Startseite Reasoning in chemistry teacher education
Artikel Open Access

Reasoning in chemistry teacher education

  • Samia Khan EMAIL logo
Veröffentlicht/Copyright: 18. Dezember 2024
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

Research on preservice science teacher’s reasoning is comparatively new in a larger field of research on reasoning. This study examines model-based reasoning among preservice science teachers to make recommendations on how reasoning can be fostered within chemistry teacher education. It coalesces over 20 years of a program of research in this area. Firstly, several empirical studies on undergraduate students and their reasoning are examined. Future chemistry teachers are drawn from this pool of undergraduate students. Secondly, empirical studies in preservice teacher education are examined to highlight reasoning among preservice chemistry teachers. Thirdly, recommendations are put forward for future research on the development of scientific reasoning among chemistry teachers as an important facet of chemistry teacher education.

1 Introduction

Reasoning in science is characterized by cognitive processes that can contribute to, or allow for, the formulation and testing of hypotheses, two important aspects of scientific endeavours (Giere et al., 2006). These cognitive processes, among others, involve formal logic, including probabilistic logic, and non-formal processes, including model-based reasoning (Khan, 2007) and analogical reasoning (Bruce et al., 2022). Several of the more non-formal cognitive processes have been associated with scientific activity at the revolutionary end of the continuum on advancement in science (Kuhn, 2012; Simon, 1996).

While science involves both formal and non-formal reasoning and problem-solving, the terms have been used interchangeably. While solving a problem, a problem-solver might engage in both forms of reasoning. As Zimmerman (2000) points out, one of the main generalizations about problem-solving is the use of heuristic searches. A problem solver constructs or develops a representation of the problem situation (i.e., the problem space) in order to generate a solution within some set of constraints. A problem space includes the initial state, a set of operators that allow movement from one state to another and a goal state or solution. It is proposed that goal-oriented behavior in general involves a search through problem spaces. In the sciences, these spaces have been thought to include a hypothesis space, experiment space, and model spaces, among other types of problem spaces (Schunn & Klahr, 2019). Reasoning, on the other hand, appears to distinguish itself from problem-solving and its use of heuristic searches in problem spaces in that direct retrieval of a solution from memory is not possible for reasoning (Zimmerman, 2000).

Magnani (2009) contends that much of scientific inquiry involves reasoning where models are generated and used in experimental and theoretical work, rather than problem-solving using heuristics. Even contrary to a more standard view of scientific reasoning as hypothetico-deductive or logic-based, abductive or model-based reasoning is arguably more descriptive of research in the sciences (Nersessian, 2008) and in chemistry (Wackerly, 2021). Modeling as an epistemic practice can yield explanatory mechanisms and manifest predictions beyond the scope of the original target domain or phenomenon (Rost & Knuuttila, 2022). Thus, models are useful for solving problems which cannot be solved by heuristics alone. Lehrer & Schauble (2005) further contend that other practices associated with science (such as argumentation) develop more as a contest about the adequacy of one model over another. This article describes several studies on model-based reasoning to generate hypotheses about teacher education and respond to the overarching research question, how can preservice chemistry teachers reason with models in teacher education?

The purpose of investigating science teacher reasoning is because teachers support students who actively reason about the world. Furthermore, advancing the UN sustainable development goals (SDGs) can occur through science teacher education. SDG 4c states that by 2030, the goal of the UN is to substantially increase the supply of qualified teachers. This goal is supported by educational research that suggests that teacher education is one of the strongest correlates of student achievement in math and science (Darling-Hammond, 2000; Monk & King, 1994). Reasoning is one of the areas that is important to investigate in teacher education as teachers may influence their own students’ reasoning competencies in classrooms. Promoting reasoning in chemistry classrooms is ever more necessary to foster greater understanding of systems in flux that models of chemistry influence, such as climate systems. Studies on reasoning continue are prevalent in science education research (DeBoer, 2019). Pertinent to science teacher education; however, the research ranges from teacher general pedagogical reasoning (Loughran et al., 2016) among others, to teacher research on reasoning competencies (Göhner & Krell, 2022; Krell et al., 2020). This article aims to contribute to the latter arena.

2 Review of literature

2.1 Reasoning in undergraduate science and chemistry education: questions and challenges from several empirical studies

Reasoning involving undergraduate education reveals specific insights about the experience of students at this level, and also of future chemistry teachers who emerge from this pool of undergraduate science students. For example, Ding (2018) investigated the level of scientific reasoning of university students in China across different years, at different types of universities, and in different fields, using the Lawson Test. It was found that regardless of which field or university, undergraduate students’ scientific reasoning showed little variation across their entire four years of undergraduate education.

Undergraduate students’ reduced breadth of studies may have slowed down their progress in scientific reasoning, according to Ding (2018). An alternative explanation, the author speculates, is that the narrowed focus of their studies may not have a significant impact but rather the kind of instruction they receive in higher education contributes little to the development of their scientific reasoning. As revealed from their results, while university students are required to continue learning advanced content knowledge in their specialized fields, their scientific reasoning skills exhibit little improvement. This perhaps has caused student reasoning to fall out of phase with their progression in content learning.

Data from the Ding study (2018) raises questions about scientific reasoning at the university level, lending credence to an earlier 21st century call for research on scientific reasoning and assessing it (Osborne, 2013). Indeed, science education research on analogical reasoning (Trey & Khan, 2008), causal reasoning (Deng & Flynn, 2021), relational reasoning (Dumas et al., 2013), spatial reasoning (Stieff et al., 2012), mechanistic reasoning (Talanquer, 2018) has burgeoned. Reasoning remains an important area of study not just for science education but chemistry education.

Reasoning in chemistry labs is used to invent reactions that have not yet been conducted during the synthesis of new compounds (Segler & Waller, 2017). To further illustrate reasoning by chemists, in a 2023 study by Button and colleagues, chemists were asked to imagine a compound that is made up of boron, oxygen, and fluorine. This compound only had three atoms, with a single atom from each element. One chemist suggested there were multiple answers, others invoked rules for their imagined structures such as, “[W]e lose entropy when we cyclize” and still others generated an analogy to glucose (Button et al., 2023). In other words, chemists reason about their invented structures, not just by drawing on heuristics, but they make inferences from data, or apply a near analogy to tackle problems not ascertainable through direct experience. In the context of chemists using analogical and other types of reasoning to advance their understanding of chemistry, the students of chemistry do not appear to be advancing their reasoning skills (Ding, 2018).

2.2 Turning the lens to science and chemistry teacher education

Valuable research on scientific reasoning in laboratory and educational contexts has occurred (Hogan, 1999; Lawson, 1978; Schauble, 1996), yet fewer studies have been done on the state and advancement of scientific reasoning among university students who choose science or chemistry teaching as a career (Lawson et al., 2000). Kind and Osborne suggest that scientific reasoning is highly dependent on content and procedural and epistemic knowledge (Kind & Osborne, 2017). It is also arguably dependent on the context, such as undergraduate contexts. Much of the research on reasoning in undergraduate contexts has students working on artificial problems (Fischer & Bidell, 1998); however, classroom contexts with authentic teachers and students can yield special insights (Krell et al., 2020). Ding’s (2018) suggestion is that the kind of instruction in undergraduate education might result in limited advancement in scientific reasoning. Less is known about what happens to reasoning in chemistry teacher education contexts.

In terms of reasoning and chemistry teachers’ backgrounds, in 2023, my colleagues and I (Krell et al., 2023) examined the contribution of three factors to the development of preservice science teachers’ scientific reasoning competencies: the amount of science education classes, the amount of science classes and the preservice science teachers’ ages as they began their teacher education programs and progressed through completion of this university program. This investigation of teachers’ backgrounds involved six universities in Germany, Chile and Canada. Preservice science teachers anonymously responded to an established multiple-choice instrument and statistical method (Krell et al., 2021) that assessed scientific reasoning over the course of their teacher education programs, taking into account the number of credit hours of science courses and science education courses of various programs in these universities and countries.

Multiple linear regression showed in this study that amount of science education classes, the amount of science classes, and age explained about 9 % of preservice science teachers’ scientific reasoning. These preservice science teachers consisted of chemistry, biology, and physics teachers. Notably, the factor: amount of science classes, was the only significant predictor of scientific reasoning among preservice science teachers. This empirical finding on content knowledge is aligned with previous research that shows a positive relationship between the two (Fischer et al., 2014), but it contrasts with other studies that point to a positive correlation between the amount of science education classes and SRCs too (Bruckermann et al., 2018). It appears that science content knowledge may be requisite for developing preservice science teachers’ scientific reasoning.

The Ding (2018) study, on the other hand, is different from the research we did in two ways: first, a different test of reasoning was employed and second, the Ding study was only deployed within a single country. The aforementioned 2023 study; however, went beyond a single country. Furthermore, and in addition to reasoning involving scientific investigations, in the 2023 study, model-based reasoning was also examined. Model-based reasoning entailed judging the purpose of models, testing models, and changing models. Analysis of the 2023 questionnaire results revealed substantial differences among the three countries, as reported in Table 1 and published in Krell et al. (2023).

Table 1:

Sample models utilized by preservice teachers from their science curricula.

Grade 10 11 12
Curricular topics Chemical reactions, life sciences Atomic theory Nature of acids and bases, dynamic equilibrium, electrochemical cells
Models Ice Atomic (Bohr) model Arrhenius and Bronsted–Lowry
Chemical respiration Chemical equilibrium
Lead-acid battery

These findings showed that Canadian preservice science teachers possess greater competencies related to model-based reasoning compared to either their German or Chilean preservice teacher counterparts. Regarding the Canadian sample, possible explanations for the significantly higher results could be that, (1) Canadian preservice teachers had more of a science background by about 1–2 years of study and that (2) teacher education or the kind of instruction had an impact on fostering model-based reasoning above and beyond their greater science backgrounds.

Model-based reasoning is a scientific practice that has been described as relational (Alexander, 2019; Sevian & Talanquer, 2014), mechanistic (Moreira et al., 2019), and analogic (Lehrer & Schauble, 2015). It has been proposed that model-based reasoning requires mental models, or internal representations of the way the world works (Pietarinen & Bellucci, 2014; Williams, 2018). These mental models are invoked when solving problems or reasoning about phenomena. One can have a mental model of climate change, Pandemics, rumours, ownership, locks and keys, atoms, or the planets, to name only a few.

Mental models can be expressed externally and socially negotiated (Campbell et al., 2015; Halloun, 2007; Khan, 2007; Khan & Chan, 2011). In teaching contexts, dialogic processes have been shown to be powerful for students engaged in model-based reasoning, especially when students themselves can point to initial activities involving modeling and author the stages (Windschitl et al., 2018). Reasoning with a model can also surface its limitations and new criteria for evaluating models (Lehrer & Schauble, 2005; Pluta et al., 2011). Epistemic messages, such as those on the purposes of adjudicating models, support greater understanding of the nature of models (Ke & Schwarz, 2021). Reasoning with models is not only done among scientists but also among teachers and students (Nersessian, 2008).

In a 2007 study on model-based reasoning in chemistry teaching contexts, Khan compared undergraduate first year chemistry classrooms for their pedagogical approaches. The approach that characterized the vast majority of chemistry teaching involved beginning the undergraduate class with defining concepts or terms in chemistry, the instructor showing how to solve a problem, and time for students to receive and practice similar problems. This approach could be termed a traditional approach to teaching chemistry in the undergraduate department, as represented by Figure 1 (Khan, 2001).

Figure 1: 
Traditional approach to teaching chemistry (Khan, 2001).
Figure 1:

Traditional approach to teaching chemistry (Khan, 2001).

One course; however, outperformed others on a general scientific reasoning test given to multiple institutions science classes (Khan, 2001). The approach to teaching by this chemistry instructor could be characterized as model-based or the generate-evaluate-modify (GEM) approach, a first case of model-based instruction inside a classroom (Khan, 2007).[1] Figure 2 depicts the GEM approach to instruction. This approach to instruction involved the teacher promoting the generation of relationships internal to a model, evaluation of those relationships (often in light of new information), and modification of their models. Evaluation and modification occurred repeatedly, until a more accurate consensus model was co-constructed with teacher guidance in the chemistry undergraduate class.

Figure 2: 
GEM instructional cycle (Khan, 2007).
Figure 2:

GEM instructional cycle (Khan, 2007).

The GEM approach that was observed in this chemistry undergraduate class involved asking students to reason about unobservable phenomena in science. In the first phase, students generate a relationship between two or more variables in a dialogic environment using information or data sets. In one example in the undergraduate chemistry classroom of an investigation of molecular models, the undergraduate teacher encouraged chemistry students to explore the relationship between vapor pressure and boiling points. Students also generated relationships between molecular weight and the mass of a compound in this phase of instruction. In the next phases of instruction observed, chemistry students continued to reason about their models of molecular compounds. In this phase, they grappled with information that led them to re-evaluate their originally formed relationships between molecular weight and boiling point. This information came from a simulation. The chemistry students noted that the higher the molecular weight, the higher the boiling point.

The chemistry students saw; however, that methanol and methyl amine fell out of the expected trend using a simulation. This was the beginning of a discussion on what kind of bond could exist between two hydroxyl groups as exclaimed by a student question, “What kind of bond would there between two hydroxyls?” While the idea of a bond is not correct, the undergraduate students went on later to invent a hidden causal factor of what would later be termed by the teacher as an intermolecular force. Their newly revised models of a chemical compound better aligned with the anomalous data points as it later took into account intermolecular forces (Khan, 2007). The GEM pedagogical approach was elucidated through several years of classroom analysis, and 34 guidance strategies to scaffold model-based reasoning were identified from observations of this approach (Khan, 2011). This approach to teaching chemistry did not require reading in advance, and the undergraduate chemistry teacher did not correct students right away. Instead, reasoning was fostered and sustained during GEM discussion. Recent research in science education settings further reports on the benefits of model-based instruction on student reasoning in undergraduate chemistry settings (Cooper et al., 2017), and even students elementary settings are using chemical models (Baumfalk et al., 2019) with similar instructional techniques.

Given that future teachers often come from this pool of undergraduate science, it is fruitful to explore whether teacher education itself could have an impact preservice teachers’ scientific reasoning competencies. In a third study, Khan and Krell (2019) used the same instrument as the international comparison (a validated pre- and post-questionnaire) to investigate a single teacher education class more closely. That is, the single teacher methods course was assessed for whether preservice science teachers’ scientific reasoning was the same before a science teacher methods as afterwards. The teacher education course contained common science methods topics, such as unit and lesson planning and assessment techniques in science.

The preservice science teachers were asked to reason about two types of problems in a validated pre- and post-questionnaire (Krell et al., 2020): investigatory-process problems and modeling problems. Statistical analyses of the data revealed that teacher education significantly contributed to the development of preservice science teachers’ competencies for those who had two previous degrees compared with those with one. Furthermore, when grouped together, a greater proportion of preservice teachers were better at planning investigations and analyzing data-moreso than any dimension associated with model-based reasoning in the questionnaire, and formulating questions and generating hypotheses. In a related study which included teacher specializations and the same questionnaire (Khan & Krell, 2021), chemistry preservice teachers had the lowest probability of answering items on the scientific reasoning questionnaire correctly compared to preservice biology teachers. Preservice chemistry teachers were found to be significantly worse at planning investigations, analyzing data, drawing conclusions, testing models, and changing their models than their biology counterparts. The questionnaire items were more procedural and epistemic rather than content-based, leading us to renew calls to examine chemistry preservice teachers.

The former findings (Krell et al., 2020) reify the notion that content knowledge may be a potential mediator of science teachers’ reasoning. The former findings are also aligned with the aforementioned cross-country comparison in 2023 of preservice teacher education (Krell et al., 2023), that affirmed that the amount of science classes is a significant predictor of SRCs in comparison to age and amount of teacher education courses. The latter findings (Khan & Krell, 2021) illuminate that chemistry preservice teachers fare significantly worse than biology teachers at scientific reasoning-including model-based- reasoning, even though both have a degree in science. Questions remain as to how teacher education might amplify the model-based reasoning of chemistry teachers.

3 Case study of a science teacher education course

While there is established quantitative and case evidence that science content courses are associated with gains in model-based reasoning skills (Khan, 2007; Krell et al., 2023), there is less evidence of similar gains in our research from participation in science teacher education courses (Khan & Krell, 2019). Investigating how teacher education programs can be designed to better promote the development of model-based reasoning skills among preservice chemistry teachers would be valuable. A current program of research is exploring this very question (Faikhamta et al., 2024; Khan, 2018; Khuyen et al., 2024). From Khan and Krell (2019)’s study in the same context as the present study, it had already been found that a science teacher education methods course had the potential to foster scientific reasoning competencies of some preservice teachers. In this study, preservice teacher competency in SRC significantly improved (for those that had 2° in science) after a teacher education course where no changes had been made to the course syllabus. This finding stands in contrast to our larger study where the amount of teacher education did not make a significant difference to SRC (Krell et al., 2023), leaving to question how intentionally designed teacher education activities might impact preservice teacher SRCs. Also, it was considered that there might be other ways to detect teacher competency at scientific and model-based reasoning, including through an analysis of preservice science teachers own actions to design and promote scientific reasoning to their students. It is thus theorized that opportunities to engage in teacher education activities to discuss model-based reasoning and foster them through the design and teaching of lesson plans will support preservice teacher’s own competency development (Khan & Krell, 2019). In this way, the kind of instruction in teacher education is hypothesized to impact model-based reasoning above and beyond preservice chemistry teacher’s previous undergraduate degree in science.

The following case is an example of a wider program of research in science teacher education to investigate high impact activities for preservice chemistry teachers. Below are excerpts of this study of a science teacher education methods course that was entirely reformed to support model-based reasoning. The revisions to the course included: (1) an expanded focus on how science proceeds using readings, depictions of science, and black box activities, (2) additional opportunities to teach and reflect upon the reasoning skills associated with modeling, including the use of a teacher design guide and class debriefs, and (3) opportunities to test teaching to promote model-based reasoning across three teaching contexts. Teacher education activities were operationalized as being successful if preservice teacher’s could plan for and facilitate their students’ model-based reasoning across multiple teaching contexts.

The design of this case study of teacher education activities followed a longitudinal pre-post design. Preservice teachers’ model-based reasoning was observed across three contexts: the science methods course itself, the practica experience, and a required assignment where preservice teachers taught in after-school organizations. Data sources included: a new pre- and post-questionnaire on the nature of modeling and model-based reasoning (different from the aforementioned SRC questionnaires), 2 sets of preservice teacher lesson plan assignments at earlier and later times in the refreshed course, and highschool classroom observations and debriefs, coded in a rubric. Data was collected during micro-teaching, on practicum, and in out-of-school settings, such as homework clubs. To illustrate preservice teacher reasoning with their students, only several excerpts from the data are presented.

The chemistry preservice teachers were advised to focus on a model from their chemistry curriculum to design and enact lessons. The models were from the secondary chemistry curriculum (constituting grades 8–12). The models reflect conceptual/symbolic (Coll & Lajium, 2011) or concrete process models, containing some abstract and concrete elements, according to the typology offered by Harrison and Treagust (2000). For example, models of electrochemical cells contained conventional elements of chemical batteries technology and wires with electron flow to and from the battery. Given that the models were evident within the secondary curriculum, they could also be referred to as “expressed consensus teaching” or “curricular models” (Chamizo, 2013) in chemistry education.

4 Results

To generate hypotheses about how science teacher education activities can be designed to better promote the development of model-based reasoning, analyses of three excerpts from the data on preservice teachers reasoning with models in their teaching is reported. The specific models selected by the preservice teachers were represented in Table 2.

Table 2:

Scientific reasoning competencies on using models across three countries.

Country Type of preservice teacher education program SRC modeling score (%)
Germany Concurrent (bachelor of science/arts with a subsequent master of education program) 39
Chile Concurrent (bachelor of education program) 35
Canada Consecutive (post-graduate bachelor of education program) 48a
  1. Note: aRepresents a SD.

Study questionnaire results pinpointed activities that preservice teachers reported that may have had an impact on their reasoning. Initial hypotheses were generated on the types of activities that could promote the development of model-based reasoning among preservice chemistry teachers.

For the first excerpt, a preservice science teacher designed a lesson plan in the teacher education course, engaging two students to reason about a model of an electrochemical cell. In this instance of microteaching, other preservice teachers served in the role of ‘students’. To begin, the preservice teacher in this excerpt asked her ‘students’ to share their ideas about a battery. After encountering data using a battery simulation, ‘the students’ identified themselves that their expressed model of a battery works is wrong. The preservice teacher prompted ‘students’ to revise their models and arrive at a consensus. The teacher reframed the discussion around modeling as the “act of building on our knowledge”.

She then was observed posing a question after asking the students to hook up the battery in a simulated circuit:

And if I can get you to label the positive and negative terminals, and then what you think the electron distribution will look like inside of this battery. I see both of you the electron flow from the positive terminal to the negative terminal….If we are saying the bulk of the electrons are being stored at the negative terminal, does that make sense? … Why would it, wouldn’t it be easier for the electrons just to flow through the battery back to the positive terminal instead of going through a circuit? Do you still think that the electrons in the new circuit will follow that pattern?

The preservice teacher, pointing to the battery, prompted her students to reason about the battery and why it would not have electrons circulate in a different pattern. This episode was coded as an example of teacher reasoning with students, because she asked students to consider a pattern (underlined) and an anomaly in the pattern (does that make sense). The student said their original models of a battery and circuit “made no sense”, and the preservice teacher responded let’s discuss why we think that.

An analogy was later brought up by the preservice teacher to help her students grapple with apparent anomaly in their electron flow models in this microteaching episode. This spontaneous teacher analogy was to water in a pipe system, and it was coded as an example of analogical reasoning using a model. The preservice chemistry teacher reasoned with her students at the start of the dialogue, “[I]f you have a lot of water at the top, in a basin at the top, and a pipe connecting the basin at the top, and then one that’s empty, just slightly lower down, where is the water going to flow as the pressure builds up? Is it going to flow from where there’s lots of water to more, or more water?” As an illustrative excerpt, the preservice chemistry teacher could be said to be engaging in reasoning with her students and their models.

In similar ways, reasoning was apparent in high school teaching contexts too. For a second illustrative example of preservice teacher reasoning about models in a high school context, two preservice teachers working in an after school club experimented with melting ice with their students, as they wrote for their debriefs:

…performed a science experiment which dealt with melting points. We encouraged the [high school] students to hypothesize which type of household seasoning (sugar, salt, cinnamon, pepper) could be used to lift an ice cube out of a container of water using only a thread … We began by asking if any of the students had an idea as to how we could accomplish the task of lifting the ice cube out of the water without touching it. The students then generated an idea about how the string could be used to pull the ice cube up. We then informed them that they would be able to also utilize one (or more) of the household seasonings to accomplish this task. The students proceeded to come up with different scenarios using the different seasonings until the desired outcome was achieved. After they had selected the correct seasoning (salt) we discussed what they though was happening, and why salt was the only seasoning to be successful.

The preservice teachers appear to be working with their high school students to develop an explanatory model of water. In a third excerpt, the preservice chemistry teachers debriefed what had happend and their reasoning processes as they asked key questions of the high school students about their models of water.

Interestingly one of the students thought that sugar would be the successful lifting agent, as he hypothesized that sugar is ‘sticky’ and should therefore result in the thread adhering to the ice cube surface. The sugar actually did result in a bit of stickiness however it was not sufficient to support the weight of the ice cube. He then decided to try a combination of salt and sugar together, however this did not work either, likely due to the ratio which was predominately sugar. The student appeared to be very frustrated when the sugar was not successful, and became visibly upset by this. Clearly this idea that sugar is sticky, and should therefore cause the thread to attach to the ice cube, was quite engrained in his mind, and as a result it was difficult [for him] to explain why salt was actually the correct ingredient. We also asked them to explain their answers as to why something did not work or did work. The students needed to understand that salt can melt ice, and the ice would refreeze the water over the thread, keeping it firm in place as you raised the ice.

The preservice teachers promoted reasoning by helping the student eliminate possibilities as underlined (e.g., salt and sugar), postulate hidden causal factors (stickiness), and better attempt to coordinate their models of water with the evidence from the melting experiment (teacher asks why something [sugar] does it not work). In terms of success, all of the preservice teachers were able to facilitate model-based reasoning in these ways over multiple teaching contexts. Compared to the pre-questionnaire which asked preservice teachers to reason with a model about chemical equilibrium, their post-questionnaire showed evidence of improvement in teacher reasoning with models, leading one to believe that activities in the course may have contributed in some way to their own competencies at model-based reasoning.

Regarding the kind of instruction in teacher education that may have contributed to an impact on the preservice chemistry teachers, they reported in the questionnaire that learning that their students’ might have alternative conceptions had the highest impact course activity. One can hypothesize that preservice science teachers learning that chemistry students have alternative conceptual models would support the idea that reasoning involves changing one’s mental models of the way the world works. Ranking close to the top were designing GEM lessons. The preservice teachers were provided a design guide to support their efforts at creating GEM lessons using this approach to foster model-based reasoning. Finally, reflection on teaching was ranked lowest by the preservice teachers, perhaps because it was a program-wide activity. The teacher education course and the community site were reported by the preservice teachers as the contexts with which preservice teachers were most able to try methods to promote model-based reasoning, according to the questionnaire. The excerpts provide a window into activities with students that might enhance their own scientific reasoning competencies.

5 Discussion

It is hypothesized that teacher education course activities may foster chemistry teachers own scientific reasoning competencies. A preservice teacher education course was intentionally designed to support preservice teacher’s model-based reasoning. Preservice teachers acknowledged that the highest impact activities in the course that supported their own understanding of model-based reasoning included learning about students’ alternative conceptions, GEM lesson planning, and enactments in the teacher education course and in community sites. The first two activities were supported by readings and a design guide, and the latter, teacher enactments, were supported by reflection activities including debriefs. Lesson observations and a post-questionnaire revealed that not just their pedagogy to facilitate high school students’ model-based reasoning improved, but there was evidence from the post-questionnaire that so did their personal reasoning competencies. A hypothesized mechanism for doing so might be that the above course activities required preservice teachers to plan for and engage in prolonged facilitation of reasoning with models. They could not anticipate fully what their students might say in the different, requiring the preservice teachers to actively reason with their students about their models. The illustrative excerpts from the data suggest preservice teachers’ reasoning with a [student] model happened when they helped high school students compare evidence with their personal models, develop explanations, eliminate less probable explanations for experimental findings, resolve anomalies, and postulate hidden causal factors. Preservice chemistry teachers were also observed spontaneously engaging in analogical reasoning to support students’ understandings of models.

Taken together, it is plausible that teacher education courses, intentionally designed, can create opportunites that foster teacher reasoning and elevate their own competencies at reasoning, as suggested by Khan and Krell (2019) in their case study of a different methods course. As Ding (2018)’s study alludes; however, SRCs may not be developed in undergraduate settings, the first degree for teachers in a consecutive program. Thus, explicit teaching of SRCs may be necessary in post-secondary education first degrees where the pool of future teachers often comes from. Despite science content courses being a significant predictor of SRCs (Krell et al., 2023), this case study suggests, in a preliminary way, that chemistry preservice teachers have the potentail to advance their SRCs from intentionally designed teacher education activities.

6 Recommendations

The research program investigating reasoning in science teacher education is especially important for chemistry education. Research on prior degrees (Krell et al., 2023) suggests further that content knowledge is a potential mediator of chemistry teachers’ capacities to reason. Therefore, the first recommendation for chemistry teacher education is that science content knowledge is considered when developing chemistry teacher education methods courses. Additionally, preservice science teachers who may teach chemistry in the future but do not have a chemistry major could be supported in teacher education with courses for non-chemistry majors. Consecutive teacher education programs appear to be the beter way to design a program that fosters teachers SRCs compared to concurrent programs (Krell et al., 2023), as content courses in an undergraduate degree are significant predictors of future SRCs.

Secondly, preservice chemistry teachers significantly underperform on questions involving scientific reasoning compared to biology preservice teachers (Khan & Krell, 2021). Based on the aforementioned research, explicit instruction of scientific reasoning would be especially important within the first chemistry degree. Teacher education interventions are also recommended. In Engelmann et al.’s (2016) meta-analysis of 15 studies to promote scientific reasoning, it was found that the interventions had a significant positive effect on scientific reasoning. Concerning interventions, this present research program in teacher education has developed targeted, high impact activities to promote model-based reasoning.

These teacher education activities include approximating practice across contexts and engaging in GEM activites that promote model-based reasoning. Science preservice teachers, despite working with a plethora of models in their first degrees, fare worse with regards to model-based reasoning than other questionnaire items (Krell et al., 2020). Course-based activities, such as those that promote model-based reasoning in various contexts, may also help preservice teachers activate more spontaneous forms of reasoning with students. It is recommended that similar high impact activities such as GEM (Khan, 2007) be integrated in teacher education and that multiple opportunities to teach in various contexts be sought.

In addition to the design of consecutive teacher education programs and intentional and explicit activities to promote reasoning in chemistry and chemistry education, additional research is necessary. First, evidence for an undergraduate class producing higher achievement than other chemistry classes was provided (Khan, 2007), and in another study, evidence for being able to teach using model-based reasoning successfully in teacher was provided (Khan & Krell, 2019). Based on the present study in teacher education that also incorporated a model-based reasoning approach, GEM, the use of this instructional approach may serve as one avenue to sustain and amplify teacher reasoning. More research; however, is needed that shows the relationship between course activities and preservice chemistry teachers outcomes in terms of their own SRC to begin to make clear claims about the impact of teacher education. It is plausible that the act of teaching itself promotes reasoning and over time and across contexts, fosters reasoning competencies as a result. The nature of the relationship is not yet fully understood or documented. Thus future research could examine this relationship in teacher education. Also, research on how to best support reasoning competencies in undergraduate education would complement ongoing projects in teacher education. Finally, greater specificity on the classroom activities that have the best possible impacts on future chemistry teachers is needed. Bolstering the reasoning competencies of teachers is one proposed way to students capacities to reason, a fruitful avenue to explore in chemistry education research.


Corresponding author: Samia Khan, Faculty of Education, The University of British Columbia, 2125 Main Mall, Neville Scarfe Building, V6T 1Z4, Vancouver, Canada, E-mail:

Acknowledgments

I would like to acknowledge the mii-STEM project for their research.

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

References

Alexander, P. A. (2019). Individual differences in college‐age learners: The importance of relational reasoning for learning and assessment in higher education. British Journal of Educational Psychology, 89(3), 416–428. https://doi.org/10.1111/bjep.12264.Suche in Google Scholar PubMed

Baumfalk, B., Bhattacharya, D., Vo, T., Forbes, C. T., Zangori, L., & Schwarz, C. (2019). Impact of model-based curriculum and instruction on 3rd-grade students’ scientific explanations for the hydrosphere. Journal of Research in Science Teaching, 56(5), 570–597. https://doi.org/10.1002/tea.21514.Suche in Google Scholar

Bruce, M. R., Bruce, A. E., & Walter, J. (2022). Creating representation in support of chemical reasoning to connect macroscopic and submicroscopic domains of knowledge. Journal of Chemical Education, 99(4), 1734–1746. https://doi.org/10.1021/acs.jchemed.1c00292.Suche in Google Scholar

Bruckermann, T., Ochsen, F., & Mahler, D. (2018). Learning opportunities in biology teacher education contribute to understanding of nature of science. Education Sciences, 8(103), 1–18. https://doi.org/10.3390/educsci8030103.Suche in Google Scholar

Button, J., Turner, D. P., & Hammer, D. (2023). How chemists handle not-knowing in reasoning about a novel problem. Chemistry Education: Research and Practice, 24(3), 956–970. https://doi.org/10.1039/d3rp00018d.Suche in Google Scholar

Campbell, T., Oh, P. S., Maughn, M., Kiriazis, N., & Zuwallack, R. (2015). A review of modeling pedagogies: Pedagogical functions, discursive acts, and technology in modeling instruction. Eurasia Journal of Mathematics, Science and Technology Education, 11(1), 159–176. https://doi.org/10.12973/eurasia.2015.1314a.Suche in Google Scholar

Chamizo, J. A. (2013). A new definition of models and modeling in chemistry’s teaching. Science & Education, 22, 1613–1632. https://doi.org/10.1007/s11191-011-9407-7.Suche in Google Scholar

Clement, J. (1989). Learning via model construction and criticism: Protocol evidence on sources of creativity in science. In Handbook of creativity (pp. 341–381). Springer.10.1007/978-1-4757-5356-1_20Suche in Google Scholar

Coll, R. K., & Lajium, D. (2011). Modeling and the future of science learning. In Models and modeling: Cognitive tools for scientific enquiry (pp. 3–21). Springer.10.1007/978-94-007-0449-7_1Suche in Google Scholar

Cooper, M. M., Stieff, M., & DeSutter, D. (2017). Sketching the invisible to predict the visible: From drawing to modeling in chemistry. Topics in Cognitive Science, 9(4), 902–920. https://doi.org/10.1111/tops.12285.Suche in Google Scholar PubMed

Darling-Hammond, L. (2000). Teacher quality and student achievement. Education Policy Analysis Archives, 8(1), 1–44.10.14507/epaa.v8n1.2000Suche in Google Scholar

DeBoer, G. (2019). A history of ideas in science education. Teachers College Press.Suche in Google Scholar

Deng, J. M., & Flynn, A. B. (2021). Reasoning, granularity, and comparisons in students’ arguments on two organic chemistry items. Chemistry Education: Research and Practice, 22(3), 749–771. https://doi.org/10.1039/d0rp00320d.Suche in Google Scholar

Ding, L. (2018). Progression trend of scientific reasoning from elementary school to university: A large-scale cross-grade survey among Chinese students. International Journal of Science and Mathematics Education, 16(8), 1479–1498. https://doi.org/10.1007/s10763-017-9844-0.Suche in Google Scholar

Dumas, D., Alexander, P. A., & Grossnickle, E. M. (2013). Relational reasoning and its manifestations in the educational context: A systematic review of the literature. Educational Psychology Review, 25, 391–427. https://doi.org/10.1007/s10648-013-9224-4.Suche in Google Scholar

Engelmann, K., Neuhaus, B., & Fischer, F. (2016). Fostering scientific reasoning in education – meta-analytic evidence from intervention studies. Educational Research and Evaluation, 22(5–6), 333–349. https://doi.org/10.1080/13803611.2016.1240089.Suche in Google Scholar

Faikhamta, C., Khan, S., Prasoplarb, T., Praisri, A., & Suknarusaithagul, N. (2024). Pre-service teachers’ conceptual understandings of models and modelling in a STEM methods course. Research in Science Education, 1–17. https://doi.org/10.1007/s11165-024-10184-3.Suche in Google Scholar

Fischer, K. W., & Bidell, T. (1998). Dynamic development of psychological structures in action. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology: Vol. 1. theoretical models of human development (5th ed., pp. 313–399). Wiley.Suche in Google Scholar

Fischer, F., Kollar, I., Ufer, S., Sodian, B., Hussmann, H., Pekrun, R., Neuhaus, B., Dorner, B., Pankofer, S., Fischer, M., Strijbos, J.-W., Heene, M., & Eberle, J. (2014). Scientific reasoning and argumentation. Frontline Learning Research, 5, 28–45.Suche in Google Scholar

Giere, N. R., Bickle, J., & Mauldin, R. F. (2006). Understanding scientific reasoning (5th ed.). Thomson/Wadsworth.Suche in Google Scholar

Göhner, M., & Krell, M. (2022). Preservice science teachers’ strategies in scientific reasoning: The case of modeling. Research in Science Education, 52(2), 395–414. https://doi.org/10.1007/s11165-020-09945-7.Suche in Google Scholar

Halloun, I. A. (2007). Mediated modeling in science education. Science & Education, 16, 653–697. https://doi.org/10.1007/s11191-006-9004-3.Suche in Google Scholar

Harrison, A. G., & Treagust, D. F. (2000). A typology of school science models. International Journal of Science Education, 22(9), 1011–1026. https://doi.org/10.1080/095006900416884.Suche in Google Scholar

Hogan, K. (1999). Thinking aloud together: A test of an intervention to foster students’ collaborative scientific reasoning. Journal of Research in Science Teaching, 36(10), 1085–1109. https://doi.org/10.1002/(sici)1098-2736(199912)36:10<1085::aid-tea3>3.0.co;2-d.10.1002/(SICI)1098-2736(199912)36:10<1085::AID-TEA3>3.0.CO;2-DSuche in Google Scholar

Ke, L., & Schwarz, C. V. (2021). Supporting students’ meaningful engagement in scientific modeling through epistemological messages: A case study of contrasting teaching approaches. Journal of Research in Science Teaching, 58(3), 335–365. https://doi.org/10.1002/tea.21662.Suche in Google Scholar

Khan, S. (2001). Developing inquiry skills while learning about unobservable processes in chemistry. Annual Meeting of the American Educational Research Association.Suche in Google Scholar

Khan, S. (2007). Model‐based inquiries in chemistry. Science Education, 91(6), 877–905. https://doi.org/10.1002/sce.20226.Suche in Google Scholar

Khan, S. (2011). What’s missing in model-based teaching. Journal of Science Teacher Education, 22, 535–560. https://doi.org/10.1007/s10972-011-9248-x.Suche in Google Scholar

Khan, S. (2018). Preservice science teacher’s adaptation of modeling strategies in the field. Teacher Education Policy in Europe (TEPE).Suche in Google Scholar

Khan, S., & Chan, V. (2011). An exploration of digital representations in chemistry education. Journal of the Research Center for Educational Technology, 7(2), 2–37.Suche in Google Scholar

Khan, S., & Krell, M. (2019). Scientific reasoning competencies: A case of preservice teacher education. Canadian Journal of Science, Mathematics, and Technology Education, 19(4), 446–464. https://doi.org/10.1007/s42330-019-00063-9.Suche in Google Scholar

Khan, S., & Krell, M. (2021). Patterns of scientific reasoning skills among pre-service science teachers: A latent class analysis. Education Sciences, 11(10), 647. https://doi.org/10.3390/educsci11100647.Suche in Google Scholar

Khuyen, N. T. T., Van Bien, N., Khan, S., Faikhamta, C., & El Islami, R. A. Z. (2024). Impacts of method courses on Vietnamese pre-service teachers’ perceptions and practices: From the perspectives of model and modeling in STEM education. Journal of Physics: Conference Series, 2727(1), 012001. https://doi.org/10.1088/1742-6596/2727/1/012001.Suche in Google Scholar

Kind, P. E. R., & Osborne, J. (2017). Styles of scientific reasoning: A cultural rationale for science education? Science Education, 101(1), 8–31. https://doi.org/10.1002/sce.21251.Suche in Google Scholar

Krell, M., Dawborn-Gundlach, M., & van Driel, J. (2020). Scientific reasoning competencies in science teaching. Teaching Science, 66(2), 32–42.Suche in Google Scholar

Krell, M., Khan, S., & van Driel, J. (2021). Analyzing cognitive demands of a scientific reasoning test using the linear logistic test model (LLTM). Education Sciences, 11(9), 1–16. https://doi.org/10.3390/educsci11090472.Suche in Google Scholar

Krell, M., Khan, S., Vergara, C., Cofré, H., Mathesius, S., & Krüger, D. (2023). Pre-service science teachers’ scientific reasoning competencies: Analysing the impact of contributing factors. Research in Science Education, 53(1), 59–79. https://doi.org/10.1007/s11165-022-10045-x.Suche in Google Scholar

Kuhn, T. S. (2012). The structure of scientific revolutions: 50th anniversary edition. University of Chicago Press.10.7208/chicago/9780226458144.001.0001Suche in Google Scholar

Lawson, A. E. (1978). The development and validation of a classroom test of formal reasoning. Journal of Research in Science Teaching, 15(1), 11–24. https://doi.org/10.1002/tea.3660150103.Suche in Google Scholar

Lawson, A., Clark, B., Cramer-Meldrum, E., Falconer, K. A., Sequist, J. M., & Kwon, Y. (2000). Development of scientific reasoning in college biology: Do two levels of general hypothesis-testing skills exist? Journal of Research in Science Teaching, 37(1), 64–81. https://doi.org/10.1002/(sici)1098-2736(200001)37:1<81::aid-tea6>3.0.co;2-i.10.1002/(SICI)1098-2736(200001)37:1<81::AID-TEA6>3.0.CO;2-ISuche in Google Scholar

Lehrer, R., & Schauble, L. (2005). Cultivating model-based reasoning in science education. In The Cambridge handbook of the learning sciences (pp. 371–387). Cambridge University Press.10.1017/CBO9780511816833.023Suche in Google Scholar

Lehrer, R., & Schauble, L. (2015). The development of scientific thinking. In Handbook of child psychology and developmental science (Vol. 2, pp. 671–714). Wiley.10.1002/9781118963418.childpsy216Suche in Google Scholar

Loughran, J., Keast, S., & Cooper, R. (2016). Pedagogical reasoning in teacher education. International handbook of teacher education (Vol. 1, pp. 387–421).10.1007/978-981-10-0366-0_10Suche in Google Scholar

Magnani, L. (2009). Model-based reasoning in science and technology: Abduction, visualization, and inference. Springer.Suche in Google Scholar

Monk, D. H., & King, J. A. (1994). Multilevel teacher resource effects in pupil performance in secondary mathematics and science: The case of teacher subject matter preparation. In R. G. Ehrenberg (Ed.), Choices and consequences: Contemporary policy issues in education (pp. 29–58). ILR Press.Suche in Google Scholar

Moreira, P., Marzabal, A., & Talanquer, V. (2019). Investigating the effect of teacher mediation on student expressed reasoning. Chemistry Education: Research and Practice, 20(3), 606–617. https://doi.org/10.1039/c9rp00075e.Suche in Google Scholar

Nersessian, N. J. (2008). Creating Scientific Concepts. MIT Press.10.7551/mitpress/7967.001.0001Suche in Google Scholar

Núnez-Oveido, M. C., Clement, J., & Rea-Ramirez, M. A. (2008). Developing complex mental models in biology through model evolution. Model based learning and instruction in science (pp. 173–193). Springer.10.1007/978-1-4020-6494-4_10Suche in Google Scholar

Osborne, J. (2013). The 21st century challenge for science education: Assessing scientific reasoning. Thinking Skills and Creativity, 10, 265–279. https://doi.org/10.1016/j.tsc.2013.07.006.Suche in Google Scholar

Pietarinen, A. V., & Bellucci, F. (2014). New light on Peirce’s conceptions of retroduction, deduction, and scientific reasoning. International Studies in the Philosophy of Science, 28(4), 353–373. https://doi.org/10.1080/02698595.2014.979667.Suche in Google Scholar

Pluta, W. J., Chinn, C. A., & Duncan, R. G. (2011). Learners’ epistemic criteria for good scientific models. Journal of Research in Science Teaching, 48(5), 486–511. https://doi.org/10.1002/tea.20415.Suche in Google Scholar

Raghavan, K., & Glaser, R. (1995). Model–based analysis and reasoning in science: The MARS curriculum. Science Education, 79(1), 37–61. https://doi.org/10.1002/sce.3730790104.Suche in Google Scholar

Rea-Ramirez, M. A., Nunez-Oviedo, M. C., & Clement, J. (2009). Role of discrepant questioning leading to model element modification. Journal of Science Teacher Education, 20, 95–111. https://doi.org/10.1007/s10972-009-9128-9.Suche in Google Scholar

Rost, M., & Knuuttila, T. (2022). Models as epistemic artifacts for scientific reasoning in science education research. Education Sciences, 12(4), 276. https://doi.org/10.3390/educsci12040276.Suche in Google Scholar

Schauble, L. (1996). The development of scientific reasoning in knowledge-rich contexts. Developmental Psychology, 32(1), 102–119. https://doi.org/10.1037/0012-1649.32.1.102.Suche in Google Scholar

Schunn, C. D., & Klahr, D. (2019). The problem of problem spaces: When and how to go beyond a 2-space model of scientific discovery. In Proceedings of the eighteenth annual conference of the cognitive science society (pp. 25–26). Routledge.Suche in Google Scholar

Segler, M. H., & Waller, M. P. (2017). Modelling chemical reasoning to predict and invent reactions. Chemistry--A European Journal, 23(25), 6118–6128. https://doi.org/10.1002/chem.201604556.Suche in Google Scholar PubMed

Sevian, H., & Talanquer, V. (2014). Rethinking chemistry: A learning progression on chemical thinking. Chemistry Education: Research and Practice, 15(1), 10–23. https://doi.org/10.1039/c3rp00111c.Suche in Google Scholar

Simon, H. A. (1996). The Sciences of the Artificial (3rd ed.). MIT Press.Suche in Google Scholar

Stieff, M., Ryu, M., Dixon, B., & Hegarty, M. (2012). The role of spatial ability and strategy preference for spatial problem solving in organic chemistry. Journal of Chemical Education, 89(7), 854–859. https://doi.org/10.1021/ed200071d.Suche in Google Scholar

Talanquer, V. (2018). Exploring mechanistic reasoning in chemistry. In Science education research and practice in Asia-Pacific and beyond (pp. 39–52). Springer.10.1007/978-981-10-5149-4_3Suche in Google Scholar

Trey, L., & Khan, S. (2008). How science students can learn about unobservable phenomena using computer-based analogies. Computers & Education, 51(2), 519–529. https://doi.org/10.1016/j.compedu.2007.05.019.Suche in Google Scholar

Wackerly, J. W. (2021). Abductive reasoning in organic chemistry. Journal of Chemical Education, 98(9), 2746–2750. https://doi.org/10.1021/acs.jchemed.1c00295.Suche in Google Scholar

Williams, D. (2018). Predictive minds and small-scale models: Kenneth Craik’s contribution to cognitive science. Philosophical Explorations, 21(2), 245–263. https://doi.org/10.1080/13869795.2018.1477982.Suche in Google Scholar

Windschitl, M., Thompson, J., & Braaten, M. (2018). Teaching and learning in a complex world: A framework for understanding the interplay between knowledge and experience. In K. R. Harris, S. Graham & T. Urdan (Eds.), Handbook of research on learning and instruction (pp. 241–274).Suche in Google Scholar

Zimmerman, C. (2000). The development of scientific reasoning skills. Developmental Review, 20(1), 99–149. https://doi.org/10.1006/drev.1999.0497.Suche in Google Scholar

Received: 2024-10-01
Accepted: 2024-11-26
Published Online: 2024-12-18

© 2024 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. The 27th IUPAC International Conference on Chemistry Education (ICCE 2024)
  4. Special Issue Papers
  5. Recent advances in laboratory education research
  6. Examining the effect of categorized versus uncategorized homework on test performance of general chemistry students
  7. Enhancing chemical security and safety in the education sector: a pilot study at the university of Zakho and Koya University as an initiative for Kurdistan’s Universities-Iraq
  8. Leveraging virtual reality to enhance laboratory safety and security inspection training
  9. Advancing culturally relevant pedagogy in college chemistry
  10. High school students’ perceived performance and relevance of chemistry learning competencies to sustainable development, action competence, and critical thinking disposition
  11. Spatial reality in education – approaches from innovation experiences in Singapore
  12. Teachers’ perceptions and design of small-scale chemistry driven STEM learning activities
  13. Electricity from saccharide-based galvanic cell
  14. pH scale. An experimental approach to the math behind the pH chemistry
  15. Engaging chemistry teachers with inquiry/investigatory based experimental modules for undergraduate chemistry laboratory education
  16. Reasoning in chemistry teacher education
  17. Development of the concept-process model and metacognition via FAR analogy-based learning approach in the topic of metabolism among second-year undergraduates
  18. Synthesis of magnetic ionic liquids and teaching materials: practice in a science fair
  19. The development of standards & guidelines for undergraduate chemistry education
Heruntergeladen am 22.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cti-2024-0099/html
Button zum nach oben scrollen