Ton De Jong

Anja Van Der Hulst

The effects of graphical overviews on knowledge acquisition in hypertext

One of the main and recognized problems of learning with hypertexts is that learners are easily distracted and end up 'lost in hyperspace'. As a result, they do not acquire complete and adequate knowledge. In this study, we enhanced a hypertext environment with a graphical overview that represented the basic structure of the domain and we designed the layout in such a way that learners were unobtrusively encouraged to follow a sequence of exploration that followed the domain structure. This so-called 'visual' lay-out was compared with two lay-outs that presented randomly positioned nodes. One of these two lay-outs contained hints (using 'highlighting') to stimulate learners to follow a domain related exploration similar to the one incorporated in the visual lay-out. The other (control) lay-out did not provide such hints. Results showed that participants from both the 'visual' and the 'hints' conditions showed a more domainrelated exploration pattern than participants from the control condition. Participants in the 'visual' lay-out did not show a better recall of the content of the nodes as such, but showed a significantly better acquisition of knowledge of structure than participants from the other two conditions. These data indicate that a visual display conveys knowledge in its own right and that knowledge gained does not depend on the exploration route followed in the hypertext material.

Introduction

Exploratory learning environments, such as hypertext or hypermedia based learning environments, are expected to engage

The effects of graphical overviews 3 learners in an active learning process. Many authors claim that this should lead to better motivation, comprehension, and transfer [START_REF] Dillon | Hypermedia as an educational technology: A review of the quantitative research literature on learner comprehension, control and style[END_REF][START_REF] Jacobson | The design of hypermedia tools for learning: Fostering conceptual change and transfer of complex scientific knowledge[END_REF]. Review studies, however, assessing the effect on learning from hypermedia have shown no overall effects compared to linear presentation formats (see e.g., [START_REF] Chen | Interacting with hypertext: A meta-analysis of experimental studies[END_REF][START_REF] Dillon | Hypermedia as an educational technology: A review of the quantitative research literature on learner comprehension, control and style[END_REF][START_REF] Tergan | Multiple views, contexts, and symbol systems in learning with hypertext/hypermedia: a critical review of research[END_REF] and some studies even show detrimental effects of the use of hypertext or hypermedia (see e.g., [START_REF] Mcknight | A comparison of linear and hypertext formats in information retrieval[END_REF][START_REF] Rada | Searching versus browsing in hypertext[END_REF]. A major barrier for fruitful exploration learning in hypertexts as signaled by several authors (e.g., [START_REF] Conklin | Hypertext: An introduction and survey[END_REF][START_REF] Foss | Effective browsing in hypertext systems[END_REF][START_REF] Marchionni | Hypermedia and learning: Freedom and chaos[END_REF][START_REF] Rouet | Interactive text processing by inexperienced (hyper)readers[END_REF] is that people easily 'get lost in hyperspace'while exploring.

Hypertext offers a large degree of freedom and users seem to have trouble handling this freedom. Early in the research on hypertext, [START_REF] Foss | Effective browsing in hypertext systems[END_REF] referred to the 'art museum phenomenon'. that is, people having spent long days, wandering around, gazing at paintings, may recall details but still have failed to discover the main lines. Disorientation, or 'losing context', according to [START_REF] Conklin | Hypertext: An introduction and survey[END_REF], is a problem that is endemic to the use of hypertext. With traditional, linear, instructional text the situation is different. This type of text is generally specifically organized for learning. The learner does not have to decide about the sequence in which to learn the nodes, and usually overviews are provided in the form of abstracts and summaries. In contrast, exploration of a hypertext document does require the learner to perform complex cognitive actions such as planning and monitoring. It is The effects of graphical overviews 4 likely that the observed ineffectiveness of learning in exploratory environments arises from an inability to meet the cognitive demands made by the exploratory environment. Learner activity is assumed desirable, but in plain exploratory environments, particularly in the more extensive ones, too much seems to be left to the learner.

Various types of solutions have been proposed to help learners to overcome the complexity of exploration. Quite restrictive support measures are the 'guided tours' as used by Allison and Hammond (1989) and Arents and Bogaerts (1993).

More freedom is given by support that helps to monitor the history of the exploration, e.g., by leaving 'footprints' [START_REF] Foss | Effective browsing in hypertext systems[END_REF][START_REF] Mcaleese | Navigation and browsing in hypertext[END_REF] sometimes combined with means to mark important sections [START_REF] Rouet | Interactive text processing by inexperienced (hyper)readers[END_REF] and annotating the material studied [START_REF] Monk | Getting to know locations in a hypertext[END_REF]. Graphical overviews have taken lead in helping users overcoming disorientation (see e.g., [START_REF] Conklin | Hypertext: An introduction and survey[END_REF][START_REF] Mcaleese | Navigation and browsing in hypertext[END_REF][START_REF] De Young | Linking considered harmful[END_REF]. Several studies have found that graphical overviews, and even the specific layout of the graphical interface, affects the effectivity and efficiency of information retrieval from the hypertext both under general conditions (Mogaheg, 1992;[START_REF] Mcdonald | Effects of text structure and prior knowledge on the learner on navigation in hypertext[END_REF] and in performing a specific (design) task [START_REF] De Vries | Using information systems while performing complex tasks an example from architectural design[END_REF][START_REF] Dee-Lucas | Information location in instructional hypertext: effects of content domain expertise[END_REF]. In a metaanalysis of 23 studies, [START_REF] Chen | Interacting with hypertext: A meta-analysis of experimental studies[END_REF] concluded that the most significant result concerned the affect of graphical overviews on results gained. [START_REF] Norman | The psychology of everyday things[END_REF] argues that one of the main principles for interface design is 'visibility' (i.e., the internal functional structure of a system must be The effects of graphical overviews 5 visible at the outside to allow the user to make deliberate decisions on how to handle the system). In line with Norman, we assume that the need for visibility also holds for an internal structure of a domain that is to be learned from a hypertext.

The effects of a visual display of the domain structure on learning from hypertext may come in two ways. First, providing learners with a systematic visual overview of the domain is expected to induce a systematic route through the domain and may thus lead to a better acquisition of the structure of the domain. This latter claim is supported by studies on the effects of the sequence in which written material is presented on learning. Many of those (older) studies compared randomly generated sequences with sequences that are based on the logical structure of the domain. Although several early studies did not report any differences [START_REF] Vlachouli-Roe | Scrambled versus ordered sequence in auto-instructional programs[END_REF][START_REF] Levin | Item scrambling in an selfinstructional program[END_REF], a number of later studies reported a significantly better acquisition of knowledge when following domain structure related sequences [START_REF] Brown | Effects of logical and scrambled sequences in mathematical materials in learning with programmed instructional materials[END_REF][START_REF] Tobias | Sequence, familiarity and attribute by treatment interactions in programmed instruction[END_REF][START_REF] Kintsch | Summarizing scrambled stories[END_REF]. [START_REF] Dee-Lucas | Learning from electronic texts: effects of interactive overviews for information access[END_REF] state that, compared to traditional text, hypertext systems make it more difficult for learners to build an integrated representation of the information because they move between units, missing the continuous line of reading that is characteristic of traditional text. Second, visual overviews are supposed to enhance acquisition of domain structure because they display the structure of the domain directly. Whereas in traditional text learners have to extract

The effects of graphical overviews 6 the overall structure (relations between higher and lower level ideas) from the running text, visual overviews display these structures directly in the titles of nodes and their interrelations. Dee-Lucas and Larkin (1995) compared learning from a hypertext with a visual overview with learning from the same hypertext with names of nodes presented as an unstructured list. They found that learners with the unstructured overview produced less coherent representations than learners with the graphical, structured overview. In another study, however, [START_REF] Jonassen | Effects of semantically structured hypertext knowledge bases on users' knowledge structures[END_REF] found that students who were provided with a graphical overview did not acquire better 'structural knowledge' than students who learned with unstructured nodes or with an interface that lead them on a node-by-node basis through the hypertext. [START_REF] Jonassen | Effects of semantically structured hypertext knowledge bases on users' knowledge structures[END_REF] even found on a recall test that the group learning with the unstructured interface outperformed the groups working with the structured interfaces.

In the present study, we investigated the effects of presenting a visual display of the domain structure in a hypertext system on knowledge acquisition. The visual display we used in our study was comparable to the visual or graphical displays from the [START_REF] Jonassen | Effects of semantically structured hypertext knowledge bases on users' knowledge structures[END_REF] and Dee-Lucas and Larkin (1995) studies. We have extended these two studies in two ways. First, we have measured the learning results in three These extensions made it possible to test some specific hypotheses. We first tested the idea that learners exploring a hypertext system in which the structure of the domain is displayed visually, will show a better acquisition of knowledge of overall structure (configural knowledge) compared to learners who explore a hypertext that lacks this visual display of the domain. We further tested the idea that acquisition of this overall structure is caused by the direct visual information and that knowledge of relations between nodes (propositional knowledge) results from a structured exploration pattern. The final assumption is that learning the content of a node (definitional knowledge) is independent of the form of the visual display and the sequence of exploration.

Method

The Domain

In the selection of the subject matter for this experiment, the prior knowledge of the learners was a crucial factor. We assume that the effectiveness of the interface The effects of graphical overviews 8 manipulation depends on the amount and nature of the learner's prior knowledge with the structure of the interfaces most likely having more effect with subject matter material that is relatively unfamiliar to the learners. Studies by [START_REF] Mohageg | The influence of hypertext linking structures on the efficiency of information retrieval[END_REF], [START_REF] Mcdonald | Effects of text structure and prior knowledge on the learner on navigation in hypertext[END_REF][START_REF] Mcdonald | Effects of text structure and prior knowledge on the learner on navigation in hypertext[END_REF][START_REF] Dee-Lucas | Information location in instructional hypertext: effects of content domain expertise[END_REF] showed that experts rely on their prior knowledge when interacting with hypertext system, whereas novices more rely on cues in the interface. Following this, we chose a domain that was largely unfamiliar to participants in our study. The hypertext system consisted of 26 hypertext fragments in the domain of fuel supply systems of a Dutch automobile. The fragments contained descriptions of the various aspects of process control in a modern fuel injection system. In such systems, several factors are measured (e.g., external temperature) to supply an optimal amount of fuel for a combustion engine to run properly. For example, in cold weather conditions, the system needs to supply relatively more fuel as more will condense in the engine and is therefore not available for combustion. The hypertext was organized in a hierarchical way. At the highest level, it contained general introductions to measure and control processes. At the subsequent level, it provided descriptions of the particular processes in a fuel supply system. Finally, at the lowest level, it gave examples of measuring or control under specific circumstances.

The effects of graphical overviews 9

Experimental Design

A hypertext system was developed with three different interfaces.

In Condition 1, the 'visual' condition, the overview was organized using a 'visual' layout. That is, we enhanced a hypertext environment (on 'fuel supply systems') with a graphical overview that represented the basic structure of the domain and we designed the layout in such a way that learners were unobtrusively encouraged to choose a sequence of exploration that followed the domain structure. This lay-out followed a vertical hierarchical structure of nodes with is-a relations (e.g., a motor temperature measurement is-a measurement), the top two horizontal levels indicated sequential, temporal relations (e.g., a motor temperature measurement is_followed_by a determination of default signal), and the lower three horizontal levels indicated causal relations (e.g., measurement of a high motor temperature causes relatively low signal correction). An impression of the interface of the 'visual' condition is given in Figure 1, but only a limited number of the nodes and relations are presented.

In Condition 2, the 'hints' condition, also a visual layout was used to present the same nodes as in the visual display of Condition 1, but now the nodes were arranged randomly on the screen (without nodes overlapping). In Condition 2, participants were provided with hints on how to traverse the system. These hints were implemented by highlighting nodes in the overview, where the sequence of The effects of graphical overviews 10 highlighting reflected the domain-related layout from Condition 1.

Condition 3, the 'control' condition, was created by using the same 'random' overview as was used in Condition 2. In this case, no hints on the sequence of traversing the system were provided.

- ------------------------------Insert Figure 1 about here -------------------------------Since our aim was to study the influence of the displayed structure of the domain on learning we decided to provide as little textual information concerning relations as possible.

Therefore, great care was taken to avoid explicit references within text fragments to other nodes. Hence, the only information on relations was provided by the visualization of those relations in the graphical overview. This visualization was the only access to the hypertext fragments and thus, though unusual, it was not possible to go from within one hypertext fragment directly to the other. Finally, in the interface the learners were allowed to move the nodes, primarily because moving usually made relations between nodes more evident. It should be noted that in all conditions participants were fully autonomous to decide on the sequence of exploration.

Participants

The participants (N = 46; 15 in the 'visual' condition, 15 in the 'hints' condition, and 16 in the 'control' condition)

The effects of graphical overviews 11 were first year undergraduates in Psychology. Participation in the experiment was part of a study requirement, though participants had the right to choose from several experiments.

The participants were assigned randomly to the conditions, with the exception that both genders were equally distributed over conditions.

Procedure

Prior to working with the hypertext system, the participants were trained on a practice domain consisting of four nodes. Dee-Lucas and Larkin (1995) showed that the influence of the form of the overview of a hypertext on participants' performance is higher when learners receive global learning goals compared to when they receive specific learning goals. Participants received a global assignment asking them to learn as much as possible from the hypertext.

They were also informed that an above moderate performance on the post-test would be (financially) rewarded. The participants were instructed to work at least 25 minutes with the hypertext and no maximum time limit was set. Participants were instructed that all text fragments should be read and each fragment could be read more than once. Differences in efficiency between conditions could emerge since the total time was not limited and we allowed participants to visit text fragments more than once. To get a clear picture of the exploratory trajectory, participants were forced to read only one fragment at a time. Finally, three types of knowledge The effects of graphical overviews 12 tests (as described later) were administered at two points in time: prior to and after the session with the hypertext.

Data Collection

Process Measures

All actions of the participants were logged with a time stamp. This provided us with information on the overall activity in the environment, time spent, and nodes visited.

The logfiles were also used to extract the exploration pattern of the participants. First, we looked at the 'connectivity' of the participants' exploration. More specifically, we looked at the number of 'jumps' made by participants. That is, did they follow links in the interface or did they traverse between unrelated nodes? Second, we looked at the 'consistency' of traversing. That is, did the exploration pattern follow the hierarchy, temporal, and causal parts of the domain structure?

We choose to use logfiles since these data can be gathered unobtrusively and this does not interfere with the learning process.

Product Measures

We have claimed that differences in the sequence of exploration would mainly affect the acquisition of knowledge of structure and not the learning of the content of the nodes.

Content of the nodes was measured by a definitional knowledge test. Knowledge of structure was first measured with a test of knowledge of direct relations between concepts (the propositional knowledge test). Second, knowledge of the The effects of graphical overviews 13 overall structure of the domain was measured by the so-called 'configural' knowledge test (cf., [START_REF] Goldsmith | Assessing structural knowledge[END_REF].

Definitional knowledge. Definitional knowledge was operationalized as knowledge of concepts acquired from the text fragments. This type of knowledge was tested by means of a multiple choice test that required reproduction of facts as given in the text fragments that described those concepts. An example item from the definitional knowledge test is: "The position of the throttle switch provides information on: 1) the amount of oxygen supplied; 2) the amount of fuel supplied;

3) both 1 and 2 are correct; 4) both 1 and 2 are incorrect; 5) no idea". The final answer option was in all cases 'no idea', this option was added since we anticipated little knowledge of the subject matter, especially in the pre-test. By instructing the participants not to guess but to use this option we hoped to avoid too large a bias due to guessing. Pre-test and posttest test both consisted of a total of 20 items with 5 possible answers.

Propositional knowledge. For measuring propositional knowledge, we developed several types of items. To test for hierarchical (i.e., generalization-specification) relations, we asked for categorization, or for identification of subclasses. Temporal relations were tested by asking for ordering in time or by asking for the missing parts of a procedure. Causal relations were tested by asking for The effects of graphical overviews 14 predictions. All relations that were present in the hypertext system (except for the absolutely trivial ones) were tested.

An example item asking for a temporal relation is: " The process of correction for external influences is amongst other things preceded by measuring: 1) the motor temperature; 2) the RPM (rotations per minute); 3) the air pressure in the inlet; 4) none of the above options is correct; 5) no idea". An example item asking for a causal relation is: "A malfunction in the motor temperature sensor during cold start causes the blocks of the basic supply signal to be: 1) too short; 1) too long; 3) irregular; 4) none of the above options is correct; 5) no idea". Pre-test and post-test both consisted of a total of 20 items, each with 5 answer alternatives, where again one of the options was the 'no idea' option.

Configural knowledge. Configural knowledge was tested by means of a card sorting task (see [START_REF] Shavelson | Concept validation: Methodology and application of three measures of cognitive structure[END_REF].

In this card sorting task, the participants were instructed to cluster the 26 nodes of the hypertext. For this task, a tool was created with an interface identical to that of the hypertext system, except that it did not allow access to the hypertext fragments. Participants were instructed to move the nodes to form stacks that were clusters of related nodes.

Neither clustering criteria nor limit on cluster-size were given. To obtain a measure for knowledge of the overall structure, the results of each participants' card sorting task were compared with a norm model. This norm model was chosen so that it resembled the original domain organization as much as

The effects of graphical overviews 15 possible. To calculate the correspondence to the norm clustering, a proximity matrix was generated from both the participant and the norm clustering. A cell of the proximity matrix is filled with a 1 if row and column concepts belonged to the same cluster, and with 0 if they belonged to different clusters. The correspondence of the participants' proximity matrix to the norm matrix was calculated using a measure for correspondence by [START_REF] De Jong | Cognitive structures of good and poor novice problem solvers in physics[END_REF]. This measure accepts matrices composed of dichotomous data as input.

Predictions

In Condition 1, the 'visual' condition, following domain- The domain related sequence of exploration in Conditions 1 and 2 also should lead to a better knowledge of the overall structure of the domain. In Condition 1, this overall structure is not only expressed in the links between nodes but is also directly displayed by the lay-out. We, therefore, predicted that participants in Condition 1 (the 'visual' condition) would gain better configural knowledge than participants in Condition 2 (the 'hints' condition), who again would acquire better configural knowledge than the participants in Condition 3 (the 'control' condition).

Since learning the content of node is independent of the overall display and of the sequence of exploration we predicted that participants in all conditions would gain the same amount of definitional knowledge.

Results

Process Measures

Time on Task and Overall Activity

Analysis of variance revealed no differences between conditions for time on task (F(2,43)=2.49, ns). The mean duration in the hypertext system in seconds for the 'visual' group was 1691 (SD=253), the 'hints' group spent 1990 seconds in the hypertext (SD=439), and the control group 1833 seconds (SD=482). Activity in terms of the number of traverses also showed no differences (F(2,43)=.85, ns). The mean number of traverses in the 'visual' group was 41.60 (SD=12.62),35.13 The effects of graphical overviews 17 (SD=16.03) in the 'hints ' group,and 43.56 (SD=24.76) in the control group. No correlation was found between time on task and the dependent variables propositional knowledge (r=-.03) or configural knowledge (r=.02).

Connectivity

The first measure of the participants' traverse pattern was called connectivity. To assess connectivity, we calculated for each participant the average distance covered in visiting two nodes that were related in the hypertext system. The distance d of a traverse was determined by means of calculating the distance covered to get from node A to a the next node in the sequence, entity B. The distance between entities A and B was defined as the number of relations of the domain structure to be traversed to get from A to B via the shortest path. This was corrected for 'necessary' traverses, e.g., if one ended up in the leaves of a fully explored branch. Connectivity was expressed as 1/d.

The connectivity scores showed a main effect for condition (F(2,43)=11,32, p<.00), where a post-hoc analysis (Scheffé) revealed that connectivity scores were significantly higher for both 'visual' (1/d = .90; sd = .04) and 'hints' group (1/d = .87; sd = .10) when compared to those of the control group (1/d = .75; sd = .13). This means that in their exploration patterns participants in the 'visual' and 'hints' groups more closely followed the links from the interface than participants in the control group.

During a qualitative analysis of the traverses of 12

The effects of graphical overviews 18 participants from the 'visual' and control group (the three best and three worst performers from both groups), we observed differences between the groups in the initial exploration phase. The trajectory of the 'visual' group was found to be highly connective from the beginning. Large 'jumps' were only observed in a later stage of exploration. In contrast, within the control group 'jumping' was certainly seen in the initial stage. A possible explanation might be that participants in the control group started browsing the material but that the 'visual' group skipped this phase. To investigate this, each trajectory was split up into an 'initial phase', incorporating the first 10 traverses and a 'final phase' incorporating the traverses 11 and on. The connectivity results for both phases can be found in Table 1.

- ------------------------------Insert Table 1 about here -------------------------------The connectivity scores for the control group were significantly lower in the initial phase (t=-5.17, df=15, p<.00) compared to the final phase. In addition, the connectivity scores of the 'visual' group were significantly higher in the initial phase (t=2.37, df=14, p<.02) compared to the final phase, while the same holds (marginally) for the 'hints' group (t=1.64, df=14, p<.06).

Consistency

In addition to whether participants followed links in the hypertext, we measured if they followed the domain structures

The effects of graphical overviews 19 in the interface. As indicated above, the hypertext system basically consisted of a number of structures, one (vertical) hierarchical structure, two (horizontal) temporal structures, and three (horizontal) causal structures. For each of these structures, we determined a measure of consistency. For each exploration of a separate structure, we determined whether it followed the links in the structure or not. This was done using a pre-defined number of links for each structure, and assessing the exploration with a 0 if the number of links was lower than the pre-defined number and a 1 if this was equal or higher than the pre-defined number. Table 2 presents the average consistency scores for the separate structures in the different experimental groups.

------------------------------Insert Table 2 about here - -----------------------------The data on consistency (see Table 2) showed an overall effect for condition, except for the first causal structure.

Analysis of variance revealed the following effects: for hierarchy (F(44,2)=45.48, p<.00), temporal-1 (F(44,2)=4.74, p<.01), temporal-2 (F(44,2)=13.81, p<.00), causal-1 (F(44,2)=1.70, ns), causal-2 (F(44,2)=16.52, p<.00), causal-3 (F(44,2)=12.25, p<.00). Post hoc analysis revealed that level of consistency in the 'visual' and 'hints' conditions is for all primitive structures significantly higher than in the control condition, again with the exception of the first causal structure.

The effects of graphical overviews 20

Product Measures

The results of the test for definitional and propositional knowledge are expressed in terms of the number of items correct (out of 20), whereas configural knowledge is expressed in terms of correspondence to a norm model. For the data on configural knowledge, due to lack of activity in the card sorting task, the results of one participant from the 'visual' condition and that of one participant from the hints condition had to be removed from the data set (N = 44). Table 3 and Figure 2 present an overview of the results.

- -----------------------------Insert Table 3

about here ------------------------------
As can be seen in Table 3, no large differences in prior knowledge were found. One way multivariate analysis of variance did not reveal a difference between conditions (F(6,80)=1.20, ns.) for the pre-test results. Although the pre-test differences for propositional knowledge were small, disregarding these may lead to inappropriate conclusions.

Hence, from here on we will use 'knowledge gain' scores that reflect the differences between pre-test and post-test scores.

These knowledge gain scores, when subjected to a multivariate analysis, showed an overall constant effect, indicating that the participants indeed learned from working with the hypertext environment (F(3,41)=64.87, p<.00). This learning effect was significant for definitional (F(1,43)=167.25, p<.00), propositional (F(1,43)=122.30, p<.00) and configural knowledge gain (F(1,43)=31.56, p<.00). The different The effects of graphical overviews 21 treatments were found to affect learning since the gain scores showed a main effect of condition (F(6,78)=3.76, p<.00). Using a univariate analysis, this main effect can be ascribed to effects on propositional and configural knowledge. For these types of knowledge gain, the results revealed significant differences between the conditions (F(2,41)=7.46, p<.00) and (F(2,41)=6.81, p<.00), respectively. Univariate analysis revealed no differences for definitional knowledge (F(2,41)=.59). The effects on propositional and configural knowledge were analyzed using a one-way univariate analysis for each of the dependent variables. Post hoc analysis (Scheffé) revealed that differences between the 'visual' condition and the 'hints' condition and differences between 'visual' condition and control condition were significant (at α = .05) for both the propositional knowledge and the configural knowledge test. Differences between the 'hints' and the control conditions were not significant for propositional knowledge. Differences between the 'hints' and the control conditions were only marginally significant (t=1.67, df=28, p<.06) for configural knowledge. For definitional knowledge, there were no significant differences between conditions.

- ------------------------------Insert Figure 2 about here -------------------------------

Conclusions and discussion

In the present study, we assessed the impact of different interfaces on learning from a hypertext system. The study was designed in such a way that effects from the graphical The effects of graphical overviews 22 interface and the learning route induced by the interface could be separately assessed. Following critical comments from review studies [START_REF] Dillon | Hypermedia as an educational technology: A review of the quantitative research literature on learner comprehension, control and style[END_REF][START_REF] Tergan | Multiple views, contexts, and symbol systems in learning with hypertext/hypermedia: a critical review of research[END_REF], we tried to use a set of knowledge measures directly tapping types knowledge relevant for the stated theoretical hypotheses. Using the logfiles, we were also able to assess the learning process in a deeper manner than has been done in previous research.

We first tested the idea that the interface may influence the exploration route that students follow in a hyptertext system. The related prediction in our study was that in presenting a visual overview of the domain (Condition 1, the 'visual' condition) and giving learners hints to follow a domain related route (Condition 2, the 'hints' condition) would encourage participants to follow a more domain related sequence of traversing the domain than participants in a condition that lacked these features (Condition 3, the control condition). Data showed that participants in the 'visual' and 'hints' conditions indeed followed the links in the interface (displaying the domain structure) more closely than participants in the control condition. A further comparison of the 'visual' with the control condition showed that in the control condition jumping through the nodes was especially dominant at the start of the learning process. Process data further showed that, overall, participants from the 'visual' and 'hints' conditions were more consistent in following the separate 'sub-structures' in the hypertext than participants from the control condition. In summary, we found that higher gain in configural knowledge than participants in the control condition. We expected this effect based on the idea that the visual overview directly displayed the configuration of the domain. Following a domain related exploration route (as was expected in both the 'visual' and 'hints' condition) would provide learners with the opportunity to experience the overall configuration of the domain but less directly than in the visual overview. This prediction was partly corroborated by the data. The difference between the 'visual ' and 'control' conditions was significant. The difference between the 'hints' and the 'control' conditions almost reached significance.

The final prediction that conditions would show no differences in knowledge of the content of nodes was supported by the data. This confirmed the idea that what students learned within a node was independent from the route that brought them there or the context in which it was embedded.

The outcomes of the present study help us to better understand results from previous studies and it underscores the value of recommendations done by several review studies.

From the results we may also learn about the theoretical underpinnings of learning from hypertext and find inspiration for the design of hypertext environments.

One of the main outcomes of our study is that different results can be found for different types of knowledge measures. This explains the seemingly contradictory results of the [START_REF] Jonassen | Effects of semantically structured hypertext knowledge bases on users' knowledge structures[END_REF] and Dee-Lucas and Larkin (1995) studies.

Using dependent measures that resembled our definitional and

The effects of graphical overviews 25 propositional test, Jonassen found no advantage to providing students with graphical overviews or node-by-node guidance.

When Dee-Lucas and Larkin used a dependent measure similar to our configural test, they found positive effects for a graphical overview compared to a textual unstructured overview. This is in line with the results of the present study. In a more general sense, this touches upon the conclusion drawn in several review studies on learning from hypermedia [START_REF] Chen | Interacting with hypertext: A meta-analysis of experimental studies[END_REF][START_REF] Dillon | Hypermedia as an educational technology: A review of the quantitative research literature on learner comprehension, control and style[END_REF][START_REF] Tergan | Multiple views, contexts, and symbol systems in learning with hypertext/hypermedia: a critical review of research[END_REF] that the results of studies reviewed are equivocal. The results of the present study support the notion that learning from hypertext may occur, though we did not compare it to more linear types of learning material. [START_REF] Dillon | Hypermedia as an educational technology: A review of the quantitative research literature on learner comprehension, control and style[END_REF] concluded from their review of studies comparing learning from hypermedia and other media that hypermedia is most useful when learning complex and extensive domains where searching for information is relevant and when data manipulation and comparison of data is important. In our study, the domain was not an extensive domain, but due to the different types of relationships involved and the nature of the domain it could be seen as not an easy topic for our participants. Of course, we can only speculate about effects when a more extensive domain is involved but we would expect, following Dillon and Gabbard, that the effect of a graphical interface would then even be more distinctive.

One of the most cited theoretical notions used in the context of learning from hypertext is cognitive flexibility theory (e.g., [START_REF] Spiro | Cognitive flexibility and hypertext: Theory and technology for the nonlinear and multidimensional traversal of complex subject matter[END_REF][START_REF] Spiro | Cognitive flexibility, constructivism, and hypertext. Random access instruction for advanced knowledge acquisition in ill-structured domains[END_REF] The effects of graphical overviews 26 &, Coulson, 1992). Basic to this notion is the idea that approaching a domain from different angles enables learners to make multiple comparisons and relations and this leads to a 'flexible' cognitive structure. Though we did not test this idea directly, the implicit assumption in cognitive flexibility theory that knowledge gained is influenced by the route followed is not supported in our study. The overall conclusion from this study is that the most significance influence on learning results comes from the graphical overview and that the effect of this interface is due to the direct display of the domain structure. Designing hypermedia environments should therefore not only look at the overall exploration pattern of learners but present the learner with graphical overviews and link these to deep representations of the domain. Recent and detailed approaches towards designing hypermedia environments [START_REF] Jacobson | The design of hypermedia tools for learning: Fostering conceptual change and transfer of complex scientific knowledge[END_REF] take this into account.

  different ways: knowledge of the overall structure of the domain (configural knowledge), knowledge of the relations between two specific nodes (propositional knowledge), and knowledge of the content of the nodes (definitional The effects of graphical overviews 7 knowledge). Jonassen concentrated on what we have called here propositional knowledge and definitional knowledge, whereas the assessment used in the Dee-Lucas and Larkin (1995) study (an analysis of summaries made by students) seems to emphasize what we have called configural knowledge. Second, we have made an analysis of the exploration behavior of learners that went beyond measuring nodes visited and time spent at each node and that tried to capture their exploration pattern.

  related traverses is induced by the domain-related visual display whereas in Condition 2, the 'hints' condition, a domain related route is stimulated by the hints that are presented. The first prediction therefore was: Participants in Condition 1 (the 'visual' condition) and Condition 2 (the 'hints' condition) will follow a more domain-related sequence of traversing the hypertext system than participants in Condition 3 (the 'control' condition). Following the first prediction, participants Conditions 1 and 2 would follow a domain-related sequence of exploration that would be absent in Condition 3. Following a domainrelated route means that traverses follow the named links between nodes. As a consequence, we predicted that participants in Condition 1 (the 'visual' condition) and Condition 2 (the 'hints' condition) would acquire higher gains The effects of graphical overviews 16 on the propositional test than the participants in Condition 3 (the 'control' condition).

  ., & Hammond, N. (1989). A learning support environment: The hitchhiker's guide. In. R. McAleese (Ed.), Hypertext: Theory into practice (pp. 62-74). Oxford: Intellect Limited. Arents, H., & Bogaerts, W. (1993). Navigation without links and nodes without contents: Intentional navigation in a third order hypermedia system. Hypermedia, 5(3), 187-204.

Figure captions Figure 1 .

 captions1 Figure captions

Figure 2 .

 2 Figure 2. Graphical display of the results on the different knowledge tests

Table 1

 1 Mean scores (and SD) for connectivity for two phases in the

	learning process
	Initial phase	Final phase
	Visual	.95	(.08)	.90	(.18)
	Hints	.92	(.16)	.86	(.09)
	Control	.63	(.04)	.81	(.11)

participants in the 'visual' condition, in which the structure of the domain was visually displayed, and participants in 'hints' condition, in which the structure of the domain was suggested by highlighting relations, showed a more domainrelated exploration pattern than participants in the control condition. This finding supports the general idea that interface aspects can influence learners' exploration routes.

Our second prediction stated that participants in the two conditions that guided the exploration route ('visual' and 'hints' conditions) would show higher gains on the propositional test than participants in the control condition.

The basic idea was that propositional knowledge test assessed the relations between separate concepts and that in the 'visual' and 'hints' conditions this would be positively affected by the domain related route that was followed. Data partly followed this prediction. Only participants in the 'visual' condition had higher gains on propositional knowledge than participants in the control condition. There was no significant difference in gain between participants from 'hints' and control condition. As was tested under our first prediction, we did not find differences in the exploration route between participants in the 'visual' and 'hints' conditions. Thus, the conclusion here can be that it is not the exploration route per se that leads to the acquisition of propositional knowledge.

The third prediction stated that participants in the 'visual' condition would gain higher configural knowledge than participants in 'hints' condition, who in turn would show a


Ton De Jong

Wouter R Van Joolingen

Janine Swaak

Koen Veermans

Renate Limbach

Simon King

Daniel Gureghian

Combining human and machine expertise for selfdirected learning in simulation-based discovery environments

SIMQUEST 1 is an authoring system for designing and creating simulation-based learning environments. The special character of SIMQUEST learning environments is that they include cognitive support for learners which means that they provide learners with support in the discovery process. In SIMQUEST learning environments we try to find the balance between direct guidance of the learning process and sufficient freedom for learners to regulate the learning process themselves. In this article we describe the basic mechanisms of the SIMQUEST learning and authoring environments. We describe the functionality authors have in providing the learner with guidance, and report on some of our experiences on how authors use these opportunities and learners employ the cognitive support they are provided with .

Introduction

In our educational systems, the instruction and learning process still is generally characterised by the traditional lecture, in which the teacher explains to the students rules and principles of the domain. There is now a general conviction that this traditional way of expository teaching is not optimal for training employees that the market requires and who need deep, flexible, and transferable knowledge. This need has led to new pedagogical philosophies in which constructivism is the key item. In this philosophy learners construct knowledge themselves, and the learning process is characterised by placing a high responsibility into the hands of the learner instead of the teacher. Simulations are extremely suited for this type of learning since they encourage discovery learning, learners experiment and construct knowledge as 'scientists': they provide the simulation with input, observe the output, draw their conclusions, and go to the next experiment (Van Joolingen & De Jong, 1997). Experience and studies, however, show that learners are not always capable of handling their own learning process. In De Jong and Van Joolingen (1997) we have summarised a large number of studies that have shown a wide variety of problems learners may experience in discovery learning. In summary we can say that learners show problems with all processes characteristic of discovery learning such as stating hypotheses, designing experiments, interpreting data and regulating the learning process (monitoring and planning).

In the EC sponsored SERVIVE project we develop an authoring system, named SIM-QUEST, that supports the creation of discovery learning environments. The SIMQUEST system is a follow-up of the SMISLE environment (see De [START_REF] De Jong | SMISLE: System for Multimedia Integrated Simulation Learning Environments[END_REF][START_REF] De Jong | The SMISLE environment: Learning with and design of integrated simulation learning environments[END_REF], still following a similar learning and authoring philosophy (Van Joolingen et al., 1996). The learning philosophy holds that in SIMQUEST applications we aim to avoid learning problems by introducing cognitive support for the learner. This support consists of several 'learning tools': Learners may ask for small exercises (so-called assignments) that help them plan their actions and that can point them to specific phenomena; while experimenting learners can ask for background information in the form of definitions, relations to the real world etc. (this can be any kind of multi-media material); the simulation model can be presented to the learner in small steps that increase the model in complexity (so-called model progression); learners have tools that help them to monitor what they have been doing in a simulation session, that help them replay simulation sessions, compare outcome series, and make sound interpretations of the data; and, finally, also learners will have tools that help learners to compose and check hypotheses.

One of central questions we have in the project concerns the level of control that can be placed in the hands of the learner or that should be taken by the system. In this article we concentrate on two characteristics of SIMQUEST learning environments that are most relevant for this question: the nature of assignments, and the timing and obligation for learners to use these assignments.

Assignments as a support for discovery learning

The educational function of assignments One of the paramount problems of learners in the discovery learning process is the regulation of their learning behaviour. Obviously, in self-directed learning environments the demand on regulative capacities of learners is larger than it is in traditional lectures. Planning and monitoring are central to regulation. Unsystematic planning and monitoring in simulation based discovery environments is a wide spread phenomenon and is, for example, reported by [START_REF] Lavoie | The nature and use of predictions skills in a biological computer simulation[END_REF]Good, 1988, Simmons and[START_REF] Simmons | Problem-solving behaviors during a genetics computer simulation: beyond the expert/novice dichotomy[END_REF], [START_REF] Shute | A large-scale evaluation of an intelligent discovery world: Smithtown[END_REF], [START_REF] Schauble | Causal models and experimentation strategies in scientific reasoning[END_REF]. [START_REF] Veenman | The generality vs domain specifity of metacognitive skills in novice learners across domains[END_REF] who followed learners in a number of simulation based discovery environments report an effect of metacognitive abilities on the discovery learning process and learning results. [START_REF] Charney | Goal setting and procedure selection in acquiring computer skills: A comparison of tutorials, problem solving, and learner exploration[END_REF] and [START_REF] Teodoro | Direct manipulation of physical concepts in a computerized exploratory laboratory[END_REF] claim that subjects have considerable problems with setting goals for themselves. Supporting the learner in the planning and goal setting process was already taken up by [START_REF] Showalter | Conducting science investigations using computer simulated experiments[END_REF] who used questions as a way to guide the learner through the discovery process. His questions focused the learners attention to specific aspects of the simulation (see also [START_REF] Zietsman | Effect of instruction using microcomputers simulations and conceptual change strategies on science learning[END_REF]. [START_REF] Tabak | Combining general and domain-specific strategic support for biological inquiry[END_REF] have added such questions with the aim of setting goals in a biological simulation. [START_REF] White | Designing computer games to help physics students understand Newton's laws of motion[END_REF][START_REF] White | ThinkerTools: causal models, conceptual change, and science education[END_REF] helped learners to set goals in a simulation by introducing games that ask learners to reach a specific state of the simulation. In the SMISLE and SIMQUEST learning environments we have introduced the mechanism of assignments to help learners in their goal setting behaviour.

Assignments in SIMQUEST learning environments Authors using the SIMQUEST authoring environment are offered templates for different types of assignments. Characteristic for all assignments is that they present the learner with a specific task. In SIMQUEST we distinguish five different types of assignments.

The first one, do-it assignments, give the learner the general assignment to explore the simulation model. As such, they do not advise the learner into a specific direction. The only thing authors can do is to put the simulation in a specific state (or more states from which the learner may select). Other types of assignment that can be created with SIMQUEST are more directive for the learning behaviour.

Investigation assignments ask learners to investigate the relation between two or more given variables. After exploring the simulation learners may select an alternative from a list of predefined alternatives (a more complex investigation assignment can be specified by asking the learner to select all the correct alternatives from the list of propositions given) and feedback is given. This feedback can be direct feedback on the alternative chosen (in the form of any type of multimedia content), or following a selected alternative the learner can be directed to, for example, another assignment.

Explicitation assignments always have an initial state or sets of initial states for the simulation associated with them, the role of the learner being to run the simulation (with these different initial sets) and to observe the impact on the simulation. Learners are then presented a set of propositions that describe the phenomenon or phenomena observed, and they are asked to select the correct alternative(s). Figure 1 gives an example of an explicitation assignment from a SIMQUEST environment in the physics domain of collisions.

In specification assignments the learner has to predict the values of certain variables when the associated simulation stops. The values predicted by the learner are allowed a deviation from the absolute values as specified by the author in either absolute or relative terms. The author also specifies when the simulation stops by assigning values to variables. When these values are reached the simulation stops.

Finally, in optimisation assignments the learner has to vary the simulation's variables' values so that the constraints specified by the author are not broken and a target specified by the author is reached. Figure 2 shows such an assignment from a SIMQUEST environment in the physics domain of motion.

Learner and author experiences

We offer authors the possibility for creating assignments so that they may help the learner in the planning of the discovery process. Assignments may help the learner to decide on what to do next and may also help (by presenting the right variables and by putting the simulation in relevant states) to cover all relevant aspects of the model to be taught. We do now have experience with a number of environments being created by authors (teachers) from university and vocational levels. In a number of cases we have carefully evaluated the authoring process (see e.g., [START_REF] Kuyper | Using WWW technologies for acquiring user requirements in distributed software development[END_REF]. From these evaluations it became clear that authors also see assignments as an important instructional tool, and they use the whole variety of available types of assignments. There are, however, two deviations from using assignments in the way we, as the development team, intended to them to be used. The first one is that authors see assignments as the central mechanism in the learning process whereas we originally saw them as a support means that a learner might use if the self-directed discovery process got stuck. A second deviation is that sometimes authors tend to use assignments as intermediate tests, telling learners first to discover the rules and then use assignments to see if they understand them. In a number of empirical studies we have been able to see the influence of assignments on the learning process. Here we report on three studies, each covering a different domain from physics. These are transmission lines (De Jong, Härtel, [START_REF] De Jong | Support for simulationbased learning; the effects of assignments in learning about transmission lines[END_REF], collisions (De Jong et al., 1997) and harmonic oscillations (Swaak, Van Joolingen, & De Jong, 1997). One of the critical observations we did in these studies was that learners saw assignments as 'the' guidance for their discovery process. In cases where they were not forced to do any assignments (see also in the next section on 'control') they made the maximum use of them and it was frequently the case that learners saw the completion of all available assignments as the goal of their work ("I am done, I have completed all assignments"). In fact, students were right in recognising the importance of assignments. In the studies mentioned above we could estimate the effect of assignments by comparing the same simulation environments with and without assignments. Overall, in conditions where assignments were present learning gains (if measured as 'intuitive knowledge', see Swaak & De Jong, 1996) were higher than in conditions where assignments were not available.

Control over the environment

Structuring the discovery process Whereas single assignments help learners to plan their immediate actions, several studies have indicated that learners also have problems in the overall planning of their learning process. For example, [START_REF] Glaser | Scientific reasoning across different domains[END_REF] found that successful discoverers make plans over experiments, whereas unsuccessful learners concentrate on local decisions. In the literature we find several attempts to lead the learner through an overall sequence of actions. These actions quite often concern steps in the overall discovery process. For example, [START_REF] Lewis | The effect of computer simulations on introductory thermodynamics understanding[END_REF], Njoo and De Jong (1993a;1993b), [START_REF] White | ThinkerTools: causal models, conceptual change, and science education[END_REF], and [START_REF] Shute | A large-scale evaluation of an intelligent discovery world: Smithtown[END_REF] are examples of studies where learners are led (in a more or less compulsory way) through a fixed sequence of actions (e.g. devise an hypothesis, design an experiment etc.). Some studies that have compared structured and unstructured environments gave beneficial effects of structuring. Other studies (e.g., [START_REF] Veenman | Differential effects of instructional support on learning in simulation environments[END_REF] could not find these effects, or could only find these effects for specific groups of subjects. In summary, existing studies are rather inconclusive on the effects of structuring the environment. The differences in outcome can possibly be attributed to the differences in how the environment was structured, but also to differences in characteristics of the domains involved and of the characteristics of the learners.

Control structure in SIMQUEST

In SIMQUEST learning environments we use a control mechanism that has a distributed character (see also Van Joolingen & De Jong, 1996). Each instructional measure has an internal state. All instructional measures can assume the states "disabled", "enabled", or "active". Specific instructional measures can add more values for the state variable. For instance, assignments also can have the state values "failed" or "succeeded", which describes the results of the learner's interaction with the assignment. The complete state of the learning environment is defined by the collection of all states of the instructional measures. Instructional measures also contain a set of rules. These rules determine the change of state. For instance, a rule may say that when the state of an assignment becomes "failed", another instructional measure must change state to "active", which means that this instructional measure will display itself and is ready for interaction with the learner. Rules can be attached to any change of state. Typically, there will be rules which arrange the learning environment (i.e. open up the appropriate simulation interface, display an introductory text etc.) and rules which respond to the behaviour of the learner (like the activation of an explanation on assignment failure or success). A special state value is that of "enabled". This means that the learner will see the instructional measure on a list and is able to inspect a description of it. The learner can select and activate instructional measures that are enabled. In this way, learners can take initiative in the learning interaction by choosing for themselves if they need support from the enabled instructional measures and when they need it. The rules inside the instructional measures will ensure that the learning environment itself remains consistent.

The control structure in SIMQUEST is dedicated for making learning environments in which the responsibility for choosing 'actions' is distributed over the learner and the environment, meaning that the initiative for activating instructional measures (e.g., assignments or explanations) can depend on both the preferences of the learner and on the internal state of the learning environment (see Van Joolingen & De Jong, 1996).

Learner experience and author experience

Our experience with authors using the control structure in SIMQUEST (and SMISLE) is that they tend to structure the environment quite strictly and force the learner to go from assignment to assignment. In this way, authors, use only a limited part of the functionality that the SIMQUEST environment offers them for structuring the learning environment. One of the reasons for authors to work in this way is that they believe that this very constrained structure is necessary for an optimal learning process. For this reason we conducted a study in which we created two environments (called CIR-CUIT), one in which the learner could always choose freely from all assignments and was never forced to perform one (all assignment were always 'enabled'), and one in which learners were led from assignment to assignment ('exiting' one assignment 'activated' another assignment). The domain was from a physics topic, electrical circuits, and the learners came from a middle vocational training college. In the 'free' environment we had 21 subjects, of which 20 worked with the 'constrained' environment. A full description of the study can be found in [START_REF] Swaak | Evaluating simulation discovery environments with learners[END_REF]. The main conclusion from the study is that constraining the students in their freedom in using support by forcing them to complete all assignments at one level of model progression before proceeding to another level did not make much difference. Neither the post-test scores nor the interaction data and navigation data as measured with the log-files, identified major differences between experimental groups. The explanation put forward for the absence of major differences between the conditions is that just the presence of assignments is sufficiently directive. Although the students in the free condition were completely free in doing or not doing assignments, they completed on the average 14 of the 19 assignments, and despite this number is significantly lower than the 17 completed assignments of the structured condition it is not substantially lower. The number of simulation runs and the amount of explanations consulted did not differ significantly for the two conditions. In addition, if we consider the navigation measures, we see that the numbers of model progression switches differ for the two experimental groups, but that the time spent at each of the model progression levels did not show any statistically significant differences. In a second study (see also Swaak et al., 1997), also in the field of electricity and in middle vocational training we introduced the same two types of environment, one free and one constrained (this environment was called ElectricA). This time we gathered in-depth data of 10 students (five in each condition). From the questionnaire data we can conclude that, though the contents of the two versions of ElectricA were exactly alike, they were perceived differently by the students. The 'free' and the 'constrained' versions of ElectricA had precisely the same outlooks, contained the same assignments, explanations, feedback etc., and only differed in the amount of freedom given to the learners. Nevertheless, most ratings from the students of the 'free' condi-tion were more favourable than the ratings from the 'constrained' condition. The students from the 'free' condition gave a higher quality rating to the several features and parts of ElectricA, appreciated the instructional measures more, reported to have less problems on keeping track of what they had learned, and on knowing how to proceed. Furthermore, students of the 'constrained' condition indicated to be more frustrated by the possibilities of the environment, reported to need more information on how to operate ElectricA, and more background information. When students of both conditions were directly asked to report on the extent to which they felt constrained, the ratings of the groups were nearly similar. Yet, students' perception of freedom, was higher in the 'free' condition as compared to the 'constrained' condition.

Conclusion

In this article we have discussed how in the SIMQUEST authoring environment we have provided authors with functionality for creating learning environments with a mixed initiative for learner and system. We have also seen that both authors and learners use these facilities in a way that does not follow its intentions and reduces simulations learning environments to restricted environments where the predominant objective seems to be to perform a fixed sequence of assignments. We think that this phenomenon is partly due to the natural tendency of authors and learners to return to something familiar: a book with exercises at specific points. We expect that part of this will disappear over time when authors and learners have more experience with simulation based discovery environments. In the meanwhile, however, we have taken two initiatives to improve the situation. One initiative concerns the learner and addresses a kind of support for regulating discovery behaviour, associated with assignments, that intends to stimulate more self directed discovery learning. The other initiative concerns the author and holds a 'pedagogical advisor' in the SIMQUEST environment, that authors may consult and that may help them in constructing more open environments. We will now elaborate a little on these two developments.

One of the reasons for learners to use assignments the way they do, is that the feedback currently given to assignments is mostly in the form of an explanation to the several alternatives presented in the assignment. Currently we are developing a tool that can be used to generate feedback that is more discovery process oriented and that analyses the experiments a learner has been doing in relation to the alternative chosen in the investigation assignment. Figure 3 gives an example of the type of feedback that can be generated. This development is related to the development of a so-called "monitoring tool" which allows the learner to store, inspect, replay, and compare experiments. In order to enable this new feedback mechanism we had to extend the control structure in SIMQUEST that we described before. In the description given, the state of the learning environment depends solely on the state of the instructional measures. In the case of explicit support on the discovery process itself this is insufficient, since most parts of the discovery process are reflected in the interaction of the learner with the simulation. For registering this we introduced the "watchdog" or "daemon". A daemon monitors the state of the simulation and changes its instructional state once a certain, pre-specified simulation state has been reached. In this way the daemon transfers events from the "simulation domain" into the "instructional domain". At the moment we have three kinds of daemon: one "timer" which measures the time that has passed since its activation and exits when a certain amount of time has elapsed, one that monitors the immediate state of the simulation and exits when a pre-specified state has been reached, and one that monitors (and analyses) the experiments performed by the learner. The latter is an example how learner modelling can be integrated within the system. Daemons can be created to monitor the experiments learners perform, the hypotheses they state, and the way they respond to questions. As daemons set an instructional state, the environment can react to these small learner models. This introduces an agent-like means of learner modelling where small daemons watch aspects of the learner behaviour. As daemons can be turned on and off during a session, the sophistication of the learner model can be adapted to the actual needs in a given situation. . For the authors we now came to realise that quite often they lack specific knowledge on how to design discovery learning environments. For this reason we will provide authors in SIMQUEST with a so-called advice tool. The advice tool is an elaborate hypertext system with textual and graphical information on how to design simulation based learning environments. The advice tool is accessible through a main window or in a context sensitive way through the element of an application that is under development. For example, when editing an assignment the author has direct access to advice on assignments. In the advice tool there are two dimensions. One is the topic on which advice can be given about (e.g., model progression, or assignments) and the other dimension concerns a classification based on questions an author may have. Here we distinguish:

• What is? Tab sheets in the 'what is?' category give a definition of for example specification assignments, or explanations; • Example. Tab sheets in this category show an example of for example instructional support, e.g., an example of a video explanation can be given; • In the considerations category we include all kinds of instructional reasons for making use of specific instructional measures, and also e.g., considerations about learner characteristics, curriculum, and context characteristics.. • The background category gives information about studies we find in the literature.

Figure 4 gives an overview of the advice tool. In the content of the tab sheets we try to use a user oriented writing style and stay as closely as possible to the daily practice of teachers. By providing the learners with tools that directly aim at stimulating central discovery processes (in addition to the learning tools we already had available) and by providing authors with good information and examples on how to create simulation based discovery environments we hope to work towards a situation in which learning is not directed by a single agent, but is the result of combined expertises of system and learner.

Figure 1 .

 1 Figure 1. Explicitation assignment from a SIMQUEST learning environment on collisions

Figure 2 .

 2 Figure 2. Optimisation assignment from a SIMQUEST learning environment on motion

Figure 3 .

 3 Figure 3. Discovery process feedback to an investigation assignment

Figure 4 .

 4 Figure 4. Example screen from the advice tool

SIMQUEST is currently under development in the SERVIVE project. This project is sponsored by the EC in its Telematics programme as project ET1020. SERVIVE is the follow-up of the SMISLE project, also sponsored by the EC. In this article we report on our experiences as we have gathered them in the SMISLE project, and as we currently have them in the SERVIVE project.

Acknowledgement:

The simulation environment on collisions (see Figure 1) was created by Hans Kingma (University of Twente) based on work by Ernesto Martin (University of Murcia). The simulation environment on motion (see Figure 2) was created by Jan van der Meij (CI-NOP). The simulation environment CIRCUIT was created by Vincent Blokhuis (ROC Oost Nederland) and ElectricA was designed by Mercedes Gutierrez (Salesianos Zaragoza).