Creating a Statistical Reasoning Learning Environment

Garfield, Joan; Ben‐Zvi, Dani; Chance, Beth; Medina, Elsa; Roseth, Cary J.; Zieffler, Andrew

doi:10.1007/978-1-4020-8383-9_3

Cited by 30 publications

(48 citation statements)

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“…To obtain student buy-in, Garfield et al (2008) emphasize that the assessments must be aligned with the learning goals. In the face-to-face sections, midterms were replaced with frequent -Authentic Quizzes‖ that aligned with the ways in which statistical reasoning was being developed throughout the course.…”

Section: Assess Understanding Of Central Ideas Using Measures Aligned With Learning Goalsmentioning

confidence: 99%

Attitudinal changes in face-to-face and online statistical reasoning learning environments

Showalter

2021

JPR

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Attitudes toward statistics play an important role in statistical understanding, postsecondary decisions, and a lifelong relationship with statistics. Unfortunately, the average undergraduate student tends to view statistics as less interesting and less valuable after completing an introductory statistics course. The product of several decades of statistics education reform, a statistical reasoning learning environment (SRLE) has shown some positive early results in cognitive domains and may impact attitudes as well. In this study, four classes of introductory undergraduate statistics (two fully online, two face-to-face) were designed as SRLEs. Students (n = 83) completed a pretest and posttest version of the Survey of Attitudes Toward Statistics-36 © . Both online and face-to-face sections showed average gains in Interest and Value that were higher than those reported in a large reference group, and these gains were practically significant.

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Section: Assess Understanding Of Central Ideas Using Measures Aligned With Learning Goalsmentioning

confidence: 99%

Attitudinal changes in face-to-face and online statistical reasoning learning environments

Showalter

2021

JPR

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“…As a result, the activity proposed in this article combines concepts from basic statistics and economics. The purpose of the activity is to create a learning environment where students can develop a deep and meaningful understanding of statistics, and therefore be encouraged to foster more analytical reasoning and critical thinking (Garfield et al 2008).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…When we use this hands-on approach for teaching statistics, the subject becomes more engaging for students and helps them to focus on concepts rather than computation. In addition, introducing R software for teaching statistics allows students to develop an understanding of abstract concepts and it offers students the opportunity to experiment with data (Garfield et al 2008).…”

Section: R Softwarementioning

confidence: 99%

The Raising Factor, That Great Unknown. A Guided Activity for Undergraduate Students

Caballer-Tarazona

Coll-Serrano

2020

Journal of Statistics Education

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In the first years of their economics degree programs, students will face many problems successfully dealing with a range of subjects with quantitative content. Specifically, in the field of statistics, difficulties to reach some basic academic achievements have been observed. Hence, a continuing challenge for statistics teachers is how to make this subject more appealing for students through the design and implementation of new teaching methodologies. The latter tend to follow two main approaches. On the one hand, it is useful for the learning process to propose practical activities that can connect theoretical concepts with real applications in the economic context. On the other hand, we should design multidisciplinary activities that link concepts from different subjects. With this goal in mind, in this article we propose a complete activity for first year students in business administration and economics degree programs, aimed to reinforce some basic statistical and economic concepts, while other basic transversal skills are also practiced, all within the subject of statistics.

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“…En la educación estadística se sugiere el uso de la informática para abordar el aprendizaje de la distribución muestral; por ejemplo, la utilización de applets disponibles en ambiente web (Inzunsa, 2007;Garfield y Ben-Zvi, 2008). Chance, Ben-Zvi, Garfield y Medina (2007) resaltan el apoyo que pueden proporcionar los recursos informáticos en actividades de automatización de cálculos y gráficos, exploración de datos, visualización de conceptos abstractos, simulación de fenómenos aleatorios, investigación de problemas reales.…”

Section: Investigaciones Previasunclassified

Comprensión de la distribución muestral mediante configuraciones didácticas y su implicación en la inferencia estadística

Martínez¹,

Galindo²,

Retamal³

2013

ensciencias

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Alvarado, H., Galindo, M. y Retamal, L. (2013 RESUMEN: En este trabajo presentamos un estudio sobre la comprensión de las distribuciones muestrales y su vinculación con la estimación de parámetros por intervalos de confianza. La enseñanza de conceptos estadísticos, dirigida a estudiantes universitarios, está basada en la implementación de configuraciones didácticas. Se aplicaron dos problemas abiertos y un cuestionario de intervalos de confianza de Olivo, Batanero y Díaz (2008) a un grupo de 113 estudiantes. Los resultados indican mejoras en la comprensión de la estimación de parámetros, la relación con coeficientes de confianza y la elección de la distribución muestral adecuada para la construcción de intervalos de confianza. Además, se confirman las dificultades de procedimientos algebraicos en las distribuciones muestrales.PALABRAS CLAVE: distribuciones muestrales, configuración epistémica, intervalos de confianza, estudiantes universitarios.ABSTRACT: We present a study on the understanding of sampling distributions and their connection with parameter estimation by confidence intervals. The teaching of statistical concepts, aimed at university students, is based on the implementation of settings configurations. Was applied two open problems and a questionnaire on confidence intervals Olivo, Batanero and Diaz (2008) to a group of 113 students. The results indicate improvements in the understanding of the estimated parameters, the coefficients relating to trust and choosing the appropriate sampling distribution to construct confidence intervals. In addition, the findings algebraic procedural difficulties of sampling distributions.

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Creating a Statistical Reasoning Learning Environment

Cited by 30 publications

References 0 publications

Attitudinal changes in face-to-face and online statistical reasoning learning environments

Attitudinal changes in face-to-face and online statistical reasoning learning environments

The Raising Factor, That Great Unknown. A Guided Activity for Undergraduate Students

Comprensión de la distribución muestral mediante configuraciones didácticas y su implicación en la inferencia estadística

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