Doutorado em Ciência da Computação

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Navegando Doutorado em Ciência da Computação por Autor "Azevedo, Caio Lucidius Naberezny"

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    Exames inteligentes: evidenciação estatística de perfis de aprendizagem, composição de banco de itens multidimensionais e personalização de avaliação
    (Universidade Federal do Espírito Santo, 2023-05-19) Silva, Wesley Pereira da; Oliveira, Elias Silva de; https://orcid.org/0000-0003-2066-7980; http://lattes.cnpq.br/2210356035827181; http://lattes.cnpq.br/8881034997521890; Baduê, Claudine Santos; https://orcid.org/0000-0003-1810-8581; http://lattes.cnpq.br/1359531672303446; Azevedo, Caio Lucidius Naberezny; https://orcid.org/0000-0001-9535-292X; http://lattes.cnpq.br/0856524274837137; Santos, Thiago Oliveira dos; https://orcid.org/0000-0001-7607-635X; http://lattes.cnpq.br/5117339495064254; Guzman, Jorge Luis Bazan
    A common challenge to the areas of knowledge is the construction of teaching strategies that are sufficiently general to suit audiences with varied learning profiles. Usually, the teacher defines his teaching plan according to learning objectives, which are evaluated through the expression of latent traits that denote proficiency on the part of the subject being examined. Thus, intelligent techniques and technological tools are opportune to contribute to increasing the quality of teaching and reducing the teaching effort in the execution of complex activities such as, for example: formulation of assessment items, application of tests and provision of feedback to students. The formal rigor in the creation of instruments for assessment, tabulation and calculation of grades is a key factor to avoid bias in conducting the assessment of learning and estimating the ability of students. Student performance is the first dimension to be considered in the assessment. The grouping of similar performances allows characterizing groups that represent learning profiles. Self-assessment and peer assessment are techniques to stimulate the student’s self-criticism in relation to himself and his classmates, seeking to discourage evaluative biases by encouraging the examinee’s coherence when exercising the role of evaluator. The logistic models derived from Psychometrics allow the quantitative characterization of the evaluation items, allowing the measurement of qualitative aspects, such as: difficulty, discrimination and propensity to kick. With psychometric models, the probability of success of the subject can be predicted when being evaluated with a certain item. Finally, the use of Natural Language Processing provides the selection of items by content similarity with a search expression, which represents a subject to be retrieved in a set of the test items bank. In this way, we seek to propose a method of creating individualized assessment trail, composed of a sequence of activities in a certain order appropriate to the ability of the examinee. Thus, we present an intelligent computerized adaptive test approach, whose execution configuration is adjustable to qualitative, quantitative and/or content teaching strategies related to pre-defined terms. The contribution envisaged with such a proposal is to extrapolate a two-dimensional parameter space of the evaluations, composed of the examinee’s performances and scores achieved by item; for a multidimensional space that considers the characteristics of the items in psychometric and semantic terms, as well as the characteristics of the examinees and historical data of subjects with similar trajectories.