A Comparison of Reasoning Processes in a Collaborative Modelling Environment: Learning about genetics problems using virtual chat

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  • This article was downloaded by: [Laurentian University]On: 11 October 2014, At: 02:55Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

    International Journal of ScienceEducationPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tsed20

    A Comparison of Reasoning Processes ina Collaborative Modelling Environment:Learning about genetics problems usingvirtual chatKai Pata a & Tago Sarapuu aa University of Tartu , EstoniaPublished online: 23 Feb 2007.

    To cite this article: Kai Pata & Tago Sarapuu (2006) A Comparison of Reasoning Processes ina Collaborative Modelling Environment: Learning about genetics problems using virtual chat,International Journal of Science Education, 28:11, 1347-1368, DOI: 10.1080/09500690500438670

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  • International Journal of Science EducationVol. 28, No. 11, 15 September 2006, pp. 13471368

    ISSN 0950-0693 (print)/ISSN 1464-5289 (online)/06/11134722 2006 Taylor & FrancisDOI: 10.1080/09500690500438670

    RESEARCH REPORT

    A Comparison of Reasoning Processes in a Collaborative Modelling Environment: Learning about genetics problems using virtual chat

    Kai Pata* and Tago SarapuuUniversity of Tartu, EstoniaTaylor and Francis LtdTSED_A_143850.sgm10.1080/09500690500438670International Journal of Science Education0950-0693 print/1464-5289 onlineOriginal Article2006Taylor & Francis000002006KaiPatakpata@ut.ee

    This study investigated the possible activation of different types of model-based reasoningprocesses in two learning settings, and the influence of various terms of reasoning on the learnersproblem representation development. Changes in 53 students problem representations aboutgenetic issue were analysed while they worked with different modelling tools in a synchronousnetwork-based environment. The discussion log-files were used for the microgenetic analysis ofreasoning types. For studying the stages of students problem representation development, individ-ual pre-essays and post-essays and their utterances during two reasoning phases were used. Anapproach for mapping problem representations was developed. Characterizing the elements ofmental models and their reasoning level enabled the description of five hierarchical categories ofproblem representations. Learning in exploratory and experimental settings was registered as theshift towards more complex stages of problem representations in genetics. The effect of differenttypes of reasoning could be observed as the divergent development of problem representationswithin hierarchical categories.

    Introduction

    Traditionally, reasoning with inductive arguments or reasoning with deductive argu-ments are considered the two modes of creating new knowledge in science (Lawson,2002). Besides these, the complex forms of model-based reasoning, which can berelated to inductive and deductive processes, have been brought to the focus inscience teaching (e.g., Gilbert, Boulter, & Elmer, 2000; Lee, 1999; Mellar & Bliss,1994; Nersessian, 1999; Stewart, Hafner, Johnson, & Finkel, 1992). The current

    *Corresponding author: Science Didactics Department, University of Tartu, 46 Vanmurse Street,Tartu, 51014 Estonia. Email: kpata@ut.ee

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    paper deals with reasoning processes in the network-based synchronous collabora-tive learning environment with modelling aids: hypothetical-predictive inductivereasoning, model-based inductive reasoning, and model-based deductive reasoning.The influence of different model-based reasoning on the structure of problem repre-sentations in the genetics domain is studied.

    The Nature of Reasoning Processes

    One theory that explains the general nature of reasoning processes is the theory ofmental models (Johnson-Laird, 1983; Johnson-Laird & Byrne, 1991). The theoryclaims that reasoning is a semantic process of model construction and manipulationin working memory. Johnson-Laird uses the three-stage processing schema forexplaining the reasoning mechanism. In the initial model-construction stage, themental model sets are constructed of separate entities, their properties, andstructural and causal relationships between them. In the conclusion-formulationstage, the inconsistent models are eliminated and consistent ones are joined informing the integrated model. At the final conclusion-validation stage, the personmust look for alternative models that might falsify the conclusion. Accordingly,peoples ability to think and solve problems in a domain depends on the quality ofunderlying mental models for that domain, which they are able to run (Gentner &Gentner, 1983). As several types of reasoning exist, it is also necessary to know whatthe influence of different types of reasoning processes on the development of mentalmodels might be.

    Several variations of inductive and deductive reasoning have been applied inscience lessons. In inductive reasoning, starting from maximally probable premisesand using correct inductive logic, one should arrive at most probable conclusions(Magnani & Nersessian, 2004). According to Romeyn (2004), the input for induc-tive reasoning includes a data set, usually consisting of observations, and possiblysome further assumptions; and the output may consist of predictions or generalstatements, where predictions concern unobserved singular states of affairs, andgeneral statements, such as empirical generalizations, concern universal states ofaffairs.

    Two main ways of practising the inductive reasoning in learning situations are thehypothetical-predictive approach (Lavoie, 1999) and the theory-supported approach(Wynne, Stewart, & Passimore, 2001). Hypothetical-predictive reasoning should bepreferable when starting new conceptual topics. According to Lavoie, hypothetical-predictive reasoning is an important thinking skill enhancing problem-solving,stimulating peer-group discussion and logical argumentation, increasing studentsmotivation, revealing prior knowledge, making students aware that a variety of opinionsare held by peers, and facilitating conceptual change. In hypothetical-predictive reason-ing, after observing some patterns, the students will be activating different knowledgefrom long-term memory, constructing separate sets of mental representations thatmight give hypothetical explanations to the phenomena. The final explanations willremain hypothetical and not supported by strong theory. In other cases, students gain

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    the domain-specific theoretical knowledge before observing the patterns and mustexplain the regularities by selecting from the set of conceptual mental models, whichare based on the previously learned theories. Mental models, not consistent with theobserved phenomena, are left aside or modified until the model that works as a rulefor explaining the pattern was found. The difference with the other inductive approachis that the final explanation will be theoretically sound.

    The deductive reasoning process is built upon the usage of connectives such asifthen, thus, and so on. Commonly, the method of deductive reasoning isbased upon the accepted theories and controlled data, permitting sound hypothesesto be formulated. True premises plus valid deductive reasoning should yield trueconclusions and the confirming of the theory (Magnani & Nersessian, 2004). Atschool, the deductive approach usually starts from approved theories, which will betested under certain circumstances. The first phase sees the mental processes thatcomprise the formation of the initial mental model, the hypothesis. Next, testingdifferent sets of options follows with some valid data set, which enables the forma-tion of an integrated understanding of the conditions under which the mental modelworks. Several authors (Klahr & Dunbar, 1988; Stewart et al., 1992) have arguedthat the strategies used by the subjects to solve problems could be described in termsof a simultaneous, mutually constraining search through experiment space consist-ing of a set of all possible experiments that might be run, and the hypothesis space,including all possible hypotheses of the solution of the problem. Finally, the alterna-tive hypotheses should be formulated and tested in order to find the contradictionsto the theory.

    In some cases, the hypotheses may be formed by inductive or hypothetical-predictive means, but tested with deductive methods. These, hypothetical-deductivemethods embody the reasoning with hypothetical entities that interact to produceemergent behaviour (Lehrer & Schauble, 2000). Lawson (2002) describes the hypo-thetical-deductive reasoning on the basis of the inventions of Galileo as the followingif/then/therefore pattern: making a puzzling observation, formulating a causalquestion, formulating one or more hypotheses, using a hypothesis and an imaginedtest to generate expected results/predictions, making actual observations andcomparing them with the expected observations, and drawing conclusions as to theextent to which the initial hypotheses have or have not been supported.

    Two Model-based Reasoning Processes in Science

    Science education should encourage students to develop insights about science as anintellectual activity (Stewart et al., 1992) that involves the development of sharedmeaning within the community by applying analogy, thought experiments, modelsand visual imagery in the concept, and theory formation by reasoning in order toovercome the working memory limitations (Nersessian, 1999). According to Lee(1999), model-based reasoning is the symbolic processing of an explicit representa-tion of the internal workings of the system (mental models) in order to predict,generate, and explain the resultant behaviour of the system, given details of its

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    structure and the behaviour of its components. Conceptual models serve as externalrepresentations for making interferences, finding characteristics, and structuring theproblem-solving (Reinhard, Hesse, Hron, & Picard, 1997). When composing themodel or when conducting inquiry with the model, different model-based reasoningprocesses may take place that might have an influence on the mental model develop-ment. Mellar and Bliss (1994) distinguish between qualitative, semi-qualitative, andquantitative modelling, which can be related to reasoning with the qualitative, soft,and hard models introduced by Lee (1999). In this study reasoning between qualita-tive and hard models was compared.

    Qualitative models capture only the fundamental aspects of the phenomena whilesuppressing much of the detail, thus facilitating the basic understanding andmanipulation of the problem. According to Lee (1999), such models are normallybased on strong theories that exploit the principles knowledge, rather than heuristicor empirical data. Qualitative models are often run only internally. An example ofturning inductive reasoning with qualitative models external is the process ofcomposing the schematic figure. Following Lee, hard models are formed on thebasis of reliable and confident data and focus on particularly well-defined problems.They are accurate and precise and well grounded by the strong and accepted domaintheories (Lee, 1999). Many computer-based inquiry models applied at schoolqualify in this category. Mellar and Bliss (1994) have described reasoning with hardmodels as quantitative modelling, which allows constructing models by use of alge-braic relations between variables.

    In this study, these two modelling methods were applied in computer-basedenvironments that have been differed in terms of their modelling capabilities asexpressive or exploratory (Chalk, 2000; Jimoyiannis & Komis, 2001). In one case,students were supported in the expressive environment (electronic whiteboard) withthe possibility to compose the schematic figure as the problem representation andcould perform inductive reasoning. Students were provided with initial elements ofthe model and had to relate them in order to explain the phenomenon. Roth (2001)assumed that the shared expressive modelling tools, such as argument graphs, mind-maps, and so on, on whiteboards can enhance learning through designarrangingdifferent elements of unique knowledge systems and providing knowledge artefacts.By representing knowledge about the phenomena in the format of visual designstructures, the latter can serve as anchors for grounding unclear matters. Instead ofthe traditional perspective of design, which requires that the learners should haveobtained all the principles of science before actually being engaged in design activity,this type of learning views design as a social process in which rational knowledgeserves as one of the resources, but is secondary in importance to the norms and prac-tices in a particular community (Roth, 2001).

    The construction of an external model on a whiteboard can enable the students torecognize the faults in their initial interpretation, activating the tunnel effect(Cornujols, Tiberghien, & Collet, 2000)the mechanism of model-based reason-ing with a single situation where the knowl...

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