedagogical Conversational Agents are systems or programs that represent a resource and a means of learning for students, making the teaching and learning process more enjoyable. The aim is to improve the teaching-learning process. Currently, there are many agents being implemented in multiple knowledge domains. In our previous work, a methodology for designing agents was published, the result of which was Agent Dr. Roland, the first conversational agent for Early Childhood Education. In this paper, we propose the use of Data Analytics techniques to improve the design of the agent. Two new techniques are applied: KDDIAE, application of (Knowledge Discovery in Databases) to the Data of the Interaction between Agents and Students – Estudiantes in Spanish, and BIDAE (use of Data Analytics to obtain information of agents and students). The use of KDDIAE and BIDAE proves the existence of a fruitful relationship between learning analytics and learning design. Some samples of rules related to learning analytics and design are the following: (Learning Analytics) Children who initially do not know how to solve the exercise, after receiving help, are able to understand  and solve it à (Learning Design) An agent for small children should be able to provide help. In addition, help should be entertaining and tailored to their characteristics because it is a resource that children actually use; or (Learning Analytics) Younger children use more voice interaction à (Learning Design) An agent interface for young children must incorporate voice commands. A complete list of rules related to learning analytics and design is provided for any researcher interested in PCA design. 72 children were able to use the new Dr. Roland after applying the learning analytics-design rules. They reported a 100 % satisfaction as they all enjoyed interacting with the agent.https://revistas.itm.edu.co/index.php/tecnologicas/article/view/1455pedagogic conversational agentlearning analyticsknowledge discovery in databaseslearning design
collection DOAJ
language English
format Article
sources DOAJ
author Diana Pérez-Marín
Silvia Tamayo-Moreno
spellingShingle Diana Pérez-Marín
Silvia Tamayo-Moreno
Designing Pedagogic Conversational Agents through Data Analysis
TecnoLógicas
pedagogic conversational agent
learning analytics
knowledge discovery in databases
learning design
author_facet Diana Pérez-Marín
Silvia Tamayo-Moreno
author_sort Diana Pérez-Marín
title Designing Pedagogic Conversational Agents through Data Analysis
title_short Designing Pedagogic Conversational Agents through Data Analysis
title_full Designing Pedagogic Conversational Agents through Data Analysis
title_fullStr Designing Pedagogic Conversational Agents through Data Analysis
title_full_unstemmed Designing Pedagogic Conversational Agents through Data Analysis
title_sort designing pedagogic conversational agents through data analysis
publisher Instituto Tecnológico Metropolitano
series TecnoLógicas
issn 0123-7799
2256-5337
publishDate 2020-01-01
description Pedagogical Conversational Agents are systems or programs that represent a resource and a means of learning for students, making the teaching and learning process more enjoyable. The aim is to improve the teaching-learning process. Currently, there are many agents being implemented in multiple knowledge domains. In our previous work, a methodology for designing agents was published, the result of which was Agent Dr. Roland, the first conversational agent for Early Childhood Education. In this paper, we propose the use of Data Analytics techniques to improve the design of the agent. Two new techniques are applied: KDDIAE, application of (Knowledge Discovery in Databases) to the Data of the Interaction between Agents and Students – Estudiantes in Spanish, and BIDAE (use of Data Analytics to obtain information of agents and students). The use of KDDIAE and BIDAE proves the existence of a fruitful relationship between learning analytics and learning design. Some samples of rules related to learning analytics and design are the following: (Learning Analytics) Children who initially do not know how to solve the exercise, after receiving help, are able to understand  and solve it à (Learning Design) An agent for small children should be able to provide help. In addition, help should be entertaining and tailored to their characteristics because it is a resource that children actually use; or (Learning Analytics) Younger children use more voice interaction à (Learning Design) An agent interface for young children must incorporate voice commands. A complete list of rules related to learning analytics and design is provided for any researcher interested in PCA design. 72 children were able to use the new Dr. Roland after applying the learning analytics-design rules. They reported a 100 % satisfaction as they all enjoyed interacting with the agent.
topic pedagogic conversational agent
learning analytics
knowledge discovery in databases
learning design
url https://revistas.itm.edu.co/index.php/tecnologicas/article/view/1455
work_keys_str_mv AT dianaperezmarin designingpedagogicconversationalagentsthroughdataanalysis
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