Architecture for an Intelligent Tutoring System that Considers Learning Styles

Morales-Rodríguez, María Lucila; Ramírez-Saldivar, José Apolinar; Hernández-Ramírez, Arturo; Sánchez-Solís, J. Patricia; Martínez-Flores, José Antonio

doi:10.13053/rcs-47-1-4

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Within the ILE you can find instructors that serve as a guide to the student, which can be represented by virtual agents with a specific architecture and function (e.g. [6,7]).…”

Section: Virtual Agents In Intelligent Learning Environmentsmentioning

confidence: 99%

Analysis of Speech Acts for the Design of a Corpus of Phrases used in an Intelligent Learning Environment

Delgado-Hernández¹,

Morales-Rodríguez²,

Rangel-Valdez³

et al. 2019

RCS

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Artificial intelligence is a fundamental part of our life today. An example is the simulators integrated by intelligent virtual characters that serve as instructors in interactive environments where no matter the place and time, people can take a class and even train in a video game. These virtual agents require a corpus of phrases to have a dialogue similar to that of a human being. This article describes an investigation of the types of speech acts immersed in an interactive dialogue. They describe the most relevant characteristics for an educational environment, such as the type of phrase, the function it performs, its meaning, etc., thus discarding those that do not belong to this environment. The main objective is to develop a specific corpus of phrases for an intelligent virtual agent belonging to a serious game for learning purposes used as an interactive learning environment.

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“…Within the ILE you can find instructors that serve as a guide to the student, which can be represented by virtual agents with a specific architecture and function (e.g. [6,7]).…”

Section: Virtual Agents In Intelligent Learning Environmentsmentioning

confidence: 99%

Analysis of Speech Acts for the Design of a Corpus of Phrases used in an Intelligent Learning Environment

Delgado-Hernández¹,

Morales-Rodríguez²,

Rangel-Valdez³

et al. 2019

RCS

Self Cite

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“…Ueno et al in [5] describes an agent that acquires domain knowledge related to the content from a learning history log database in a learning community and automatically generates motivational messages to the learner. Other online platforms as proposed by Lucila et al in [6] take serious consideration of personalized and self-paced interaction. They take into a consideration the students' learning style and select the suitable teaching strategies that can effectively support the student's learning behavior.…”

Section: Introductionmentioning

confidence: 99%

I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment

Rodzman

Bakar

Choo

et al. 2020

IJEECS

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Intelligent systems are created to automate decision making process that is similar to human intelligence. Incorporating intelligent component has achieved promising results in many applications, including in education. Intelligence modules in a tutoring system would bring the application and its capability closer to a human's ability to serve its human users and to solve problems. However, the majority of the online learning provided in the literature review especially in Malaysia, normally only provide the lecture notes, assignments and tests and rarely suggest or give feedbacks on what the students should study or do next in order to fully understand the subjects. Hence, the researchers propose an online learning environment called Intelligent Online Assessment and Revision (I-OnAR). It facilitates the learning process at multiple learning phases such as test creation, materials revision, feedback for improvement and performance analysis. These components are incorporated into the tutoring system to assist self-pace learning at anytime and anywhere. The intelligent agent uses a Rule-based Machine Learning method for the adaptive capabilities such as automated test creation and feedbacks for improvement. The system has been tested on a group of students and found to be useful to support learning process. The results have shown that 60% of the subjects’ performance have improved with the help of the system. The students were given feedbacks on the topic they did poorly as well as how to improve their performance. This proves that the Intelligent Online Assessment and revision (I-OnAR) can be a useful tool to help online students intelligently, systematically and efficiently. For the future works, the researchers would like to apply the usage of other techniques such as Fuzzy Logic to strengthen the analysis and decision of the current system.

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