Academic journal article Science Educator

Pre-Service Science Teachers' Interpretations of Graphs: A Cross-Sectional Study

Academic journal article Science Educator

Pre-Service Science Teachers' Interpretations of Graphs: A Cross-Sectional Study

Article excerpt

Abstract

This study focuses on pre-service science teachers' interpretations of graphs. First, the paper presents data about the freshman and senior pre-service teachers' interpretations of graphs. Then it discusses the effects of pre-service science teacher training program on student teachers' interpretations of graphs. The participants in the study were 117 pre-service science teachers. Fifty-six of the participants were freshman. The data of the study were gathered with a questionnaire adapted from Aoyama (2007). The questionnaire includes two graphs each based on a different context, and having four questions about the interpretation of the graphs. The participants' interpretations of graphs were analysed in five levels. The levels were listed from low to high level: Idiosyncratic, Basic Graph Reading, Rational/Literal, Critical, and Hypothesising and Modelling. The results of this study reveal that pre-service science teacher can read values and trends in graphs, but they are not successful at the higher levels within the interpretations of graphs hierarchy. Moreover, the Turkish pre-service science teacher training program does not promote student teachers' interpretations of graphs. These findings suggest that teaching modules should be designed to promote pre-service science teachers' interpretations of graphs and challenge them to go beyond basic graph reading. Furthermore, the factors that affect the pre-service science teachers' interpretations of graphs, such as the type of graphs and the context of the graphs, can be useful as the findings of the study can be used to aid in designing graphs teaching modules.

Keywords: Graphs, interpretations of graphs, teacher education, science education, cross-sectional study.

Introduction

In the 21st century, we frequently use graphs in our daily lives while reading newspapers, magazines, articles, watching TV news, and surfing on the net. Economic developments, election results, the results of public reports in the fields of education and health and so on are presented by graphs. Therefore, graphing competence is important and crucial for all the citizens who often need it in their daily life outside of the school environment. Graphs are used in many scientific disciplines (e.g., geography, economy, health, etc.) to analyse and organize the collected data (either qualitative or quantitative) and present them in a visual format (Batanero, Arteaga, & Ruiz, 2009; Shah & Hooeffner, 2002). Graphs serve many purposes. First, graphs present complex data concisely and precisely (Alacaci, Lewis, O'Brien, & Jiang, 2011; Bowen & Roth, 2005; Monteiro & Ainley, 2004). After the data are processed and put into graphs, the correlation and co-variation between the variables can be inteipreted more easily (Bowen & Roth, 2005; Connery, 2007; Glazer, 2011; Vekiri, 2002). In addition, graphs help to determine the meaning of the data and help in making deductions and decisions (Doig & Groves, 1999; Bowen & Roth; 2002; Vekiri, 2002; Roth & Bowen, 2003). In this respect, graphing competence is not an easy task as it requires high-level cognitive abilities (Bowen & Roth, 2002; Bowen & Roth, 2005; Glazer, 2011; Grueber, 2011; Sharma, 2006).

Graphing competence includes both graph construction and graph interpretation skills (Aoyama, 2007; Glazer, 2011). Graph construction is the procedure of data processing (National Ministry of Education [NME], 2005; Monteiro & Ainley, 2003; Temiz & Tan, 2009). Technological advancements have both increased the graph variety (Vekiri, 2002) and contributed to the graph construction (Amer & Ravindran, 2010). For example, it has become easy for the individuals to enter only their data to form a graph with some computer programmes. There are even laboratory environments that save the data and convert the data into graphs automatically. So, technologic developments have reduced the skills that individuals are required to have for graph construction. …

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