Examining pre-service science teachers’ development and utilization of written and oral argument and representation resources in an argument-based inquiry environment

Abstract

This study investigated the development and utilization of argument and representation resources in pre-service science teachers’ (PSTs’) written and oral arguments over two semesters in an argument-based inquiry environment of General Chemistry Laboratory I and II courses. The study employed a form of mixed methods research that is known as ‘data-transformation variant of convergent design’ which allows quantification of qualitative data. Data sources included PSTs’ 180 laboratory reports and 20 video recordings. A Friedman test and a Spearman-Brown correlation were conducted for statistical analysis. The results revealed that the quality of argument and representation were intertwined in both written and oral argumentation. While the PSTs’ quality of written argument and representation significantly increased from the first-time phase to the following time phases, in oral argumentation the quality remained stable after the second time phase. There was also a positive correlation amongst the PSTs’ quality of written and oral argument and representation. The PSTs’ representational competency increased over time and they connected more representations in written arguments. The results suggest that students should be provided with opportunities to engage in sustained talking, writing, and reading practices both publicly and privately in order to critique and construct arguments, develop representational competency, and integrate ideas.

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Current reforms in science education have placed an emphasis on language use and argumentation in learning and doing science (NRC, 2012; Ministry of National Education [MONE], 2018). New curricula have highlighted the need for students to be engaged in scientific practices such as using and communicating with language (listening, talking, reading and writing) including a range of modes of representation such as formulas, models, graphs and tables to explain concepts, ideas, and phenomena both in oral and written forms (NRC, 2012; MONE, 2018). Students should also be involved in constructing and critiquing arguments based on evidence, and be provided with opportunities to support their ideas using different reasoning, and construct counterarguments to refute their peers’ claims (MONE, 2018; Cavagnetto et al., 2020). In these learning environments, it is necessary for teachers to guide the discussions that their students present, which includes encouraging students to base their claims on valid data using reasoning (MONE, 2018). However, a critical concern for Yore and Treagust (2006) is that there has been little consideration of the role of language and argumentation in science teacher education programs as a means to prepare teachers for these more engaging environments. As teachers tend to teach the way they were taught, it is important to provide pre-service science teachers with a learning environment in which they can have first-hand experience in language use, and in constructing and critiquing written and oral arguments (Kaya, 2013; Özdem et al., 2013; Suh, 2016; Yaman, 2020, 2021).

These new curricula reforms have shifted a previous emphasis on science as inquiry to incorporate a much richer focus on argumentation where students utilize rational reasoning through engagement in complex scientific practices as they construct and critique knowledge claims (Duschl and Osborne, 2002; Berland and McNeill, 2010; Ryu and Sandoval, 2012; Asterhan and Schwarz, 2016). While there have been different perspectives on how best to promote argument and argumentation within classrooms, recent studies have suggested that students need to be immersed in argumentation practices as a means to understand scientific inquiry (Yore and Treagust, 2006; Cavagnetto, 2010; Walker and Sampson, 2013; Manz, 2015), and further suggest that sustained engagement in such practices is needed to promote the quality of argumentation (Ryu and Sandoval, 2012; Walker and Sampson, 2013; Chen et al., 2016). However, a critical problem for Ford and Forman (2006) is that science classrooms are generally places where students have limited opportunities to construct and critique their written and oral arguments resulting in an underutilization of scientific language.

One particular argument approach that incorporates an emphasis on language is the Science Writing Heuristic (SWH) approach. The SWH approach is an immersive approach to argument-based inquiry (Cavagnetto, 2010; Ardasheva et al., 2015; Hand et al., 2020), and is centered on assisting students in engaging in complex reasoning and the use of language by embedding arguments within scientific practices (Cavagnetto, 2010). Students are required to ask research questions, outline their design, engage in investigations, gather data, generate claims supported with evidence, consult experts and present their findings to compare with others through oral and written argumentation as well as multiple representations (Ardasheva et al., 2015; Hand et al., 2020). The SWH approach places emphasis on the need for students to construct and critique scientific argumentation as the basis for building knowledge. Ardasheva et al. (2015) argue that to do this, students need to authentically engage in argument as they learn about argumentation while they are required to negotiate their understanding publicly and privately across multiple situations and representations such as pictures, graphs, or equations (Hand et al., 2017b). The concept of language writ large is that the language of science can contain many different representational forms such as, equations (mathematical and chemical), graphs, tables, pictures, diagrams, as well as text (both oral and written) (Hand, 2017). In this study, we used multiple levels of representations used in chemistry education (e.g. macroscopic, symbolic, microscopic and algebraic) since they are key model for improving students’ understanding of chemistry (Nakhleh and Krajcik, 1994; Gabel, 1999) and students have difficulties in understanding and connecting these representations to explain chemical phenomena (Kozma and Russell, 1997; Hinton and Nakhleh, 1999; Treagust et al., 2003; Ardac and Akaygun, 2004; Chandrasegaran et al., 2008; Stieff and DeSutter, 2020).

There have been a number of studies that have looked at the role of language in different components of this type of argument-based inquiry. For example, a study by Chen et al. (2016) examined whole class dialogue and the role of language as a critical element of the process. They showed that the most productive outcomes occurred when middle school students simultaneously use talk and writing rather than either condition alone in science courses. However, they did not examine the impact of this oral dialogue on the written arguments that were produced as a consequence of the oral dialogue. At the undergraduate level in chemistry laboratory courses, Greenbow and his colleagues (Burke et al., 2005; Greenbowe and Hand, 2005; Choi et al., 2013) examined the role of oral dialogue between the instructor and students and the impact on student learning. They showed that when instructors and students were involved in rich dialogue, the results obtained on standardized American Chemical Society tests were significantly better when compared to situations where there was little dialogical interaction between the instructor and students. Almost a decade ago, Walker and her colleagues (2013) used an approach called Argument-Driven Inquiry, which focused on peer-review of written arguments as a means to improve the quality of written arguments. In their more recent work, Walker and her colleagues (Walker et al., 2019; Hosbein et al., 2021) focused on students’ oral argumentation in general chemistry 1 and 2 courses, and showed that students increased their scientific argumentation resources (cognitive, epistemic and social) when they engaged in argumentation. In a more recent study, in general laboratory 1 and 2 courses, Yaman (2020) was able to show through examining written laboratory reports that pre-service science teachers in SWH environments were able to build their representational (e.g. macroscopic, symbolic, microscopic and algebraic levels) and argumentative competency simultaneously through their engagement in the laboratory activities. However, there have been very few studies that have examined the relationship between oral and written dialogue (a written argument is viewed as a dialogue between the author and his/her audience) in terms of building the essential competencies necessary for promoting the outcomes of argument-based inquiry.

Understanding the role of both oral and written dialogue as critical elements of argument-based inquiry has the potential to help us better explain what Norris and Phillips (2003) refer to as ‘the fundamental sense of science literacy’. The fundamental sense of science literacy recognizes the critical role of language as an epistemic tool that promotes students’ generation of knowledge. Importantly, language is required to build arguments, and reasoning is required in using both language and argument. While our initial work (Yaman, 2020; 2021; Çıkmaz et al., 2021) has shown that these three elements of argument-based inquiry (language, argument and reasoning) are utilized simultaneously, this current research is focused on building a much richer and deeper understanding of two critical elements of a generative learning environment – public oral dialogue and individual written arguments – through examining the use of language, and argument. In doing so, we believe that we will be able to build a richer understanding of the fundamental sense of science literacy. Therefore, we followed a group of pre-service science teachers embedded within a chemistry course centered on using the SWH approach during their freshman year over a 28 week program. We focused on whole class discussion (public oral dialogue) in building questions, designs, claims and evidence, and students’ individual written arguments completed at the end of each laboratory activity. We analyzed such factors as quality of argument and representational levels used (e.g. macroscopic, microscopic symbolic and algebraic). The research questions that guided this study were as follows:

(1) How do the quality of arguments and representations change over time in PSTs’ written and oral argumentation?

(2a) How does the quality of written arguments relate to the quality of written representations, and the quality of oral arguments relate to the quality of oral representations over time?

(2b) How do the quality of oral argument and representations relate to the quality of written argument and representations over time?

(3) Are the PSTs able to connect representations in a manner that represents an understanding of the role of language in the learning process?

Theoretical background

Dialogue

There has been an emerging interest in the role of dialogue as a critical element of the learning environment. In their recent handbook, Resnick et al. (2015) highlight the critical role that talk and dialogue have in learning, and suggest that there is more research needed to advance this field of work. Mercer et al. (2019) draw attention to the critical role that dialogue plays within learning environments, but they also emphasize the need to examine the “interdependency” between dialogic pedagogy and the role of different technologies. In this research we begin to explore this interdependency by examining the role of oral and written dialogue. An important and critical condition when examining this interdependency is the recognition that writing is not talk written down (Emig, 1977) – talk (oral) and writing are two different forms of dialogue.

Oral and written argumentation

Argumentation refers to goal-directed communicative activities that generally occur in dialogue which may take oral and written communicative exchange to deal with a controversial issue or disagreement (Walton, 1990; Del Longo and Cisotto, 2014; Asterhan and Schwarz, 2016; Shi et al., 2019). It is also a problem-solving activity where there is a considerable demand on an individual's mental processes and self-regulation capacity (Del Longo and Cisotto, 2014). In fact, both written and oral argumentation practices assume a dialogue with an audience and aim to convince them of the correctness or validity of a claim (Walton, 1990; Berland and McNeill, 2010; Del Longo and Cisotto, 2014; Asterhan and Schwarz, 2016; Shi et al., 2019). However, they can be distinguished from each other. In written argumentation, even though the writers do not expect a direct response from the audience, they need to organize a series of reasonings in a coherent and connected way in the text and take into consideration both arguments and counterarguments aimed at a particular audience (Walton et al., 2008; Berland and McNeill, 2010; Del Longo and Cisotto, 2014; Asterhan and Schwarz, 2016; Shi et al., 2019). Written argumentation thus requires extensive pre-writing and planning strategies, as well as metacognitive and self-regulated practices for editing and revising (Del Longo and Cisotto, 2014). In oral argumentation, the speakers expect a direct respond from the audience. In fact, even if speakers can establish common ground with the audience for understanding and rely on immediate listener feedback, the articulation and coordination of speech and gestures is complex. It is difficult to use prosody (and other linguistic features) as a persuasive and organizational tool (Del Longo and Cisotto, 2014).

In supporting engagement in scientific practices, written and oral argumentation practices have received attention in how it helps develop students’ knowledge and understanding of disciplinary core concepts and epistemic practices (Driver et al., 2000; Manz, 2015; Chen et al., 2016). To date, even though there have been some studies that have examined students’ oral argumentation in small or whole class discussions (Erduran et al., 2004; Osborne et al., 2004; Duschl, 2007; Berland and Reiser, 2011; Özdem et al., 2013; Chen, 2020; González-Howard and McNeill, 2020), studies have overwhelmingly investigated students’ written arguments (Takao and Kelly, 2003; Hohenshell and Hand, 2006; McNeill et al., 2006; Choi et al., 2013; Sampson et al., 2013; Manz, 2015; Aguirre-Mendez et al., 2020; Chen et al., 2020; Yaman, 2020, 2021). In oral argumentation, the studies have focused on assessing science teachers’ argument development (Erduran et al., 2004; Osborne et al., 2004), students’ reasoning (Duschl, 2007; Özdem et al., 2013), developing a learning progression for argumentation (Berland and McNeill, 2010), or managing uncertainty in scientific argumentation (Chen et al., 2020). In written argumentation, research has investigated development of students’ written arguments (Choi et al., 2013; Yaman, 2020, Yaman, 2021), students’ argumentative writing skills and conceptual understanding (Hohenshell and Hand, 2006; Sampson et al., 2013), assessment of evidence in students’ argumentative writing (Takao and Kelly, 2003). There have been very limited studies that have investigated students’ development of both written and oral argumentation (Sampson and Clark, 2009; Berland and McNeill, 2010; Walker and Sampson, 2013; Chen et al., 2016).

Prior research has indicated that students have difficulties in writing effective arguments at different educational levels (Graham and Perin, 2007), producing arguments of high content quality (Kelly and Takao, 2002) or dialogic quality, constructing and critiquing scientific arguments (Ryu and Sandoval, 2012), and/or providing backing or a sound rationale for claims (Walker and Sampson, 2013). In particular, three argumentative processes appear to be the most difficult aspects to improve through interventions. These are: (1) making claims without justifications, (2) drawing on evidence to weaken a claim and (3) justifying the relations between claims and data or evidence (Kuhn and Moore, 2015; McNeill and Krajcik, 2007; Ryu and Sandoval, 2012; Fischer et al., 2014). Ryu and Sandoval (2012) contend that one of the reasons for students’ argumentation not improving is the short time duration of interventions (e.g. a few weeks or months). Supportive evidence has shown that improving the quality of arguments (Chen et al., 2016) and constructing counterarguments takes time (Berland and McNeill, 2010). However, research also indicates that prolonged time itself does not guarantee that students will improve their written arguments (Kuhn et al., 2013). Rather, students need to be provided with regular argument practices (Osborne et al., 2004; Firetto et al., 2019).

Recently, to overcome these difficulties several studies have developed new approaches of oral argumentation, including small group or whole class discussions, to improve students’ written arguments within longer time periods. These studies have found that students’ written argumentation increased over time through interventions within oral argumentation (Hemberger et al., 2017; Murphy et al., 2018; Firetto et al., 2019; Shi et al., 2019). Studies suggest that students’ talking and writing practices need to be embedded in argumentation so that they can develop their written and oral argument quality (Walker and Sampson, 2013; Chen et al., 2016). Importantly, the role of audience and purpose are viewed as essential components to achieve successful writing while attempting to link oral and written arguments (Berland and McNeill, 2010; Walker and Sampson, 2013; Chen et al., 2016; Jang and Hand, 2017; Shi et al., 2019). When argumentation in inquiry is embedded with talking and writing activities, and students are provided with an audience (e.g. peers), they are able to positively correlate their written and oral argumentation as well as increase the quality of oral and written arguments (Walker and Sampson, 2013; Chen et al., 2016). However, there have been limited studies that investigated students’ quality of written and oral argument and representation when they are embedded in argument-based inquiry.

Assessment of written and oral arguments

In science education, students' written and oral arguments have been analyzed using an analytical framework or a holistic framework (Erduran et al., 2004; McNeill and Krajcik, 2012; Osborne et al., 2004; Choi et al., 2013; Kaya, 2013; Çetin, 2014; Yaman, 2020). Toulmin's argument model is the most widely used analytical framework for analyzing students' written and oral arguments in science education (Erduran et al., 2004; Osborne et al., 2004; Kaya, 2013; Çetin, 2014). This model has six components: data (the assemblies that make up our view), claim (an assertion put forward), warrant (a link between data and claim), qualifiers (phrases that limit cases where the claim is considered true), backing (generalizations that support the acceptability of warrants) and rebuttals (outstanding or remarkable cases that may weaken the strength of the supporting arguments) (Erduran et al., 2004; Osborne et al., 2004). Studies using this model have focused on identifying and using the structural features of the argument (Erduran et al., 2004; Osborne et al., 2004; Kaya, 2013; Çetin, 2014). Due to deficiencies in Toulmin's model (for example, analyzing small group discussions or analyzing long complex and dialectic discussions) some of the studies used a modified version of Toulmin's structure (Zohar and Nemet, 2002; McNeill and Krajcik, 2012). For example, Zohar and Nemet (2002) grouped Toulmin's data, warrant, and backing components under a single category as justification to analyze the quality of students' written arguments. McNeill and Krajcik (2012) used the structure of claim, evidence and reasoning, simplifying Toulmin's framework to analyze students' written and oral arguments. Another analytical framework used in science education is Walton's argument schemes (Walton et al., 2008). Walton identified 60 argument schemes, and some of these schemes (e.g. argument from sign, analogy, cause to effect, evidence to hypothesis) were used in science education to examine students' reasoning skills in their oral arguments (Duschl, 2007; Özdem et al., 2013). Some studies have used a holistic framework to evaluate students' written and oral arguments. For example, Hand and his colleague (Hand and Choi, 2010; Çıkmaz et al., 2021; Yaman, 2020) used the holistic framework to analyze whether students were constructing reasonable arguments, regardless of where the argument components (question, claims, evidence, and reflection) were used. Some studies used both analytical and holistic frameworks to analyze students’ arguments (Choi et al., 2013; Yaman, 2021). In this study, drawing on our previous research we used both analytical and holistic frameworks in analyzing PSTs’ written and oral arguments.

Multiple levels of representations (language)

The importance of the role of language in the practice of science has recently gained greater recognition given its use as an epistemic tool in science and chemistry education in particular (Kozma, 2000; Wu, 2003; Yore et al., 2003; NRC, 2012; Prain and Hand, 2016; MONE, 2018). We use the concept of language writ large, that is, the language of science containing many different modal forms such as, equations (mathematical and chemical), graphs, tables, pictures, diagrams, as well as text (both oral and written) (Hand, 2017). Chemistry can be described as a representational and symbolic discipline, with teachers and researchers using representations as a language to communicate with students and scientists (Kozma and Russell, 1997; Taber, 2009). For teaching and doing research, researchers and teachers generally use macroscopic, microscopic, symbolic and algebraic levels (Nakhleh and Krajcik, 1994; Gilbert and Treagust, 2009; Talanquer, 2011; Yaman, 2020).

Research has indicated that utilizing multiple levels of representations is viewed as a key model for improving students’ understanding of chemistry (Nakhleh and Krajcik, 1994; Gabel, 1999). Studies have used various instructional models such as computer animations, simulations or laboratory activities to improve students’ use of representations and representational competency (Kozma et al., 2000; Wu, 2003; Ardac and Akaygun, 2004; Chansagaran et al., 2008; Tang and Abraham, 2016; Samon and Levy, 2017; Stieff and DeSutter, 2020). However, although teachers attempt to explain the use of multimodal representations, including the need to use them simultaneously, students have difficulties in understanding and connecting these levels of representation (Gabel, 1999), as well as selecting, generating, interpreting, and using representations to explain chemical phenomena (Kozma and Russell, 1997; Hinton and Nakhleh, 1999; Treagust et al., 2003; Ardac and Akaygun, 2004; Chandrasegaran et al., 2008; Stieff and DeSutter, 2020). The reasons put forward include students not having the capability of using the symbolic level as a communication tool, not having enough experience with macroscopic levels of representations, and the difficulties in visualizing the matters at the microscopic level (Gabel, 1999; Gilbert and Treagust, 2009; Taber, 2009, 2013).

To help students develop representational competency and to be able to connect these different representational levels, researchers have argued students should be provided with opportunities to use, generate, or select the representations to describe and explain observable chemical phenomena and molecular entities for appropriateness of a particular purpose. Moreover, students need to be given opportunities to use representations as evidence to support claims and draw inferences, to discuss these representations through any observed changes in their experiments, and to explain and connect them through reflecting on their meaning as they work in groups or with peers (Kozma and Russell, 1997, 2005; Ardac and Akaygun, 2004; Treagust and Chandrasegaran, 2009; Yaman, 2020). Kozma and Russell (1997, 2005) have suggested that representational skills can be best developed and used within the context of student discourse and scientific investigations by asking questions, designing investigations, constructing an apparatus, carrying out investigations, analyzing data, drawing conclusions and presenting findings. They have also argued for adding talking and writing activities to science curricula as a means to help students use and understand these representations better. When students are immersed in argument-based inquiry with talking and writing opportunities, they are able to use, generate, connect, and reflect on multiple levels of representation and develop representational competency in their written arguments over time (Yaman, 2020). Research also indicates that students should use and move between macroscopic, microscopic, and symbolic levels of representations at the appropriate stage of reasoning to display understanding (Chandrasegaren et al., 2008). However, currently there are a limited number of studies addressing these concerns and thus there is a need for research into these representationally rich learning environments.

Argumentation and representation in pre-service science teacher education

Over the past two decades, argumentation has been used in science classrooms to improve students’ learning and doing science with many countries addressing the importance of constructing and critiquing science arguments in their science curricula (Erduran et al., 2004; Osborne et al., 2004; McNeill and Krajcik, 2012; Chen et al., 2016). Science teachers are expected to incorporate inquiry-based argument in their science classes (NRC, 2012; MONE, 2018). As a consequence, researchers have made a call for science teachers to be provided with pedagogical learning and teaching experiences in their pre-service teaching programs so that they can implement inquiry-based arguments when they become teachers (Kaya, 2013; Özdem et al., 2013; Namdar, 2017).

This call has been reflected in science teacher education programs with recent research showing that there is a growing interest in implementing argumentation in pre-service science courses (Kaya, 2013; Çetin, 2014; Aydeniz and Doğan, 2016; Aydeniz and Özdilek, 2016; Çiğdemoğlu et al., 2017; Namdar, 2017; Demiral and Çepni, 2018; Yaman, 2018; Yaman, 2020; Yaman, 2021). Research has shown that argumentation is used to investigate the effect of argumentation on PSTs’ conceptual understanding in a variety of chemical concepts (e.g. chemical equilibrium, acid and bases, reaction rate) (Kaya, 2013; Çetin, 2014; Aydeniz and Doğan, 2016; Çiğdemoğlu et al., 2017); to determine argument schemes used (Özdem et al., 2013); to investigate chemical literacy and attitude towards chemistry (Çiğdemoğlu et al., 2017); to uncover the effect of argument on socio-scientific issues (Çapkınoğlu et al., 2021); self-efficiency to teach science thorough argumentation (Aydeniz and Özdilek, 2016); to improve critical thinking and content knowledge (Demiral and Çepni, 2018); and to investigate PSTs’ argumentative writing and scientific argumentation (Yaman, 2018). These studies generally have implemented argumentation in shorter time periods such as 3–4 weeks for specific chemistry concepts such as acid and bases or chemical reactions (Kaya, 2013; Özdem et al., 2013; Çetin, 2014; Aydeniz et al., 2016; Çiğdemoğlu et al., 2017). Studies generally focused on PSTs’ written arguments (Kaya, 2013; Çetin, 2014; Namdar, 2017; Yaman, 2018; Yaman, 2020; Çıkmaz et al., 2021; Yaman, 2021). Limited studies focused on PSTs’ oral argumentation (Özdem et al., 2013) or combination of written and oral argumentation (Çiğdemoğlu et al., 2017). However, these studies did not focus on the relationship between written and oral argumentation. In some studies, research has focused on the intersection of argument and representation in pre-service science teacher education programs in their written arguments (Namdar, 2017; Yaman, 2020; Çıkmaz et al., 2021). In our previous studies (Yaman, 2020; Çıkmaz et al., 2021), we were able to show that PSTs’ written argument and representations are intertwined when immersive argument-based inquiry is used in longer time periods (such as 28 weeks) focusing on variety of chemistry concepts (e.g. chemical reactions, chemical equilibrium, chemical reactions, acid and bases). However, none of those studies has focused on the relationship between oral and written arguments and representational use over a longer time period. This paper is an attempt to begin this examination.

Method

Research design

The data-transformation variant of convergent design, which is a particular form of mixed method research (Creswell and Clark, 2011), was used to explore how PSTs developed and utilized the quality of written and oral argumentation and representation in an argument-based environment over two semesters. The data-transformation variant of convergent design approach was used for this study because it allowed the researchers to use the implementation of the convergent design with a predetermined unequal priority, placing greater emphasis on the quantitative strand (Creswell and Clark, 2011). In other words, the data-transformation variant allowed us to quantify the qualitative findings, and this approach also allowed the results from the qualitative data set to be combined with the results from the quantitative data through direct comparison, interrelation and further analyses (Creswell and Clark, 2011). In this study, we derived qualitative themes from the qualitative data (written and oral arguments and representations), and then scored the themes using rubrics (which were explained in data analysis section) for each of the participants. These quantified scores were then analyzed using correlations to identify relationships between oral and written arguments and representations, and using a statistical test for repeated measures to understand the development and utilization of arguments and representations.

As indicated above, in this study, the data obtained from the written and oral arguments and representations of the PSTs were subjected to numbers and statistical analysis by passing through certain processes. Although numbers and figures are often referred to as quantitative research types, it is possible to reduce qualitative data to numbers in a certain order. The purpose of reducing qualitative data to numbers is not to make generalizations by applying statistical methods (Patton, 1990; Yıldırım and Simsek, 2018). There are several main purposes in digitizing qualitative data. The first of these is that digitization increases reliability. For example, Weber (1985) mentions three types of reliability: constancy, repeatability, and accuracy. Constancy is defined as the same result being achieved if the analysis of a data set is repeated later by the same researcher. Repeatability means that if more than one person is involved in the analysis of a data set, there must be coherence and consistency between the individuals in terms of the analysis of the data. The term accuracy describes whether the themes or categories that form the basis of the analysis of a data set are made according to a certain standard or norm (Yıldırım and Simsek, 2018). The second goal is to reduce bias. The purpose of reducing bias is not to capture objectivity or to objectify qualitative data. Rather, the aim of bias reduction in this study is to address bias in the digitization of qualitative data (which is also a form of data analysis) to ensure that the resulting interpretations are made more equitably (Yıldırım and Simsek, 2018). Third, quantifying the qualitative data to a certain extent may enable us to make comparisons between the themes or categories that emerged as a result of the data analysis (Yıldırım and Simsek, 2018).

Participants

The participants consisted of nine female pre-service science teachers (PSTs) enrolled as freshmen in the science education department at a university located in a central region of Turkey. These PSTs were required to take General Chemistry Laboratory I and II courses in their first and second semesters of their first year. There were only nine PSTs in the first and second semesters. These PSTs in this study did not interact with other students who were not on the pre-service teacher track because only PSTs can register for the General Chemistry Laboratory I and II courses in the science education department in the faculty of education. This study gained ethical approval from the university where the study took place and the researchers obtained informed consent from each participant. The PSTs were informed about the purpose of the research, specific procedures, duration of participation, risks, benefits, compensation, confidentiality, and voluntary nature of participation. The PSTs, whose ages ranged from 18–20, voluntarily participated in the study.

Context

The PSTs participated in the General Chemistry Laboratory I and II courses in two semesters as part of their compulsory curriculum, and were immersed in classrooms that used the argument-based inquiry approach known as the SWH. The PSTs completed a total of 20 SWH activities, ten in each semester. In the first and second semesters, the PSTs engaged with the topics related to chemical and physical changes, gases, factors that affect reaction rate, acid-base titration, factors affecting chemical equilibrium, equilibrium of solubility and so on (Yaman, 2020). Details of 20 experiments can be seen in Appendix A (ESI†).

Data collection

The data included the video recordings and laboratory reports for each PST. To gain insight into PSTs’ quality of written and oral arguments and multiple levels of representation (language), 180 laboratory reports from the nine PSTs and video recordings of 20 experiments were collected and analyzed over two semesters. The PSTs used the student template of the SWH approach for their written laboratory reports including the beginning question, design, observation, claim, evidence, reading and reflection components (Yaman et al., 2019; Yaman, 2020). The focus of the video analysis was on the whole class dialogue because these were times that the group was required to negotiate and come to consensus on such issues as beginning questions, design and claims and evidence. The total time of each laboratory activity across the 20 experiments ranged from 101 to 199 minutes. The total pages for each laboratory report ranged from 5 to 18 pages.

Description of the setting

As previously mentioned, this study was conducted over a span of two semesters and lasted 28 weeks in total. The PSTs conducted 10 SWH lab experiments in each semester and were taught by the same lecturer. At the beginning of the first semester, the PSTs participated in a lecture on the SWH approach and its student template (Yaman et al., 2019; Yaman, 2020; Çıkmaz et al., 2021). For each laboratory section, the PSTs were required to be involved in pre, during and post laboratory activities.

Pre-laboratory activities were primarily focused on oral dialogue. Once the PSTs arrived at the laboratory, they were required to participate in small group and whole class discussions. They discussed their individual beginning questions with group members and wrote their group beginning questions on the board. They then shared their questions with the whole class as to arrive at a consensus on the capacity for questions to generate research and variables to be tested. After the PSTs had reached an agreement on the whole class beginning questions, they then discussed and chose an appropriate design based on their understanding from the lab manual related to steps or strategies required to perform the experiments with discussion about additional design elements they needed to answer the questions agreed upon.

During-lab activities were also centrally focused on oral dialogue. Once the PSTs reached a consensus on the design to be used, each small group of three performed their experiment and collected their data. The PSTs looked for patterns and determined if there were anomalies in analyzing the collected data. From their small group discussions, the PSTs wrote their data on the board in order to discuss their claims and evidence as a whole class. After the whole class discussion, they reached an agreement on evidence for a claim based on the data they collected and the inferences they made with the data (data plus reasoning). Through the small group and whole class discussions, the PSTs were required to critique and construct their own ideas, to consider the ideas proposed by the other groups, and to reach agreement on the fundamental ideas. In other words, the PSTs decided on their own claims and evidence in their small groups and then each group presented their claims and evidence to the other groups in whole class discussions so that the other groups could provide critiques. Whole class discussions continued until agreements of the fundamental ideas were reached. Throughout whole class discussions the groups could change their group claims and evidence based on the critiques from other groups. These groups were supposed to reflect on their changed ideas in the reflection part of written arguments. In other words, in written arguments the PSTs utilized their agreed claims and evidence, and incorporate feedback from the group members to reflect on them in written arguments in the reflection part.

Throughout these dialogues, the PSTs used representations such as drawings for chemical substances, molecular formula drawings, calculations, graphs, chemical equations, and tables, which were put onto the whiteboard for all to see. The PSTs used writing and talking throughout this process. In these dialogical environments the PSTs were required to apply argumentative reasoning, to critique and construct ideas being proposed by their classmates. As a part of the dialogue during the discussions of the design and claim and evidence components, the PSTs used these representations which could be represented at the macroscopic (MAS), microscopic (MIS), symbolic (SYM) and algebraic (ALG) levels to support their reasoning. Throughout the process, the PSTs used worksheets and electronic devices in the laboratory to keep record of what occurred in order to complete the laboratory write up at home, which was due within four or five days. The whole class discussions were recorded using one camera located at the center of the lab from which the white board and 9 PSTs could easily be seen.

In post-laboratory activities, the central focus was on written dialogue based on completing the write up of the argument template, including the components on reading to compare their outcomes against disciplinary norms, and a reflection component. The PSTs were required to search for at least three different sources to compare and contrast their claims and evidence with the disciplinary norms, and subsequently to explain how their ideas may have changed. The PSTs were encouraged to write their laboratory reports for an audience who does not know the topic, not the lecturer herself. Throughout the laboratory activities including pre and post activities, the PSTs were not given any specific instruction on how to use or embed multiple levels of representation (the macroscopic, microscopic, symbolic, and algebraic levels). However, they were invited to generate and use representations such as drawings of chemical equipment, chemical and mathematical signs, graphs, calculations, tables, molecular drawings of substances, symbols and formulas, chemical equations, etc.

Data analysis

The analysis of written and oral arguments and representation is described below.

Analysis of written and oral argument. The quality of PSTs’ written and oral arguments was analyzed using analytical and holistic frameworks both of which were scored with a matrix ranging from 0 to 15 as seen in Appendix B (ESI†) (Yaman, 2020, 2021). An analytical framework was designed to evaluate if the PSTs were able to distinguish the components of question, claim, evidence and reading and reflection. Reading and reflection component was not analyzed in the oral argumentation since the PSTs were required to do it after they completed lab activities. The analytical framework was also designed to investigate if the PSTs were able to record each component (e.g. claim, evidence and reading and reflection) in the suitable component. For example, if the PSTs were able write or discuss their claim in the claim component, or discuss their evidence in the evidence component. A holistic framework was designed to examine the connectedness and strength of the argument components regardless of where they were recorded (Hand and Choi, 2010; Yaman, 2020, 2021). A statistical analysis was completed taking into consideration the PSTs’ written and oral holistic argument scores. The PSTs’ written and oral arguments were analyzed using the same holistic framework as seen in Appendix B (ESI†). To ensure validity, an independent analysis was conducted. Inter-rater reliability for written and oral argumentation and representations was checked by randomly selecting 20% of video recordings and lab reports of 20 weeks. In this regard, the four transcripts of video recordings (video transcripts of 4 week) and 36 laboratory reports were randomly chosen and given to a researcher in chemistry education along with the scoring rubrics for argument. An inter-rater reliability of the analysis was found to be 0.86 for coding the quality of oral and 0.88 for written arguments. Appendices C and D (ESI†) show PST7's oral examples from whole class claim and evidence and PST7's written scores using analytical and holistic frameworks related to Experiment 13, the effect of temperature on reaction rate in particular because Experiment 13 lasted 183 min and she wrote an 18-page lab report.

PST7's holistic oral argument is awarded as 15 points because she strongly connected beginning questions, claims and evidence components in her discussions. In class discussion in beginning question, she improved her group questions and demonstrated understanding of independent and dependent variables, and helped others to determine appropriate application of what the lab could result in. As a result of beginning question section, whole class came up with four questions. In whole class claim and evidence section, she drew graphs and wrote calculations at white board (red colored graphs and calculations). She expressed four claims for all data. Even though her explanation and interpretation for concentration is somewhat partially correct, she was able to reconcile her ideas with other groups.

When scoring PST7's holistic written arguments, 15 points is awarded because there is a strong connection between question, claim, evidence and reading and reflection components. The SWH flows smoothly from one area to another area. The PST7 developed strong arguments. The PST7 wrote four testable and meaningful questions that show an understanding of dependent and independent variable. As a response to these four questions, she made four valid sound and accurate claims based on all data. In evidence section, she restated her claims and defended them with logical statements. She explained and interpreted chemical equations, calculations and graphs using correct figures and units. She drawn inferences and used rich reasoning to explain and interpret her data and observation. In reading and reflection section, she stated how her ideas have changed, and she compared her claims and evidence with other sources.

Analysis of PSTs’ written and oral multiple levels of representation. The PSTs’ use of oral multiple levels of representation was analyzed by investigating the video records of the design and whole class discussion for the claim and evidence component for each experiment. Moreover, the PSTs’ laboratory reports were analyzed in terms of representational levels located in the design, observation, claim and evidence and reading and reflection components. The PSTs’ written and oral representations were analyzed in three ways including by the type of representations used, by the cohesiveness of these representations, and by the statistical analysis.

For the first step of the analysis the type of representations were used in the oral and written components. The PSTs’ use of representational levels was classified into 4 categories as seen in Table 1: the single type, two-connected, three-connected and four-connected representations. In this regard, the macroscopic (MAS), microscopic (MIS), symbolic (SYM), or algebraic (ALG) was classified as the single type when one of these four representations was separately used or not linked to any other representations to explain the same topic or concept in oral and written arguments. However, since the microscopic level was not used separately in the oral and written dialogues, we removed this category from the single type of representation in the findings section. The representations were classified as two-connected when two different levels of representation (e.g. the symbolic and macroscopic levels or the macroscopic and microscopic levels) were used within one or several dialogues in oral argument or within a sentence or paragraph to explain the same topic or concept in written arguments. For instance, if the symbolic and algebraic levels were linked to each other, the two-connected representation was formed and was shown as “SYM-ALG”. Similarly, multiple levels of representation were classified as three-connected when three different levels of representations (e.g. the macroscopic, the microscopic and the symbolic level) were used within one or several dialogues in oral or one or several sentences to explain the same topic in written arguments. For instance, if the macroscopic, symbolic, and algebraic levels were linked to each other, this means that three connected levels of representation of the symbolic-algebraic-macroscopic was formed and was shown as “SYM-ALG-MAS”. The same analysis was valid when four different types of representation were connected. In this regard, it was shown as “MAS-MIS-SYM-ALG”.

Table 1 Multiple levels of representation (MLR) the PSTs used in their laboratory dialogues and laboratory reports

Category	Codes	Description of the codes	Examples from laboratory dialogues	Examples from laboratory students’ laboratory reports
Single type	MAS	The macroscopic level includes entities and phenomena that are tangible and observable in the world (Johnstone, 1982) or matter that has bulk properties (Nakhleh and Krajcik, 1994)	• The oxygen gas formed as a result of the reaction. *The color of the compound changed from blue to grey	• When we mixed two solutions, a brown precipitation occurred
			• If there is precipitation at the bottom of the test tubes, this means that the precipitation reaction occurred	• When we heated, the color of the substance changed from blue to grey
			• A brown precipitation occurred when the solutions mixed	• A pale gas formed in the test tube, and the gas gave a pop sound when we put fire in the test tube
	SYM	The symbolic level includes, chemical equations, symbols, formulas, diagrams, and molecular structure drawings (Johnstone, 1982; Treagust et al., 2003)	• CuSO4 + K2CrO4 → K2SO4 + CuCrO4; • Fe^+2(aq) + OH^−(aq) → Fe(OH)2(s)	• FeSO4(aq) + NaOH(aq) → Fe(OH)2(s) + Na2SO4(aq); • CaCl2; NH4Cl; S8; Fe, Mg, H2SO4
			• BaCl2; CH3COONa; HNO3; S8; K;	• . • ; • M1 × V1 = M2 × V2
			• ; * P × V = n × R × T
	MIS	The microscopic level consists of moving atoms, molecules, and ions (Johnstone, 1982; Treagust et al., 2003), as well as atomic structure of matter or bonding theory (Hinton and Nakhleh, 1999)	None of the conversations felt in this category.	None of the students’ writings felt in this category.
	ALG	The algebraic level includes the relationship of matter is represented and manipulated using formulas and graphs (Nakhleh and Krajcik, 1994)	None of the conversations felt in this category.	Volume of irregular shaped solid = 58.01–49.30 = 8 m3;
Two connected representations	SYM-MAS	When symbolic level and macroscopic level are connected to each other in a dialogue or several dialogue; or one or several sentences to explain the same topic, the combination of the symbolic and macroscopic level forms	• We created this graph (Symbolic) as a result of the observations and this shows that when the amount ofacid increases the pH of the solution decreases (Macroscopic). There was not a permanent color change at the pH of 12 (Macroscopic)	• When we mixed pale solutions of KI and Pb(NO3)2, PbI2(symbolic) precipitated with yellow (Macroscopic). (symbolic)
	SYM-ALG	When symbolic and algebraic level are connected to each other in a in a dialogue or several dialogue; or one or several sentences to explain the same topic, the combination of the symbolic and algebraic level forms	• Ptotal = Psteam + Pgas (Symbolic); 1 atm = 0.02 atm + Pgas (Algebraic); Pgas = 0.98 atm	• Kcc = [Pb^2+]. [I^−]^2 = [0.01 M. 0.1 L/100.15 × 10^−3 L]. [0.2 M. 0.15 × 10^−3 L/100.15 × 10^−3 L] = 8.8 × 10^−10
			• n = PH2.V/R.T (Symbolic); n = (0.98 atm). (33.5 × 10^−3 l)/(0.082 atm l mol^−1 K^−1) 291 K (Algebraic); n = 1.3 × 10^−3 mol	• nH2 = PH2.V/R.T (Symbolic); n = (0.974 atm).(38 × 10^−3 l)/(0.082 atm l mol^−1 K^−1). 295 K (Algebraic); n = 1.53 × 10^−3 mol
			• MA = m/n (Symbolic); MA = 0.03 g/0.0013 mol = 23.17 g mol^−1 (Algebraic)	• Reaction rate = Δ[ ]/Δt (Symbolic); r = [0.053–0.005]/(40-0); r = 1.2 × 10^−3 M s^−1
			• Error% = 100% − yield% (Symbolic); error% = 100 − (23.07/24 × 100) = 3.825 Algebraic)	• M = n/V(Symbolic); M = 0.53 mol/0.005 L = 106 mol L^−1 (Algebraic)
	MAS-MIS	When macroscopic and microscopic levels are connected to each other in a dialogue or several dialogue; or one or several sentences to explain the same topic, the combination of the macroscopic and microscopic level forms	• Pb^2+ ion and I^− ion (Microscopic) combined and a yellow precipitation occurred (Macroscopic)	• The substances that release H^+ ion (Microscopic) to water, and change color of litmus paper and has sour taste (Macroscopic) called as acidic
Three connected representations	MAS-MIS-SYM	When macroscopic, microscopic and symbolic levels are connected to each other in a dialogue or several dialogues; or one or several sentences to explain the same topic, the combination of the macroscopic, microscopic and the symbolic level forms	• As seen in the meeting point (showing the drawings on the board-Symbolic) NH3 molecules moved faster than HCl molecules (Microscopic). When the two different gas molecules came across, white gas (Macroscopic) which is NH4Cl formed. We can show the chemical equation of this reaction as NH3(aq) + HCl(aq) → NH4Cl(g) (Symbolic)	• Blue color of the component of CuSO4.5H2O (Symbolic) turned to grey when we heated the compound (Macroscopic) because water molecules (Microscopic) around the compound moved away
	MAS-SYM-ALG	When macroscopic, microscopic and algebraic levels are connected to each other in a dialogue or several dialogues; or one or several sentences to explain the same topic, the combination of the macroscopic, symbolic and the algebraic level forms.	• We prepared the solution of 0.1 M 100 mL K2Cr2O7 which was our concentrated solution. We then diluted it by adding water and its volume became 250 mL. We saw that the color of the diluted solution of K2Cr2O7 was lighter than the concentrated one (Macroscopic). When we calculated the concentration of the diluted solution like this:
			M concentrated × Vconcentrated = Mdiluted × Vdiluted (Symbolic); 0.1 × 100 = Mdiluted × 250; Mdiluted = 0.04 (Algebraic). We found that its concentration was 0.04 M. This result shows that we can dilute the solution by adding water, and we can understand it by looking at the color of diluted solution and calculations	• The pH graph of strong base and strong acid titration (symbolic) shows that pH decreases as strong base added. At the beginning, the color of solution was pink (macroscopic) and pH value was 14. We measured it using a pH stript. At the same time we calculated the pH using formula of pH = −log [H3O^+] (symbolic). NaOH was 1 M, so OH^− = 1 M. [H3O^+].[OH^−] = 1 × 10^−14, [H3O^+].1 M = 1 × 10^−14 (algebraic), pH = −log[1 × 10^−14], pH = 14. After adding 64 mL HCl added, the pH value decreased dramatically and we started adding HCl slowly. When volume reached 70 mL titration occurred and color of solution became pale (Macroscopic). When we measured the pH, we saw that it was 7, which showed us that we reached the equilibrum point
Four Connected Representations	MAS-SYM-MIS-ALG	When four of the representational levels are connected to each other in a dialogue or several dialogue or one or several sentences to explain the same topic, this code forms	None of the conversations felt in this category	In Fig. 1(symbolic), the space between the particles (Microscopic) of K2CrO4(symbolic) is less and its color is darker (Macroscopic) therefore it is concentrated.We added water into this solution as seen in Fig. 2(Symbolic), the space between the particles (Microscopic) of K2CrO4 is more and its color is lighter (Macroscopic) therefore it isiluted. We can support this with calculation since the number of particles are equal in two solutions the formula nconcentrated = ndiluted is used. Then, Mconcentrated × Vconcentrated = Mdiluted × Vdiluted(Symbolic); 0.1 M × 100 mL = Mdiluted × 250 mL; Mdiluted = 0.02 molar (Algebraic); when we looked at this data, the concentration of the concentrated solution is 0.1 M and the concentration of diluted solution is 0.02 M. Consequently, we support our claim that when we add solvent to a solution, the solution becomes dilute

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Cohesiveness was the second focus used to analyze the PSTs’ written and oral representations. Four categories (no coherence, weak, moderate, and strong coherence) were used to holistically analyze the connectedness of written and oral representations. Connectedness of the representations was analyzed in written and oral forms separately. In other words, connectedness was not related to connectedness between the written and oral representations. In this regard, if the oral and written representations used in each SWH component had no explanation (e.g. if the PSTs drew a graph but did not do any explanation about it) and if the representations were not related to each other, it was designated as having no coherence and zero points was given. If the representations used in each component of SWH were related to each other, but had a weak or moderate relationship with the context, it was considered as weak or moderate coherence and given 5 or 10 points, respectively. If the representations used in each component of the SWH report were related to each other and strongly tied to the text or had powerful relationships with the context, it was considered as strong coherence and given 15 points. The type (e.g. MAS, MIS, MAS-MIS-SYM) and cohesiveness of the oral and written representations (e.g. weak, moderate, strong) were determined and categorized to give a holistic representation score for each experiment. For example, if a PST uses 12 different representations in total in a lab report, 10 of which are categorized as strong coherence and 2 of them categorized as moderate coherence, the PSTs’ cohesiveness is scored as strong coherence and 15 points is awarded because the majority of the written presentations are in this category. Finally, statistical analysis was run. Moreover, an independent analysis was conducted to ensure validity of the rubric scores. As mentioned in the written and oral argument section, the four transcripts of video recordings and 36 laboratory reports were randomly chosen and given to another researcher along with the scoring rubrics of multiple levels of representation. An inter-rater reliability of the analysis was found to be 0.90 for coding the oral representations and 0.91 for coding the written representations.

A Friedman test which is a nonparametric statistical test used for repeated measures (Smalheiser, 2017), and a Wilcoxon signed-rank test for post-hoc were conducted to determine whether PSTs’ scores on written and oral arguments, and representations quality improved over time, and whether there was a significant difference between their average scores. Moreover, a Spearman-Brown correlation coefficient was run to investigate the relationship between the quality of written and oral argument; between written and oral representation quality; between the quality of written argument and written representation; and between the quality of oral argument and oral representation (Büyüköztürk et al., 2017).

In this study, the findings are represented as assertions with several steps that were followed in order to determine the assertions. First, each video was watched, transcribed and read, and each laboratory report was read thoroughly. Second, the quality of arguments and the multiple levels of representation were coded and the constant comparative method was used to analyze oral and written argumentation across all experiments. Third, the quality of arguments, the multiple levels of representations were counted, categorized or scored. Fourth, two semesters were divided into four-time phases because of time breaks between experiments: Time Phase 1 (Experiments 1–5), Time Phase 2 (Experiments 6–10), Time Phase 3 (Experiments 11–15), and Time Phase 4 (Experiments 16–20). There was a one-week break between time Phases 1 and 2, and time Phases 3 and 4 because of mid-term exams, and there was an 8 week-break between time Phases 2 and 3 because of a semester break. Fourth, the statistical analysis was run. Fifth, several tactics (such as noting patterns, counting, noting relationship between variables, and building a logical chain of evidence) were used to draw and verify conclusions to develop the assertions (Miles et al., 2013).

Results

Given that the PSTs were able to participate in such an environment, there were a number of assertions that emerged.

Assertion 1: Regardless of the form of dialogue, there was a very strong relationship between the quality of argument, quality and use of representations, and this relationship grew significantly across time.

Assertion 1 emerged from the analysis of the first research question. In this regard, the first step was to calculate the PSTs’ holistic arguments and multiple levels of representation scores in oral and written arguments, and then these results were later analyzed through a Friedman test for repeated measures.

Oral Dialogue: Table 2 shows the Friedman results for oral argument quality, and representational quality in each time phase of the project. As seen in Table 2, the average scores were 6.6, 12.2, 12.2 and 12.2 for the quality of oral argument, 7.7, 11.7, 12.2 and 12.2 for representational quality, respectively. The Friedman test results indicated that qualities of oral argument (X_F²(3) = 24.000, p = 0.00, p < 0.001), and representational use (X_F²(3) = 19.800, p = 0.00, p < 0.001) changed significantly across the time phases. After examining these differences using the Wilcoxon signed-rank test, the results indicated there was a significant difference between PSTs’ oral argument (T = 36.00, z = −2.55, p = 0.011, p < 0.05), and representational quality (T = 28.00, z = −2.41, p = 0.016, p < 0.05) between Time Phase 1 and 2 in favor of Time Phase 2. The scores in Time Phase 3 were significantly higher than Time Phase 1 in PSTs’ oral argument (T = 36.00, z = −2.55, p = 0.011, p < 0.05), and representational quality (T = 28.00, z = −2.40, p = 0.017, p < 0.05). In a similar vein, post hoc tests using the Wilcoxon sign-ranked test show that the PSTs’ mean scores in Time Phase 4 were significantly higher than Time Phase 1 related to the quality of oral argument (T = 36.00, z = −2.55, p = 0.011, p < 0.05), and representational use (T = 28.00, z = −2.39, p = 0.017, p < 0.05). Such results show that the PSTs’ oral argument quality and oral representation quality remained stable after Time Phase 2 and showed a similar pattern.

Table 2 Friedman test results of oral argument and multiple levels of representation (MLR) qualities in each time phase

Oral	Time phase (TP)	N	Mean	sd	Mean rank	df	χ ^2	p	Significant differences^a
*p < 0.05. **p < 0.01. ^aPost hoc test using Wilcoxon signed rank test.
Oral argument quality	TP1	9	6.6	3.97	1.17	3	24.000	0.000**
	TP2	9	12.2	2.63	2.94				1–2
	TP3	9	12.2	2.63	2.94				1–3
	TP4	9	12.2	2.63	2.94				1–4
Oral MLR quality	TP1	9	7.7	4.09	1.33	3	19.800	0.000**
	TP2	9	11.7	3.53	2.78				1–2
	TP3	9	12.2	2.63	2.94				1–3
	TP4	9	12.2	2.63	2.94				1–4

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Written Dialogue: As seen in Table 3, the PSTs’ average scores were calculated as 7.6, 13.0, 12.6, and 13.2 for written argument quality, and 9.3, 12.7, 12.7 and 13.0 for written representation quality for each time phase, respectively. According to the Friedman test results of variance analysis in the repeated measurements in which the statistical significance of the differences between the averages were tested, the PSTs’ written argument qualities (X_F²(3) = 21.532, p = 0.00, p < 0.001), and written representation qualities (X_F²(3) = 22.417, p = 0.00, p < 0.001), displayed significant difference across the time phases. The differences between time phases were examined post-hoc using a Wilcoxon signed-rank test. There was a significant difference between the mean scores of the PSTs’ written argument quality (T = 45.00, z = −2.67, p = 0.008, p < 0.05), and written representation quality (T = 45.00, z = −2.67, p = 0.008, p < 0.05), between Time Phase 1 and Time Phase 2 in favor of Time Phase 2. This difference was also seen between Time Phase 1 and Time Phase 4 in favor of Time Phase 4 – written argument quality (T = 45.00, z = −2.67, p = 0.008, p < 0.05), and written representation quality (T = 45.00, z = −2.67, p = 0.008, p < 0.05). There was also a significant difference between Time Phase 3 and Time Phase 4 in favor of Time Phase 4 – written argument quality (T = 21.00, z = −2.20, p = 0.027, p < 0.05), and written representation quality (T = 15.00, z = −2.12, p = 0.034, p < 0.05). These results show that the quality of the PSTs’ written argument quality, and representations continuously increased over time.

Table 3 Friedman test results of written argument, multiple levels of representation (MLR) qualities and reasoning in each time phase

Written	Time phase (TP)	N	Mean	sd	Mean rank	df	χ ^2	p	Significant differences^a
*p < 0.05. **p < 0.01. ^aPost hoc test using Wilcoxon signed rank test.
Written argument quality	TP1	9	7.6	1.45	1.00	3	21.532	0.000**	1–2
	TP2	9	13.0	1.47	3.00				1–3
	TP3	9	12.6	1.89	2.50				1–4
	TP4	9	13.2	1.42	3.50				3–4
Written MLR quality	TP1	9	9.3	1.85	1.00	3	22.417	0.000**	1–2
	TP2	9	12.7	1.91	2.89				1–3
	TP3	9	12.7	1.91	2.67				1–4
	TP4	9	13.0	1.78	3.44				3–4

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Assertion 2.a. Regardless of the time phase and the form of dialogue, the quality of the arguments and representational use increased.

Assertion 2.a. emerged from the analysis of the Spearman-Brown correlation as a response to the second-a research question. The results from the Spearman-Brown correlation coefficient analysis indicate there was a high level of positive and significant correlation between the quality of written arguments and written representations except for Time Phase 1 (r = 0.531 in TP1; r = 0.909, p < 0.01 in TP2; r = 0.952, p < 0.01 in TP3; r = 0.983, p < 0.01 in TP4). When the determination coefficients are taken into account for all time phases (TP1 r² = 0.28; TP2 r² = 0.82; TP3 r² = 0.91; TP4 r² = 0.96), it can be seen that 28%, 82%, 91%, and 96% of the total assumptions (variability) in the quality of the written arguments are due to the quality of written representations. Such results suggest that as the quality of written arguments increases, the quality of written representations increase in all time phases.

Parallel findings were found for oral dialogue. There was a high level of positive and significant correlation between the quality of oral arguments and representations in all time phases (r = 0.887, p < 0.01 in TP1; r = 0.949, p < 0.01 in TP2; r = 1.0, p < 0.01 in TP3; r = 1.0, p < 0.01 in TP4). When the determination coefficients are taken into account according to time phases (r² = 0.78 in TP1; r² = 0.90 in TP2; r² = 1.0 in TP3; r² = 1.0 in TP4), it can be said that 78%, 90%, 100% and 100% of the total assumption in the quality of oral arguments are due to the quality of the oral representations. As with the results for writing, these results suggest that as the quality of oral arguments increases, the quality of oral multiple representations and oral reasoning increases.

Assertion 2.b. There was a positive correlation between the quality of written and oral argument, and between the quality of written and oral representational use.

Assertion 2.b was made as a response to the second-b research question. The findings showed while a significant positive correlation only occurred in Time Phase 2, the results showed that a positive relationship exists between the quality of written and oral arguments in all time phases (r = 0.217 in TP1; r = 0.745, p < 0.05 in TP2; r = 0.489 in TP3; r = 0.528 in TP4). The findings also showed that a positive relationship exists between the quality of written and oral representational use in all time phases (r = 0.039 in TP1; r = 0.557 in TP2; r = 0.575 in TP3; r = 0.627 in TP4).

Assertion 3.a. While the PSTs developed representational competence in both oral and written arguments over time, they were able to connect more representations in their written arguments.

Assertion 3.a. emerged from an analysis of the third research question. All representations were coded, counted and categorized as single, two-connected, three-connected, or four-connected representations with Fig. 1 showing the distribution of percentages of type of representations in oral and written arguments over time. Whilst the percentages of a single type of representations decreased in both written (58%, 18%, 12%, 12%, respectively) and oral arguments (31%, 35%, 23%, 11%, respectively), the percentages of the three-connected representations increased in both written (10%, 27%, 31%, 32%) and oral arguments (22%, 29%, 23%, 26%) across all time phases. However, four-connected representations only appeared in written arguments and also increased (8%, 19%, 36% and 37% respectively), across all time phases.

When we investigated where these representations were predominantly used, the results indicated that the more connected (e.g. three and four connected) representations were prominently used in the evidence component in both written and oral arguments (as seen in Fig. 2 and 3), for each time phase.

Fig. 1 The distribution of percentages of type of representations in oral and written argumentation across time phases.

Fig. 2 The distribution of percentages of type of representations in components of SWH in oral argumentation in each time phase.

Fig. 3 The distribution of percentages of type of representations into components of SWH in written argumentation in each time phase.

Fig. 2 shows the distribution of percentages of single, two-connected, and three-connected representations in the design and claim and evidence (C&E) components of oral arguments for each time phase. While the PSTs mostly used the single type of representations in the design components (64%, 99%, 71%, 76%), they predominantly used more connected level of representations (e.g. three-connected representations) in the claim and evidence components of oral arguments in each time phase (62%, 88%, 71%, 52%).

Fig. 3 shows the distribution of percentages of type of representations into component of the SWH (design, observation, claim and evidence (C&E), reading and reflection (R&R)) for each time phase in the written arguments. The use of a single type of representation mostly occurred in either the design (39% in TP2, 47% in TP4) or reading and reflection components (40% in TP1, 54% in TP3). Two-connected representations were mostly used in the observation component across all time phases (43%, 46%, 51%, 52%), while three-connected representations were used either in observation (50% in TP1, 44% in TP3) or claim and evidence (43% in TP2; 46% in TP4) components. However, four-connected representations were mainly used in the claim and evidence component across all time phases (77%, 73%, 79%, 68%). This result shows that the claim and evidence component is the place where the PSTs connected more representations (e.g. the three and four connected) in their written arguments, which is similar with PSTs’ oral argument results.

Fig. 4 shows total percentages of representations in components of SWH approach in both the oral and written argumentation in each time phase. In the oral arguments, the PSTs used most of the representations in the design components in each time phase (percentages of 64%, 55%, 62%, and 69% respectively). Such a result may indicate that in the claim and evidence components the PSTs were more selective in which representations they used. In the written arguments the PSTs generated most of the representations in the observation components across all time phases (38%, 38%, 48% and 37%, respectively). The results in the claim and evidence components (26%, 27%, 26%, and 29% respectively) and reading and reflection components (21%, 17%, 14% and 20%, respectively) of written arguments, show that the PSTs did not use all the representations they generated in observation. These results indicate that the evidence component is the place where the PSTs were selective in terms of the representations they used.

Fig. 4 Total percentages of representations in components of SWH in written and oral argumentation in each time phase.

Taken together, these results show that the PSTs developed representational competency over time because they used single type of representations with higher percentages in the first time phase, but then they decreased the percentage of using this type of representation in later time phases (Fig. 1). On the other hand, the PSTs started making connections between different representations, and used more connected representations (e.g. three and four connected representations) with higher percentages (Fig. 1). Moreover, the PSTs used more connected representations in the evidence component to support their claims (Fig. 2 and 3), and they were selective in terms of representations they generated in design of oral argumentation and in observation components of written argumentation (Fig. 4).

Assertion 3.b. The symbolic and macroscopic levels of representation played an important role for interconnectedness of representations in PSTs’ written and oral arguments.

Assertion 3.b. was made in response to the third research question. In this regard, the type of the representations PSTs’ used was coded and counted. Fig. 5 shows the total percentages of each representation in oral and written arguments. The analysis of representations used in four time phases showed that the symbolic level (SYM, 75%) in single type, the combination of the macroscopic and symbolic level (SYM-MAS, 92%) in two-connected representations, and the combination of the macroscopic, symbolic level and the algebraic level (SYM-MAS-ALG, 60%) in three-connected representations were the most utilized representations of the potential combinations in the PSTs’ oral arguments. As stated earlier, there was no four-connected level of representation in PSTs’ oral arguments across all time phases.

Fig. 5 Percentages of type of representations in detail in oral and written argumentation in total.

As seen in Fig. 5, while individual symbolic (SYM, 30%) and macroscopic (MAS, 62%) levels of representation, and their combination (MAS-SYM, 85%) were the most used single type and two-connected types of representations, both combinations of three-connected types (MAS-MIS-SYM, 49%; MAS-SYM-ALG, 51%) were used almost equally. The combination of four levels of representation was used in PSTs’ written arguments across four-time phases. As a result of the findings gathered from representations in PSTs’ written and oral arguments, we believe that the symbolic and the macroscopic level of representations can be viewed as a reference point (a point in which the PSTs started connecting different levels of representations) since they were frequently used as one type of representation and appear to be dominant to connect microscopic and algebraic levels.

Discussion

This study investigated the development and utilization of PSTs’ quality of arguments, and multiple levels of representation in their oral and written arguments over the span of two semesters. The results revealed that the quality of argument and representation were intertwined in both written and oral argumentation (1st Research Question, RQ), showed parallel patterns (1st RQ) of use and had a positive and significant correlation between the quality of written argument and representation as well as between the quality of oral argument and representation (2nd a RQ) across all time phases. While the quality of PSTs’ written argument and representation significantly increased from the first time phase, they remained stable after the second time phase in oral argumentation (1st RQ). There was also a positive correlation between PSTs’ quality of written and oral argument and written and oral representation quality (2nd b RQ). The PSTs’ representational competency increased over time, with more connected representations in their written arguments compared to their oral arguments (3rd RQ).

Taken together, the results are important in that they show that even when writing and talking are complementary (2nd b RQ), writing nonetheless serves a different function from talking (1st, and 3rd). As such, these results do provide preliminary insight into the “interdependency” of talk and writing, but they also provide evidence that more work needs to be done in understanding this dependency. While much research has focused on talk, these results show the critical importance of the interaction with writing particularly because of the greater understanding now emerging of writing as an epistemic tool (Prain and Hand, 2016). Drawing from the results of this research and previous research, we believe that the learning environment, language, audience, and writing-to-learn activities are critical factors that should be considered when arriving at these conclusions.

We argue that the intertwined nature of the quality of argument and representation (1st and 2nd a RQs) is related to the learning environment and language use. This finding aligns with Yaman's (2020) study, which showed that the PSTs’ quality of argument and representations are intertwined in written arguments. The PSTs engaged in an argument-based inquiry environment in which arguments that emphasized science language were linked with inquiry, and in which evidence was not separated from reasoning (Hand et al., 2017a; Hand et al., 2020). Such an outcome supports the work of Klein et al. (2014) who highlighted that the SWH approach can be classified as a genre system that includes oral, print and graphical representations and complex reasoning. In this learning environment, the PSTs explained and interpreted chemical equations, calculations, and graphs using proper Turkish with clear logical statements and drawn inferences (reasoning) to support their claims (Burke et al., 2005; Hand et al., 2017a; Hand et al., 2020). Moreover, there was much emphasis on the sustained engagement of language, with Kozma and Russell (1997) contending that language is the glue that holds different representations together. We believe that embedding PSTs in different language practices including writing, talking, and reading in argumentation may have played an important role as a glue to hold different types of representations together.

The role of audience

The positive correlation results between written and oral arguments and representation (2ndb RQ) can be attributed to an audience factor. Studies that have attempted to connect written and oral arguments or determine that dialogue is a path for argumentative writing highlight that students need a clearly defined audience to communicate with in order to develop rich and convincing arguments in written form and to link written and oral arguments (Walker and Sampson, 2013; Berland and McNeill, 2010; Chen et al., 2016; Shi et al., 2019). The finding from this current research stands in contrast to findings that contend there is a limited transfer between students’ oral and written argumentation (Firetto et al., 2019) and that students’ oral argument is more complex than their written arguments (Berland and McNeill, 2010). However, the findings do align with the concept that students’ written and oral arguments are positively related to each other (Reznitskaya et al., 2001; Walker and Sampson, 2013; Chen et al., 2016; Hemberger et al., 2017; Murphy et al., 2018; Shi et al., 2019), which may suggest that such arguments are complementary. Indeed, the purpose of both written and oral arguments is to convince the other party of the validity and strength of a claim in the presence of an audience within a dialogue (Del Longo and Cisotto, 2014).

However, research indicates that writers do not always consider the presence of the audience in the written arguments (Del Longo and Cisotto, 2014). In considering the applicable counter arguments, Del Longo and Cisotto (2014) suggest that the writers therefore need to mentally represent their audience and organize their reasoning in a coherent and cohesive manner. Student writers who lack an audience beyond the instructor are generally at risk of filling the pages with what they believe their teachers want to hear (Shi et al., 2019). However, when such students are asked to write to an audience other than the teacher, a series of translation processes occur, in that the students are forced to break down certain terminology. In so doing, the students need to first translate scientific knowledge into colloquial language so that they can understand the concepts before translating the language to inform and explain the scientific knowledge for their intended audience (Gunel et al., 2009; Hand, 2017; Jang and Hand, 2017; Yore and Treagust, 2006). Gunel et al. (2009) reported that when students are asked to write to their peers or younger audiences (in other words, those who have less knowledge about the topic), the students are able to explain the processes more explicitly, thereby indicating an increase in metacognitive skills. In the current study, the PSTs were asked to write their lab reports to someone who does not know the topic; their peers represented audience members whom they had to persuade through oral argumentation. Walker and Sampson (2013) reported that undergrad students who were provided with writing and talking practice while embedded in an argument-focused chemistry laboratory environment over a semester were able to connect their written and oral arguments when tasked with writing to their peers. Therefore, we argue that asking PSTs to write to a clearly defined audience may have helped them connect oral and written arguments and improve their quality of written argument and representation.

Extension of writing to learn focus

The findings related to increasing the quality of written argument and representations (1st RQ) are an extension of the writing-to-learn activities (that are promoting students’ learning, improving conceptual understanding by unpacking meanings, and cognitively demanding) that the PSTs are engaged in (Bangert-Drowns et al., 2004; Klein, 2006). These findings are consistent with past studies that used the SWH approach in terms of increasing the quality of the argumentative writing (Choi et al., 2013; Chen et al., 2016; Yaman, 2020, 2021), and support the conclusion that argument-based approaches similar to the SWH approach can improve the argumentative writing of undergraduate chemistry students over time (Walker and Sampson, 2013). The reason for this association may be due to the sustained practices of reasoning and representation that the PSTs implement through their written arguments (Hand et al., 2017a and 2017b; Yaman, 2020). Reznitskaya et al. (2001) and Firetto et al. (2019) support the idea that students need to be provided with repetitive and varied written argumentation experiences to develop argumentative skills over time. The PSTs used the student template of the SWH approach, which is a writing-to-learn activity, and this template also included a reading and reflection component that provided the PSTs metacognitive thinking and self-reflection on how their ideas have changed through negotiations (Hand et al., 2017a and 2017b; Hand et al., 2020). Moreover, we argue that writing also serves as a tool for integrating a variety of representations as well as learning and complex reasoning (Applebee, 1984), as the PSTs connected more representations in their written arguments when compared to oral arguments (3rd RQ).

Talk is not writing

Even though the PSTs engaged in sustained practices of oral argumentation, the findings indicate that the quality of their oral argumentation, and representation increased up to the second time phase and subsequently remained stable (1st RQ). We argue that writing is not talk recorded and serves a function that is separate from talking (Emig, 1977). During oral argumentation, the PSTs attempted to arrive at a consensus by working as a whole class to embed arguments that involved beginning questions, design, claims and evidence. While the increase in oral argumentation up to the second time phase may suggest that the PSTs developed the resources needed for critiquing and constructing arguments, the stability after the second phase suggest that the PSTs utilized these sources.

Building of representational competency

The results indicate that the PSTs’ representational competency increased over time (3rd RQ), which is consistent with previous research that focused on PSTs’ written argument and representations using the SWH approach over two semesters (Yaman, 2020). In the first- time phase of this learning environment, the PSTs tended to use a single representation particularly at the symbolic and macroscopic level with higher percentages; later, however, the PSTs were able to connect representations (Dreyfus, 2002). We thus contend that interpreting and explaining the meaning behind the data and calculations to support claims – the chemical sign (symbolic level) to describe observable chemical phenomena (macroscopic level) in terms of underlying molecular entities (microscopic level) – may have helped them connect these representations (Kozma and Russell, 1997, 2005; Kozma et al., 2000; Yaman, 2020). Moreover, when designing the investigations in the laboratory, the PSTs decided upon the appropriate variables, discussed the representational form of the data they would collect, and assessed why a particular representation may work better for their purposes (Kozma et al., 2000; Kozma and Russell, 2005).

When discussing or explaining their design, the PSTs drew their equipment, wrote formulas, equations, and described the relationship between physical changes they expected to observe. They then made observations, took measurements and recorded data. Stieff and DeSutter (2020) reported that the generation and selection of representations did not improve students’ representational competence skills when they employed drawings. However, throughout the process, the PSTs generated representations such as chemical equations, graphs, calculations in the design, observation, evidence and reading and reflection components. They also selected and connected different type of representations as evidence to support claims, and explained why certain inferences were appropriate for a particular purpose in their writing and talking (Kozma and Russell, 1997, 2005; Kozma et al., 2000; Yaman, 2020). We contend that by using representations in these ways, the PSTs developed representational competence (Kozma et al., 2000; Hand et al., 2020).

In this study, the PSTs were involved in an argument-based inquiry approach known as the SWH approach, which is an example of an immersive approach to argument (Cavagnetto, 2010; Manz, 2015). We argue that our results have the potential to represent any of the approaches within this broad classification. While findings from this study support findings from other studies, it extends these findings and provides richer evidence about the development and utilization of argument and representation while constructing written and oral arguments. As a result of this study, we suggest that students should be provided with an audience other than the instructor. In addition, students should be immersed in argument-based inquiry for longer periods with sustained talking, writing and reading practices to develop representational competency and improve science understanding using argument, and representation simultaneously. Furthermore, students need to be engaged in writing-to-learn activities to integrate ideas, construct understanding and increase metacognitive thinking. We also suggest that science teacher education programs incorporate argument-based inquiry, language including talking, writing and reading, and writing-to-learn practices so that pre-service science teachers can engage as learners, which will positively influence their epistemic orientation as future teachers.

Limitations and further research

This study was limited in that it had a small sample size, so the results of this study should be interpreted cautiously. While the sample size may be an important factor in direct comparison studies, a small sample size in a repeated measure study may be more appropriate as more than one measurement and in-depth analysis is performed over time. In this context, 180 laboratory reports of 5 to 19 pages in length and 20 video recordings of 101 to 199 minutes were analyzed over two semesters. A second limitation is the sex representation; all participants in the study were female with low or moderate socioeconomic status from rural areas in Turkey. Therefore, this population may not be representative of all students who take chemistry laboratory courses. Future research needs to be expanded to more diverse groups and cultural settings other than Turkey. A third limitation is related to the collection of data. Written (lab reports) and oral documents (video recordings) were collected over two semesters. Further research might collect additional data such as interviews or vignettes. In this study, we were only interested in analyzing PSTs’ written and oral arguments and representations. For further research, more in-depth analysis of oral discussions (e.g. more examples of talk/utterances) amongst the class should be conducted.

Conflicts of interest

There are no conflicts to declare.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d2rp00152g

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