O papel das representações vetoriais de palavras na pontuação automática de ensaios: uma abordagem baseada em deep learning no contexto de learning analytics

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dc.contributor.advisorSilveira, Ismar Frango da
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/3894359521286830por
dc.contributor.authorCoelho, Orlando Bisacchi
dc.creator.Latteshttp://lattes.cnpq.br/7904738562111617por
dc.date.accessioned2020-04-29T20:31:03Z
dc.date.accessioned2020-05-28T18:08:04Z
dc.date.available2020-05-28T18:08:04Z
dc.date.issued2019-08-19
dc.description.abstractA quite recent development in Machine Learning, Deep Learning is having a huge impact in Data Analytics, virtually replacing Artificial Neural Network for classification, regression and time series forecasting tasks. The motivation for the work herein presented derives from the question: What impact if any is Deep Learning making in Learning Analytics and Educational Data Mining? In order to answer this question a systematic review of the literature was carried out. It managed to identify and document the very first applications of Deep Learning in these areas and the quite fast increase of related publications in the second half of the current decade. The review also documented the main tasks in Learning Analytics and Educational Data Mining that can benefit from this new approach, namely multimodal learning analytics and, more generally, any Learning Analytics or Educational Data Mining task that can be modeled as a supervised learning task where raw, unprocessed data is available. In order to develop a Learning Analytics application in this guise, an experiment in automated essay scoring was developed. The architecture used for the experiment was the stacked bidirectional LSTM. An innovative aspect of the experiment was to study the effect of different word embedding techniques has on the learning of the task.eng
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dc.identifier.citationCOELHO, Orlando Bisacchi. O papel das representações vetoriais de palavras na pontuação automática de ensaios: uma abordagem baseada em deep learning no contexto de learning analytics. 2019. 104 f. Tese (doutorado em Engenharia Elétrica e Computação) - Universidade Presbiteriana Mackenzie, São Paulo, 2019.por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/24299
dc.keywordslearning analyticseng
dc.keywordseducational data miningeng
dc.keywordsdeep learning.eng
dc.keywordsword embeddingseng
dc.keywordsautomated essay scoring.eng
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.subjectanalítica de aprendizagem.por
dc.subjectmineração de dados educacionaispor
dc.subjectdeep learningpor
dc.subjectrepresentação vetorial contínua de palavraspor
dc.subjectpontuação automática de ensaios.por
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA ELETRICApor
dc.titleO papel das representações vetoriais de palavras na pontuação automática de ensaios: uma abordagem baseada em deep learning no contexto de learning analyticspor
dc.typeTesepor
local.contributor.board1Peres, Sarajane Marques
local.contributor.board2Ochoa, Xavier
local.contributor.board3Basile, Antonio Luiz
local.contributor.board4Lopes, Fábio Silva
local.publisher.countryBrasilpor
local.publisher.departmentEscola de Engenharia Mackenzie (EE)por
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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Engenharia Elétrica e Computação - Teses - EE Higienópolis