Bayesian item response theory and latent dirichlet allocation applied to textual professor performance profiling
Fecha
2019
Autores
Profesor Guía
Formato del documento
Thesis
ORCID Autor
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Editor
Universidad de Valparaíso
Ubicación
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ISSN
item.page.issne
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Facultad
Facultad de Ciencias
Departamento o Escuela
Facultad de Ciencias, Instituto de Estadística
Determinador
Recolector
Especie
Nota general
Magíster en Estadística. Universidad de Valparaíso. 2019.
Resumen
The evaluation of teaching and the perception about the academic environment are only obtained through students. Measure the teachers’ performance and subject relevance has an increasing importance for universities and academic institutions since these evaluations are subjective. Thus, a natural question is: how to develope an unbiased measure through subjective answers? This thesis uses several statistical tools in order to measure professor performance and subject relevance. An strategic diagram is constructed to compare these latent variables. Item response theory (IRT) is used to propose a method to measure teachers’ performance and subject relevance, also Latent Dirichlet allocation (LDA) is used to model textual data, and clustered in order to profile professors using students’ comments.
Descripción
Lugar de Publicación
Valparaíso
Auspiciador
Palabras clave
ANALISIS BAYESIANO, DESEMPEÑO HUMANO, EVALUACION, ENCUESTAS, ESTUDIANTES