Scientific control is deductive reasoning

Cognitive processes as the basis of scientific thinking and reasoning in early and middle childhood

Summary

According to Kuhn (2011), scientific thinking refers to the ability of individuals to apply scientific processes and ways of thinking in order to evaluate information and generate knowledge. The contribution shows to what extent the basic cognitive processes of induction, deduction and abduction are important for scientific thinking and reasoning. Subareas such as the evaluation of evidence, the variable control strategy and the argumentation are examined and the state of research in early and middle childhood is presented. The article closes with the relationship between scientific thinking and domain-specific knowledge, which refers to an iterative process of meaning construction.

Abstract

According to Kuhn (2011), scientific reasoning refers to the ability of individuals to apply scientific procedures and ways of thinking in order to evaluate information and generate knowledge. In this paper, we highlight the relevance of the basic cognitive processes of induction, deduction, and abduction for scientific reasoning and argumentation. We review research on subdomains such as evidence evaluation, control of variable strategy, and argumentation in early and middle childhood. We conclude with the relationship between scientific reasoning and domain-specific knowledge as an iterative process of meaning construction.

introduction

Scientific thinking is described as thinking that deals with phenomena of the world with the aim of gaining knowledge. It relates in particular to the ability of individuals to apply scientific processes and ways of thinking in order to evaluate information and generate knowledge and is described as a key competence for participation in the “knowledge society” (Fischer et al. 2014). Basic cognitive processes such as inductive and deductive reasoning are used in scientific thinking (Lawson 2010; Morris et al. 2012; Zimmerman 2007). The knowledge gained from these processes has the consequence, among other things, that domain-specific knowledge is constructed, which is available as so-called “theoretical knowledge” for further thought and problem-solving processes (Kuhn 2011). According to Kuhn and Pearsall (2000), a broad theoretical concept is legitimate in the context of scientific thinking, which includes the category knowledge, interpretations and explanatory knowledge of individuals (see also Sandoval et al. 2014). In particular, research findings in developmental psychology and scientific didactics show that components of scientific thinking - with specific task and requirement-related restrictions - can already be observed in kindergarten age and that systematic progress can be made up to early adulthood (Butler 2020; Sandoval et al. 2014 ; van der Graaf et al. 2018).

In addition to theoretical knowledge, scientific thinking also includes knowledge of epistemic activities and the skills to apply them (Koerber et al. 2015; Zimmerman 2007). These epistemic activities include ways of thinking, working and acting such as asking questions, observing, planning investigations, conducting investigations, collecting data, measuring, interpreting and documenting (Hetmanek et al. 2018). In addition, the variable control strategy is classified as an important aspect of scientific thinking in the area of ​​data generation and interpretation. Knowledge gains from epistemic activities result from the coordination of theory and evidence, i. H. in the comparison of empirically generated knowledge with the hypotheses derived from the theory or with the knowledge components already constructed by individuals. Sandoval et al. (2014) also point out that in addition to the application of methods to obtain empirical evidence, a plausibility check of the claim (claim) is central, which i.a. takes place through source reliability and ascribed expertise. The long-term goal of gaining knowledge through scientific thinking is therefore also to be able to assess the respective theoretical knowledge as true or untrue through the use of (empirical) evidence (Kuhn and Pearsall 2000).

Scientific thinking is according to the model Scientific Discovery as Dual Search (SDDS) understood by Klahr and Dunbar (1988) as a search in the so-called hypothesis room and in the experiment room, whereby it is assumed that the searches in the hypothesis room and in the experiment room are mutually restrictive. In addition, the authors differentiate between “evoking” and “inducing” a hypothesis in the hypothesis formation phase. In evoking a hypothesis, it is assumed that the retrieval of prior knowledge and theories stored in memory are the basis of hypothesis formation; when inducing a hypothesis, primarily observation and pattern recognition take place before a first hypothesis can be formulated. These processes indicate the importance of basic cognitive processes such as deduction and induction when comparing search processes in the two search spaces.

From a co-constructivist point of view, when dealing with theoretical knowledge and evidence, the importance of scientific argumentation arises, since with arguments certainties and assumptions can be questioned and theories can be sharpened through evidence and support of arguments in the discourse (Mercier 2012). According to Fischer et al. (2014) Arguments related to assessing the appropriateness of a test design, interpreting the evidence, and understanding the validity and truth of knowledge as the center of scientific thought. Arguments are typically structured as statements that combine at least two pieces of evidence, two theories or one theory and one piece of evidence and contain at least one reason (cf. Toulmin 2003; Furtak et al. 2010). A justification is often not enough, however, but often requires one or more supporting statements. The latter, in turn, can be theories with which evidence or subjective experiences are made explicit and on which one relies to substantiate the trustworthiness of the justification. Arguments with several justifications and supporting statements (which can also include the anticipation of a counter-argument and its refutation) are seen as the better arguments with regard to scientific thinking (Toulmin 2003). In addition, theoretically or empirically obtained evidence (empirical evidence) to support a justification is particularly important in scientific argumentation and is assessed as being of high quality (Chinn and Malhotra 2002; Furtak et al. 2010; Mercier 2018).

In this overview, we focus on the central cognitive processes of deduction, induction, and abduction, based on the literature on scientific thinking and reasoning. We show what importance these can have for the development of basic skills in essential sub-areas such as theory-evidence coordination and to what extent concept development is related to scientific thinking. In addition, the studies published in the special issue are classified in this context.

Basic cognitive processes

The encoding of relevant information and its mental representation are considered fundamental for the cognitive processes of scientific thinking (Morris et al. 2012). The increasing ability of children to encode information relevant to gaining knowledge is supported both by the acquisition of coding strategies and by the acquisition of domain-specific knowledge (Siegler 1989). The resulting mental representations include the prerequisite for the application of cognitive processes such as deduction and induction, whereby they are neither conceptualized as separate from one another nor as hierarchical (Goel and Waechter 2017; Lawson 2010). In the following, however, an analytical approach is chosen that allows research strands to be ordered accordingly - knowing that this order is not to be understood exclusively or conclusively.

Deduction

In deductive reasoning, a statement is assumed that in the broadest sense comprises a theory or is part of a theory (Goel and Waechter 2017). Deduction is understood as conditional thinking (“if I remove this building block, the tower will collapse”), counterfactual thinking (“if I hadn't removed this building block, the tower would have stopped”) and transitive thinking (linear syllogism: “if the smallest tower was built with these building blocks and the second largest tower as well and both remain standing, the largest tower made of these building blocks will also remain standing ”, cf. also Goswami 2011). The aim of deduction is therefore to classify phenomena in a given theory under certain conditions and to draw a logical conclusion about it. Deductive reasoning is used in particular to provide evidence in which a certain statement is derived from one or more statements (Johnson-Laird et al. 2018). With deduction, more specific theories can be derived from general theories, whereby deduction in the narrower sense can be understood as a formal rule that does not aim at truth, but at the logical validity of statements (Goel and Waechter 2017). Lawson (2010) points to the importance of deductive reasoning for scientific reasoning. The article Hardy, Stephan-Gramberg and Jurecka (in this issue) explains how evidence-based justification is to be classified as a process of deductive reasoning when comparing hypotheses with empirical evidence.

As early as the age of six, children can logically deduce if the given premises relate to specific, confirmed cases, even if the children have to imagine them (e.g. "If something is a car, it has an engine. that you see a car. Do you think that it has an engine? ”cf. Markovits and Thompson 2008). In the same study, however, it was also shown that six-year-old children had greater difficulty in correctly assessing conclusions in the following form and thus rejecting them: “Assume that you see something that has an engine. Is it sure it's a car? ”This type of reasoning became more common with age (children aged 6, 7, and 9 were examined). However, even six-year-old children reach conclusions of this kind if they did not have to imagine the cases but were shown them. It is also possible for preschool children to draw deductive if-then conclusions based on empirically incorrect premises, provided that these are anchored in a fantasy context that the children can understand (Leevers and Harris 2000). Recent studies have also shown that evoking flexible thinking (divergent thinking) shortly before solving deductive tasks, the probability of their solution being increased by preschool children (de Chantal and Markovits 2017). The authors attribute this to the fact that it encourages children to think about alternative premises. The latter studies in particular support the assumption that the understanding of a hypothetical character of premises is to be understood as a component of deductive thought processes (Barrouillet et al. 2008).

induction

Inductive reasoning involves perceiving or observing a series of cases and finding a system or rule that organizes these cases. As soon as such a system has been discovered, it becomes possible to extrapolate theoretical knowledge from this, which not only classifies the perceived cases, but also future cases (Dunbar and Klahr 2012). Induction can thus lead to the construction of hypotheses, conceptual knowledge and theories (Goel and Waechter 2017) and is considered to be one of the central cognitive processes with which individual differences in performance in scientific thinking can be explained (Morris et al. 2012; Zimmerman 2007). People with little prior knowledge tend to use easily recognizable perceptual features instead of abstract commonalities when comparing cases, which is particularly common in preschoolers with little domain-specific knowledge (Namy and Gentner 2002). For example, younger children justify the similarities between a straw and a plant stem with the fact that both are long and thin, while older children say that the straw and the plant stem can transport nutrients (Gentner 1988). Recent studies indicate that the activation of prior knowledge z. B. by enabling systematic comparisons of several specifically coordinated cases or by using linguistically focussing references leads to the fact that more abstract than perceptual features are ordered and relational similarities are found (Gentner and Namy 1999; Schalk et al. 2011).

Cognitive components of comparison processes can be related to the theory of Structural alignment analyzed (e.g. Gentner and Smith 2012). These include the retrieval of relevant information from long-term memory, the identification as well as the comparison and evaluation of similarities and differences between two (or more) cases. Conclusions are drawn based on a comparison, thereby creating an abstract schema (i.e. theoretical knowledge). This process is to be understood as a relational pattern completion that proceeds in several steps, which allows the previously implicit relations of two (or more) cases to emerge more and more clearly and allows the formation of more abstract schemata (Gentner and Hoyos 2017; Gentner and Smith 2012). A large number of studies confirm these assumptions for different age groups and domains (cf. for an overview Gentner and Maravilla 2018). In the context of scientific thinking, it was also possible to show that preschool children also find it easier to predict and explain the swimming behavior of objects if inductive processes are stimulated by assessing two comparison objects (Hardy et al. 2020).

Abduction

Abduction can be understood as a special case of induction that allows possible explanations for unexpected events to be developed. In the literature, there is often no explicit distinction between induction and abduction. For example, Chinn and Brewer (1993) understand induction to mean that links are made between theory, evidence and alternatives, which are checked and explained in a continuous process with regard to their plausibility. The distinction between induction and abduction, however, makes it possible to take a closer look at the process of finding explanations. Abduction focuses on the interplay of explanations, finding and checking hypotheses as an iterative process (Koslowsky 2018). In contrast to deduction and induction in the narrower sense, formal rules or probabilities of occurrence are weighted less strongly with abduction, since abduction focuses on the meaning of true and relevant information and theories. With the SDDS model by Klahr and Dunbar (1988), in addition to induction and deduction, abductive processes can also be located, since this model focuses on the interplay of explanations, finding and checking hypotheses. According to the abduction, the search in the hypothesis and experiment room is followed by an investigation, either mentally or actively, which aims to reach a comprehensive conclusion as the “best explanation”. For this, possible explanations have to be evaluated and compared to competing explanations. The characteristics of abduction can also be found in the theory of problem solving according to Dewey (2009), which identifies “finding the best explanation” as the goal of scientific thinking. In addition, it is assumed that in abductive processes the explanation is assessed in terms of whether it is consistent with established knowledge (Lombrozo 2012). According to this, abduction is associated with the claim to find true theories that plausibly expand existing theories and can serve as an explanation for future events.

Central aspects of scientific thinking in this special issue

The focus on underlying cognitive processes should serve to classify the findings from studies on scientific thinking in models of cognitive psychology and to derive further research. Based on four empirical contributions to the scientific thinking of children between the ages of four and twelve, the following central areas are addressed in the special issue: Hypothesis formation and evidence-based reasoning, variable control, scientific reasoning and justification as well as the connection between early scientific thinking and conceptual knowledge. The spectrum of the addressed sub-areas of scientific thinking thus also shows the range of references to contexts and domains of pre-school and school learning.

Hypothesis building and evidence-based reasoning

The basis for revising assumptions and theoretical knowledge is to recognize the results of experiments as information carriers and to see experiments as an important tool for obtaining evidence. In an intervention study, Schulz et al. (2007) show that preschool children are able to predict the outcome of a novel experiment on the basis of experimentally collected evidence patterns. In addition, a study by Piekny et al. (2014) that preschool children can assess conclusive and partially conclusive evidence accordingly. In particular, it was found that the agreement between the children's existing assumptions and the evidence presented facilitated the correct assessment, while assumptions contradicting the evidence hindered the correct assessment (Koerber et al. 2005). Bonawitz et al. (2012) found, on the other hand, that 6 to 7 year old children can certainly succeed in revising their assumptions on the basis of contradicting evidence, provided they are not given further explanations that have nothing to do with their assumptions. Hardy, Stephan-Gramberg and Jurecka (in this issue) examined the extent to which training with different adaptive support measures (Scaffolding) supports the correct evaluation of evidence for given hypotheses. They found a training effect when answers were modeled in the training context and supported adaptively. As can be deduced from the literature on deductive reasoning, this relates in particular to the evaluation of events that are irrelevant to the hypothesis.

Variable control strategy

The quality of justifications depends not only on the relationship between the hypothesis and the data obtained, but also on the correct implementation of experimental test arrangements. The variable control strategy is understood to mean the ability to recognize and produce unconfirmed experiments and to correct experimental arrangements if necessary (Klahr and Li 2005). Initial studies by Inhelder and Piaget (1964) found that 7- to 9-year-olds changed several variables at the same time and were not aware that no valid conclusions could be drawn about the result in this way. Chen and Klahr (1999) assume that higher performance when using the variable control strategy can be achieved through the presentation of clearly distinguishable variables, but can also be related to a development-related improvement in the application of learned strategies. This was confirmed by a study by van der Graaf et al. (2015) in the context of the inclined plane, which showed progress in applying the variable control strategy in children between 4 and 6 years of age. Although these can be traced back to the children's level of development, a longer intervention proved to be quite successful. In addition, van der Graaf et al. (2019) show that direct instruction and verbal support measures during an experiment promote the use of variable control strategies. A long-term study by Schalk et al. (2019) with primary school children finally shows that the acquisition of the variable control strategy is also possible through a consecutive, implicit implementation of science lessons with a high proportion of research-based learning. On the basis of these results, Laufs and Kempert (in this issue) examined how the variable control strategy was conveyed to suit individual interests. Compared to teaching the variable control strategy without taking individual interests into account, this had a positive effect on the students' interest in the subject matter. However, the analyzes also show that the students in both groups achieved a similar learning gain in the variable control strategy.

Scientific reasoning and reasoning

A large number of research papers investigate the characteristics of scientific thinking and reasoning, especially in the field of natural sciences (see overview Ball and Thompson 2018; Driver et al. 2000; Fischer et al. 2018; Holoyak and Morrison 2012). The argumentation as well as the modeling often implied in the natural science context include the formulation of arguments and their (empirical) evaluation as well as the usually discourse-based weighing of counter-arguments. Even preschool children are able to present arguments to negotiate decisions and to make joint decisions based on similarities in their arguments (Köymen et al. 2014). It also seems to be possible for 3 to 5 year old children to make strong arguments (“The dog walked this way because I saw that it was walking in this direction”) from circular inferences (“The dog walked this way, because he went in this direction ”) (Mercier et al. 2014, 2018). Ryu and Sandoval (2012) showed that elementary school children benefited from a classroom intervention in which scientific reasoning was focused on the making and evaluation of claims in different scientific content areas. In another study, the relationship between the use of evidence-based arguments in classroom discourse and the importance of impulses from the teacher was examined and confirmed their importance - albeit on a generally low level of justification (Hardy et al. 2010). In addition to arguing in the classroom discourse, the establishment of justifications can also be recorded with written test procedures (Brown et al. 2010). The study by Peteranderl, Edelsbrunner and Deiglmayr (in this issue) shows that in sixth graders the use of advanced, evidence-based arguments could be promoted through an intervention in the variable control strategy. The use of such evidence-based arguments usually focused on the justification of main effects and not interaction effects between two variables.

Connections between scientific thinking and conceptual knowledge

In teaching research in particular, arguing and the acquisition of scientific knowledge are placed in connection with the development of conceptual knowledge. Although this connection is theoretically plausible, it has so far been little researched empirically. It is assumed that the following prerequisites must be met for a far-reaching concept change: The individual must be dissatisfied with the previous concept, the target concept must be understandable, plausible and transferable to other contexts, i.e. serve as a basis for further knowledge gains (Posner et al. 1982). Other models of conceptual development also attach great importance to the instructional conditions for the restructuring of concepts (Vosniadou et al. 2001). Accordingly, Edelsbrunner et al. (2018) found that pupils with a higher degree of experimental strategies benefited more from physics lessons with research components relating to the development of conceptual knowledge than a comparison group. In the younger age groups, however, the relationship between the components is less clear. In an empirical study in kindergarten, van der Graaf et al. (2018) that scientific thinking predicted the acquisition of conceptual knowledge. Koerber and Osterhaus (in this issue), on the other hand, found in a longitudinal study that the domain-specific conceptual knowledge of five-year-olds predicted their performance in scientific thinking at the end of kindergarten. Scientific thinking was collected using a comprehensive instrument aimed at recording experimentation strategies, data interpretation and understanding of science. The found connection can be interpreted to the effect that an early developed conceptual knowledge in the field of natural phenomena is helpful to draw attention to aspects that can be scientifically investigated or substantiated.

In summary, it can be said that scientific thinking and reasoning in the service of knowledge generation are based on the cognitive processes of induction, deduction and abduction in the coordination of theory and evidence (Dewey 1910; Lombrozo 2012). The development of new concepts also depends on the application of corresponding cognitive processes. It has already been shown in some empirical studies (e.g. Edelsbrunner et al. 2018; van der Graaf et al. 2018) that it makes sense to link research on concept development with research on scientific thinking. If the assumptions of Posner et al. (1982) continued on the concept change, for example the confrontation with new theories as well as with evidence that does not conform to expectations could trigger dissatisfaction with the existing conceptual knowledge in individuals. Such a “conflict strategy” was often used as a basis for the research-oriented approach in science lessons in order to promote the need to build up new theoretical knowledge (Limon 2001). Correspondingly, the argumentative processing and modeling of such new stocks of knowledge in educational opportunities is playing an increasing role (Duschl 2008; Sandoval et al. 2014). Arguments must be subjected to a plausibility check and should, e. B. on the basis of deductive logical conclusions, be comprehensible (cf. Lombrozo 2012; Sandoval et al. 2014). Processes of inductive reasoning also help to check individual observations for regularity and to extrapolate plausible theories. Finally, through abduction, stocks of knowledge are used to generate hypotheses and to find explanations. Thus, the development of conceptual knowledge in educational opportunities can also be understood as an iterative process of scientific thinking and reasoning, with knowledge gained through epistemic activities such as observation and data collection as well as the comparison of theory with evidence.

outlook

The articles in the special issue provide insights into the development and support approaches of scientific thinking and argumentation in children between the ages of four and twelve. In three of the articles in the thematic booklet, different learning and instruction formats and their effects on the development of early scientific thinking skills are examined in experimental training studies. Another contribution deals with the connections between scientific thinking and conceptual knowledge in a longitudinal study (Koerber and Osterhaus in this issue). In all contributions, scientific thinking is linked with contextual contexts from everyday life and / or the natural sciences that are valid. The results supplement the current state of research, which has been manageable so far, in particular with regard to which instructional contexts of (early) childhood can be useful for promoting scientific thinking and argumentation. Training settings were implemented with different instructional strategies, which reflect the individual importance of the learning context (Laufs and Kempert in this issue), the task context and content (Peteranderl, Edelsbrunner and Deiglmayr in this issue) and the individual learner support (Hardy, Stephan-Gramberg and Jurecka in this issue). In addition, the work provides information on where further research is required in order to be able to specify the underlying cognitive mechanisms and relationships of scientific thinking with individual entry requirements of the learner. Such a continuation of the knowledge can be undertaken from the perspective of basic cognitive processes, in which scientific thinking is understood as an alternation of inductive, deductive and abductive processes that promote the development of conceptual and process-related knowledge in the long term.

literature

  1. Ball, L. J., & Thompson, V. A. (Eds.). (2018). Routledge international handbook of thinking and reasoning. New York: Routledge.

    Google Scholar

  2. Barrouillet, P., Gauffroy, C., & Lecas, J.-F. (2008). Mental models and the suppositional account of conditionals. Psychological Review, 115(3), 760-771. https://doi.org/10.1037/0033-295X.115.3.760.

    Article Google Scholar

  3. Bonawitz, E. B., van Schijndel, T. J., Friel, D., & Schulz, L. (2012). Children balance theories and evidence in exploration, explanation, and learning. Cognitive Psychology, 64(4), 215-234. https://doi.org/10.1016/j.cogpsych.2011.12.002.

    Article Google Scholar

  4. Brown, N.J., Furtak, E.M., Timms, M., Nagashima, S.O., & Wilson, M. (2010). The evidence-based reasoning framework: assessing scientific reasoning. Educational Assessment, 15(3–4), 123–141.

    Article Google Scholar

  5. Butler, L. P. (2020). The empirical child? A framework for investigating the development of scientific habits of mind. Child Development Perspectives, 14(1), 34-40. https://doi.org/10.1111/cdep.12354.

    Article Google Scholar

  6. de Chantal, P.-L., & Markovits, H. (2017). The capacity to generate alternative ideas is more important than inhibition for logical reasoning in preschool-age children. Memory & Cognition, 45(2), 208-220. https://doi.org/10.3758/s13421-016-0653-4.

    Article Google Scholar

  7. Chen, Z., & Klahr, D. (1999). All other things being equal: acquisition and transfer of the control of variables strategy. Child Development, 70(5), 1098-1120. https://doi.org/10.1111/1467-8624.00081.

    Article Google Scholar

  8. Chinn, C.A., & Brewer, W.F. (1993). The role of abnormal data in knowledge acquisition — a theoretical framework and implications for science instruction. Review of Educational Research, 63(1), 1-49. https://doi.org/10.3102/00346543063001001.

    Article Google Scholar

  9. Chinn, C. A., & Malhotra, B. A. (2002). Children's responses to anomalous scientific data: how is conceptual change impeded? Journal of Educational Psychology, 94(2), 327-343. https://doi.org/10.1037/0022-0663.94.2.327.

    Article Google Scholar

  10. Dewey, J. (1910). How we think. New York: Dover.

    Book Google Scholar

  11. Dewey, J. (2009). As we think. Zurich: Pestalozzianum.

    Google Scholar

  12. Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84(3), 287–312.

    Article Google Scholar

  13. Dunbar, K. N., & Klahr, D. (2012). Scientific thinking and reasoning. In K. J. Holoyak & R. G. Morrison (Eds.), The Oxford handbook of thinking and reasoning (Pp. 701-718). New York: Oxford University Press.

    Google Scholar

  14. Duschl, R. A. (2008). Science education in 3 ‑ part harmony: balancing conceptual, epistemic and social goals. Review of Research in Education, 32, 268–291.

    Article Google Scholar

  15. Edelsbrunner, P. A., Schalk, L., Schumacher, R., & Stern, E. (2018). Variable control and conceptual change: a large-scale quantitative study in elementary school. Learning and Individual Differences, 66, 38-53. https://doi.org/10.1016/j.lindif.2018.02.003.

    Article Google Scholar

  16. Fischer, F., Chinn, C. A., Engelmann, K., & Osborne, J. (Eds.). (2018). Scientific reasoning and argumentation. The roles of domain-specific and domain-general knowledge. New York: Routledge.

    Google Scholar

  17. Fischer, F., Kollar, I., Ufer, S., Sodian, B., Hussmann, H., Pekrun, R., & Eberle, J. (2014). Scientific reasoning and argumentation: advancing an interdisciplinary research agenda in education Frontline learning research 2(3). http://journals.sfu.ca/flr/index.php/journal/article/view/96/182. Accessed: February 3, 2020.