Publication: Factores que determinan el éxito académico de los estudiantes en las áreas de ciencia, tecnología, ingeniería y matemáticas (Stem)
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2018
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Abstract
Existe una brecha en la obtención de títulos en careras relacionadas con las Ciencias, Tecnología, Ingeniería y Matemáticas (STEM, en inglés) entre estudiantes que pertenecen a grupos poco representado en Estados Unidos. A fin de incrementar la retención de este grupo estudiantes, el gobierno financia programas de entrenamiento en investigación. Como punto de partida, esta investigación analizó una base de datos 305 estudiantes que participaron de un programa de entrenamiento en investigación en ciencias. Con el fin de conocer el vínculo entre la culminación de PhD en ciencias o no (variable dependiente) y las variables independientes de género, programa académico, internado de verano, tiempo en el programa, promedio académico, se realizó una regresión logística múltiple. El valor de Cox & Snell indica que, 9% de la varianza en la variable dependiente es explicada por el modelo lógico, mientras la R cuadrada de Nagelkerke’s muestran una baja relación (13%) entre las variables dependientes e independientes. Es así como planteamos que, conocer los factores que facilitan que estudiantes hispanos continúen estudios graduados en ciencias requiere incorporar variables psicosociales, antes no contempladas en el modelo. En esta dirección, replanteamos el objeto de estudio con un acercamiento metodológico mixto aplicando un diseño exploratorio secuencial. Este diseño permite, primeramente, recopilar información cualitativa para explorar el fenómeno y posteriormente compilar información de naturaleza cuantitativa que permita explicar las relaciones que se determinan de la información cualitativa. Concretamente, este diseño permitirá explorar los factores que un grupo de expertos académicos identifican como relevante para que los estudiantes que participan de programas de entrenamiento en investigación durante sus estudios de grado continúen doctorados en áreas relacionadas con las ciencias naturales. Habiendo identificado las variables se procederá a alinear los instrumentos con factores paralelos con el fin de poner a prueba las relaciones en un nuevo modelo.
There is a gab in degree completion in Science, Technology, Engineering and Mathematics (STEM) among underrepresented minority (URM) students in the United States. In order to increase URM students’ retention, the government is supporting undergraduate research experiences (URE) programs. As a starting point for this research we analyzed data from 305 students that participated in a URE program. To explore the relation among the binary dependent variable (doctoral degree completion in science or not) and a group of five independent variables (sex, academic concentration, time within the program, grade point average, and summer internship), a Multiple Logistic Regression was executed. The Cox & Snell value indicated that 9% of the variation in the dependent variable is explained by the logistic model, whereas the Nagelkerke’s R Square showed a low relationship of 13% between the independent and the dependent variable. This low relationship made us realized that to better understand factors relevant to when hispanic students are more likely to pursue a scientific career, our model should consider psychosocial variables. In this direction, we refocused the subject matters with a mixed method study applying an exploratory sequential design. During the first phase we will gather qualitative information to explore the phenomena, followed by a second phase of quantitative data collection to explain relationships found in first phase. Specifically, this design will help us explore factors that, a group of academic experts identify as relevant for undergraduate students that participated in URE to pursue a science related doctoral degree. Once variables are identified, we will incorporate instruments, with parallel factors, to test the relations in the new model.
There is a gab in degree completion in Science, Technology, Engineering and Mathematics (STEM) among underrepresented minority (URM) students in the United States. In order to increase URM students’ retention, the government is supporting undergraduate research experiences (URE) programs. As a starting point for this research we analyzed data from 305 students that participated in a URE program. To explore the relation among the binary dependent variable (doctoral degree completion in science or not) and a group of five independent variables (sex, academic concentration, time within the program, grade point average, and summer internship), a Multiple Logistic Regression was executed. The Cox & Snell value indicated that 9% of the variation in the dependent variable is explained by the logistic model, whereas the Nagelkerke’s R Square showed a low relationship of 13% between the independent and the dependent variable. This low relationship made us realized that to better understand factors relevant to when hispanic students are more likely to pursue a scientific career, our model should consider psychosocial variables. In this direction, we refocused the subject matters with a mixed method study applying an exploratory sequential design. During the first phase we will gather qualitative information to explore the phenomena, followed by a second phase of quantitative data collection to explain relationships found in first phase. Specifically, this design will help us explore factors that, a group of academic experts identify as relevant for undergraduate students that participated in URE to pursue a science related doctoral degree. Once variables are identified, we will incorporate instruments, with parallel factors, to test the relations in the new model.
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