Using Concept Maps to Facilitate Collaborative Simulation-Based Inquiry Learning

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Using Concept Maps toFacilitate CollaborativeSimulation-Based InquiryLearningHannie Gijlers a & Ton de Jong aa Department of Instructional Technology ,University of TwenteAccepted author version posted online: 21 Nov2012.Published online: 02 Jan 2013.

To cite this article: Hannie Gijlers & Ton de Jong (2013) Using Concept Maps toFacilitate Collaborative Simulation-Based Inquiry Learning, Journal of the LearningSciences, 22:3, 340-374, DOI: 10.1080/10508406.2012.748664

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THE JOURNAL OF THE LEARNING SCIENCES, 22: 340–374, 2013Copyright © Taylor & Francis Group, LLCISSN: 1050-8406 print / 1532-7809 onlineDOI: 10.1080/10508406.2012.748664

Using Concept Maps to FacilitateCollaborative Simulation-Based

Inquiry Learning

Hannie Gijlers and Ton de JongDepartment of Instructional Technology

University of Twente

This study investigates the effect of a shared concept-mapping task on high schoolstudents’ learning about kinematics in a collaborative simulation-based inquiry set-ting. Pairs of students were randomly assigned to a concept-mapping condition(12 pairs) or a control condition (13 pairs). Students in the concept-mapping con-dition had a computer-supported collaborative concept-mapping tool that aimedto integrate concepts and propositions. Students in the control condition used thesame learning environment without the concept-mapping tool. Students’ interac-tions with each other and with the simulation were tracked by log files. Learningwas assessed with tests of intuitive and structural knowledge and a proposition test.Students in the concept-mapping condition exchanged significantly more chat mes-sages related to experimentation, interpretation, and drawing conclusions and weremore engaged in integration-oriented consensus building, which regression analysisshowed to be positively related to learning gains for both intuitive and structuralknowledge. Students in the concept-mapping condition outperformed their peerson the intuitive and structural knowledge tests. These results suggest that con-cept maps can positively influence consensus-building activities and learning in acollaborative inquiry-learning setting. The findings of the current study thereby con-tribute to the ongoing debate about (shared) graphical representations as scaffoldsfor collaborative learning.

This study focuses on supporting the learning process of students who collaboratewithin a simulation-based inquiry-learning environment. In such an environment

Correspondence should be addressed to Hannie Gijlers, Department of Instructional Technology,Faculty of Behavioural Sciences, University of Twente, P.O. Box 217, 7500 AE, Enschede, TheNetherlands. E-mail: [email protected]

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FACILITATING COLLABORATIVE SIMULATION-BASED INQUIRY 341

students are expected to construct knowledge through an inquiry cycle thatconsists of processes such as orientation, questioning, hypothesis generation,experimentation, data interpretation, and drawing conclusions (see, e.g., Kuhn,Black, Keselman, & Kaplan, 2000; Njoo & de Jong, 1993). Pure inquiry is difficultfor students and does not always lead to learning (Mayer, 2004). Therefore, mostinquiry-learning environments incorporate various forms of guidance or scaffoldsto assist students’ inquiry-learning processes (see, e.g., Hmelo, Duncan, & Chinn,2007). Simulation-based learning environments that include support often lead tosuccessful learning (Eysink et al., 2009; Linn, Lee, Tinker, Husic, & Chiu, 2006),as a recent meta-analysis made clear (Alfieri, Brooks, Aldrich, & Tenenbaum,2011). Beyond the scaffolds provided for individual learners, interaction witha fellow student might also give added support for learning. In collaborativelearning, students’ plans, ideas, and reasoning must be made explicit and under-standable for the collaborating partner (Kyza, 2009; Teasley, 1995). The processesthat students engage in when they try to provide explanations for their partnerare believed to help students gain greater conceptual understanding themselves.However, like inquiry learning, collaboration often does not run smoothly on itsown, and therefore also requires support.

In this study we investigated the effect of a specific tool, a shared conceptmap, that was intended to support collaborative simulation-based inquiry learning.To contextualize the study, we first discuss how inquiry learning can be scaffoldedand then consider collaborative inquiry learning and its support.

INQUIRY LEARNING AND SCAFFOLDS

Within a simulation-based inquiry-learning environment, students’ main task is tofind the properties of a simulated domain by engaging in knowledge inference pro-cesses. The processes students engage in can be assigned to two major categories(Njoo & de Jong, 1993): transformative and regulative processes. Transformativeprocesses are those in which learners directly create or generate knowledge, suchas orientation, hypothesis generation, experimentation, interpretation, and draw-ing conclusions. During orientation students form an idea of the structure of thesimulated domain by identifying important variables and relations; activating priorknowledge; and connecting their prior knowledge to the variables, conditions, andphenomena that are presented in the learning environment. Hypothesis genera-tion refers to the formulation of a statement or a set of statements concerning thevariables and relations in the domain. Experimentation includes designing exper-iments, making predictions concerning the experimental outcomes, and runningthe experiment. During interpretation and drawing conclusions students interpretexperimental outcomes and review their hypotheses in light of the experimen-tal data they collected during the experimentation phase. Regulative processes

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342 GIJLERS AND DE JONG

refer to the planning and monitoring of the learning process. Each of these pro-cesses is known to pose specific problems for learners (e.g., de Jong, 2006a),problems for which a whole series of support measures for students have beendesigned. This has led to the development of a variety of computer-based inquiryenvironments that provide students with inquiry facilities and integrated support-ive scaffolds. Examples of such learning environments are Belvedere (Suthers,Weiner, Connely, & Paolucci, 1995), BGuILE (Reiser et al., 2001), Inquiry Island(White, Frederiksen, Eslinger, Loper, & Collins, 2002), GenScope (Hickey &Zuiker, 2003), SimQuest-based environments (de Jong et al., 1998), Co-Lab (vanJoolingen, de Jong, Lazonder, Savelsbergh, & Manlove, 2005), WISE (Linn,Davis, & Bell, 2004), STOCHASMOS (Kyza, Constantinou, & Spanoudis, 2011),and SCY (de Jong et al., 2010, 2012).

Scaffolding refers to support that helps students with tasks or parts of a taskthat they cannot complete on their own. The different types of scaffolds thatare integrated in a software learning environment and their effects on knowl-edge acquisition have been reviewed in several studies (de Jong, 2006b; Scaliseet al., 2011; J. Zhang, Chen, Sun, & Reid, 2004). Within inquiry-learning envi-ronments, scaffolds are targeted at the different learning processes that constituteinquiry learning. For example, they can help students to create hypotheses (vanJoolingen & de Jong, 1991), design experiments (Lin & Lehman, 1999), makepredictions (Lewis, Stern, & Linn, 1993), formulate interpretations of the data(Edelson, Gordin, & Pea, 1999), or reflect upon the learning process (Davis,2000).

In the current study students worked with a collaborative inquiry-learningenvironment centered around a computer simulation in the physics domain ofkinematics. The support offered by the environment included its design based onprinciples of model progression and its provision of an extensive set of assign-ments. Model progression (Swaak & de Jong, 2001b; White & Frederiksen, 1990)can be used to create a stepwise introduction to the simulation model. The basicidea behind using model progression is that students might be overwhelmed bythe model in its full complexity. By moving through intermediate steps (or levels)of increasing complexity, students gradually learn the full model. At each pro-gression level the relevant variables and relations are presented to the studentsin assignments. Assignments are short exercises that provide learners with short-term goals (Swaak, van Joolingen, & de Jong, 1998) and guide them through thekey elements of the simulation model. Swaak and de Jong (2001a) reported apositive effect of the use of assignments on the development of students’ intu-itive knowledge. The learning environment also included two scaffolds that werespecifically related to the collaborative character of the learning environment:shared proposition tables and shared concept maps. The supportiveness of sharedconcept maps was the topic of the current study, and the presence of concept mapswas varied across conditions.

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COLLABORATIVE INQUIRY LEARNING

There is a growing awareness that knowledge construction processes are influ-enced by the social setting in which they take place. Collaboration is widely usedand recognized as a way to enhance student learning (Lou, 2004; Lou, Abrami, &d’Apollonia, 2001). The positive effects of collaboration can be explained by thefact that engagement in a collaborative learning task provides students with theopportunity to talk about their own understandings and ideas.

Inquiry-learning tasks allow students to express and explore their own strate-gies and conceptions. During inquiry learning, students must make many deci-sions (e.g., which propositions to test, which variables to change), and in acollaborative inquiry-learning setting, students are invited to share these plans andideas with their partner(s). This means that when students work collaboratively,they need to externalize their ideas: They must provide arguments and explana-tions so that their partner is able to understand and evaluate their ideas and plans(Teasley, 1995). Externalizing thoughts and ideas is believed to increase students’awareness of flaws and inconsistencies in their own reasoning or theories andto stimulate students to revisit their initial ideas. A study by Okada and Simon(1997) compared the inquiry-learning behavior of individual students and dyadsin a molecular biology learning environment. They found that dyads consideredmore alternative hypotheses and carried out more useful experiments than individ-uals. The generation of an alternative hypothesis was often triggered by a questionor a remark from the learning partner. To benefit from collaboration students mustrecognize conflicting information and need access to the knowledge and skillsneeded for the resolution of the conflict (de Vries, Lund, & Baker, 2002; Gijlers& de Jong, 2009).

Specific scaffolds might assist this externalization process. For example,Gijlers and de Jong (2009) introduced a tool that visualized students’ conflict-ing ideas and prompted students to think about these ideas. Students supportedby this tool achieved higher learning gains than their peers in the control con-dition. However, students often moved quickly from one visualized conflict toanother without considering how a resolved disagreement related to their overallunderstanding of the domain (Gijlers & de Jong, 2009).

Relating newly obtained knowledge to existing knowledge is an importantaspect of learning. Novak and Gowin (1984) argued that meaningful learn-ing occurs when students choose to relate new knowledge to relevant conceptsand propositions they already know. When students move quickly betweenexperiments and assignments in the inquiry-learning environment without try-ing to explain inconsistent ideas or integrate new information with their currentunderstanding of the domain, this might result in fragmented domain knowledge.

A more structured understanding of the domain is essential for deep under-standing and flexible problem solving (Jonassen, Beissner, & Yacci, 1993). Jointly

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created representations can play an important role in this process (Fischer &Mandl, 2005; Suthers & Hundhausen, 2003). The construction of a shared graph-ical representation that reflects the students’ prior knowledge and the knowledgeobtained in the learning environment might stimulate students to interrelate ideasand concepts obtained in the learning environment and help students to orga-nize their domain-related knowledge (Jonassen et al., 1993) and improve theirconceptual understanding (Kwon & Cifuentes, 2009).

CO-CONSTRUCTING GRAPHICAL REPRESENTATIONS

In order to become knowledgeable in a specific domain, students not only mustrecall single concepts and formulae but also have to develop an understandingof how different concepts relate to each other and to the domain as a whole.In simulation-based inquiry-learning environments, students engage in variousexperiments. The creation of a graphical representation of the simulated domainstimulates students to use the concepts and ideas newly obtained from exper-imentation in order to elaborate and refine their representation (Zimmarro &Cawley, 1998). In order to create such a representation students have to rethinktheir ideas and knowledge, select the information they want to present, and thinkabout the connections between the elements (concepts) in their representation.Fischer, Bruhn, Gräsel, and Mandl (2002) indicated that concept-mapping activ-ities make students aware of missing explanations and links. Simulation-basedinquiry-learning environments provide students with the opportunity to investi-gate these missing links. H. Z. Zhang and Linn (2008) also argued that creatinga representation might stimulate students to revisit the original learning material.The results of their study demonstrated that the creation of a shared representationhelped students to reflect on their original ideas and resulted in higher levels ofknowledge integration. Allowing students to construct their own representationsof a domain might stimulate them to integrate conceptual information obtained inan inquiry-learning setting and might positively affect the inquiry-learning pro-cess as well as the collaborative learning process (van Boxtel, van der Linden, &Kanselaar, 2000). For example, Suthers (2006) stated,

Similarly, disciplinary representations such as models, simulations and visualiza-tions also offer negotiation potentials and serve as resources for conversation. Ratherthan being vehicles for communicating expert knowledge, such representationsbecome objects about which learners engage in sense-making conversations . . . andcan be designed to lead to productive conversation. (p. 327)

Creating a shared representation, such as a concept map, might be particularlymeaningful in combination with inquiry-learning tasks.

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With respect to the collaborative process, collaboratively constructed represen-tations can help students to attain and maintain a shared focus (Suthers, 2003;Suthers & Hundhausen, 2003). Suthers and Hundhausen (2003) explored the useof evidence graphs in a collaborative learning setting. They found that a sharedrepresentation might serve as a group memory, reflecting prior findings or ideasthat students can refer to and build upon during the collaborative learning process.Similar findings were reported by Roth and Roychoudhury (1993), who askedstudents to collaboratively construct a concept map. The interaction protocols col-lected by Roth and Roychoudhury illustrate how concept mapping enhances thenegotiation of meaning. During their construction of a concept map, students fre-quently discussed the nature of a relation between two concepts. Collaborativediscussion about concepts can positively affect students acquisition of concep-tual knowledge. Comparing the learning effects of an individual and collaborativeconcept-mapping activity Kwon and Cifuentes (2009) found that both concept-mapping activities had a similar positive effect on a traditional knowledge test.However, the quality of the concept maps was significantly higher for studentswho constructed the concept maps collaboratively. Kwon and Cifuentes arguedthat students’ exchange and verbalizations of knowledge and ideas promote theunderstanding of interrelations among concepts.

In order to successfully complete a collaborative (inquiry) learning taskstudents need to reach at least a specific consensus about the learning task(Weinberger, Stegmann, & Fischer, 2007). The construction of a shared repre-sentation of the domain creates such a need for consensus-building activitiesbecause students have to agree on the concepts and ideas they want to includein their map (Ryve, 2004). Feeling the shared responsibility for a representationmight stimulate students to explore, and on some occasions integrate and critique,each other’s viewpoints and ideas (Damsa, Kirschner, Andriesen, Erkens, & Sins,2010).

The social mode of knowledge co-construction describes how learners reactto their partners’ contributions (Weinberger & Fischer, 2006) and is relatedto individual students’ learning outcomes (Teasley, 1997). Weinberger andFischer (2006) distinguished five social modes of co-construction: externalization,elicitation, quick consensus building, integration-oriented consensus building, andconflict-oriented consensus building. Externalization refers to situations in whichstudents articulate ideas to the group without referring to their partners. Duringelicitation students question their partner to receive additional information. Quickconsensus building occurs when students accept the contributions of their part-ner in order to continue with the task. Integration-oriented consensus buildingis characterized by building on the ideas of a partner, integrating multiple ideasor viewpoints, or taking over the perspective of a partner. Conflict-oriented con-sensus building refers to situations in which students operate on their partners’reasoning by critiquing and modifying their contributions or presenting them with

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alternatives. As Teasley (1997) has found, integration-oriented consensus buildingand conflict-oriented consensus building are associated with learning.

In summary, collaborative inquiry learning provides opportunities for sciencelearning. Software scaffolds might assist students in externalizing domain-relatedknowledge and ideas, but for successful collaborative inquiry learning it is impor-tant that students also connect their ideas to existing knowledge and construct anintegrated understanding of the domain. A review of the literature reveals thatconcept-mapping activities have the potential to support construction of an inte-grated understanding of the domain and to improve students’ dialogue. Becausein most prior studies students created concept maps based on textual information,it remains unclear how a concept-mapping activity that is embedded in a collab-orative inquiry-learning environment will affect students’ learning processes andoutcomes.

In the present study we investigated the effect of a shared concept-mappingtool added to a collaborative simulation-based inquiry environment. Guidancefor students’ inquiry was provided in the learning environment by incorporatingmodel progression, assignments, and a shared proposition tool into the simulation.The concept-mapping tool was an add-on to this. The opportunity to construct ashared representation was expected to have a positive effect on students’ learningoutcomes and students’ constructive dialogue about the outcomes of experimentsin the inquiry-learning environment. More specifically, the nature of the concept-mapping tool provided was expected to increase knowledge of interrelations in thedomain and result in higher levels of consensus building. Our analyses focusedon the students’ consensus-building activities, their learning outcomes, and theinterrelation of these.

METHOD

Participants

Fifty students participated in the study. The approximate age of the participatingstudents was 15 years old. All students were following a university preparationtrack and had completed an introduction to the domain of kinematics. The studentswere recruited from two different schools.

Students were assigned to heterogeneous groups based on prior knowledge.Research has indicated that students in heterogeneous groups generally performbetter than students in homogeneous groups (Webb, Nemer, & Zuniga, 2002),but the difference between students should not be too large (Gijlers & de Jong,2005). Because the inquiry-learning task focused on the acquisition of intuitiveunderstanding rather than definitional knowledge, we chose to assign students todyads based on students’ results on an intuitive knowledge pretest (see “Tests”

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for a detailed description). Based on the students’ results, we composed threegroups: high-scoring students, medium-scoring students, and low-scoring stu-dents. Taking this information into account, we constructed dyads consisting ofeither a low-scoring student and a middle-scoring student or a middle-scoringstudent and a high-scoring student. Dyads were randomly assigned to the twoconditions: 13 dyads participated in the control condition and 12 dyads in theconcept-mapping condition. Three dyads had to be excluded from the data anal-yses. We removed two dyads from the control condition. The first dyad was notable to complete the experimental session because of technical difficulties, andthe second dyad was excluded because the students were not able to attend bothsessions (pretest and experiment). One dyad from the concept-mapping conditionwas excluded from the analyses because one of the partners did not attend thecomplete experimental session. As a result, we analyzed five high/medium andsix low/medium dyads for the control condition and six high/medium and fivelow/medium dyads for the concept-mapping condition. All students were familiarwith pen-and-paper concept-mapping tasks. They participated in the experimenton a voluntary basis and received a small reward for their participation.

Materials

There were two conditions in this study based on two versions of the same learningenvironment. For students in both conditions, the simulation-learning environmentincluded support in the form of model progression, assignments, and a sharedproposition table. In the concept-mapping condition, a concept-mapping tool wasadded to the simulation environment. Student dyads worked at two separate com-puters with a shared interface and communicated only through a chat tool. Controlover the cursor could be obtained with a mouse click, so that the students reallyworked together in a shared learning environment. Students in both conditionswere instructed to communicate their ideas and work toward a shared solution forthe presented assignments. Both versions of the learning environment as well asall aforementioned tools are described next.

Learning Environment. The inquiry-learning environment used in thisstudy was called Motion and covered the physics domain of one-dimensional kine-matics. The Motion environment was developed with the SimQuest authoring tool(van Joolingen & de Jong, 2003). The learning environment included several scaf-folds to increase the effectiveness of the learning experience. Model progression,assignments, and a shared proposition table were used to assist students in bothconditions.

Model Progression. Model progression means that students are not exposedto the full complexity of a domain at the start; rather, the complexity is gradually

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increased. The Motion environment contained three complexity levels. Studentswere free to start at any level and to move back and forth between the levels.The first level focused on initial velocity, acceleration, time, and final velocity.The second level introduced distance moved, and in the third and final level theconcepts of mass and friction were introduced.

Assignments. A total of 35 assignments were available in the environ-ment. Basically, assignments presented the students with a goal and a simulationsetup. Through these goals, the assignments stimulated students to explore certainimportant aspects of the simulation environment and perform experiments withthe simulations. A sample assignment is presented in Figure 1. Students in bothconditions received the same assignments.

Shared Proposition Table. The shared proposition table is based on thework by Njoo and de Jong (1993), who found that working with a predefinedhypothesis (or proposition) had a positive effect on the number of task-relatedactions and simulation runs performed by students. Providing students with pre-defined propositions ensures that students work with syntactically correct propo-sitions that are testable in the learning environment. Gijlers and de Jong (2009)

FIGURE 1 Screenshot of the simulation with an assignment (color figure available online).

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developed a collaborative version of the predefined hypothesis list, the sharedproposition table, in which discrepancies between the ideas of two collaboratingstudents can be visualized.

The shared proposition table used the results of a proposition test in whichindividual students were presented with a list of 26 domain-specific propositions.Three questions were asked for each proposition. First each student had to indicatewhether he or she recognized the proposition. Then the student was asked to assigna truth value (true, probably true, probably false, or false) to the proposition. Andfinally, the student had to indicate whether he or she considered the proposition tobe a relevant candidate for testing. After individually completing the test, studentslogged into the collaborative part of the environment and the system combinedthe students’ individual responses into one shared proposition table. The sharedproposition table displayed the individual opinions of both students concerningthe truth value of the proposition as well as the wish to test the proposition (seeFigure 2 ). Differences in students’ answers were highlighted by color. Finally, ifa dyad decided to perform an experiment to test a certain proposition, the studentscould indicate this (by clicking the button “Simulation”); in that case, they wereprovided with the simulation in a specific state together with an assignment thatwas suited to test this particular proposition. Students were allowed to adjust theiropinion after their investigations.

When students entered a specific model progression level the start screen con-tained a simulation, a list of available assignments, and the shared propositiontable. The presented assignments and propositions matched the complexity of theselected model progression level. Students were instructed to explore the vari-ables that influenced final velocity and displacement. For example, in the firstprogression level students were told to investigate the relation between initialvelocity, acceleration, and time on final velocity. They could do this by using thesimulation button that was integrated into the proposition table, but they were also

FIGURE 2 Screenshot of the shared proposition table (color figure available online).

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free to use the available simulation and assignments to gather additional evidenceor information.

Concept-Mapping Tool. Initially, Novak and Gowin (1984) defined a con-cept map as a “schematic device for representing a set of concepts embedded ina framework of propositions” (p. 15). Concept mapping enables students to exter-nalize, articulate, and interrelate the ideas and concepts that they are studying(Jonassen, Reeves, Hong, Harvey, & Peters, 1997; Zimmarro & Cawley, 1998).A concept map can be seen as a visual representation of students’ understand-ing of a knowledge domain, consisting of nodes and labeled lines. The nodesrepresent the important terms and concepts in the domain. The lines denote therelations between the concepts. In the present study, a concept-mapping tool wasavailable for students in the concept-mapping condition. When they entered aspecific model progression level, dyads in the concept-mapping condition wereasked to work collaboratively to build a concept map displaying all of the rela-tions between the key concepts in that particular level. The concept-mapping toolprovided students with a facility for graphing nodes and arcs. By double-clickingthe concept-mapping canvas, students activated a pop-up window with a fill-inbox (see Figure 3). Students could define concepts using the fill-in box and dragthe concept nodes around the canvas. A line could be drawn to connect two con-cept nodes; by double-clicking this line, students could activate another pop-upwindow in which the relation between the connected concepts could be defined.Students were allowed to use their own words and specifications for describing thenodes and lines. A student could get control of the cursor with one mouse click,and the student who controlled the cursor was able to edit the concept map. Bothstudents could observe the edits in real time. Students were free to move betweenthe concept map, the assignments, and the shared proposition table.

A typical dyad would explore a progression level by using the simulation,completing a number of assignments, or checking a few propositions presentedin the shared proposition table and then would continue with the construction ofa concept map representing their prior knowledge and knowledge gained duringthe aforementioned activities. During the construction of the concept map studentscould use the simulation, assignments, and shared proposition table to check ideasand search for additional information. After completing the concept map for onelevel, the students moved on to the next level. The constructed concept maps (seeFigure 3 for an example) were saved by the system, and students were able to revisittheir map and adjust it to include it new concepts and relations at later stages.

Procedure

Before the actual experiment took place, students participated in a testing andtraining session. In this session the prior knowledge of all students was assessed.

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FIGURE 3 Screenshot of the concept-mapping tool (color figure available online).

All students completed a definitional knowledge test, the pretest version of a what-if test for intuitive knowledge, the pretest version of a proposition test, and astructural knowledge test. After completing the tests, students received an intro-duction to the learning environment. The introduction focused on the structure ofthe learning environment, the operation of the system, and the scaffolds that wereavailable for all students (model progression, assignments, and the propositiontable).

Before the actual experiment, students received an introduction to computer-based concept mapping in a SimQuest learning environment (an overview of theexperimental session is provided in Table 1). Students were already familiar with

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TABLE 1Overview of the Experimental Session

Activity Time Scale

Training and testingStructural knowledge test 15 minIntuitive knowledge test 15 minDefinitional knowledge test 20 minProposition test 15 minIntroduction to SimQuest 20 min

Experimental sessionIntroduction to concept mapping withSimQuest

10 min

Short break 5 minInteraction with the Motion environment 90 minPosttests 50 min

paper-based concept-mapping techniques. Computer-based concept mapping wasintroduced to them as a general technique that could be useful for various domainsand that would enable them to share their concept maps easily. In the introduction,students were taught to place links between concepts and label the relations repre-sented by the links. After the introductory instruction, the actual learning sessionstarted. Students in both conditions were asked to interact collaboratively with thelearning environment, perform experiments, and learn more about the relationsbetween the different concepts in the domain. Students were free to stop whenthey were ready, but the majority of students continued to work until they werelogged out of the system by the experiment leader after approximately 90 min.We checked whether there was a difference between the two conditions concern-ing time on task, but there did not appear to be one, F(1, 42) = 0.044, p = .834.The average study time in the concept-mapping condition was 91.81 min (SD =2.08), and in the control condition students worked for an average of 91.68 min(SD = 2.21).

Following this learning period, all students were asked to complete the posttestversions of the tests of intuitive knowledge, proposition judgments, and structuralknowledge.

Process Data

Students’ interactions with the simulation and their chat communications werecaptured with log files.

Simulation Log Files. The simulation log files provided data on the use ofthe simulation and assignments by each dyad. Within the learning environment,

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the simulation could be run either in the context of an assignment or as a freesource of information (free simulation run). Records of students’ use of the sim-ulation for free and assignment-related runs provided a data source for dyads’activity within the learning environment.

Chat Logs. Students’ chat logs were used as a data source for students’collaborative interaction. The chat logs were coded along two dimensions: thefirst dimension focused on students’ inquiry-learning processes, and the seconddimension focused on the social mode of the collaboration.

First, all of the dialogues were segmented into utterances. An utterance wasdefined as a distinct message from one student to another student or to himself orherself. Second, each utterance was categorized as on- or off-task communication.Off-task communication was not further categorized. Third, all on-task utteranceswere further coded along both dimensions (inquiry-learning processes and socialmodes). The coding scheme is presented in Table 2.

TABLE 2Overview and Examples of the Coding Categories

Category Code Examples From Students’ Interaction

Off task OT “Hey, I really like the skirt Sandra is wearing today.”“Did you also go to the concert last Saturday?”

Technical T “I cannot see the chat window.”“Can you move the chat window to the right?”

Regulative R “We have 20 minutes left and we are still working on the firstlevel; let’s skip to the next.”

“Do you agree with me on this idea?”Transformative TR

Orientation O “Look at the line; it is not straight but a curve.” “What doesthis N next to mass mean?”

Proposition generation P “If the initial velocity increases the final velocity will alsoincrease.”

“I think acceleration is negative when you are slowing down.”Experimentation E “I think it is a good idea if we test this idea.”

“Let’s see what is changing if we double the acceleration.”Interpretation andconclusion

I “It seems like our car is moving faster now.”“So the line is steeper, we can see that speed is increasing

fast.”Social mode

Externalization EX “So we have acceleration.”Elicitation EL “Oh why is this line a curve?”Quick consensus building QC “Okay.”Integration-orientedconsensus building

IO “I also think that acceleration is negative when you areslowing down.”

Critical consensusbuilding

CC “Okay, but isn’t it more important to include mass?”

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Concerning the inquiry-learning dimension, we categorized messages as tech-nical, regulative, or transformative. All utterances related to technical features ofthe learning environment, such as closing and opening an assignment or window,were coded as technical. Messages related to the planning or monitoring of thelearning process were coded as regulative. Communication that directly yieldedknowledge was coded as transformative. All transformative messages were furthercategorized as related to orientation, proposition generation, experimentation, orinterpretation and conclusion (a more detailed description of the transformativeprocesses is presented in “Inquiry Learning and Scaffolds”). A second codercoded about 20% of the data. The interrater reliability of coding utterances interms of on- and off-task communication reached .97 (Cohen’s kappa). Interraterreliability for coding utterances as technical, regulative, or transformative reached.88 (Cohen’s kappa), and the interrater reliability of coding the transformativeprocesses reached .71 (Cohen’s kappa). The results presented in this article arebased on the codes assigned by the first coder.

On-task messages were also coded according the social mode dimension.On the social mode dimension we categorized messages as externalizations, elic-itations, quick consensus building, integration-oriented consensus building, andcritical consensus building (a more detailed description of the social modes of thecollaboration is provided in “Co-Constructing Graphical Representations”). Thesecond coder coded about 20% of the protocols, and the interrater reliability forcoding the social modes of the discussion reached .83 (Cohen’s kappa).

Tests

Four different tests were administered for definitional knowledge, intuitive knowl-edge, proposition judgments, and structural knowledge. Participants were testedas individuals, not as dyads, on all pre- and posttests.

The definitional knowledge test focused on students’ definitional knowl-edge about the domain and contained questions about concepts, formulae, anddefinitions that were relevant for the domain at hand. The test consisted of25 multiple-choice items with four possible choices (example items are presentedin Figure 4). Cronbach’s alpha reached .62. This test was used to check differencesin initial knowledge between groups. Because the inquiry-learning task focused onthe acquisition of intuitive understanding rather than definitional knowledge, thedefinitional knowledge test was administered as a pretest only.

Inquiry learning is believed to promote an intuitive understanding of scien-tific domains that is not easily assessed by traditional knowledge tests (Thomas& Hooper, 1991). Swaak and de Jong (2001a) argued that intuitive knowledgeis related to the action- and perception-driven elements in inquiry learning andthat this type of knowledge can be characterized as the quick perception of antic-ipated situations. An intuitive knowledge test in the so-called what-if format was

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FIGURE 4 Two sample items from the definitional knowledge test.

developed to assess these qualities of intuitive knowledge. Each item on the intu-itive knowledge test consisted of three parts: conditions, actions, and predictions(Swaak & de Jong, 2001a). Each item started with a description of a condition (aparticular state in which the simulation could be). The condition was presentedwith a screenshot of the simulation and a short textual description. The action (achange in a variable) was presented to the students in text form. Finally, threepredicted states of the simulation were presented to the students in the form ofa screenshot or text. Students were asked to decide which of the predicted statesfollowed from the presented condition and action (an example is presented inFigure 5). The intuitive knowledge test consisted of 21 of these what-if items and

FIGURE 5 Sample item from the intuitive knowledge test.

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was administered as a pre- and a posttest. The pre- and posttest versions of the testconsisted of the same items but presented in a different order and also with dif-ferent answer choices. Cronbach’s alpha for the intuitive knowledge test reached.61 for the pretest and .66 for the posttest.

The proposition test focused on students’ knowledge of relations within thedomain. This test asked students to give their judgment of the truth or falsity ofa list of 26 propositions from the domain (see “Shared Proposition Table”). Theproposition test was administered as a pre- and a posttest; both versions containedthe same propositions but presented in a different order. A sample item from theproposition test is displayed in Figure 6. A student’s score on the proposition test

FIGURE 6 Sample item from the computerized proposition test (pretest version) (colorfigure available online).

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was calculated as the total number of propositions the student correctly identifiedas true (or probably true) or false (or probably false). Cronbach’s alpha for theproposition test yielded .64 for the pretest and .67 for the posttest.

An essay question was used to assess students’ structural knowledge aboutthe domain. In this structural knowledge test students were asked to describethe movement of a shuffle stone that slides across the surface of a shuffleboardcourt. They were specifically asked to describe the physical factors that influencethe movement of the stone by identifying important concepts and describing therelations between the concepts. The essay question was scored on the number ofcorrectly used concepts and on the completeness of the description, compared withthe answer key. The score on the essay question was determined from the num-ber of relevant concepts the students used and the quality of the links betweenconcepts (propositions) as they were constructed by the students. Interrater agree-ment (Cohen’s kappa) between two judges on 10% of the essays reached .76 forthe number of relevant concepts and .68 for the quality of the propositions.

RESULTS

In this section we combine a qualitative analysis of students’ chat exchanges andtheir interactions with the learning environment with quantitative data to fullyexplain the differences between conditions. First, to gain a deeper understandingof the collaborative learning processes in both conditions, we discuss four rep-resentative excerpts, providing examples of students’ chat interaction and theirinteraction with learning environment. Second, we present the results of the mul-tivariate analyses performed on the coded process data and test scores. Finally, wereport the relation between test scores and the type and amount of inquiry-learningand consensus-building activities in student interaction.

Case Analyses

In the following paragraphs we present excerpts from students’ dialogues to pro-vide examples of how students worked with the tools that were provided in bothversions of the learning environment. Our analysis focuses on the collaborativeinquiry-learning process and the consensus-building activities students engagedin during their interaction with the learning environment. The excerpts presentedhere provide examples of the inquiry-learning processes and consensus-buildingactivities presented in the introduction and in Table 2. More specifically, the casestudies reveal how the support available in both conditions affected students’inquiry-learning process as well as their interaction. To gain insight into howdyads worked in the different conditions, we selected two example excerpts fromeach condition.

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Control Condition. First we look at students’ interaction in the control con-dition. Students in the control condition were scaffolded by model progression,assignments, and a proposition table. The selection of a specific propositionwas typically followed by the completion of the assignment associated with thisproposition or a discussion about the truth value of the proposition. The excerptspresented in Tables 3 and 4 give examples of how students used the propositiontable and the assignments during their collaborative learning process.

TABLE 3Excerpt 1: Sample Episode From the Chat Communication of Marc and Clare

Codes

Turn Student Chat Message Main Inquiry Social

1 Marc Let’s see 1.2 R EX2 Clare Both lines are horizontal. I think that proposition 1.2 is true. TR I IO3 Clare Marc, have a look at 1.3, we disagree there R EX4 Marc I think that one is false TR P QC5 Marc Look, here at the 5 second point in the graph. Velocity at that

point is not twice as large as the 2.5 second pointTR I CC

6 Clare But it’s constant and positive, so acceleration should also bepositive. The car is speeding up.

TR I CC

7 Marc Okay, true TR I QC8 Clare I think 1.4 is true TR O EX9 Marc I agree TR O QC10 Clare Yes, yes TR O QC11 Marc That proposition is true indeed TR I QC12 Marc I will click it R QC13 Clare This one is also true TR O EX14 Clare See, at 5 seconds it is 20 and 10 seconds later it is 30. TR I EX17 Marc Yes, I see. TR I QC18 Marc And true again. TR O QC19 Clare So acceleration is zero TR I EX20 Clare Yes, again a horizontal line, acceleration is zero TR I IO21 Marc We have completed this list of propositions. R EX22 Clare No, there are two more left R EX23 Marc Check these two quickly. R EL24 Marc 1.10 is definitely true, because it depends on the initial speed TR I EX25 Clare Acceleration of the car is constant TR O IO26 Marc The distance covered also depends on the initial velocity TR I IO27 Clare Okay R QC28 Clare Check that box in the table and next R QC

Note. CC = critical consensus building; EL = elicitation; EX = externalization; I = interpreta-tion and conclusion; IO = integration-oriented consensus building; O = orientation; P = propositiongeneration; QC = quick consensus building; R = regulative; TR = transformative.

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TABLE 4Excerpt 2: Sample Episode From the Chat Communication of Nancy and Frank

Codes


1 Nancy We failed that assignment TR O EX2 Frank Okay move on R EX3 Frank Continue with this one? R EL4 Nancy Yes R QC5 Frank What is this, I don’t understand this question TR O EL6 Nancy I don’t get it either. TR O QC7 Nancy Let’s run the simulation TR E EX8 Frank I think we should go for answer 4 TR O EX9 Nancy I agree TR O QC10 Nancy Correct TR O QC11 Frank So that proposition should be marked as true. TR I QC

Note. E = experimentation; EL = elicitation; EX = externalization; I = interpretation andconclusion; O = orientation; QC = quick consensus building; R = regulative; TR = transformative.

Excerpt 1. Control condition: Marc and Clare. In the excerpt presented inTable 3, Marc and Clare are working on time, velocity, acceleration, and displace-ment. They are in the control condition and work on the propositions that werepresented in the proposition table.

Marc and Clare mainly use the proposition table. They work their way throughthe list of propositions that is presented to them in the shared proposition table.Clare focuses Marc’s attention on a proposition they disagree upon (turn 3). Marcstates his opinion regarding the proposition (turn 4) and tries to support it withexperimental evidence, and Clare responds by presenting her own interpretationof the experimental data (turns 5 and 6). When the students click the simula-tion button in the proposition table they are provided with a simulation state andan assignment that is suited to test the selected proposition. The fact that Marcis interpreting a graph suggests that this dyad performed an experiment to testthe proposition. In response to Clare, Marc changes his opinion (turn 7). In turn8 Clare presents her idea about the next proposition. Marc and Clare do not dis-cuss this proposition but just decide that it is true (turns 8–12) and move to thenext propositions. Agreeing without further explanation or exploration is consid-ered quick consensus building. Clare continues and presents her idea about thetruth value of another proposition and supports it with an interpretation of a graph(turns 13 and 14). Marc agrees without further discussion.

Excerpt 2. Control condition: Frank and Nancy. In the excerpt presentedin Table 4, Frank and Nancy are also working in the control condition. They have

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just failed an assignment associated with a proposition (turn 1), and Frank sug-gests that they should move on. They do so by selecting a new proposition andfocusing on the associated assignment.

Frank indicates that he does not understand the question (turn 5). Nancy alsoindicates her lack of understanding and suggests that they could run a simulation(turns 6 and 7). Frank does not respond to her idea but proceeds by suggestingan answer (turn 8). Nancy answers his question by agreeing with his suggestion(turn 9) without asking for further explanations. The answer is correct (turn 10),and Frank remarks that they should mark the proposition as true. From an inquiry-learning perspective their interaction is characterized by orientation moves. Frankand Nancy’s interaction is characterized by the exchange of externalizations.Their discussions about the proposition and assignment are rather brief and theyuse no domain-related concepts, suggesting quick consensus-building activities.Completing the assignment is treated as a formality.

The examples provided in Tables 3 and 4 reveal that discussions betweenstudents in the control condition are focused on the rapid completion of theshort-term goals provided by the propositions and the assignment. Students there-fore engage in quick consensus-building activities, which may prevent them fromparticipating in integration-oriented consensus-building activities.

Concept-Mapping Condition. Students in the concept-mapping conditionwere asked to work collaboratively on the construction of a shared conceptmap displaying the key concepts in relations in a particular level of the learn-ing environment. Students typically started exploring progression level by usingthe simulation and then checking a few propositions or assignments before theycontinued with the construction of a shared concept map.

Excerpt 3. Concept-mapping condition: Angela and Cathy. In theexcerpt presented in Table 5, Angela and Cathy are constructing a concept map.They are trying to find information about the relation between velocity and themass of a vehicle, and they use the simulation to find additional information (turn8). They conduct an experiment in order to find information about the relationsbetween various concepts. Cathy looks at the environment and decides that thereis a way to find the information through an experiment (turns 9 and 10). Shedesigns and performs an experiment in which she reduces the weight of the vehi-cle. They save the graph (turn 13), and Angela builds on Cathy’s experiment andincreases the mass of the vehicle. She observes that this makes the vehicle slower(turn 16). Cathy decides that building a concept map based on this informationis too simple (turn 1). This suggests that Angela and Cathy are actually tryingto incorporate their experimental findings in the concept map and that the cre-ation of this map increases their awareness of missing links. They revisit the ideasthat are already represented in the concept map and argue about its completeness

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(turns 18–22). Angela states that velocity is influenced by acceleration (turn 23),and Cathy agrees (turn 25). Angela and Cathy build on each other’s ideas andreasoning. When Angela states (turn 29) that if acceleration increases the vehiclewill move faster, Cathy responds (turn 30) by indicating that if mass increases thevehicle will move slower and summarizes (turn 31) that they have mass, acceler-ation, and velocity in their map. Later on, Cathy and Angela discuss the relationbetween mass and acceleration in more detail (turns 31–40). They end up includ-ing mass, force, and acceleration in their concept map. The concept-mapping taskprovides a meaningful context for searching for information about the relationbetween variables. Angela and Cathy not only try to solve the disagreements pre-sented to them in the proposition table, they discuss the ideas of their partner andthe results from the simulations and work with these ideas and results.

Excerpt 4. Concept-mapping condition: Colin and Tom. In the excerptpresented in Table 6, we again present the dialogue of two students working withthe shared concept-mapping tool. Colin and Tom are conducting an experimentand are focusing on the graph that represents the output of the experiment. Colinwants to work on the graph, Tom agrees, and they notice that the graphing toolhas a zooming function (turns 1–3). Colin notices that the output is a straight line(turn 4). Tom builds on this idea and uses a more precise formulation indicat-ing that the line is straight and horizontal (turn 5). Colin concludes that the linerepresents constant velocity. Tom agrees, and Colin repeats all of the facts (turns6–8). Tom suggests drawing it in the map (turn 9) and notices that their map isnot symmetrical (turn 10). Colin does not seem to care (turn 11) and starts talkingabout the domain again (turn 12). Tom explains that the graph (probably referringto the slope) will go up if there is acceleration (turn 13) and suggests includingthis in the graph (turn 14). Colin prefers to test this idea with the simulation, andthey observe that the line indeed goes up (turn 16). In turn 18 Tom suggests doinganother experiment.

The examples provided in Tables 5 and 6 illustrate that students in the concept-mapping condition refer to knowledge that is represented in the shared conceptmap and build on their partners’ ideas and reasoning. Moreover, these excerptsshow that the concept map might increase students’ awareness of missing conceptsand information and that some students prefer testing ideas before they actuallyinclude them in their concept map. This indicates that students might feel a sharedresponsibility for the quality of their concept map. In contrast to students in thecontrol condition, students in the concept-mapping condition focus less on the listof assignments or the shared proposition table. Angela and Cathy notice that theirconcept map is missing information, and they actively use the resources in thelearning environment to search for a solution. Colin and Tom also focus on thecreation of their concept map. They perform an extra experiment to be sure thatthe information represented in their map is correct.

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TABLE 5Excerpt 3: Sample Episode From the Chat Communication of Angela and Cathy

Codes


1 Angela Maybe you can give it a try. R EX2 Angela Think about the relation between those two. TR R EL3 Cathy Okay I will think with you R QC4 Angela Okay. R QC5 Cathy I think that if the mass of the vehicle is larger ( followed by

pause)TR P EX

6 Cathy Well then the velocity will also be larger. TR P EX7 Angela I don’t think so. TR P QC8 Angela Can we make up an experiment to find out? R EL9 Cathy I will look. R EL10 Cathy Yeah. We can test it R EL11 Cathy What are you doing? R EL12 Cathy I am making the vehicle lighter TR O EL13 Angela Let’s save the graph R EL14 Cathy It is saved. R EL15 Angela And now increase the mass of the vehicle. Let’s add a person TR E EX16 Angela It is slower now. TR E IO17 Cathy But there must be something in between. The map is too

simple.TR O EX

18 Cathy We have mass and velocity. TR O EX19 Cathy That does not explain it. TR I CC20 Angela Well it does (pause) TR I CC21 Angela Velocity. TR O CC22 Cathy Is there more we can change? TR O EL23 Angela Velocity has something to do with acceleration. TR P IO24 Cathy Yeahh T P QC25 Cathy When you increase acceleration you certainly go faster TR I IO26 Angela Whoawww OT27 Cathy Hehe OT28 Angela Well let’s think R29 Angela If acceleration increases we will go faster TR P EX30 Cathy If mass increases we will go slower. TR P IO31 Cathy So we have the circles with mass, acceleration and velocity. TR O IO32 Angela Yeahh. TR O QC33 Cathy Is acceleration influenced by mass? TR P EL34 Angela It is the formula with force, mass and acceleration in it? TR O IO35 Cathy What did that formula look like? TR O EL36 Angela Can we see if we can find it out with an experiment? R EL37 Cathy You do it. R QC38 Cathy We must remember better. OT39 Cathy Don’t you remember? OT40 Angela Okay let’s put it this way, mass and force influence

acceleration.TR P IO

Note. CC = critical consensus building; E = experimentation; EL = elicitation; EX = externaliza-tion; I = interpretation and conclusion; IO = integration-oriented consensus building; O = orientation;OT = off task; P = proposition generation; QC = quick consensus building; R = regulative; T =technical; TR = transformative.

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TABLE 6Excerpt 4: Sample Episode From the Chat Communication of Colin and Tom

Codes


1 Colin Can I try? R EL2 Tom It’s okay. R EL3 Tom Great, you can use a zoom on the graph. T EX4 Colin It’s straight. TR I EX5 Tom There is no acceleration and in that case

there is a straight horizontal line.TR I IO

6 Colin Hmm, that is constant speed. TR I IO7 Tom Yep constant. TR I IO8 Colin Okay constant, it does not accelerate and the

line is horizontal.TR O IO

9 Tom I will draw it in our map. R EX10 Tom Our map is not symmetrical. R EX11 Colin Who cares. R QC12 Colin Acceleration constant, acceleration increases TR P EX13 Tom Put that in there also, I think the line will go

up.TR P IO

14 Tom Shall I draw it or do you want to check? R EL15 Colin I will check first. R EL16 Colin Heh, it goes up, faster and faster. TR I EX17 Colin And our next step is? R EL18 Tom I told you already, what if you double the

acceleration.TR E IO

Note. E = experimentation; EL = elicitation; EX = externalization; I = interpretation and conclu-sion; IO = integration-oriented consensus building; O = orientation; P = proposition generation; QC= quick consensus building; R = regulative; T = technical; TR = transformative.

Process Measures

The case studies provided insight into students’ interaction with the learning envi-ronment and the inquiry-learning and consensus-building activities they engagedin. To corroborate the findings of the case studies we provide a more quantita-tive account of students’ chat messages. Chat messages were coded accordingthe coding scheme discussed in the Method section (see Table 2). Because stu-dents worked in dyads, we used the dyad as the unit of analysis in our statisticalanalyses of chat messages. The first dimension of the coding scheme focusedon the inquiry-learning processes students discussed during their collaboration.To explore differences between the two conditions on this dimension we per-formed a multivariate analysis of covariance (MANCOVA) with dyads’ utterancesin the different inquiry-learning categories as dependent variables, controlling for

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TABLE 7Mean Number of Remarks Related to the Different Learning Categories

Control ConditionConcept-Mapping

Condition

Type of Remark M SD M SD

On task 113.45 63.72 121.73 35.33Technical 4.55 4.25 5.82 3.74Regulative 41.90 11.75 38.00 16.61Transformative 67.00 52.20 77.91 25.73Orientation 21.82 23.33 20.18 11.38Proposition 13.82 10.40 13.64 13.31Experiment 20.91 18.97 28.73 11.67Interpretation and conclusion 10.45 6.85 15.36 6.51

Note. n = 11 for each condition.

the difference in the total number of utterances. Mean scores and standard devi-ations are presented in Table 7. A significant effect of condition on the numberof on-task utterances was found after we controlled for the total number of utter-ances, F(1, 20) = 8.78, p < .05, Cohen’s d = 0.42, with the number of on-taskutterances higher for students in the concept-mapping condition. Furthermore,significant differences between conditions were found for the number of utter-ances related to transformative processes after we controlled for the total numberof utterances, with the number of utterances related to transformative processessignificantly higher for students in the concept-mapping condition, F(1, 20) =4.98, p < .05, Cohen’s d = 0.95. Within the category of transformative pro-cesses, dyads in the concept-mapping condition generated a higher number oftransformative utterances related to the experimentation process, F(1, 20) = 4.40,p < .05, Cohen’s d = 0.89, and to interpretation and conclusion, F(1, 20) = 5.96,p < .05, Cohen’s d = 1.04, after we controlled for the total number of utterances.No significant differences between conditions were found regarding the numberof orientation-related messages, F(1, 20) = 0.245, ns, or the number of generatedhypotheses, F(1, 20) = 0.011, ns.

The second dimension focused on consensus-building activities. To investigatethe differences between the two conditions regarding consensus-building activitieswe performed a MANCOVA with condition as the independent variable and thenumber of utterances in the different social modes of co-construction as depen-dent variables, controlling for the difference in the total number of utterances.Mean scores and standard deviations are presented in Table 8. The MANCOVArevealed an overall effect of condition on the social modes of co-construction,F(1, 20) = 5.01, p < .05. Subsequent analyses of covariance revealed that thenumber of quick consensus-building activities was significantly higher for dyads

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TABLE 8Mean Number of Remarks Related to the Different Consensus-Building Activities

Control ConditionConcept-Mapping

Condition

Type of Remark M SD M SD

On task 113.45 63.72 121.73 35.33Externalization 35.18 26.07 42.36 14.90Elicitation 24.45 10.37 26.18 9.49Quick consensus building 29.73 14.06 18.00 9.55Integration-oriented consensus

building18.18 11.78 28.09 8.01

Conflict-oriented consensus building 5.91 9.92 7.10 2.38


in the control condition, F(1, 20) = 16.52, p < .01, Cohen’s d = 1.73. Dyadsin the concept-mapping condition produced more externalizations, F(1, 20) =5.89, p < .05, Cohen’s d = 0.96, and engaged more in integration-orientedconsensus-building activities, F(1, 20) = 12.42, p < .01, Cohen’s d = 1.50.No significant differences between conditions were found regarding the num-ber of elicitations, F(1, 20) = 0.34, ns, or the number of conflict-orientedconsensus-building activities, F(1, 20) = 1.08, ns.

Chat files also provided information about the number of times concepts andrelations were reused and integrated into new questions, explanations, and argu-ments. The number of reused concepts and relations (controlling for the differencein total number of utterances) was significantly higher in the concept-mappingcondition, F(1, 20) = 4.64, p < .05, Cohen’s d = 1.04

The log files also provided information about the use of the assignments andthe simulation. Within the learning environment, students could choose from35 assignments and were free to run the simulation. We counted the number ofassignments and simulation runs used by each dyad (see Table 9). An analysis of

TABLE 9Mean Number of Free Simulation Runs and Assignments Conducted

by Dyads in Each Condition

Assignments Simulation Runs

Condition M SD M SD

Control 13.55 2.32 2.64 0.78Concept mapping 16.73 2.19 3.55 1.14


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variance was performed with the number of completed assignments and simula-tion runs for each dyad as dependent variables and condition (concept mapping orcontrol) as the independent variable. A significant and moderate effect of condi-tion was found for the number of assignments completed by the dyads, F(1, 21)= 21.88, p < .01, η2 = .342. The dyads in the concept-mapping condition per-formed more assignments than the dyads in the control condition. No significantdifference between the two conditions was found for the number of simulationruns.

Knowledge Tests

Next we examined the effect of the concept-mapping tool on students’ knowledgeacquisition on the intuitive knowledge test, the proposition test, and the structuralknowledge test.

Intuitive Knowledge Test. The what-if test for intuitive knowledge wasgiven as a pretest and a posttest. It consisted of 21 multiple-choice items with threeanswer choices. The mean number of correctly answered items on the intuitiveknowledge pre- and posttests is given in Table 10. A repeated measures analysisof the test scores for the intuitive knowledge test showed a statistically significantstrong learning effect from pretest to posttest, F(1, 42) = 105.95, p < .01, ηG

2

= .67, and a significant but small interaction between learning effect and con-dition, F(1, 42) = 7.121, p < .05, ηG

2 = .03. Students in the concept-mappinggroup gained significantly more intuitive knowledge than students in the controlcondition.

TABLE 10Mean Scores and Standard Deviations for the Different Knowledge Pre- and Posttests for

the Control and Concept-Mapping Conditions

Knowledge Measure

DefinitionalKnowledge

Test

Intuitive KnowledgeTest Proposition Test

Structural KnowledgeTest

Condition Pretest Posttest Pretest Posttest Pretest Posttest

ControlM 13.45 12.73 14.45 6.50 11.41 6.86 8.97SD 3.95 2.11 2.30 2.23 2.01 2.46 2.53

Concept mappingM 12.81 13.73 16.81 7.09 11.04 6.16 10.21SD 4.70 2.16 2.82 3.31 2.25 2.16 2.64


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Proposition Test. The proposition test was administered as a pre- and aposttest. It consisted of 26 proposition judgment items with four response choices:true, possibly true, possibly false, or false. The mean number of propositionscorrectly identified as true or false for the pre- and posttest is presented inTable 10.

A repeated measures analysis showed a statistically significant and moderatelearning effect for students in the concept-mapping condition as well as studentsin the control condition for the number of correctly identified propositions frompre- to posttest, F(1, 42) = 75.64, p < .01, ηG

2 = .53, and no significant effect ofcondition.

Structural Knowledge Test. An essay question was used to assess whetherstudents were able to use concepts in the field of kinematics in an interrelatedway when describing the movement of a certain object. This was done in a sim-ilar way on both the pretest and posttest. The results of the structural knowledgetest are presented in Table 10. A repeated measures analysis of the test scoresshowed a statistically significant and strong learning effect for scores from pretestto posttest, F(1, 42) = 168.59, p < .01, ηG

2 = .74, and a small but significant inter-action effect between learning effect and condition, F(1, 42) = 16.98, p < .01, ηG

2

= .08. Again, learning gains in the concept-mapping condition were significantlyhigher than in the control condition.

Knowledge and Process Measures Related

In order to explore the relation between students’ learning gains (posttest minuspretest scores) on the intuitive knowledge test, the proposition test, and the struc-tural knowledge test on the one hand and the number of chat messages studentsgenerated on the two dimensions of the coding scheme on the other hand, weconducted a stepwise regression analysis. A stepwise regression analysis withlearning gains on the intuitive knowledge test as a dependent variable and the num-ber of chat messages students generated in a certain category as predictors resultedin a significant model (adjusted R2 = .833), F(2, 42) = 13.71, p < 0.01. The num-ber of messages related to integration-oriented consensus building (social mode ofcollaboration) and the number of messages related to orientation (inquiry-learningprocess) were significant predictors. Integration-oriented consensus building waspositively related (β = .179, p < .01) to learning gains on the intuitive knowledgetest and orientation was negatively related (β = –.051, p < .05).

A regression analysis of the results of the proposition test revealed no sig-nificant relation between the number of chat messages in a certain category andlearning gains on the proposition test. A regression analysis of the results of thestructural knowledge test revealed that the number of chat messages related tointegration-oriented consensus building was significantly and positively related to

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the learning gains on the structural knowledge test (β = .349, �R2 = .122), F(2,42) = 5.829, p < .05.

DISCUSSION AND CONCLUSION

Although we found that students in both conditions improved on the intuitiveknowledge test, the proposition test, and the structural knowledge test, a key find-ing of this study is that students in the concept-mapping condition demonstratedhigher levels of transactivity in their dialogues and showed greater learning gainson the intuitive knowledge test and the structural knowledge test than studentsin the control condition. This is in line with the general idea that concept map-ping could have a positive effect on students’ knowledge about relations in adomain (Jonassen et al., 1993; Jonassen & Wang, 1993). The findings indicate thatconcept-mapping activities can positively affect students’ dialogues and learningoutcomes in an inquiry-learning task.

Our analyses of student interactions show that this result can be traced backto students’ collaborative learning processes. Based on our literature review weasserted that the construction of a self-generated graphical overview of the domainwould promote deeper learning processes (e.g., van Amelsvoort, Andriessen, &Kanselaar, 2007). From the analyses of the chat messages it became apparent thatstudents in the concept-mapping condition devoted more utterances to interpre-tations and conclusions than their peers who were working with only the sharedproposition table.

Our analysis of the social mode of student dialogue indicated that the studentsin the concept-mapping condition also engaged more often in integration-orientedconsensus-building activities, in contrast to the students in the control condition,who engaged more often in quick consensus-building activities. Regression analy-sis revealed that integration-oriented consensus-building activities were positivelyrelated to students’ learning gains on the intuitive and structural knowledge tests.The literature on collaborative learning processes suggests that higher levels ofconsensus building, such as we observed here for students’ integration-orientedand critical consensus-building activities, are associated with greater learning(Teasley, 1997). The results of the present study confirm this and indicate thatcollaborative concept-mapping techniques might facilitate integration-orientedconsensus building and learning in the context of a collaborative inquiry-learningtask.

The case studies show that students in the control condition were very focusedon completing the listed assignments and resolving all cases of disagreementpresented to them in the proposition table. These students did not always dis-cuss the assignments and answer them in detail, and mutual efforts to understand

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each other’s viewpoint and reasoning were rare. It seems that these students didnot perceive understanding of the simulated domain as their main learning taskbut instead focused on the short-term goals provided by the assignments and theproposition table. This focus on short-term goals might have prevented them fromrelating newly obtained understanding to knowledge obtained at earlier stages ofthe learning process.

In contrast, students in the concept-mapping condition not only used theassignments and propositions as short-term goals but also used their partner,the available assignments, and the simulations as sources of information dur-ing the concept-mapping task. Working with the shared concept map as a focalpoint representing agreed-upon knowledge might have stimulated students tobuild upon the reasoning of their partner, which provides a possible explanationfor the greater prevalence of messages related to integration-oriented consensus-building activities in the concept-mapping condition. This is in line with the ideathat shared representations help students to maintain a persistent overview oftheir developing knowledge (Dillenbourg & Traum, 2006) and to work towardconvergence of knowledge (Fischer & Mandl, 2005; Roschelle, 1992).

The format of a concept map stresses the interrelatedness of domain-relatedconcepts and propositions (van Boxtel, van der Linden, Roelofs, & Erkens, 2002).The shared representation provided by a collaboratively created concept mapvisualizes concepts and relations between concepts. Therefore, the shared con-cept map also makes it easier for students to refer to concepts and relations thatwere discussed at earlier stages of the learning process (Hron, Cress, Hammer,& Friedrich, 2007; Suthers & Hundhausen, 2003) and to include this knowledgein new arguments and explanations. Our quantitative analysis of the number ofreused concepts revealed that students in the concept-mapping condition moreoften included concepts and relations they had discussed at an earlier stage intheir argumentation. The results of our qualitative as well as quantitative anal-yses suggest that shared concept-mapping activities can contribute to students’knowledge-building dialogues in simulation-based inquiry settings. This resultsnot only in higher quality discussions, as reflected in higher level consensus-building activities, but also in a shared focus on domain-related concepts andrelations.

Excerpts from students’ interactions indicate that students may have perceivedof the concept map as a group product that was part of their collaborative learn-ing task and for which they had shared responsibility. Students who feel sharedresponsibility for a group task do not simply rely on their partners’ contribu-tions but try to understand these contributions (Damsa et al., 2010; J. Zhang,Scardamalia, Reeve, & Messina, 2009). The excerpt from Angela and Cathy’schat presented in Table 5 illustrates that the students build on their partner’s com-ments, and their dialogue suggests that they feel a shared responsibility for thequality of the concept map. Angela and Cathy think that their concept map is

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too simple and worry about the quality of their map. They feel that their map ismissing information, and in response both students search for extra information,indicating that they share responsibility for the quality of the map.

Other studies also suggest that concept mapping supports knowledge acquisi-tion and elaboration in collaborative learning settings (Alfieri et al., 2011; Ryve,2004; Sizmur & Osborne, 1997; van Boxtel et al., 2002). However, not all studiesconfirm the positive effects of concept mapping. In a recent study, Karpicke andBlunt (2011) showed that retrieval practice resulted in higher learning gains onconceptual and elaborative knowledge tests than learning through concept map-ping. Therefore, these authors questioned whether concept mapping is the bestmethod for acquiring conceptual knowledge. The learning task used by Karpickeand Blunt, however, differed from the learning task used in our study. One differ-ence is that Karpicke and Blunt instructed individual students to create conceptmaps. In our study, a concept-mapping task was used in a collaborative context.The literature suggests that concept-mapping activities might be particularly suc-cessful in provoking high-quality interaction, which in turn promotes learning(Roth & Roychoudhury, 1993). It would be interesting to test our inquiry-learningenvironment with individual students to investigate whether similar results wouldbe obtained when students individually construct a concept map. Another dif-ference is that students in the Karpicke and Blunt study created concept mapsbased on a given text. In our study students created a concept map based oninformation that they collected themselves in a simulation-based inquiry-learningenvironment. In our case, the shared concept-mapping tool provided students withthe opportunity to create their own overview of their developing understanding ofthe domain. The additional benefits of creating shared concept maps representingknowledge students have obtained and agreed upon might be greater because cre-ating such maps stimulates students to revisit information that they have collectedthemselves, which may differ from summarizing given information in a conceptmap.

In the present study we investigated the effect of one specific situation in whichcollaborative inquiry learning and concept mapping were combined. The domainand nature of the concept-mapping task used might have affected the results. It istherefore worthwhile to further investigate how different types of concept maps(Kinchin & Hay, 2005) or a combination of individual and shared concept maps(Engelmann & Hesse, 2010) affect collaborative inquiry learning. The presentstudy involved 22 dyads working with the learning environment for only a rela-tively short period. The results of this study should therefore be interpreted withcare. The practical implications of this study, although tentative, pertain to theuse of concept-mapping tasks in collaborative inquiry-learning settings. Basedon these results, teachers might consider using shared concept-mapping activi-ties in combination with inquiry-learning activities to stimulate students to createan overall integrated overview of the domain and to externalize their ideas and

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findings and to facilitate students’ communication about the represented ideas andrelations.

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Documents

Using Concept Maps to Facilitate Collaborative Simulation-Based Inquiry Learning