Visualização espacial, raciocínio indutivo e rendimento acadêmico em desenho técnico

Abstract

As dificuldades de aprendizagem de Desenho Técnico que experimentam os estudantes de Engenharia relacionam-se com seu nível de aptidão. Para melhorar o processo didático, seria necessário detectar de imediato os estudantes que requerem mais apoio. Este estudo descreve a utilidade de um teste de Visualização Espacial e um teste de Raciocínio Indutivo para prever o rendimento dos estudantes em Desenho Técnico. A amostra foi composta por 484 estudantes do primeiro ano do Curso de Engenharia de quatro centros brasileiros de Educação Superior. Os dados foram analisados com o modelo de Rasch. Os resultados sugerem que a aptidão de Visualização Espacial é o melhor previsor.

Rasch Measurement of the Brief Situational Test of Emotional Management in a Large Portuguese Sample

“…In RM, calculations transform the persons and item parameters to a unit “measure” ( θ or theta) that is distributed along a continuum. Each unit of measure of θ corresponds to log-odd units or “logits,” a scale with theoretical ranges being ±infinity but typically ranges between ±5 (Prieto & Velasco, 2006), where 0 localizes the average difficulty for the measure. Thus, the RM analysis has the advantage of estimating items’ parameters independently from the participants who enter the sample and providing a robust assessment of construct validity and representation for more consistent scoring and interpretation.…”

“…Infit mean square (Infit MNSQ) and outfit mean square (Outfit MNSQ) are calculated to estimate the model fit to the data, both for items and persons. The infit refers to the weighted MNSQ, providing information about items and possible structural problems (Baker, 2001; Prieto & Velasco, 2006). According to Linacre (2011), infit statistics values between .5 and 1.5 are productive for measurement; values greater than 2.0 can degrade the measurement, values between 1.5 and 2.0 are unproductive for measurement construction, and values smaller than .5 are less productive for measurement but not degrading.…”

Developing a fluid intelligence scale through a combination of Rasch modeling and cognitive psychology.

“…The simple correlation was .62 with Raven’s Advanced Progressive Matrices, .42 with general sequential reasoning, .46 with spatial visualization, and .36 with crystallized intelligence. A latent variable modeling of a general factor using Raven’s Advanced Matrices indicated that the loadings from the Form A and B scales were .86 and .78, respectively (Prieto & Velasco, 2006; Primi, 1998, 2002; Primi, Santos, & Vendramini, 2002).…”

Assessment of Emotional Management ability: a Rasch Model Analysis of the brief Situational Test of Emotional Management (STEM-B)

“…In Rasch Models, calculations transform the persons and item parameters to a unit "measure" (θ or theta) that is distributed along a continuum, like a ruler. Each unit of measure of θ are log-odd units or "logits", a scale with theoretical ranges being ± infinite, but typically ranges between an amplitude of ± 5 (Prieto & Velasco, 2006), and 0 localizes the average difficulty point set for the measure.…”

“…Infit Mean Square (Infit MNSQ) and Outfit Mean Square (Outfit MNSQ) are observed to estimate the fit of the data to the model, both for items and persons. The Infit refers to the weighted mean square, providing information about items and possible structural problems (Baker, 2001;Prieto & Velasco, 2006). According to Linacre (Linacre, 2011), Infit statistics values between 0.5 and 1.5 are productive for measurement; values greater than 2.0 can degrade the measurement; values between 1.5 and 2.0 are unproductive for measurement construction; and values smaller than 0.5 are less productive for measurement, but not degrading.…”