TOM: An intelligent tutor for the construction of knowledge represented in concept maps

Boguski, Rodrigo Ruy; Cury, Davidson; Gava, Tânia Barbosa Salles

doi:10.1109/fie43999.2019.9028615

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…In addition, concepts, similar to vocabulary, are important factors that affect the outcomes of reading comprehension, and they can be learned or retained by connecting the new concept with learned concepts to form concept maps. TOM, an intelligent tutor, is developed to mine the concepts from the text and build reference concept maps automatically or semiautomatically (Boguski et al, 2019 ).…”

Section: Literature Review: Reading Models and Computer Technology In...mentioning

confidence: 99%

Reading bots: The implication of deep learning on guided reading

Huang

Dou²,

Zhang³

2023

Front. Psychol.

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This study introduces the application of deep-learning technologies in automatically generating guidance for independent reading. The study explores and demonstrates how to incorporate the latest advances in deep-learning-based natural language processing technologies in the three reading stages, namely, the pre-reading stage, the while-reading stage, and the post-reading stage. As a result, the novel design and implementation of a prototype system based on deep learning technologies are presented. This system includes connections to prior knowledge with knowledge graphs and summary-based question generation, the breakdown of complex sentences with text simplification, and the auto-grading of readers' writing regarding their comprehension of the reading materials. Experiments on word sense disambiguation, named entity recognition and question generation with real-world materials in the prototype system show that the selected deep learning models on these tasks obtain favorable results, but there are still errors to be overcome before their direct usage in real-world applications. Based on the experiment results and the reported performance of the deep learning models on reading-related tasks, the study reveals the challenges and limitations of deep learning technologies, such as inadequate performance, domain transfer issues, and low explain ability, for future improvement.

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Section: Literature Review: Reading Models and Computer Technology In...mentioning

confidence: 99%

Reading bots: The implication of deep learning on guided reading

Huang

Dou²,

Zhang³

2023

Front. Psychol.

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“…The evolution of technology has made the integration of ICTs in higher education classes an important component with which to enhance a student's learning process. Some examples of ICTs are MOOCs [32], Virtual and Experiential environments [33], Educational Robotics [34,35], Web-based learning [36], Maker spaces [37], Mixed reality laboratories [38], M-learning [39], intelligent tutors [40], data science [41], machine learning [42], and cloud technologies [43]. For the case study presented in this article, the key technological player is Cyber-Physical Systems, which will be further discussed in Section 1.3.…”

Section: Education 40mentioning

confidence: 99%

A Challenge-Based Learning Experience in Industrial Engineering in the Framework of Education 4.0

et al. 2021

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Current tendencies of product, project and services development focus on a higher consideration of the User Experience (UX). Therefore, traditional training and teaching methodologies need to adapt to prepare the students to develop strategies for problem solving for their professional education. Such needs have risen and interest in tendencies such as education and Industry 4.0 has grown. This paper presents and analyzes the process and results of a teaching implementation methodology based on Challenge-Based Learning (CBL). The paper describes the process followed, explaining the methodology precedents that led to the final implementation case. It also mentions previous experiments on product analysis and home automation developments that are linked to implementation of the technology. This case’s implementation, analysis and experimentation integrated the use of Emotional Domotics (ED) Tools for the UX analysis, to grant feedback and compare the students’ results with the bio-metrical and emotional computational analysis. The methodology, described through this document, allowed the students to better understand and experience some of the implications of an interconnected system with instant information feedback. This allowed them to better grasp part of the impact that the tendency towards the Internet of Things (IoT) is currently having, and the impact of the improvement proposals from the students.

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“…Como trabalhos futuros, os próximos passos são aprimorar a eficácia do arcabouço, fazendo validações semânticas mais amplas e promover a utilização de bases ontológicas para aprimorar a identificação de relações parte-todo no âmbito das implicações locais. Propomos também, integrar o arcabouço, funcionando como uma API (Application Programming Interface), em um ambiente de aprendizagem assistida por tutores inteligentes sobre mapas conceituais [Boguski et al 2019], a fim de que a identificação automática das implicações em um mapa conceitual produzido por um aluno possa permitir que um tutor interaja com ele, em tempo real, com base no conhecimento que ele foi capaz de expor naquele instante, para obter maiores indícios de aprendizado, além da confirmação das implicações obtidas. Como consequência, o tutor pode sugerir novas ideias, novos conceitos, novos relacionamentos e levantar questionamentos ao aluno a partir daquilo que ele está representando no momento da construção do seu mapa.…”

Section: Considerações Finaisunclassified

Utilizando análise semântica para descobrir implicações significantes em mapas conceituais

Moreira

Boguski

Cury

2021

Anais Do XXXII Simpósio Brasileiro De Informática Na Educação (SBIE 2021)

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Este artigo apresenta um arcabouço capaz de efetuar análise semântica de mapas conceituais, por intermédio das implicações significantes definidas por Piaget (local, sistêmica e estrutural). O objetivo é fornecer uma dimensão semântica mais profunda na análise das implicações significantes e extrair informações mais precisas sobre a representação do conhecimento e a compreensão de um indivíduo a partir da representação de seu mapa. O arcabouço utiliza técnicas de processamento de linguagem natural para previsão e casamento de palavras a partir do cálculo de similaridade semântica utilizando redes neurais. A fim de validar sua eficácia, realizamos experimentos em sala de aula, obtendo resultados bastante satisfatórios.

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TOM: An intelligent tutor for the construction of knowledge represented in concept maps

Cited by 9 publications

References 5 publications

Reading bots: The implication of deep learning on guided reading

Reading bots: The implication of deep learning on guided reading

A Challenge-Based Learning Experience in Industrial Engineering in the Framework of Education 4.0

Utilizando análise semântica para descobrir implicações significantes em mapas conceituais

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