Reading Strategies and Hypertext 1 Running head: READING STRATEGIES AND HYPERTEXT COMPREHENSION Reading Strategies and Hypertext Comprehension

The literature on assessing the cognitive processes involved in hypertext comprehension during the last fifteen years has yielded contradictory results. In this paper we explored a possible factor affecting this situation, mainly the potential effects on comprehension of reading strategies in hypertext. In experiment 1, results showed that reading strategies affect selectively the textbase and the situation model level. The number of different nodes read mainly affected the textbase, whereas the reading order influenced the situation model. In experiment 2, the analysis of reading strategies was used in order to replicate the effect of knowledge and coherence (McNamara & Kintsch, 1996) found in the literature on linear text comprehension, but not replicated in hypertext. Low knowledge participants learnt more by following a high coherent reading order, whereas high knowledge learnt more by reading the hypertext in a low coherence order. Finally, we discussed the theoretical and methodological consequences of this approach for the study of hypertext comprehension. Reading Strategies and Hypertext 3 Reading Strategies and Hypertext Comprehension Hypertexts are information systems in which the contents are organized in an interrelated network where the nodes are documents and the links are the relations between these documents. Hypertexts constitute a real alternative to paper documents in fields such as education. Research assessing the cognitive processes involved in hypertext comprehension has grown jointly with the development of these systems in educational fields. However, reviews of the literature published up to 1999 showed little reliable evidence about the processes involved in hypertext comprehension (Dillon & Gabbard, 1998; Unz & Hesse, 1999). In this paper we first describe the results found in the literature assessing the effects of overviews on hypertext comprehension since 1999, and conclude that the null and contradictory results of previous works still exist. Second, we propose a theoretical and methodological approach intended to explore the relation between reading strategies and text comprehension. Third, we describe two experiments designed to support our proposal. Recent research on hypertext comprehension Most of the research on hypertext comprehension has followed the ConstructionIntegration (C-I) model of text comprehension (Van Dijk & Kintsch, 1983; Kintsch, 1988; 1998). The model distinguishes between two of the mental representations that a reader forms from the text: the textbase, a hierarchical propositional representation of the information within the text; and the situation model, which integrates that information with readers’ prior knowledge. According to the C-I model, many factors contribute to text comprehension, but prior knowledge and coherence are the main factors. Text coherence refers to the extent to which a reader is able to understand the relations between ideas in a text. In general, readers with high domain knowledge comprehend better at both the textbase and the situation level (Moravcsik & Kintsch, 1993). But when the analysis takes account of both prior knowledge and text coherence, it has been found that readers with low domain knowledge construct better situation models from a highly coherent text than from an incoherent one, whereas readers with high domain knowledge actually learn more from an incoherent text than from a highly coherent one (McNamara, E. Kintsch, Songer & W. Kintsch, 1996; Reading Strategies and Hypertext 4 McNamara & Kintsch, 1996). The explanation for this effect of knowledge and coherence is that naïve readers cannot fill in gaps in the incoherent text without explicit guidance about relationships among information items; on the other hand, expert readers who are overguided will not actively use their own prior knowledge to form the situation model of the text. These effects have been the starting point of most of the experiments exploring the effects of overviews on hypertext comprehension. Overviews are writing devices that emphasize the contents of a text and their organization (Lorch, 1989). This is one of the most active areas on hypertext comprehension, and one in which the situation described by Dillon and Gabbard (1998) and Unz and Hesse (1999) become apparent. Starting from research conducted with linear text (e.g. Snapp & Glover, 1990), it has been hypothesized that the overview would act as an “advance organizer,” improving readers’ memory of the contents. In the experiments reviewed an overview containing structural information on the contents was compared to an unstructured one (e.g. list of contents, linear version). In most cases, comprehension measures were taken for both the textbase (e.g. recall, text based questions) and for the situation model (e.g. inference questions, essay, cued association, sorting task, concept map). In addition, some of the experiments considered readers’ prior knowledge. However, a review of recent empirical work does not allow to draw a clear conclusion about the effect of overviews in comprehension (see Table 1). For low knowledge readers, some experiments show that overviews facilitate textbase construction (Moeller & Mueller-Kalthoff, 2000; Potelle & Rouet, 2003), whereas other present null (Brinkerhoff, Klein & Koroghlanian, 2001; De Jong & van der Hulst, 2002; Hofman & van Oostendorp, 1999; Mueller-Kalthoff & Moeller, 2003; Puntambekar, Stylianou & Hübscher, 2003; Shapiro, 1998, 1999) and negative effects (Naumann, Waniek & Krems, 2001; Quathamer & Heineken, 2002). Regarding the situation model construction for low knowledge readers, some experiments show positive effects of overview (De Jong & van der Hulst, 2002; Potelle & Rouet, 2003; Puntambekar, Stylianou & Hübscher, 2003; Shapiro, 1999, 2000), whereas other present null (Moeller & Mueller-Kalthoff, 2000; Mueller-Kalthoff & Moeller, 2003) and negative effects (Hofman & van Oostendorp, 1999; Shapiro, 1998; Waniek, Brunstein, Naumann & Krems, 2003). Reading Strategies and Hypertext 5 For high knowledge readers, experiments agree on a null effect of structured overviews in hypertext comprehension both in textbase and situation model construction (Hofman & van Oostendorp, 1999; Moeller & Mueller-Kalthoff, 2000; Mueller-Kalthoff & Moeller, 2003; Potelle & Rouet, 2003; Shapiro, 1999). These results on hypertext comprehension reveal an unclear situation, just as the earlier results reviewed by Dillon and Gabbard (1998) and Unz and Hesse (1999). The heterogeneity of the results for low knowledge readers regarding both the direction of the effect (positive, null and negative) and the type of comprehension (textbase and situation model) suggest that there is no easy explanation for the contradictory data. Some suggestions for clarifying the state of the art in the field propose to improve the methodological rigor of experiments (e.g. pre-testing of prior knowledge) (Dillon & Gabbard, 1998), to use several measures for text comprehension (Hofman & van Oostendorp, 1999) and to understand the interdependence between navigation behavior and the learning performance (Unz & Hesse, 1999). In the present work we explore the last suggestion, focusing on the role of reading strategies in hypertext comprehension. ---Insert Table 1 about here ---The role of reading strategies One variable that might play an important role in comprehension is the reader’s strategy. Reading strategies in hypertext can be considered as the decision rule that a reader follows to navigate through the different nodes of a hypertext. For example, a reader can read through the contents selecting those nodes that contain interesting information for him / her, or those related to the previous paragraphs read. The relation between strategy use and comprehension have been widely reported on the literature of text comprehension in linear text (Chi, Bassok, Lewis, Reimann & Glases, 1989; Chi, De Leeuw, Chiu & LaVancher, 1994; Goldman & Saul, 1990; Goldman, Saul & Coté, 1995; Magliano, Trabasso & Graesser, 1999; McNamara & Scott, 1999; Pressley, Symons, McDaniel, Snyder & Turnure, 1988; Trabasso & Magliano, 1996; Wagner & Sternberg, 1987). Different reading strategies influence the way the reader process the text and hence his / her text comprehension. Reading Strategies and Hypertext 6 Research on hypertext comprehension has also studied the relation between reading strategies and comprehension (Barab, Bowdish, & Lawless, 1997; Barab, Bowdish, Young, & Owen, 1996; Barab, Fajen, Kulikowich & Young, 1996; Barab, Young & Wang, 1999; Britt, Rouet & Perfetti, 1996; Foltz, 1996; Horney & Anderson-Inman, 1994; Lawless & Kulikowich, 1996, 1998; Lawless, Mills & Brown, 2002; Niederhauser, Reynolds, Salmen & Skolmoski, 2000; Rouet, Favart, Britt & Perfetti, 1997). In most cases, this relation has been studied focusing on the analysis of the navigational path of the reader. The general approach consists of identifying similar groups of navigational paths using a multidimensional scaling technique, and of analysing possible comprehension differences between groups, as in the studies by Lawless & Kulikowich (1996, 1998). The authors have identified three main navigational groups: knowledge seekers, feature explorers and apathetic hypertext users. Knowledge seekers spend most of the reading time on content related documents, whereas feature explorers do that on the special features of the hypertext (e.g. images, videos, maps). Finally, apathetic users spend short intervals of time on content related documents, and seem to follow a random reading order. Regarding the comprehension outcome for each group, the authors found that knowledge seekers learned more than the other groups. However, other categories have been proposed in the literature based on features of the particular hypertext used. This is due to the fact that the grouping of reading strategies on the basis of the features of a particular hypertext fails when a hypertext does not possess these features. We propose that reading strategies in hypertext can affect comprehension indirectly by leading the reader to process a particular text in terms of reading order and amount of information accessed. Different reading orders of the same text influence text comprehension in linear text (Danner, 1976; Kintsch & Yarbrough, 1982; Lodewijks, 1982; Mayer, 1976; Schnotz, 1982, 1984, 1993). Reading order has been manipulated following different criteria: self-regulated order vs. experimenter-regulated; object order vs. aspect; logical order vs. random. Each manipulation of the reading order could produce different comprehension outcomes, and moreover, could interact with reader characteristics. For example, Schnotz (1982) reported an experiment in which two groups of participants read an expository text with Reading Strategies and Hypertext 7 the same contents but organized in different orders. The different paragraphs of the text were organized by object or by aspect. The author argued that an organization by aspect contains several thematic ruptures in which the object is changed, so this type of organization could hamper text coherence, and the opposite must hold for the object organization. Results showed an interaction between order and prior knowledge : low knowledge readers recalled more information from the object organization while high knowledge recalled more information from the aspect organization. This result mimics the effect of knowledge and coherence, and could be explained in a similar way (McNamara et al., 1996; McNamara & Kintsch, 1996). Furthermore, reading strategies in hypertext can determine the amount of information a reader accesses from a particular text. For example, a reader following a strategy consisting of selecting the most interesting nodes could stop reading when he / she already read all the paragraphs considered interesting. In most of the experiments on hypertext comprehension it is the participant who decides when he / she has finished reading. As already stated, reading strategies can affect both the amount of information obtained and reading order. These two features of the text can have different effects on the text representation build by the reader. Specifically, we propose that the amount of information read by a given reader affects the textbase and that the order followed influences the situation model. The textbase representation consists of information derived from the original text. This representation would be richer as a reader reads more portions of the text, that is, visited more different nodes. Some experimental evidence supports this prediction. Lawless and Kulikowich (1996) distinguished among groups of readers in a hypertext according to the number of different nodes accessed between other measures. These groups differed on the score of textbased questions. The situation model consists of information both from the text and from the prior knowledge of the reader. During text processing the reader has to construct this representation by finding the appropriate place to connect each new piece of information with the knowledge structure acquired so far. The process of integrating the information on a coherent representation could be affected by the reading order of the information. For example, if a particular idea is Reading Strategies and Hypertext 8 stated in node A and a conclusion derived from that is described in node D, the connection of both statements would require extra processing (e.g. in the form of bridging inferences) as the information (nodes) read between them increases (Kintsch & van Dijk, 1978). Some experimental results partially support this hypothesis (Foltz, 1996). This author analyzed the reading order of the participants in a hypertext comprehension experiment, considering the coherence between the contents of the nodes transited. A transition between two nodes was considered coherent if both nodes were connected in the macrostructure of the text. The number of coherent transitions correlated with the number of important ideas included in an essay assessing the comprehension of the text. Therefore, we hypothesize that the number of nodes accessed influences mainly the construction of the textbase, whereas the transitions between nodes is critical for the construction of the situation model. We tested these predictions in an experiment in which participants had to read a hypertext and perform some tasks testing both textbase and situation model comprehension. Data on participants’ reading behavior was used a posteriori to analyze their comprehension scores. Experiment 1 The experimental hypotheses were as follows: (1). An increase in the amount of information read in a hypertext facilitates the construction of the textbase, as assessed by textbased questions; (2). Different orders in reading the sections in a hypertext are associated with differences in the construction of the situation model, as assessed by inference questions and a cued association task. Method Participants Forty-one University of Colorado undergraduates participated for class credit. Materials Hypertext. An expository text on atmosphere pollution was adapted to a hypertext containing 24 nodes and 3,855 words. It consisted of three main sections with three levels of depth. The text readability was as follows: Flesh Reading Ease = 33.9; Flesh-Kincaid Grade Reading Strategies and Hypertext 9 level = 12. We constructed an overview presenting the hierarchy of contents that followed the paragraphing of the original text (see Figure 1). Participants must access the nodes clicking on the titles provided on the overview. Once they read a node, they must return to the overview in order to decide what to read next. The hypertext and the rest of materials were implemented using HyperCard (R) and were run in Apple Macintosh (R) computers. ---Insert Figure 1 about here ---Coherence between nodes. Coherence between nodes was analyzed using Latent Semantic Analysis (LSA) with the General Reading Space (available at the URL of the LSA group at the University of Colorado, http://lsa.colorado.edu) that incorporates expository texts from high school textbooks up to the first year of college. The text of all the nodes was analyzed with the matrix analysis contrast (document to document comparison) that compares the contents of each node with every other. LSA cosines provide a measure of the degree of argument overlap between texts that is assumed to reflect the level of coherence between them (Foltz, Kintsch & Landauer, 1998). The rationale for this approach is that frequently when two propositions are in fact related semantically, there exists a shared argument between them (Kintsch, 1992). LSA cosines were used here to explain possible differences between reading orders in comprehension outcomes. Prior knowledge questions. Participants were given a pretest of eight true/false questions to determine individual differences in domain knowledge previous to the reading phase. Half of the questions were true and the other half false. An example of this type of question was: The Montreal Protocol is accepted by nations agreeing to restrict the release of ozone depleting chemicals. (True) Text-based questions. We constructed a test consisting of 22 true/false questions for which the question and the answer appeared in a single node and did not require to infer information. Each question referred to the contents of a different node. Half of the questions were true and the other half false. An example of a text-based question was: Reading Strategies and Hypertext 10 The two layers of the atmosphere closest to the earth's surface are critical in regulating earth climate. (True) The answer to this question appeared in the following paragraph of a node: The atmosphere consists of a relatively narrow shell of air encircling the earth that supports animal and plant life. Human activity specially affects the two layers of the atmosphere closest to the earth's surface: the troposphere which extends from the surface to about 12 miles, and the stratosphere, which extends from 12 miles up to approximately 30 miles. These portions of the atmosphere are critical in regulating our climate. Cued association task. Participants were given a list of the 24 most important concepts in the text and were instructed to write down the three concepts that first came to mind after reading each concept on the list. Each response that contained a concept from the original list was computed. If the response was written first, it received a value of 1; if second, 0.66; and if third, 0.33. A PhD in Atmospheric Science of the National Center for Atmospheric Research provided expert ratings after reading the original text. We used these scores in order to compare the participants’ solution with an expert one. The final score was obtained by calculating the proportion of each participant’s links that were also present in the expert matrix. These scores were obtained by adding up the link strength values for only those connections in the participant matrix that were also included in the expert matrix, and dividing the result by the sum of all links of each participant matrix. The cued association task has been developed in the framework of the C-I model of text comprehension, and it has been validated to asses situation model comprehension (Ferstl & Kintsch, 1999). The C-I model assumes that during reading a text a reader forms a text representation network of the contents. Then the response pattern on the cued association task is assumed to correspond to the activation pattern on this network after probing with a concept cue. Inference questions. We created 10 true/false inference questions that required the participant to relate information contained in at least two different nodes. Thus this task was Reading Strategies and Hypertext 11 also intended to assess situation model comprehension. Half of the questions were true and the other half false. An example of an inference question was: While the ozone in the higher and lower levels of the atmosphere is chemically identical, its environmental effects differ greatly. (True) To answer this question participants had to relate information contained in three different nodes: (1) Ozone is a naturally occurring gas molecule containing three atoms of oxygen. It is mainly found in two parts of the atmosphere: most (about 90%) resides in the upper atmosphere or stratosphere, where it forms the stratospheric ozone layer ; the remaining ozone, referred to as ground level ozone or tropospheric ozone, is present in the lower region of the atmosphere. (2) A range of negative environmental and human health impacts associated with ozone depletion can be identified, although their exact nature is difficult to quantify. Known effects include increased incidence of skin cancers and eye disorders (e.g. cataracts), damage to the immune system and adverse effects on plant development and phytoplankton growth. (3) Observed effects of ground level ozone on human health include irritation of the eyes and air passages, damage to the mechanisms that protect the human respiratory tract and for some asthma sufferers, increased sensitivity of the airways to allergic triggers. Procedure First participants went through a pre-test of eight true/false questions assessing their domain knowledge. They were then instructed on how to use the hypertext. After that, they were required to read the contents during 20 minutes. The instructions stressed that they had to read the text carefully in order to answer a series of questions after the time was concluded. At this point, participants had to perform a cued association task. Finally, participants had to answer 22 true/false text-based questions and 10 true/false inference questions mixed randomly. Design Reading Strategies and Hypertext 12 We used a quasi-experimental design with reading order (see bellow) and different nodes accessed as independent variables, and the scores on text-based questions, cued association and inference questions as dependent variables. Results Analysis of amount of information read For all experiments, differences declared as significant have p < .05. The first hypothesis stated that an increase in node access would facilitate the construction of the textbase. We performed a regression analysis with the number of different nodes accessed as independent variable, and the score on the text-based questions as dependent variable. Results showed that node access significantly predicted the score on the text-based questions, R = 0.11, F(1, 39) = 4.85. As node access increased, so did text-based scores. Follow up analysis were made in order to explore a possible influence of prior knowledge on this effect. Participants were divided in two groups based on a mean split of their prior knowledge scores. 18 participants were included in the low knowledge group, M = 3.17, SD = 1.03, and 23 in the high knowledge, M = 6.33, SD = 1.24. Regression analysis for each group revealed that the effect of node access was significant for low knowledge readers, R = 0.25, F(1, 21) = 6.98, but not for high knowledge, F < 1. In addition, we also expected that node access would not predict comprehension at the situation model level. Supporting the null hypothesis, none of the analyses showed significant results either for the cued association scores, R = 0.03, F(1, 39) = 2.34, p < .15., or for the inference questions, R = 0.06, F(1, 39) = 2.83, p < .15. Furthermore, no significant differences were found when the analysis were performed for each group of prior knowledge. Analysis of reading order A look at the node-transition matrixes reveled at least two main reading orders: order 1 corresponding to participants following the map of contents in a linear fashion and order 2 corresponding to participants following a top down path, starting visiting the highest nodes of the hierarchy and continuing to the lowest levels. We constructed two theoretical matrixes representing both orders, and correlated them with the node-transition matrixes of all Reading Strategies and Hypertext 13 participants. Participants’ matrixes with a correlation higher to the 75% percentile were grouped into the corresponding order. Participants’ matrixes with a lower correlation were grouped under a third order, which included participants that followed a combination of order 1 and 2, and those that read the contents in a different order. Participants were distributed as follow: order 1, 13 participants; order 2, 11; and order 3, 17. Hypothesis 2 predicted that participants following different reading orders would differ in comprehension at the situation model level. We performed an ANOVA with reading order as independent variable, and cued association scores as dependent variable. Results showed a main effect of reading order, F(2, 38) = 7.81, MSE = 0.02. Participants following the order 1 had better cued association scores (M = 0.48, SD = 0.16) than those of the order 2 (M = 0.35, SD = 0.1) and order 3 (M = 0.29, SD = 0.11). Similar results were found with inference questions as dependent variable, F(2, 38) = 4.15, MSE = 266.24. Participants following the order 1 successfully answered more inference questions (M = 83.5% correct, SD = 11.4) than those of the order 2 (M = 71.4%, SD = 19.2) and order 3 (M = 66.4%, SD = 17.4). In order to account for possible influences of prior knowledge in the effects found, we performed two ANOVAs including prior knowledge as covariate. In both cases (cued association and inference questions scores) the differences between group 1 and groups 2 and 3 remained significant. In addition, we expected that reading order would not differ on the text-based questions scores. Supporting the null hypothesis, no differences were found between the order on the textbased questions scores, F(2, 38) = 1.38, MSE = 268.4, p < .3. In order to explain the differences found between reading orders we compared the different groups on different dependent variables: level of prior knowledge , nodes accessed and the coherence of the transitions (measured as the mean LSA cosine of all the transitions). Participants of the three order groups did not differ in prior knowledge, F < 1. However, they differed on the nodes accessed, F(2, 38) = 4.76, MSE = 5.54. Participants in the group 1 accessed more different nodes (M = 24.07, SD = 1.18) than those in the group 3 (M = 21.41, SD = 2.62), and both of them were not different from the group 2 (M = 22.81, SD = 2.89). In addition, reading order groups differed on the coherence of their transitions, F(2, 38) = 19.77, Reading Strategies and Hypertext 14 MSE = 0.01. Participants of the order 1 followed a more coherent path (mean cosine, M = 0.5, SD = 0.01) than those of the order 2 (M = 0.44, SD = 0.02) and order 3 (M = 0.45, SD = 0.03). Discussion The results of experiment 1 support the hypothesis that the amount of information accessed and the reading order influence the reader’s comprehension level in two different ways. First, the different number of nodes accessed predicts scores on text-based questions for low knowledge readers. Participants that read more different texts form a better textbase of the contents. Although this result can be seen as an obvious statement, it is relevant for the literature on hypertext comprehension, since in most experiments on hypertext comprehension is the participant who decides when he / she has finished reading the contents. The results also show that this effect is influenced by prior knowledge: low knowledge readers learn more by reading more nodes, whereas high knowledge are not affected by it. A possible explanation for this effect is that high knowledge readers could use their prior knowledge to try to fill in gaps in the information presented in the nodes not read. For that reason, the loss of relevant information for the textbase due to a incomplete reading of the materials is lower for high knowledge than for low knowledge readers. Second, the groups based on the reading order of the materials differ on two situation model tasks (cued association and inference questions). Differences due to the reading order seem to rely on two different variables: nodes accessed and coherence between node transitions. On the one hand, the better learning of the reading order group 1 compared to the group 3 seem to be influenced by the nodes accessed (higher number for group 1 than for group 3). This result suggests that in order to construct an appropriate situation model a minimum number of nodes must be read. On the other hand, providing that a similar number of nodes are read (i.e. group 1 versus group 2), differences on the learning outcome seem to be related to the coherence between node transitions (Foltz, 1996). Participants that read the contents in a high coherent order form a better situation model of the text. This effect can be explained by the fact that transitions between two paragraphs that do not share arguments (coherence) will require extra processing (e.g. in the form of bridging inferences) in order to maintain the coherence of the text Reading Strategies and Hypertext 15 representation (Kintsch & van Dijk, 1978). Although these results of reading order seem to be independent of the prior knowledge of the reader, the method used (analysis of covariance) and the limited number of participants per reading order group prevent us from making any strong conclusion. For that reason, in experiment 2 the role of prior knowledge and reading order will be addressed in more detail. Since most of the previous works in the literature have not controlled for these effects, they can be considered as a possible factor affecting the confusing state of the literature on hypertext comprehension. If these effects are not controlled, comprehension outcomes for a condition could depend on the particular distribution of participants following the different strategies. Since reading strategies can influence comprehension by leading the reader to read a particular text, it can be expected that a failure to control its influence could particularly mask those expected effects related to text characteristics. For example, Foltz (1996) designed an experiment with two conditions, one intended to provide high text coherence by including extra information for understanding the contents of a node when a non-coherent transition was made, and another without such a help. Contrary to expectations, there were no comprehension differences between conditions. This result could be explained due to the fact that both groups of participants followed similar reading orders that lead them to read an alike text in terms of coherence (Foltz, 1996, p. 128). A similar problem could arise while attempting to replicate the effect of knowledge and coherence, not thus far replicated in hypertext comprehension: low knowledge readers form a better situation model from a coherent text than from an incoherent one, whereas high knowledge readers learn more from an incoherent one (McNamara et al., 1996; McNamara & Kintsch, 1996). In order to replicate this effect, a traditional experiment would present two different overviews trying to promote low and high coherence. But since in each condition participants could follow different reading orders, the path followed could affect comprehension independently of the overview used (e.g. Foltz, 1996). Therefore, we propose that the effect of knowledge and coherence could be replicated in hypertext if participants follow a low and a high coherence reading order. Reading Strategies and Hypertext 16 This approach was tested in experiment 2, in which we tried to replicate the effect of knowledge and coherence not thus far replicated in hypertext. In a pilot study we provided an overview in which the titles of the contents were distributed in a 6 x 4 array. We found that 17 out of 37 participants followed a strategy consisting on reading the contents from the first row from left to right and continuing next row down. Then, we decided to construct two overviews which organization provided low and high coherence respectively (in terms of reading order) if a reader followed the left-right strategy. By doing that we expected to show that learning differences could be found between reading orders, but not necessarily between overviews. Therefore, we expected that the effect of knowledge and coherence would appear when comparing participants following a low and a high coherence transition order, but not when comparing the overviews. Experiment 2 The hypotheses were as follows: (1). Participants with high domain knowledge will construct a better situation model (assessed by inference questions and a cued association task) when following a strategy that lead to a low coherence order than when following a strategy that lead to a high coherence order; (2). Participants with low domain knowledge will construct a better situation model when following a strategy that lead to a high coherence order than when following a strategy that lead to a low coherence order. Method Participants Eighty-two University of Colorado undergraduates participated for class credit. Materials Hypertext. We used the same hypertext presented in experiment 1 except for the overview provided. Two different overviews were created, in which nodes were arranged in a 6 x 4 array. Coherence between contents was assessed using LSA as in experiment 1. In one overview nodes were arranged in a manner that provided the lowest coherence between transitions, when reading from left to right and from top to down. This was done by arranging the nodes in an order in which the sum of LSA cosines between nodes was the lowest possible. Reading Strategies and Hypertext 17 In a second overview nodes were arranged for providing the highest coherence between transitions, when reading from left to right and from top to down. This was done by arranging the nodes in the order in which the sum of LSA cosines between nodes was the highest possible. Comprehension tasks were the same as used in experiment 1. Procedure Procedure was identical to that of experiment 1, except for the reading phase. Since the effect of knowledge and coherence is mainly related to situation model comprehension, we tried to control the effect of the variable nodes accessed on the textbase found in experiment 1 using a different procedure. Specifically, in experiment 2 participants had to read all the contents without time limit. Participants were not able to reread nodes. Design We used a 2 x 2 between groups design with prior knowledge (low and high), and overview (low and high coherence) as independent variables. The two levels of prior knowledge were defined according to the mean split of the answers to the eight true/false questions about the participants’ domain knowledge. The mean score was 5.62, SD = 1.23. Participants with scores below the mean were classified as low knowledge (n = 39, M = 4.51, SD = 0.64) and those above as high knowledge (n = 43, M = 6.63, SD = 0.79). We also used reading order (low and high coherence) as a quasi-experimental variable. For that purpose we analyzed the coherence of the reading sequence as in experiment 1 (mean LSA cosine of all the transitions). We used the extreme tiers for the coherence values, the lower boundary being the 40th percentile (cosine = 0.38), M = 0.32, SD = 0.03, and the higher being the 60th percentile (cosine = 0.41), M = 0.47, SD = 0.03. Therefore, the distribution of participants by prior knowledge and reading order was as follows: low knowledge low coherence 21 participants; low knowledge high coherence 16; high knowledge low coherence 11; and high knowledge high coherence 19. The dependent variables were scores on the textbased and inference questions and on the cued association task. Results Reading Strategies and Hypertext 18 In order to show the consequences of not considering the reading order we performed two ANOVAs. First, we conducted an ANOVA with prior knowledge (low and high), and overview (low and high coherence) as independent variables, and cued association scores as dependent variable. There were no significant differences (F(1, 78) = 2, MSE = 0.02, p < .2 for the interaction). The same null results were found for the dependent variable inference questions scores (F(1, 78) = 2.18, MSE = 393.7, p < .15 for the interaction). Therefore, in agreement with previous research, the effect of knowledge and coherence did not appear when considering all participants without taking into account the reading order. Second, we performed another two ANOVAs with reading order (low and high coherence) instead of overview. In this case, the interaction for cued association scores was significant, F(1, 63) = 8.38, MSE = 0.02. Participants with low knowledge performed better on the cued association task when following a strategy leading to high coherence (M = 0.4, SD = 0.17) than when following a low coherence one (M = 0.28, SD = 0.09); whereas the opposite was found for participants with high knowledge (M = 0.29, SD = 0.11 and M = 0.39, SD = 0.17 respectively). Simple effects were analyzed for prior knowledge (low and high). Results showed a significant difference for low knowledge participants, F(1, 63) = 7.45, MSE = 0.02 and a close to significant difference for high knowledge, F(1, 63) = 3.35, MSE = 0.02, p = 0.07. Similar results were obtained with inference questions scores as dependent variable. Only the interaction was significant, F(1, 63) = 7.21, MSE = 2.49. Participants with low knowledge scored higher when following a strategy leading to high coherence (M = 67.2% correct, SD = 15.7) than when following the a coherence one (M = 55.4%, SD = 17.5); whereas the opposite was found for participants with high knowledge (M = 53.3%, SD = 24.2 and M = 68.2%, SD = 20.4 respectively). Simple effects analysis for prior knowledge revealed that these differences were close to significant for low knowledge readers, F(1, 63) = 3.26, MSE = 2.49, p = 0.07, and significant for high knowledge, F(1, 63) = 3.96, MSE = 2.49. Since in experiment 2 participants could decide the time spent reading the text, the influence of reading time was assessed. First, an ANOVA showed no effect for prior knowledge Reading Strategies and Hypertext 19 nor for reading order, (F < 1 for the interaction). Second, correlation analysis showed no significant relations between reading time and any of the comprehension variables. Finally, although it was not considered in our hypotheses, we also run an ANOVA with prior knowledge (low and high), and reading order (low and high coherence) as independent variables, and text-based scores as dependent variable. The objective was to replicate the effect found in experiment 1 showing that the reading order does not affect the construction of the textbase. Supporting this idea, neither the main effect of reading order nor the interaction were significant (F < 1 in both cases). There was only a close to significant effect of prior knowledge, F(1, 63) = 3.29, MSE = 5.43, p = 0.07. Participants with low knowledge scored lower than high knowledge (M = 61.5% correct, SD = 10.4 and M = 66.8%, SD = 12.7 respectively). Discussion The results of experiment 2 show that the effect of knowledge and coherence are replicated in hypertext on those participants following a particular strategy that lead them to read the contents in a low or high coherent order. Participants with low knowledge benefit more at the situation model level when reading the contents in a high coherence order, whereas participants with high knowledge learn more from a low coherence order (McNamara et al., 1996; McNamara & Kintsch, 1996; Schnotz, 1982). Results show that this effect is not related to the reading time of the materials. Moreover, when considering only the experimental conditions manipulated by the experimenter (type of overview) this effect disappears, masked by the joint effect of the different reading strategies followed by participants. Therefore, it can not be expected that an effect on hypertext comprehension would hold for all participants of a condition, but only for those participants following a particular strategy that allows the experimental manipulations to become effective (in this case high and low coherence due to the reading order). In addition, data of the text-based questions show that the reading order does not affect the textbase construction. Participants following a low or high coherent order do not differ on the text-based scores, although they do on two situation model measures. This result supports Reading Strategies and Hypertext 20 the effect found in experiment 1, showing that text-based scores are positively related to the number of different nodes read, but not to the reading order. General discussion The two experiments reported here reveal that reading order and amount of information read have distinctive effects on the representation of the text that readers form when reading a hypertext. While the amount of information read influences mainly the construction of the textbase, the reading order influences the construction of the situation model. In addition, these results stress the importance of text coherence as a feature derived from the reading order (Foltz, 1996), and its different effect on comprehension depending on the domain knowledge of the reader (McNamara et al., 1996; McNamara & Kintsch, 1996). Differences were found between low knowledge readers following a strategy that lead them to read the text in a coherent order, and high knowledge readers following a strategy that lead them to read the text in an incoherent order. Considering that previous research has paid little attention to these effects, it could be affirmed that a failure to control for these effects could be one of the possible reasons for the inconsistent results found in the literature. As shown in experiment 2, comprehension effects due to text characteristics only appear after the reading order is considered. Therefore, an important issue that needs to be addressed is how to control these effects in hypertext comprehension experiments. The effect of amount of information accessed could be easily controlled by forcing the participants to read all the paragraphs of the experimental text, as done in experiment 2. But the effect of the reading order is hard to control since freedom of choosing a reading order is the very essence of reading a hypertext. A possible solution could consist of using appropriate criteria for the comparison of different reading orders. In the present work, the coherence between nodes assessed by LSA was revealed as an important variable affecting comprehension (Foltz, Kintsch & Landauer, 1998). Therefore, researchers could consider this variable as a possible comparison criterion between reading orders. In the present work we assessed the effects on comprehension of reading strategies due to their influence on the final text read by the reader in terms of amount of information read and Reading Strategies and Hypertext 21 reading order. In that sense, these effects can be considered bottom-up. However, readingstrategies could also influence comprehension in a top-down fashion (e.g. Magliano et al.,1999). Therefore, researchers need to consider both the different types of strategies that readersfollow while reading a hypertext, and their different effects on comprehension. Previous worksin the literature on text comprehension in linear text could be a possible starting point for thatpurpose. When reading a linear text a reader can move through the different sections of the text,e.g. for revisiting information previously read. Goldman and Saul (1990) have proposed theStrategy Competition Model, that states that readers progress through a text trying to establishglobal discourse coherence. If at one point the reader detects a gap in his / her comprehension ofthe contents, he / she would move through the text looking for the necessary information inorder to fill this gap. Therefore, it is important to consider in further research if readers of ahypertext use text coherence as a rule for selecting what node to read next. While the results ofthe first experiment presented here stress the importance of text coherence in hypertextcomprehension, they cannot be considered as an strong evidence for this hypothesis. Instead,participants seemed to rely on the overview to read the contents, so in this case coherence couldbe considered as an indirect consequence of their reading strategy. Further research will berequired to fully understand the effects of reading strategies on hypertext comprehension.ReferencesBarab, S. A., Bowdish, B. E., & Lawless, K. A. (1997). Hypermedia navigation: profilesof hypermedia users. Educational Technology Research and Development, 45, 23-42.Barab, S. A., Bowdish, B. E., Young, M. F, & Owen, S. V. (1996). Understanding kiosknavigation: using log files to capture hypermedia searches. Instructional Science, 24, 377-395. Barab, S. A., Fajen, B. R., Kulikowich, J. M. & Young, M. F. (1996). Assessinghypertext navigation through Pathfinder: prospects and limitations. Journal of EducationalComputing Research, 15, 185-205.Barab, S. A., Young, M. F., & Wang, J. (1999). The effects of navigational andgenerative activities in hypertext learning on problem solving and comprehension. InternationalJournal of Instructional Media, 26, 283-309. Reading Strategies and Hypertext 22 Brinkerhoff, J. D., Klein, J. D., & Koroghlanian, C. M. (2001). Effects of overviews andcomputer experience on learning from hypertext. Journal of Educational Computing Research,25, 427-440.Britt, M.A., Rouet, J.-F., & Perfetti, C.A. (1996). Using hypertext to study and reasonabout historical evidence. In J.-F. Rouet, J. J. Levonen, A. Dillon & R. J. Spiro (Eds.),Hypertext and cognition (pp. 43-72). Mahwah, NJ: Lawrence Erlbaum Associates.Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, R., & Glaser, R. (1989). Self-explanation: How students study and use examples in learning to solve problems. CognitiveScience, 13, 145-182.Chi, M. T. H., De Leeuw, M., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477.Danner, F. W. (1976). Children's understanding of intersentence organization in therecall of short descriptive passages. Journal of Educational Psychology, 68, 174-183De Jong, T., & Van der Hulst, A. (2002). The effects of graphical overviews onknowledge acquisition in hypertext. Journal of Computer Assisted Learning, 18, 219-231.Dillon, A., & Gabbard, R. (1998). Hypermedia as an educational technology: A reviewof the quantitative research literature on learner comprehension, control, and style. Review ofEducational Research, 68, 322-349.Ferstl, E. C., & Kintsch, W. (1999). Learning from text: Structural knowledgeassessment in the study of discourse comprehension. In H. Van Oostendorp & S. R. Goldman(Eds.), The construction of mental representations during reading (pp. 247-277). Mahwah, NJ:Lawrence Erlbaum Associates. Foltz, P. W. (1996). Comprehension, coherence, and strategies in hypertext and lineartext. In J.-F. Rouet, J. J. Levonen, A. Dillon & R. J. Spiro (Eds.), Hypertext and cognition (pp.109-136). Mahwah, NJ: Lawrence Erlbaum Associates.Foltz, P. W., Kintsch, W., & Landauer, T. K. (1998). The measurement of textualcoherence with Latent Semantic Analysis. Discourse Processes, 25, 285-307. Reading Strategies and Hypertext 23 Goldman, S. R., Saul, E., & Coté, N. (1995). Paragraphing, reader, and task effects ondiscourse comprehension. Discourse Processes, 20, 273-305.Goldman, S. R., & Saul, E. (1990). Flexibility in text processing: a strategy competitionmodel. Learning and Individual Differences, 2, 181-219.Hofman, R., & Van Oostendorp, H. (1999). Cognitive effects of a structural overview ina hypertext. British Journal of Educational Technology, 30, 129-140.Horney, M. A., & Anderson-Inman, L. (1994). The electrotext project: hypertextreading patterns of middle school students. Journal of Educational Multimedia andHypermedia, 3, 71-91.Kintsch, W. (1988). The role of knowledge in discourse comprehension: Aconstruction-integration model. Psychological Review, 95, 163-182.Kintsch, W. (1992). How readers construct situation models for stories: The role ofsyntactic cues and causal inferences. In A. F. Healy, S. N. Kosslyn & R. M. Shiffrin (Eds.),From learning processes to cognitive processes: Essays in honor of William K. Estes (pp. 216-278). Hillsdale, N.J.: Lawrence Erlbaum Associates.Kintsch, W. (1998). Comprehension: A paradigm for cognition. New York: CambridgeUniversity Press.Kintsch, W., & van Dijk, T. A. (1978). Toward a model of text comprehension andproduction. Psychological Review, 85, 363-394.Kintsch, W., & Yarbrough, J. C. (1982). The role of rhetorical structure in textcomprehension. Journal of Educational Psychology, 74, 828-834.Lawless, K. A., & Kulikowich, J. M. (1996). Understanding hypertext navigation through cluster analysis. Journal of Educational Computing Research, 14, 385-399.Lawless, K. A., & Kulikowich, J. M. (1998). Domain knowledge, interest, andhypertext navigation: a study of individual differences. Journal of Educational Multimedia andHypermedia, 7, 51-70.Lawless, K. A., Mills, R., & Brown, S. W. (2002). Children’s hypermedia navigationalstrategies. Journal of Research on Computing in Education, 34, 274-284. Reading Strategies and Hypertext 24 Lodewijks, H. (1982). Self-regulated versus teacher-provided sequencing of informationin learning from text. In A. Flammer & W. Kintsch (Eds.), Discourse processing (pp. 509-520).Amsterdam: North-Holland.Lorch, R. F., Jr. (1989). Text-signaling devices and their effects on reading and memoryprocesses. Educational Psychology Review, 1, 209-234.Magliano, J. P., Trabasso, T. & Graesser, A. C. (1999). Strategic processing duringcomprehension. Journal of Educational Psychology, 91, 615-629.Mayer, R. E. (1976). Some conditions of meaningful learning for computerprogramming: advance organizers and subject control of frame order. Journal of EducationalPsychology, 68, 143-150.McNamara, D. S., Kintsch, E., Songer, N., & Kintsch, W. (1996). Are good textsalways better? Interaction of text coherence, background knowledge, and levels ofunderstanding in learning from text. Cognition and Instruction, 14, 1-42.McNamara, D. S., & Kintsch, W. (1996). Learning from text: effect of prior knowledgeand text coherence. Discourse Processes, 22, 247-288.McNamara, D. S., & Scott, J. L. (1999). Training reading strategies. Proceedings of theTwenty-first Annual Meeting of the Cognitive Science Society (pp. 387-392). Hillsdale, NJ:Lawrence Erlbaum Associates.Moeller, J., & Mueller-Kalthoff, T. (2000). Lernen mit hypertext: effekte vonnavigationshilfen und vorwissen [Learning with hypertext: the impact of navigational aids andprior knowledge]. Zeitschrift für Pädagogische Psychologie, 14, 116-123.Moravcsik, J. E., & Kintsch, W. (1993). Writing quality, reading skills, and domain knowledge as factors in text comprehension. Canadian Journal of Experimental Psychology,47, 360-374.Mueller-Kalthoff, T., & Moeller, J. (2003). The effects of graphical overviews, priorknowledge, and self-concept on hypertext disorientation and learning achievement. Journal ofEducational Multimedia and Hypermedia, 12, 117-134. Reading Strategies and Hypertext 25 Naumann, A., Waniek, J. & Krems, J. F. (2001). Knowledge acquisition, navigation andeye movements from text and hypertext. In U. D. Reips & M. Bosnjak (Eds.), Dimensions ofInternet Science (pp. 293-304). Lengerich, Germany: Pabst.Niederhauser, D. S., Reynolds, R. E., Salmen, D. J., & Skolmoski, P. (2000). Theinfluence of cognitive load on learning from hypertext. Journal of Educational ComputingResearch, 23, 237-255.Potelle, H., & Rouet, J.-F. (2003). Effects of content representation and readers’ priorknowledge on the comprehension of hypertext. International Journal of Human-ComputerStudies, 58, 327-345.Pressley, M., Symons, S., McDaniel, M. A., Snyder, B. L., & Turnure, J. E. (1988).Elaborative interrogation facilitates acquisition of confusing facts. Journal of EducationalPsychology, 80, 268-278.Puntambekar, S., Stylianou, A., & Hübscher, R. (2003). Improving navigation andlearning in hypertext environments with navigable concept maps. Human-Computer Interaction,18, 395-428.Quathamer, D., & Heineken, E. (2002). Kohärenzbildung beim lesen von texten: fishye-views als kognitive werkzeuge [Maintaining global coherence during reading: fisheye views ascognitive tools]. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 34,72-79.Rouet, J.-L., Favart, M., Britt, M. A. & Perfetti, C. A. (1997). Studying and usingmultiple documents in history: Effects of discipline expertise. Cognition & Instruction, 15, 85-106. Schnotz, W. (1982). How do different readers learn with different text organizations? InA. Flammer & W. Kintsch (Eds.), Discourse processing (pp. 87-97). Amsterdam: North-Holland.Schnotz, W. (1984). Comparative instructional text organization. H. Mandl, N. L. Stein,& T. Trabasso (Eds.), Learning and comprehension of text (pp. 53-81). Hillsdale, N. J.:Lawrence Erlbaum Associates. Reading Strategies and Hypertext 26 Schnotz, W. (1993). Adaptive construction of mental representations in understandingexpository texts. Contemporary Educational Psychology, 18, 114-120.Shapiro, A. M. (1998). Promoting active learning: The role of system structure inlearning from hypertext. Human-Computer Interaction, 13, 1-35.Shapiro, A. M. (1999). The relationship between prior knowledge and interactiveoverviews during hypermedia-aided learning. Journal of Educational Computing Research, 20,143-167.Shapiro, A. M. (2000). The effect of interactive overviews on the development ofconceptual structure in novices learning from hypermedia. Journal of Educational Multimediaand Hypermedia, 9, 57-78.Snapp, J. C., & Glover, J. A. (1990). Advance organizers and study questions. Journalof Educational Research, 83, 266-271.Trabasso, T., & Magliano, J. P. (1996). Conscious understanding during textcomprehension. Discourse Processes, 21, 255-288.Unz, D. C., & Hesse, F. W. (1999). The use of hypertext for learning. Journal ofEducational Computing Research, 20, 279-295.Van Dijk, T. A., & Kintsch, W. (1983). Strategies of discourse comprehension. NewYork: Academic Press.Wagner, R. K. & Sternberg, R. J. (1987). Executive control in reading comprehension.In B. K. Britton & S. M. Glynn (Eds.), Executive control processes in reading. Hillsdale, NJ:Lawrence Erlbaum Associates.Waniek, J., Brunstein, A., Naumann, A., & Krems, J. F. (2003). Interaction between text structure representation and situation model in hypertext reading. Swiss Journal ofPsychology, 62, 103-111. Reading Strategies and Hypertext 27 Author NotesThis research was funded by research grants from the Fulbright Commission Spain andthe Spanish Ministry of Education and Culture to the first author. We thank John R. Surber,John Dunlosky and two anonymous reviewers for insightful comments on an early version ofthe manuscript; and Gabriele Petron for her help in elaborating the materials. Reading Strategies and Hypertext 28 Table 1. Reported effects of structured overviews in comprehension, by prior knowledge (lowand high) and mental representation (textbase and situation model). A plus sign means apositive effect of structured overview, a minus sign means a negative effect, and a equals signmeans a null effect. (1) Hofman & van Oostendorp (1999) found a null effect for both textbaseand situation model questions tapping the macrostructure level (i.e. main ideas), and a negativeeffect for situation model questions at the microstructure level (i.e. local ideas). (2) Potelle &Rouet (2003) found a positive effect for questions focusing at the macrostructure level and anull effect for questions focusing at the microstructure level. Low knowledgeHigh knowledgeTextbase Sit. model Textbase Sit. modelBrinkerhoff, Klein & Koroghlanian, 2001=De Jong & van der Hulst, 2002=+Hofman & van Oostendorp, 1999= / = / = (1) ==Moeller & Mueller-Kalthoff, 2000+===Mueller-Kalthoff & Moeller, 2003====Naumann, Waniek & Krems, 2001-Potelle & Rouet, 2003= / + = / + (2) ==Puntambekar, Stylianou & Hübscher, 2003=+Quathamer & Heineken, 2002-Shapiro, 1998=-Shapiro, 1999=+==Shapiro, 2000+Waniek et al., 2003Reading Strategies and Hypertext 29 Figure 1. Overview used in experiment 1.