Fionnuala O'donnell

Brendan Tangney

Towards 'Phenomenaria' in the Teaching of Distributed Systems Concepts

Introduction

For Perkins [START_REF] Perkins | Technology meets Constructivism: Do they make a marriage?[END_REF], any learning environment can be parsed into five facets, not all of which are always present. These five facets are information banks, symbol pads, construction kits, 'phenomenaria' and task managers [START_REF] Perkins | Technology meets Constructivism: Do they make a marriage?[END_REF].

Information banks are sources of explicit information about a topic like textbooks. Symbol pads are surfaces for the construction and manipulation of symbols like word processors. Construction kits refer to a collection of building blocks that when moulded together form an entity. Lego Mindstorms is an example. 'Phenomenaria' are areas for the specific purpose of presenting phenomena and making them accessible to scrutiny and manipulation. SimCity, a program that models a city's environment, is an example. Task managers are elements of the environment that sets tasks to be undertaken in the course of learning guide and provide feedback regarding process and/or product. The teacher is an example [START_REF] Perkins | Technology meets Constructivism: Do they make a marriage?[END_REF].

Taken together these five facets offer a perspective on the general structure and style of the learning environment and its underlying assumptions about the nature of learning and teaching. Typically today's lecture theatre contains three of the five facets (information banks, symbol pads and task managers). Implicit in this profile are the premises that learning occurs through telling students about things, that students cannot manage their own learning and that solving problems rather than constructing entities is primary [START_REF] Perkins | Technology meets Constructivism: Do they make a marriage?[END_REF].

On the contrary, understanding is not something that comes free with full databanks; it is something won by the struggles of the student to learn, to conjecture, to probe and to puzzle out [START_REF] Perkins | Technology meets Constructivism: Do they make a marriage?[END_REF]. Thus, a shift in emphasis is needed away from the information banks and more towards construction kits or phenomenaria. The reason for this shift is because the latter facets place the learner directly and emphatically in the position of having something to make sense of or with, respectively. Moreover the role of task manager falls to that of the student albeit with scaffolding from the teacher. This shift in emphasis is what the author is proposing for the teaching of distributed systems concepts. But first what is a distributed system and why the need of phenonmenaria in this subject domain?

A collection of processes, which are distinct, spatially separated, and which communicate by exchanging messages constitutes a distributed system [START_REF] Lamport | Time, clocks and the ordering of events in a distributed system[END_REF].

A distributed algorithm defines the steps to be taken by each process within that system, including the transfer of messages. It must be able to deal with the failure of one or more of the processes involved in its computation and also the failure of one or more message transmissions. This makes the task of describing all states of the algorithm difficult [START_REF] Coulouris | Distributed Systems: Concepts and Designs[END_REF].

A major problem in teaching the latter material is the ability to capture the dynamic movement of data. Typically, when demonstrating data movement on a white board, part of the existing data configuration must be erased in order to show the new configuration. Moreover, the lecturer must decide the appropriate pace at which to reveal the configurations. However, no matter what pace the lecturer chooses it will be the wrong pace for some students because different students learn at different speeds [START_REF] Stern | A Strategy for Managing Content Complexity in Algorithm Animation[END_REF].

A phenomenarium offers a way forward for both lecturers and students in that it provides mechanisms not only to animate but to simulate dynamic movement in visual form. Moreover, it can be housed within an environment that permits 'anytime anywhere' learning, like the world wide web, and as such offers the possibility of direct manipulation of visual display in real time, something simply not possible with static media like the whiteboard. Simply stated, a phenomenarium permits a student to interact with elements of a given algorithm in their own time, and to perform operations appropriate to that algorithm thereby gaining knowledge as to the workings of that algorithm. This paper is an argument in favour of the use of the latter in the teaching of distributed algorithms.

A Phenomenarium

A phenomenarium is a model of some phenomenon or activity that users learn about through interaction with the phenomenarium [START_REF] Alessi | Simulations, in Multimedia for Learning (Methods and Development)[END_REF]. It is a space designed by some external agent to help the user understand the domain. Its aim is to teach about something (conceptual) as opposed to teaching how to do something (procedural) [START_REF] Alessi | Designing educational support in system dynamics based interactive learning environments[END_REF]. As such it is analogous to a physical simulation. A physical simulation is one in which the underlying computer model of a system is transparent to the user. In a physical simulation, one learns by manipulating the various objects or variables and observing how the overall system changes as a result [START_REF] Alessi | Simulations, in Multimedia for Learning (Methods and Development)[END_REF].

Similarly, in a phenomenarium, a user learns by making changes to inputs while the latter executes and by watching the resulting changes dynamically reflected in the model's representation. Thus, phenomenaria build conceptual understanding through experimentation.

Where phenomenaria differ from other instructional models is in the degree to which the underlying model is visible to the user and in the instructional approach employed. A phenomenarium favours the use of an expository approach [START_REF] Alessi | Designing educational support in system dynamics based interactive learning environments[END_REF]. The latter is one were students are given complete representations of a system with which to experience, explore, experiment and practice [START_REF] Alessi | Simulations, in Multimedia for Learning (Methods and Development)[END_REF]. Automated visualisation systems like phenomenaria differ from traditional methods of teaching distributed algorithms in that they allow a student to learn in their own time and permit direct manipulation of visualised content in real time. Traditional methods can be defined as those that make use of text and a visual display medium, like a blackboard, to teach the algorithms. Alternatively in a discovery approach, students learn by modelling that is by building their own representations of a domain. Their aim is to try to figure out the underlying model thus making the latter visible would be counterproductive [START_REF] Alessi | Designing educational support in system dynamics based interactive learning environments[END_REF]. Such an approach to learning is exemplified in Papert's classic treatment of the microworld concept using the Logo programming language. A microworld is a place where learners can build, create their own conceptual understandings of a domain through the language of that domain [START_REF] Papert | MindStorms: Children, Computers and Powerful Ideas[END_REF]. This language can be either a symbol notation or a programming language. This paper favours the use of phenomenaria as they allow for the easy integration of support facilities and do not require any language proficiency. Often the need to be proficient in the language of the system overrides the need to understand the complex interoperations of the system's model.

A phenomenarium does not simply replicate a phenomenon. It simplifies it by omitting, changing or adding details or features. The purpose is to help learners build their own mental models of the phenomena and provide them with opportunities to explore, practice, test and improve their models safely and efficiently. This can be done more effectively when the model is simplified [START_REF] Alessi | Simulations, in Multimedia for Learning (Methods and Development)[END_REF]. The universe of possible actions within a phenomenarium is constrained. This simplifies the choices to be made by the student and annihilates extraneous information. Students make decisions with a predetermined set of tools. Also the concept of time within a phenomenarium is user defined. A phenomenarium should embody expository learning and it should engender elements of procedural thinking that is it should allow a user to repeatedly perform operations and to reset the state of the representation if need be.

Token Ring Phenomenarium.

The underlying model encapsulated in the above is the token ring algorithm and its design is informed by principles outlined in section 3. It is initially presented to the student in its simplest form, bereft of failure, monitors and holding a minimum of two nodes. The aim is to allow a user to build algorithm representations of increasing complexity. He or she can vary the size of the network, create different network configurations, such as include a monitor or exclude a monitor and determine token speed constraints. Through a predefined set of controls the student can analyse, observe the effect of such layouts on the ability of nodes to send messages, to cope with token failure, packet failure, monitor failure and/or node failure. The controls enable expository learning. With respect to node failure, a student can cause a node to fail at any time during algorithm execution. The same is true of packet, monitor and token failure. The scope of the phenomenarium is limited and restricted to the failure scenarios exhibited by the token ring algorithm. The latter are considered to be the most problematic areas for students to learn and understand. Additional features provided by the above are control mechanisms for manipulating the presentation of an algorithm and a display mechanism for textual feedback during algorithm execution. With respect to the former a student has the option of playing, resetting, pausing, stepping forward or stepping backwards through the algorithm. The algorithm can be reset or temporarily suspended at any time during execution. All operations are repeatable.

Where the above differs from other visualisation systems in the same domain is in the level of detail afforded to behaviour scenarios and in particular monitor failure. Once a node detects monitor failure, a new window is open to depict the election process that ensues. The latter follows the same design principles as those outlined in section 3 and as such is itself a phenomenarium.

Future Direction.

Of immediate concern is the creation of more phenomenaria.

Next, hypertext material should be developed to wrap around each phenomenarium to give it a context. Finally, an investigation of the contribution of the latter to the learning process is warranted.

Proceedings of the The 3rd IEEE International Conference on Advanced Learning Technologies (ICALT'03) 0-7695-1967-9/03 $17.00 © 2003 IEEE


INTERNAL AND EXTERNAL COOPERATION SCRIPTS IN WEB-BASED COLLABORATIVE INQUIRY LEARNING Effects on the acquisition of domain-specific and general knowledge

Cooperation scripts are a powerful means to improve collaborative learning. Scripts can be designed to support argumentative knowledge construction. However, not only externally induced cooperation scripts but also the learners' internal scripts on argumentative knowledge construction influence argumentative processes and what kind of knowledge is acquired during collaboration. In this study, 98 students (49 dyads) of two German secondary schools participated. We implemented two versions (high vs. low structured) of an external cooperation script directed towards supporting argumentative knowledge construction into a web-based collaborative inquiry learning environment. Further, we classified the learners' internal scripts as either high or low structured, thereby establishing a 2x2-factorial design. We investigated how external and internal scripts as well as their interaction influenced the acquisition of both domain-specific and general knowledge. Results suggest that learners' internal scripts are more important for acquiring domain-specific knowledge, whereas external scripts are more influential for the acquisition of general knowledge.

INTRODUCTION

In theory and research on computer-supported collaborative learning (CSCL) as well as in instructional psychology, cooperation scripts are considered a powerful means to improve processes and outcomes of collaborative learning (Kollar, [START_REF] Fischer | Cooperation Scripts for Computer-Supported Collaborative Learning[END_REF]. For example, Weinberger, Reiserer, Ertl, Fischer and Mandl (in press) demonstrated that cooperation scripts embedded in different net-based learning environments were able to improve the learners' ability to apply theory-based knowledge to transfer problems as well as their recall rates of learned information. One main advantage of cooperation scripts is that they can be tailored to support a variety of activities like problem solving, learning from text or learning concrete procedures (for an overview see O 'Donnell, 1999). One area in which instructional support in the nature of cooperation scripts especially is demanded is collaborative argumentative knowledge construction. Many studies have found that students often experience difficulties in engaging in argumentation [START_REF] Kuhn | Effects of dyadic interaction on argumentive reasoning[END_REF][START_REF] Brem | Explanation and evidence in informal argument[END_REF]. Especially in the U.S. this problem was addressed by the development of several computer-or web-based inquiry learning environments (e.g., WISE, CoLAB, BGuILE) providing learners with the opportunity to deal with evidence for a specific problem, to plan and run experiments on science issues etc. (e.g., [START_REF] Schwartz | Towards the development of flexibly adaptive instructional design[END_REF][START_REF] Reiser | BGuiLE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms[END_REF]. That way, learners are enabled to acquire knowledge both concerning domain-specific content like for example mechanisms that underlie scientific phenomena as well as about scientific methods of experimenting or hypothesis generation. However, in most of these environments there is considerable potential to improve the provided instructional support concerning argumentative knowledge construction.

WHAT IS ARGUMENTATIVE KNOWLEDGE CONSTRUCTION?

Research on argumentation and argumentative knowledge construction appears to be scattered. At least two different approaches to argumentative knowledge construction can be found. On the one hand, there is a structural perspective, which seeks to evaluate the quality of single arguments on the basis of the structural components it includes [START_REF] Toulmin | The Uses of Argument[END_REF]. On the other hand, there is a dynamic perspective on argumentation, dealing with how argumentation develops in discourse. In such a view, argumentative sequences like "argumentcounterargument -reply" become important for collaborative knowledge construction [START_REF] Leitão | The potential of argument in knowledge building[END_REF]. Since learners engage in elaborating content while generating longer argumentative sequences, this is also supposed to result in an acquisition of more domain-specific content knowledge. Moreover, by engaging in meaningful sequences of argumentation, learners might internalize these processes and apply them even when they are not explicitly asked to do so.

In most previous research, these two accounts to argumentative knowledge construction have been treated separately -studies often focused either on the structural (e.g., [START_REF] Means | Who reasons well? Two studies of informal reasoning among children of different grade, ability, and knowledge levels[END_REF] or on the dynamic perspective of argumentative knowledge construction (e.g., [START_REF] Resnick | Reasoning in conversation[END_REF]. Few studies have considered both perspectives, a research gap that is accounted for in this study.

SCRIPTS FOR ARGUMENTATIVE KNOWLEDGE CONSTRUCTION IN INQUIRY LEARNING

As mentioned above, one promising way to facilitate students' argumentative knowledge construction is to provide learners with cooperation scripts [START_REF] O'donnell | Scripted Cooperation in student dyads: A method for analyzing and enhancing academic learning and performance[END_REF], which specify the processes according to which collaboration should proceed. Such cooperation scripts typically induce specific activities to be carried out by the (two or more) learning partners, prescribe a specific sequence according to which these activities are supposed to be carried out, and distribute roles among the learning partners (see Kollar et al., 2003). What activities are induced by a cooperation script depends on what the actual outcome of collaboration is supposed to be. Of course, the cooperation script should be designed in a way that guarantees a match between the induced activities and the desired learning outcomes. It is clear that the constraints that are inherent in the script instructions should have the function to reach these objectives. Yet, if wrong placed, these constraints might restrain learners to engage in activities that also might be beneficial with respect to the intended learning outcomes. Thus, it seems adequate to assess different kinds of learning outcomes in order to get a clearer profile of what particular aspects are influenced by the instructions of an externally induced cooperation script. Despite the rather positive effects of cooperation scripts that have been demonstrated in the literature, it does not seem plausible that externally provided script instructions are the only factor that guides collaborative learning processes. Rather, learners can be viewed as possessing knowledge and strategies about how to proceed in a collaborative learning situation on their own. In cognitive psychology, [START_REF] Schank | Scripts, plans, goals and understanding[END_REF] refer to scripts as a form of culturally shared knowledge about everyday events. We view "argumentation" as one example of an event that does have everyday significance, but about which individuals may hold different scripts that guide their actual behaviour in argumentative discourse. Yet again, differences in the learners' internal scripts might also lead to different profiles of what individuals learn during collaborative argumentative knowledge construction.

As a consequence, in this paper we focus on both internal (cognitive) and external (instructional) scripts, thereby investigating their each specific effects as well as the effect of their interrelation concerning the acquisition of different types of domain-specific content knowledge as well as argumentation-specific knowledge (as an instance of domain-general knowledge) during collaborative learning in a web-based collaborative inquiry learning environment.

GOALS OF THE STUDY

The main objective of this study is to analyse different cognitive outcomes of students' argumentative knowledge construction during learning in a web-based inquiry learning environment. In order to investigate the relationship between external and internal scripts in facilitating argumentation, we developed two versions of an external cooperation script (low vs. high structured) and implemented them into a curriculum project of the computer-based inquiry learning environment WISE (Web-based Inquiry Science Environment; [START_REF] Slotta | How do students make sense of Internet resources in the science classroom[END_REF]. It was then our aim to investigate how these externally provided cooperation scripts and the learners' internal scripts on argumentative knowledge construction interact with respect to both the acquisition of different forms of domain-specific content knowledge as well as domain-general knowledge about argumentation in inquiry learning environments. We set up two competing hypotheses, (a) an interactive effects hypothesis and (b) an additive effects hypothesis.

Interactive effects hypothesis: A highly structured externally provided cooperation script will especially facilitate the acquisition of content-specific and argumentation-specific knowledge of learners holding low structured internal scripts, whereas a low structured external script will lead learners holding high structured internal scripts to acquire more content-and argumentation-specific knowledge. This is either because the high structured external script will compensate the deficits low structured internal scripts obtain or because it unnecessarily puts constraints to the learning processes of learners holding high structured internal scripts.

Additive effects hypothesis: A highly structured externally provided cooperation script will support argumentative knowledge construction of all learners, independently from the nature of their internal scripts on argumentative knowledge construction, because even the contents of a high structured internal script will play out only when additional instructional support is provided.

METHOD

Participants

98 students from grades 8 to 10 from five classes of two German Gymnasiums participated in the study.

Design

An experimental 2x2-factorial design was established with the internal cooperation script (high vs. low structured) and the external cooperation script (high vs. low structured) as independent variables. Dyads were homogeneous with respect to the learners' internal scripts and gender and were randomly assigned to one of the two external script-conditions. Learners were identified as holding a high or a low structured internal script by assessing their performance in a test, in which they were asked to evaluate a fictitious discourse between two students about a science topic. This discourse included "good" and "bad" arguments and argumentative sequences in the sense of the models proposed by [START_REF] Toulmin | The Uses of Argument[END_REF] and [START_REF] Leitão | The potential of argument in knowledge building[END_REF]. I.e., some utterances contained complete arguments, whereas others did not, and sometimes, argumentative sequences had the "argument -counterargument -integrative argument"-structure proposed by [START_REF] Leitão | The potential of argument in knowledge building[END_REF], whereas in other cases, they had not. The students' task then was to individually identify these "good" and "bad" arguments or argumentative sequences and specify why they were good or bad. On the basis of their answers, students received a test score ranging from 0 to 20. The median score of 3 was used as the criterion according to which learners were classified as holding either a low or a high structured internal script resulting in 48 learners classified as holding a low structured and 50 learners as holding a high structured internal script. Reliability of the used scale was sufficient. The design of the study can be viewed in table 1.

Procedure

The study was conducted in two sessions. In the first session, which took part about two weeks before the actual collaboration phase, learners had to complete several questionnaires on demographic variables, prior domain-specific content knowledge, and collaboration as well as computer experiences. Most importantly, in this first session learners were asked to answer the test assessing their internal scripts.

For the actual collaboration phase two weeks later, homogenous dyads were established with respect to the degree of structuredness of the learners' individual scripts and gender, i.e. that only dyads existed consisting of two girls or two boys both holding either a low or a high structured internal script. They then collaborated on the WISE-project "The Deformed Frogs Mystery", which is described below. Two versions of the "Deformed Frogs" project were realized, one containing the low structured external cooperation script and the other the high structured one (see below). Dyads were randomly assigned to one of these two conditions. The collaboration phase was 120 minutes.

Immediately after collaboration, learners had to complete several questionnaires to assess the domain-specific content knowledge and domain-specific knowledge on argumentation that was acquired during collaboration (see below). Completing these questionnaires required about 40 minutes.

Setting and learning environment

Dyads worked on a German version of the WISE project "The Deformed Frogs Mystery". In this project, learners were introduced to the phenomenon that many frogs with massive deformities on legs and eyes had been found in the late 90's. However, among biologists it is not yet clear what the underlying mechanism for these deformities is. Therefore, the project provides learners with two competing hypotheses. The Parasite Hypothesis states that a small parasite called "trematode" burrows into the tadpole near where the legs will develop, and the Environmental-Chemical Hypothesis implies that a hormone-like substance in the water of the dumps frogs live in causes legs and eyes to develop strangely. In order to discuss the pro's and con's of these two hypotheses, the web-based learning environment contains different kinds of background information, e.g. journal articles, maps about the distribution of the deformities, or photographs of the deformed frogs. Discussion should then focus on evaluating the two hypotheses on the basis of this background information. The two learning partners of each dyad worked together in front of one computer screen and could talk face-to-face. There were always between three and five dyads located in the same room, but they were separated from each other to achieve rather controlled experimental conditions. A teacher was not present.

External cooperation script

The two versions of the external cooperation script were implemented in the "Deformed Frogs" project. At the end of each information unit, all learners were asked to discuss the two hypotheses on the basis of the information they had just viewed and to type their arguments. However, the two conditions differed in the way how this typing and discussion phase was structured. In the low structured version of the external script, learning partners were simply asked to discuss the two hypotheses on the basis of the information of the particular unit. In the high structured version of the external script, however, learners received additional guidance in how to discuss the two hypotheses, based on the models of [START_REF] Toulmin | The Uses of Argument[END_REF] and [START_REF] Leitão | The potential of argument in knowledge building[END_REF]. The first time learners had to discuss the two hypotheses, learning partner A was assigned to defend the parasite hypothesis. In order to do that, he or she was asked to give an argument containing of data, a claim and a warrant. To achieve that, the learner was supposed to type each component (data, claim, warrant) in a separate text field including adequate cues (e.g., "It was found that…" for data). Next, his or her learning partner was asked to type in a counterargument, again containing the three argument components and the respective cues. Finally, both learners should try to formulate an integrative argument, which again had to consist of data, a claim, and a warrant. After that, for the environmental-chemical hypothesis, roles were switched. During the course of collaboration, which learner had to advocate which hypothesis was varied. Moreover, script instructions were continually faded out to avoid the problem of "over-scripting" [START_REF] Dillenbourg | Over-scripting CSCL: The risks of blending collaborative learning with instructional design[END_REF]. Moreover, a recent study by McNeill, Lizotte, Krajcik and Marx (2004) has demonstrated that fading might even contribute to higher learning gains than leaving instructions unfaded over time.

Instruments and dependent measures

Dependent measures were the individual students' performance in the subsequent knowledge tests. One knowledge test aimed to assess the learners' domain-specific content knowledge, whereas the second test assessed their domain-general knowledge on argumentation. In order to give a more detailed measure of the content-specific knowledge acquired, we split up the content-specific knowledge test into two subscales. The first subscale (knowledge about mechanisms) measured knowledge that was related to the two proposed mechanisms (parasite vs. environmental-chemical substance) as well as to evidence connected with them, whereas the second subscale (knowledge about scientific methods) assessed the learners' knowledge concerning how to proceed to determine the reason for the deformities. Reliabilities for the subscales were sufficient. The same contentspecific knowledge test was also used to assess the learners' prior knowledge. There, reliabilities of the used scales were sufficient for the overall measure as well as for knowledge about scientific methods, but not for knowledge about mechanisms. Therefore, the pretest measure of knowledge about scientific methods was not included in our analyses.

For the domain-general knowledge about argumentation test, only one scale was computed. In this test, learners were supposed to name the three components of a complete argument as well as the three components of a argumentative sequence. Moreover, they were asked to give examples for a complete argument and a complete argumentative sequence. Learners received points for each correctly named component of a single argument and an argumentative sequence as well as for each of these components that was included in their examples. Reliability of this scale was sufficient.

Statistical analyses

Concerning both domain-specific content knowledge and domain-general knowledge on argumentation, we computed ANCOVA's with internal and external scripts as fixed factors and the scores in the specific outcome measures as dependent variables to test the two hypotheses. For determining the effects of internal and external scripts on domain-specific content knowledge, the each specific contentspecific prior knowledge measures were included as a covariate (except for the knowledge about mechanisms because of its low reliability), although learners in the four conditions did not differ significantly concerning their content-specific prior knowledge (F(1,95) < 1.06; n.s.). As a covariate for domain-general knowledge on argumentation, the actual point score in the internal scripts-test was used. For all analyses, the alpha-level was set to 5 %.

RESULTS

Learning prerequisites

Before testing our hypotheses, we controlled for differences in the pretest measures. Concerning the domain-specific prior knowledge measures, we found no statistical significant differences between the four experimental conditions (F(1,94) < 1.06; n.s.).

Domain-specific content knowledge

On the overall measure of domain-specific content knowledge, learners holding high structured internal scripts received higher scores than learners with low structured internal scripts. There were only marginal differences between learners having collaborated on the basis of the high structured and the low structured external script. An ANCOVA (with the overall domain-specific content knowledge as dependent variable and the overall prior knowledge as control variable) revealed a significant main effect for the internal script (F(1,93) = 10.33; p < .05).

The same pattern of results could be observed for knowledge about mechanisms. Yet, the differences between the four conditions were even more distinct than in the overall domain-specific content knowledge test. An ANOVA yielded a significant main effect for the internal script indicating that learners holding high structured internal scripts received significantly higher scores than learners with low structured internal scripts (F(1,93) = 4.24; p < .05).

For knowledge about scientific methods, a different pattern occurred. There, learners holding high structured internal scripts who had collaborated on the basis of the low structured external script reached the highest scores, followed by learners with low structured internal scripts who were provided with the low structured external script. Learners who collaborated on the basis of the high structured external script reached lower scores, especially when they held low structured internal scripts. An ANCOVA revealed a slightly significant main effect for the external script (F(1,93) = 3.18; p = .08) indicating that learners having worked with the low structured external script reached higher scores than learners having been supported by the high structured external script. Post hoc t-tests revealed that learners holding high structured internal scripts who had collaborated on the basis of the low structured external script were significantly better than both conditions that included the high structured external script.

Domain-general knowledge on argumentation

For domain-general knowledge about argumentation, learners with the combination of high structured internal and high structured external scripts received the highest scores. However, in this measure, the second successful group was the low structured internal/high structured external condition, followed by high structured internal/low structured external and low structured internal/low structured external. An ANCOVA with the actual point score in the initial internal script-test as a covariate revealed a significant main effect for the external cooperation script (F(1,93) = 12.96; p < .01) indicating that the high structured external script led learners to acquire more argumentation-specific knowledge than the low structured external script. Neither the main effect for the internal script nor the interaction between the two factors reached statistical significance (F(1,93) < 1.15; n.s.)

CONCLUSIONS

We argued that for inquiry learning environments to be effective, additional instructional support should be provided that helps learners to engage in effective argumentative knowledge construction (see [START_REF] Reiser | BGuiLE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms[END_REF]. Engaging in argumentative knowledge construction, in turn, should lead to the acquisition of more overall domain-specific content knowledge, but what content would be learnt would be influenced by the different script types. On the one hand, instructional (external) scripts prescribe nature and sequence of specific activities like "giving an argument consisting of data, claim, and warrant" and distribute roles among the learning partners (Kollar et al., 2003). However, we argued that collaborative argumentative knowledge construction is not only guided by the instructions provided in an externally provided cooperation script but also by the learners' internal scripts on argumentative knowledge construction. We therefore investigated how these two script types interact with regard to their effects on the acquisition of different aspects of domain-specific content knowledge as well as domain-general knowledge on argumentation.

In order to investigate this question, we set up two competing hypotheses, an interactive effects hypothesis and an additive effects hypothesis. Overall, the results we found largely support the additive effects hypothesis. For overall domainspecific content knowledge as well as for knowledge about mechanisms, we found that learners with high structured internal scripts learned more than learners holding low structured internal scripts, regardless of the structure of the external cooperation script. Thus, it can be said that the learners' internal scripts on argumentative knowledge construction were more influential than the externally provided cooperation script with respect to the acquisition of domain-specific content knowledge. One possible explanation for the fact that internal scripts played a more important role than external scripts is that since the learners' internal scripts have developed over long periods of time (see [START_REF] Schank | Scripts, plans, goals and understanding[END_REF], learners can use them effortlessly just like a very familiar tool. Thus, it is plausible that students will rather rely on strategies they have developed for years and which have been proven functional for them than to adopt a strategy that is new to them as long as they have the opportunity to do so. For the high structured external cooperation script to boost the learners' acquisition of domain-specific content knowledge, maybe more content-specific scaffolds like content-related questions might be helpful. However, maybe stronger effects of the high structured external script used in this study might be observable in longitudinal studies since it might take time to get learners to internalize the argumentation-specific script instructions, which might help them to engage in deep elaborations of the learning material.

However, for knowledge about scientific methods, we found that individuals learned more when they did not receive further support by the high structured external cooperation script. This result might be attributed to the fact that the high structured external script set constraints for activities, which could have contributed to the acquisition of knowledge about scientific methods, which was not the case for activities that contributed to an acquisition of knowledge about mechanisms. In fact, the high structured external script was more directed towards discussing the two hypotheses and related evidence than towards thinking about what research methods might be able to determine what causes the frog deformities. Thus, our results prove that the instructions provided in an external cooperation script can be tailored to specific outcomes. However, designers of cooperation scripts should be careful to induce activities, which contribute to the desired learning outcomes and not restrict further activities that might also be fruitful with respect to these learning outcomes.

Similarly, the results for domain-general knowledge on argumentation provided support for the additive effects hypothesis. There was a strong effect of the external script on the acquisition of domain-general knowledge on argumentation indicating that learning with the high structured external script was superior to learning with the low structured version of the externally induced cooperation script. This result provides strong support for the additive effects hypothesis: The high structured external cooperation script helped all learners, independently of their internal scripts on argumentative knowledge construction. Moreover, the supportive effect of the high structured external script was similar for both learners with high and low structured internal scripts since there was no interaction between external and internal scripts. Therefore, in order to reach the objective to let learners acquire domain-general knowledge on argumentation, we recommend to provide them with high structured external scripts, which specify in a detailed way how to proceed in collaborative argumentative knowledge construction.

In sum, our results are in favour of the additive effects hypothesis and provide counter-evidence to the interactive effects hypothesis. The high structured external script supported both learners with high and low structured internal scripts on acquiring domain-general knowledge on argumentation and simultaneously did not impede learning of domain-specific content. However, since learners reached on average rather low scores in the pretest, which assessed their internal scripts, we speculate that individuals having been classified as holding high structured internal scripts in this study still did not have enough argumentative skills to fully play them out in a rather unstructured learning environment. It might be that if learners' internal scripts on argumentative knowledge construction were structured even higher than were the internal scripts of the learners assigned to this condition in this study, the interactive effects hypothesis might receive stronger support. Subsequent research is needed to address this issue.

CONSEQUENCES

Our results support quite clearly our initial assumption that argumentative knowledge construction is guided by both externally provided instruction and the learners' internal scripts. However, the structure of these scripts has an impact on what kind of knowledge in fact is acquired. Therefore, cooperation scripts must be designed carefully to assure that the activities they induce match with the desired learning outcomes. The likelihood of students' engagement in activities that do not contribute to these learning objectives should be restricted by structuring the external cooperation script accordingly. Yet, further research should aim to investigate the interplay of internal and external scripts also in other domains. For more in-depth analyses, however, process analyses are necessary which we have just begun. By looking at process data from transcripts of the oral discussions within each dyad and at logfile data, we hope to understand more clearly how different scripts play together in inquiry learning environments.

On a theoretical level, a distributed cognition perspective (e.g., [START_REF] Perkins | Person-plus: a distributed view of thinking and learning[END_REF]) might be a valuable frame of reference for describing the impact of internal and external effects on learning. This perspective states that learners and their (social, artifactual, and also instructional) surround can form a learning system, in which the executive function is distributed over several system components, namely the individual learner, his or her learning partner, the computer-environment and the imposed external script. Then, it is an important question how to orchestrate the system components (or more specifically the different scripts) in a way that they promote effective learning.

On a practical level, our results demonstrate that the benefits of inquiry learning environments can be augmented by adding additional micro-structuring facilities to them. Scripts can be designed to improve the learners' abilities to argue with hypotheses and evidence that are provided in inquiry learning environments without impeding their content-related learning. Yet, it must be clear that different learners may require different forms of such instructional support in order to reach equal learning gains. There might be conditions, in which learners with high structured internal scripts can benefit more from a rather open learning environment, whereas in other settings, they might require more external structuring. This points to the need of developing "flexibly adaptive instructional designs", as [START_REF] Schwartz | Towards the development of flexibly adaptive instructional design[END_REF] put it.

AFFILIATIONS

Ingo Kollar, University of Tuebingen, Frank Fischer, Knowledge Media Research Center, Tuebingen.

Table 1 .

 1 Design of the empirical study.

			External cooperation script
			Low structured	High structured
	Internal scripts on	Low structured	N = 26 (13 dyads)	N = 22 (11 dyads)
	argumentative knowledge construction	High structured	N = 26 (13 dyads)	N = 24 (12 dyads)


Jan Van Der Meij
email: j.vandermeij@utwente.nl

Ton De Jong

Progression in Multiple Representations Supporting students' learning with multiple representations in a dynamic simulation-based learning environment

Keywords: Multiple representations, Instructional technology, Scientific discovery learning, Simulation-based learning environment

Relating multiple representations and translating between them is important to acquire deeper knowledge about a domain. To relate representations, learners have to mentally search for similarities and differences. To translate between representations, learners need to interpret the effects that changes in one representation have on corresponding representations. The question is how presenting representations may improve or hinder the processes of relation and translation. In this study we examined the effect of sequencing dynamic representations on learning outcomes. Two versions of the same simulation-based learning environment, that of the physics topic of moments, were compared: a learning environment providing the representations step-by-step (experimental condition) and a learning environment providing all representations at once (control condition). The subjects were 120 students from secondary vocational education (aged 15 to 21). Overall, we found the subjects learned from working in the learning environment; the post-test scores on the domain and understanding items were significantly better than the pre-test scores. This was true for both the subjects with and without prior knowledge on the domain. Moreover, the subjects with prior knowledge scored significantly better on both the pre-test and the post-test compared to the subjects without prior knowledge. Despite our expectations, no differences were found between the two experimental conditions. The subjects learned equally well regardless of the way in which the representations were presented. Also, the extent to which the subjects' experienced complexity of both the topic and the learning environment did not differ between the experimental conditions.

Introduction

By using multiple representations in simulation-based learning environments, learners are assumed to acquire deeper knowledge about a domain and therefore to be able to use their knowledge in other learning situations. Mental transference between representations forces learners to reflect beyond the boundaries and details of the first ______________ representation to anticipate on correspondences in the second [START_REF] Petre | Cognitive questions in software visualization[END_REF]. This is believed to lead to a deeper level of cognitive processing and may expose glitches that might otherwise have been missed. A familiar representation can support understanding, and reasoning with, unfamiliar ones (the constraining function; [START_REF] Ainsworth | The functions of multiple representations[END_REF]). In addition, representations can complement each other by containing complementary information or by supporting different complementary processes (the complementing function; [START_REF] Ainsworth | The functions of multiple representations[END_REF]).

In a multi-representational learning environment, learners can choose those representations that fit their prior knowledge and preference [START_REF] Ainsworth | The functions of multiple representations[END_REF]. However, to be able to learn from multiple representations, learners have to: (1) understand the syntax of each representation; (2) understand which parts of the domain are represented; (3) relate the representations to each other if the representations are (partially) redundant; and (4) translate between the representations, that is, interpret similarities and differences of corresponding features of two or more representations (van der Meij & de Jong, in press). Several studies (e.g., [START_REF] Kozma | The material features of multiple representations and their cognitive and social affordances for science understanding[END_REF]e.g., Tabachneck, Leonardo, & Simon, 1994) have shown the last two abilities -relating and translating between representations -are difficult for learners. This is problematic, because these cognitive processes are important for deeper learning to occur. Learners find most difficulty in translating between representations with different representational codes (for example, pictorial, arithmetical or textual) [START_REF] Ainsworth | The functions of multiple representations[END_REF].

This leads to an interesting question for instructional designers: can the way in which multiple representations are offered improve or hinder the cognitive processes of relating and translating?

Supporting the relating and translating process

An important requirement for learning with multiple representations in simulation-based learning environments is how to support learners in the process of relating and translating. Both integration and dynamic linking of representations [START_REF] Ainsworth | The interaction between informational and computational properties of external representations on problem-solving and learning[END_REF][START_REF] Chandler | Cognitive load theory and the format of instruction[END_REF][START_REF] Mayer | A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory[END_REF]van der Meij & de Jong, in press) are of proven value. However, both also have their limitations and drawbacks.

Integrating

Physical integration of representations can make relations between representations explicit for the learner (e.g., [START_REF] Chandler | Cognitive load theory and the format of instruction[END_REF]. Integrated representations appear to be one representation showing different aspects of the domain. By integrating representations, relations between them are shown directly to the learner. Having all related elements in the same place makes it easier to interpret the similarities and differences between corresponding features and therefore integration also supports the translation process. Several studies conclude that learning with integrated representations leads to better knowledge compared to learning with non-integrated representations [START_REF] Ainsworth | The interaction between informational and computational properties of external representations on problem-solving and learning[END_REF][START_REF] Bodemer | The active integration of information during learning with dynamic and interactive visualisations[END_REF][START_REF] Chandler | Cognitive load theory and the format of instruction[END_REF][START_REF] Mayer | A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory[END_REF][START_REF] Tabbers | Multimedia instructions and cognitive load theory: Split-attention and modality effects[END_REF]. However, integration does not always lead to better learning outcomes. [START_REF] Bodemer | The active integration of information during learning with dynamic and interactive visualisations[END_REF] found that learners working with integrated representations only learned more compared to learners working with non-integrated ones, when they had to actively integrate the representations themselves. Moreover, [START_REF] Bodemer | External and mental referencing of multiple representations[END_REF] only found positive effects of active integration when learners were able to integrate the representations correctly. [START_REF] Chandler | Cognitive load theory and the format of instruction[END_REF] only found positive effects of integration when individual units could not be understood separately.

Dynamic linking

For simulation-based learning environments with dynamic representations (representations that change over time or change according to input of the learner), dynamic linking can be provided to make the relations between different representations explicit for the learner [START_REF] Ainsworth | The functions of multiple representations[END_REF]. With dynamically linked representations, actions performed on one representation are automatically shown in all other representations. If a learner, for example, changes the value of a force in a numerical representation, the corresponding representation of the force in an animation is updated automatically. It is expected that dynamic linking decreases cognitive load by freeing learners from having to establish the relationships between the representations (e.g., [START_REF] Kaput | Linking representations in the symbol systems of algebra[END_REF][START_REF] Scaife | External cognition: How do graphical representations work[END_REF]. However, a potential problem with dynamic linking might be learners' selective attention to control their cognitive load (see [START_REF] Lowe | Extracting information from an animation during complex visual learning[END_REF]. With multiple dynamically changing representations, learners need to attend to and relate changes that occur simultaneously in different regions of various representations. Another problem might be that dynamic linking allows a learner to be too passive in the relating and translating processes [START_REF] Ainsworth | The functions of multiple representations[END_REF]. Dynamic linking may discourage mental relation and translation, hindering the learner to construct the required understanding. Despite the potential problems, dynamic linking seems to be a promising approach to support learning with multiple representations.

Integrating plus dynamic linking

In a study comparing three simulation-based learning environments, van der Meij and de Jong (in press) extended known research on integration by examining its role using dynamic representations instead of static representations. In three experimental conditions, the same learning environment on the physics topic of moments was presented using separate, non-linked representations, using separate, dynamically linked representations and using integrated, dynamically linked representations. Furthermore, they examined the role of the complexity of the domain and the learning environment. The learning environment was divided into parts of low complexity and high complexity. They found better learning results on domain knowledge when the representations were both integrated and dynamically linked. However, they did not find learning progress on transfer knowledge, whereas transfer is an important argument for using the type of learning environments evaluated in their study. The learning environments they used can be characterized as guided discovery learning environments (e.g., [START_REF] Mayer | Should there be a three-strikes rule against pure discovery learning[END_REF][START_REF] Van Joolingen | SimQuest: Authoring educational simulations[END_REF]. In discovery learning environments, learners are engaged in active exploration of the learning materials in order to understand the concepts of a domain. It is expected that learners who explore a domain themselves acquire deeper knowledge of that domain. However, they only gain from the discovery process if it is adequately guided by, for example, assignments and explanations.

Representation progression

Another way to support learners in simulation-based learning environments, is providing model progression [START_REF] White | Causal model progressions as a foundation for intelligent learning environments[END_REF]. Model progression sequences the learning environment from simple to complex. This study was a first attempt to relate model progression to representational progression. Based on the model progression used, we increased the number of representations iteratively. As a result, the number of relations and possible translations increased likewise. Starting with a few relations and possible translations and then introduce more relations and possible translations step-by-step might support learners in relating the representations and translating between them. In addition, sequencing the representations might be a solution to the selective attention problem mentioned earlier.

Research questions

The goal of this study was to determine if sequencing dynamic representations has an effect on learning outcomes. This was examined in a simulation-based learning environment with dynamic representations.

The context of the study was a guided discovery simulation-based learning environment called Moments. Subjects studied the physics topic moments by means of multiple representations of an open-end spanner tightening a bolt. Two versions of the same simulation-based learning environment were compared: a learning environment providing the learners with representations introduced step-by-step (experimental condition -R-Step) and a learning environment presenting all the representations at once (control condition -R-Once).

Method

The experiment was conducted with subjects who already had and who had no prior knowledge about the domain.

Subjects

Subjects with prior knowledge in the domain

The subjects were students at the end of their first year of secondary vocational education. They were between 16 and 19 years old and took either a course in mechanical engineering (class 1) or architecture (class 2). Subjects came from two schools, and from one class at each school. A between subjects design was used, in which participants were randomly assigned to one of the two experimental conditions. Thirty-five students started the experiment; two subjects were not representative because they were orienting for following the course and of one student the post-test results were lost, resulting in analyses done with 32 subjects.

Subjects without prior knowledge in the domain

Subjects were students at the start of their first year of secondary vocational education. They were between 15 and 21 years old and took either a course in mechanical engineering (classes 3 and 6) or architecture (classes 4 and 5). Subjects came from two schools, and from one class at school one and three classes at school two. A between subjects design was used, in which participants were randomly assigned to one of the two experimental conditions. Ninety-five students started the experiment; two subjects were not representative because they already participated in a subjects with prior knowledge session, two subjects had no Internet access and as a result were not able to do the pre-test and post-test, subject identifications of two subjects were probably mixed up and one subject was removed from the analyses as an outlier (score of 1 on the pre-test); resulting in the analyses being performed with 88 subjects. Table 1 shows how the subjects were distributed across the conditions and the schools. [START_REF] De Jong | Authoring discovery learning environments: In search for reusable components[END_REF][START_REF] Van Joolingen | SimQuest: Authoring educational simulations[END_REF]. Subjects studied the physics topic moments in the context of mechanical engineering. The topic is important for these students, since it forms the basis for static mechanics. The learning environment is based on guided discovery learning (de Jong & van Joolingen, 1998). The learner has to engage in discovery activities in order to learn about the properties of the simulation model and is guided in the discovery process by 'cognitive tools' such as model progression, assignments and explanations. Learners explore the simulation model by manipulating values of the input variables and observing the behaviour of output variables. By understanding the relations between the variables, it is expected that learners acquire a deep understanding of the domain and are able to transfer their knowledge to similar 'problems' in other situations.

The learning environment consists of an introduction and 16 assignments. The introduction gives an overview to 'moments' by giving everyday examples in which moments play a role. After this introduction, learners explore specific aspects of the domain by choosing an assignment from the menu. When opening an assignment, a corresponding simulation interface opens. Each assignment starts with a short description of an aspect of the domain, asks the learner to explore this aspect and asks the learner to answer a question about it.

Supported by assignments (right screen in Figure 1), the learner can perform experiments in the simulation interface (left screen in Figure 1). The learner can manipulate the force and length input variables and can observe the moment output variable 5) a dynamically changing table (showing one row that is dynamically updated when variables are manipulated by the learner). Table 2 gives an overview of the instructional support with corresponding representations. Only the variables in representation 2 are introduced explicitly in assignments 2, 3, 4, 5, 7 and 8. The assignments are the same for both experimental conditions. 5) also supports the understanding of numerical relations. It contains one row representing the actual values of all variables involved. The dynamically changing equation and table could not be integrated with representations 1 to 3 because their forms are too divergent. We chose to dynamically link all the representations. In addition to the table, an experiment table is introduced in assignment 12 (see Figure 2). This table has the same format as representation number 5 (the dynamically changing table), except that learners can save, compare, structure, replay and delete their experiments. Experiments are saved by clicking a save button; this adds a row to the table showing the variables values in a static fashion. Learners can replay an experiment by selecting a table row and clicking a start button. All representations then represent the values of the table row.

Learners can manipulate the input variables by using the provided sliders. If a learner manipulates a slider, the corresponding changes are shown in the representations in real time. So, if a learner moves the force-slider, the element representing force is updated continuously and immediately, as is the change in moment. Learners can compare situations by moving the slider back and forth between different states of the simulation. They can also compare situations when they have access to the experiment table (see Figure 2).

In the experimental condition, the representations are introduced one by one; starting with the concrete representation, followed by the diagrammatic, then the numerical and ending with the table (Figure 3 shows the first step; Figure 1 shows the final step).

Tests and questionnaires

Subjects' prior domain knowledge was assessed using an online pre-test. This was administered directly before working with the learning environment. An online post-test was administered directly after working with the learning environment. The pre-test consisted of 20 items, both multiple-choice and open answer items; 10 items testing domain knowledge and 10 items testing understanding of the domain. The posttest consisted of 40 items, both multiple-choice and open answer items; 10 items testing pure domain knowledge, 10 items testing understanding of the domain, 10 testing the ability to relate representations and 10 items testing the ability to translate between representations. The domain and understanding items corresponded with the post-test items. The post-test items differed slightly from the pre-test by differing the item and alternative answer orders. Since subjects did not know which items had been changed, they could not rely on a memory strategy.

For each pre-test and post-test item, a subject received a score of 1 if the answer was correct or a score of 0 if the answer was incorrect. The maximum scores for the pre-test and post-test were 20 and 40 respectively. Figure 4 shows examples of one test item from each category. The domain items tested whether the subjects were able to reproduce the content they were explicitly asked to explore in the learning environment. The understanding items tested whether the subjects had insight in the domain. To answer these items correctly, the subjects needed to apply their acquired knowledge in new situations. These were new contexts and relations between variables that were not directly asked for in the learning environment, but that could be derived from the domain knowledge. The relate items tested whether students were able to relate different representations. These items asked students to relate similar variables of representations with different representational codes. To be able to answer translate items correctly, the subjects had to make a mental translation from manipulations on one representation to the effects in another representation, having a different representational code.

An electronic questionnaire based on Swaak's S.O.S. scale [START_REF] Swaak | What-if: Discovery simulations and assessment of intuitive knowledge[END_REF] was used to assess the subjects' opinions on the complexity of the learning environment and the domain. The questionnaire asked subjects to score the topic as easy, average, or difficult (Q1) and whether they found working with the simulation easy, average, or difficult (Q2). The questionnaire was given three times to subjects while they worked with the learning environment; after assignments 6, 11 and 16. Subjects had to complete the questionnaire before they could continue. The possible answers: easy, average, or difficult.

Procedure

The experiments were held at the participating schools and consisted of three experimental sessions: pre-test, working with the learning environment and post-test. Subjects were randomly assigned to one of the two conditions using their seating placement.

Before the pre-test participants were informed about the experiment and were told the test measured their prior knowledge on force, arm and moment. If necessary, a brief description was given. Participants were asked to fill in all test items, even if they were unsure about the right answer. Subjects had a maximum of thirty minutes to fill in the pre-test.

The learning environment session took place proximally 45 minutes after the start of the pre-test session, so that all subjects had at least a 10-minute break between the sessions. Subjects could work in the learning environment in their own pace, but not longer than an hour. They worked on their own and could question the teacher or experiment leader on operating the learning environment. Subjects were asked to do all 16 assignments. When ready they could ask to do the post-test.

The post-test took place directly after the learning environment session. The participants could work a maximum of forty-five minutes on this test. The participants were not allowed to use the learning environment during the test and were asked to fill in all test items, even if they were unsure about the right answer.

Results

Pre-test and post-test

Subjects with prior knowledge in the domain

The overall mean score on the pre-test was 12.16 out of 20 test items (SD = 2.99). These data indicate the subjects had moderate prior knowledge in the domain. The overall mean score on the post-test domain plus understanding items was 13.31 out of 20 test items (SD = 2.81). Table 3 shows the means and standard deviations of the scores on the item categories in the pre-test and post-test. A repeated measures ANOVA showed the overall combined domain and understanding post-test score of the 32 subjects was significantly better than the overall pre-test scores (F(1,31) = 9.07, p < .01). Repeated measures ANOVAs for each item category showed a trend for pre-test to post-test scores on domain items (F(1,31) = 3.89, p = 0.06). Post-test scores on understanding items were significantly better than pre-test scores on these item types (F(1,31) = 7.52, p < .05). Table 4 shows the means and standard deviations of the pre-test and post-test scores for the four item categories for each condition. One-way ANOVAs showed no significant differences between the experimental conditions on pre-test domain scores and understanding scores (F(1,30) = 1.68, p = .21; F(1,30) = .90, p = .35). This means that subjects in the experimental conditions did not differ in prior knowledge.

One-way ANOVAs showed no significant differences between the experimental conditions on post-test domain scores, understanding scores, relate scores and translate scores (F(1,30) =.02, p = .90; F(1,30) = .04, p = .84; F(1,30) = .01, p = .92; F(1,30) = .02, p = .90).

Subjects without prior knowledge in the domain

The overall mean score on the pre-test was 9.65 out of 20 test items (SD = 2.88). These data indicate the subjects had little prior knowledge in the domain. The overall mean score on the post-test domain plus understanding items was 11.64 out of 20 test items (SD = 3.10). Table 5 shows the means and standard deviations of the scores on the item categories in the pre-test and post-test. A repeated measures ANOVA showed the overall combined domain and understanding post-test score of the 88 subjects was significantly better than the overall pre-test scores (F(1,87) = 60.68, p < .01). Repeated measures ANOVAs for each item category showed that the post-test scores on domain and understanding items were significantly better than the pre-test scores on these item types (F(1,87) = 67.51, p < .01 and F(1,87) = 15.54, p < .01). Table 6 shows the means and standard deviations of the pre-test and post-test scores for the four item categories for each condition. One-way ANOVAs showed no significant differences between the experimental conditions on pre-test domain scores and understanding scores (F(1,86) = 2.05, p = .16; F(1,86) = .05, p = .82). This means that subjects in the experimental conditions did not differ in prior knowledge.

One-way ANOVAs showed no significant differences between the experimental conditions on post-test domain scores, understanding scores, relate scores and translate scores (F(1,86) = .33, p = .57; F(1,86) = .64, p = .43; F(1,86) = .94, p = .33; F(1,86) = .09, p = .77).

Subjects with and without prior knowledge in the domain taken together

The overall mean score on the pre-test was 10.32 out of 20 test items (SD = 3.10). These data indicate the subjects had some prior knowledge in the domain. The overall mean score on the post-test domain plus understanding items was 12.08 out of 20 test items (SD = 3.10). Table 7 shows the means and standard deviations of the scores on the item categories in the pre-test and post-test. A repeated measures ANOVA showed the overall combined domain and understanding post-test score of the 120 subjects was significantly better than the overall pre-test scores (F(1,119) = 67.38 p < .01). Repeated measures ANOVAs for each item category showed the post-test scores on domain and understanding items were significantly better than the pre-test scores on these item types (F(1,119) = 61.66, p < .01 and F(1,119) = 22.57, p < .01). Table 8 shows the means and standard deviations of the pre-test and post-test scores for the four item categories for each condition. One-way ANOVAs showed no significant differences between the experimental conditions on pre-test domain scores and understanding scores (F(1,118) = 2.68, p = .10; F(1,118) = .04, p = .85). This means that subjects in the experimental conditions did not differ in prior knowledge.

One-way ANOVAs showed no significant differences between the experimental conditions on post-test domain scores, understanding scores, relate scores and translate scores (F(1,118) =.25, p = .62; F(1,118) = .74, p = .39; F(1,118) = .99, p = .32; F(1,118) = .16, p = .69).

Comparison of subjects with and without prior knowledge

A one-way ANOVA showed that subjects with prior knowledge scored significantly higher on the pre-test than subjects without prior knowledge (F(1,118) = 17.49, p < .01). The same effect was found for the post-test (F(1,118) = 11.17, p < .01).

Experienced domain complexity

The experienced domain complexity was measured by the questionnaire question: "I find the topic at this moment: easy, average, or difficult." The question appeared three times during working with the learning environment. One-way ANOVAs for all three appearances showed no significant differences between the experimental conditions on experienced domain complexity for neither subjects with prior knowledge and subjects without prior knowledge.

Experienced learning environment complexity

The experienced learning environment complexity was measured by the questionnaire question: "I find working with the simulation at this moment: easy, average, or difficult". The question appeared three times during working with the learning environment. One-way ANOVAs for all three appearances showed no significant differences between the experimental conditions on experienced learning environment complexity for neither subjects with prior knowledge and subjects without prior knowledge.

Discussion

The aim of this study was to determine if sequencing dynamic representations has an effect on learning outcomes. This was examined in a simulation-based learning environment on the physics topic of moments.

Overall, we found that subjects learned from working with the learning environment; post-test scores on the domain and understanding items were significantly better than pre-test scores. In contrast with our expectations, no differences where found between experimental conditions. So, subjects learned equally well regardless of the way the representations were presented. Also, subjects' complexity experience of both the topic and learning environment did not differ between the experimental conditions. This leaves us with the question: Why did sequencing representations not support learners in relating and translating between representations? Do we have to adapt our theory? In search of an answer to these questions we analysed the log files to get insight in the way learners worked through the learning environments. The data suggest that an intervening variable played an important role: the instructional support consisting of assignments and explanations. The instructional support had a great impact on how learners worked with the learning environment. It was the same for both conditions, but was organised according to the steps in the experimental condition where we sequenced the representations. The assignments and explanations directed the subjects' attention to the newly introduced representations and variables. It looks like the instructions supported the subjects in the progression of the learning material; sequencing the material from simple to complex. Thus, it may have affected the subjects processing of the representations.

Although we tried to encourage the subjects to explore the simulation and reflect on their actions by asking them to prove their answers by experiments done and to provide an explanation for their given answers, the log files showed that learners did not explore the simulation for other features than explicitly indicated in the assignments and their reflections were very brief. In short, the instructions guided the subjects through the learning environment with little else being attended to. As a result, the subject did not focus on relating representations and translating between them. Therefore, the expected support from representational progression was not found in this study.

Despite of our attempt to engage the subjects in relating representations and translating between them, they do not seem to do so if they are not explicitly asked to. We believe the intervening effect of instructional support in the present study can help us to improve the effects of providing multiple representations in the future. In a follow-up study we are going to use the current results to adapt the instruction. Instead of focusing on domain knowledge in the instruction, we are going to try to encourage learners to relate and translate between representations by explicitly asking them to do that. We believe that sequencing the representations are of additional support here. They avoid overloading learners by directing their attention only to the representations they are asked to relate and translate between. Step-by-step learners are guided to relate more representations and translate between them.

  . The assignments stimulate learners to explore both the relation between the variables in the simulation model and the relation between the representations given. The types of representations used are: (1) a concrete representation (animation of an open-end spanner); (2) a diagrammatic representation (an abstract representation of the variables playing a role in the concrete situation); (3) a numerical representation (showing the values of the variables involved); (4) a dynamically changing equation; and (

Figure 1 .

 1 Figure 1. Assignment with simulation interface showing all representation types

Figure 2 .

 2 Figure 2. Experiment table

Figure 3 .

 3 Figure 3. First representation progression step (experimental condition)

Figure 4 .

 4 Figure 4. Example of a (1) domain, (2) understanding, (3) relate and (4) translate item

Table 1 .

 1 Distribution of subjects over conditions

	Condition

m = male, f = female

2.2 Materials

2.2.1 Learning environment

Subjects worked with the Moments learning environment that was built in the SimQuest authoring environment (de Jong, van Joolingen, Veermans, & van der Meij,

Table 2 .

 2 Instructional support with corresponding representations

	Representations

Representations: 1. concrete representation (animation of open-end spanner) 2. diagrammatic representation (an abstract representation of the variables playing a role in the concrete situation) 3. numerical representation (showing the values of the variables involved) 4. dynamically changing equation 5. dynamically changing table (showing one row that is dynamically updated when variables are manipulated by the learner)

Table 3 .

 3 Means and standard deviations of pre-test and post-test scores

			Pre-test			Post-test
		Mean	(SD)	%	Mean	(SD)	%
	Domain items (max. 10)	6.97	(1.51)	70	7.50	(1.44)	75
	Understanding items (max. 10)	5.19	(1.94)	52	5.81	(2.07)	58
	Total (max. 20)	12.16	(2.99)	61	13.31	(2.81)	67
	Relate items (max. 10)				8.63	(1.21)	86
	Translate items (max. 10)				3.78	(1.98)	38
	n = 32

Table 4 .

 4 Means (standard deviations) of pre-test and post-test scores per condition

	R-Step	R-Once

Table 5 .

 5 Means and standard deviations of pre-test and post-test scores

			Pre-test			Post-test
		Mean	(SD)	%	Mean	(SD)	%
	Domain items (max. 10)	5.70	(1.62)	57	6.99	(1.68)	70
	Understanding items (max. 10)	3.96	(1.78)	40	4.65	(1.91)	47
	Total (max. 20)	9.65	(2.88)	48	11.64	(3.10)	58
	Relate items (max. 10)				7.82	(1.50)	78
	Translate items (max. 10)				2.95	(1.39)	30
	n = 88

Table 6 .

 6 Means (standard deviations) of pre-test and post-test scores per condition

	R-Step	R-Once

Table 7 .

 7 Means and standard deviations of pre-test and post-test scores

			Pre-test			Post-test
		Mean	(SD)	%	Mean	(SD)	%
	Domain items (max. 10)	6.03	(1.69)	60	7.13	(1.63)	71
	Understanding items (max. 10)	4.28	(1.90)	43	4.96	(2.01)	50
	Total (max. 20)	10.32	(3.10)	52	12.08	(3.10)	60
	Relate items (max. 10)				8.03	(1.47)	80
	Translate items (max. 10)				3.18	(1.60)	32
	n = 120

Table 8 .

 8 Means (standard deviations) of pre-test and post-test scores per condition

	R-Step	R-Once