Processes and Reasoning in Representations of Linear Functions

Adu‐Gyamfi, Kwaku; Bossé, Michael J.

doi:10.1007/s10763-013-9416-x

Cited by 43 publications

(27 citation statements)

References 11 publications

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“…Uno de ellos es la mayor dificultad intrínseca de las representaciones verbales, que tienen una forma menos definida y se entrelazan con el lenguaje natural como medio de comunicación (Bossé et al, 2011). Otro es la menor atención que habitualmente suelen prestar los docentes a este registro (Adu-Gyamfi y Bossé, 2014;Bossé et al, 2011). Además, los alumnos también suelen infravalorar este registro, por ejemplo, en las anotaciones que toman de las clases frente a otros registros como el simbólico o el numérico (Arce, Conejo y Ortega, 2016).…”

Section: Discusión De Los Resultados Y Conclusionesunclassified

Estrategias y errores de conversión entre representaciones de intervalos de la recta real

2019

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En este artículo se presentan los resultados de un estudio empírico llevado a cabo a través de diferentes ciclos de investigación con alumnos de 4.º de la ESO y 1.º de Bachillerato, centrado en el concepto de intervalo de la recta real. Los objetivos son identificar las estrategias que utilizan los alumnos para realizar conversiones entre diferentes representaciones de intervalos no acotados e interpretar los posibles errores y dificultades derivados de aplicar estas estrategias. Se han detectado diferentes estrategias focalizadas en aspectos particulares, lo que ha corroborado que muchas dificultades están asociadas a la utilización de conversiones por congruencia. A partir de los resultados, se proponen varias orientaciones didácticas para facilitar el aprendizaje de este concepto a través de sus representaciones.

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Section: Discusión De Los Resultados Y Conclusionesunclassified

Estrategias y errores de conversión entre representaciones de intervalos de la recta real

2019

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“…From Figure 3, the students seem to understand dependence relationship among with ( ) and ( ) with ( ). Implicitly, it shows that SCU students understand about symbolic representation registers (Adu-Gyamfi & Bossé, 2014;Boote, 2014) and are able to interpretation composition functions concept.…”

Section: Compartementalized Understandingmentioning

confidence: 90%

“…Eventually, the students assumed that ∘ (0) = 1. Based on the working result and interview, it could be assumed that TCU students understood table register (Adu-Gyamfi & Bossé, 2014) and were able to interpret the of composition function concept (Boote, 2012).…”

Section: Compartementalized Understandingmentioning

confidence: 99%

Characteristics of Students’ Mathematical Understanding in Solving Multiple Representation Task based on Solo Taxonomy

Afriyani

Sa’dijah

Subanji

et al. 2018

Int Elect J Math Ed

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This articles discusses the characteristics of students' mathematical understanding in solving multiple representation tasks. Qualitative explorative methods were used to clarify the characteristics of mathematical understanding. Data were obtained by assigning multiple representation tasks to and interviewing 25 students. It is concluded that there are two characteristics of mathematical understanding in solving multiple representation tasks: flexibility and compartmentalization. Flexible understanding consists of complete and incomplete flexibility. SOLO taxonomy level for students who have flexible understanding is relational. Multi-structural level refers to students whose comprehension is incomplete flexible, while uni-structural level refers to students whose understanding is compartmentalized. The findings of this study can be used as a guide to assess the depth of students' mathematical understanding and a foothold in developing learning mathematics based multiple representations.

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“…However, it has been found that students may work within a representation and at the same time, they do not understand a mathematical concept that is studied [54]. For example, they may to memorize keystrokes, actions, and commands in a computer environment, but cannot understand a concept in full.…”

Section: Multiple Representationsmentioning

confidence: 99%

Efficiency of Dynamic Computer Environment in Learning Absolute Value Equation

Jokić

Takači

2020

Symmetry

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The presented study analyzes the usage of the didactic efficiency of multiple representations in a computer environment in learning absolute value functions and equations. It is known that the axis of symmetry of the graph of the absolute value function is the y-axis. The research was applied at the University of Novi Sad, Serbia. The data were collected by testing a group of 226 students: major chemistry and physics students at the beginning of their common calculus course. The students worked individually in two groups: the experimental and control group. The experimental group of students practiced using GeoGebra software, and the control group of students practiced using paper and pencil. At the end of the experiment, which lasted for two weeks (six school classes), both groups were tested with a post-test of knowledge without using a computer. It can be concluded that GeoGebra software had a positive influence on the students’ achievements in solving absolute value equations.

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Processes and Reasoning in Representations of Linear Functions

Cited by 43 publications

References 11 publications

Estrategias y errores de conversión entre representaciones de intervalos de la recta real

Estrategias y errores de conversión entre representaciones de intervalos de la recta real

Characteristics of Students’ Mathematical Understanding in Solving Multiple Representation Task based on Solo Taxonomy

Efficiency of Dynamic Computer Environment in Learning Absolute Value Equation

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