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10/13/2003 Learning from Text: Facilitating and Enhancing Comprehension Danielle S. McNamara, Rachel Best & Corina Castellano | |
| Correspondence should be addressed to Danielle S. McNamara, Department of Psychology, The University of Memphis, e-mail d.mcnamara@mail.psyc.memphis.edu Abstract: The students’ability to comprehend challenging textbooks typically used in classrooms is questionable -- particularly those covering scientific material. McNamara’s research addresses this problem by investigating the effects of manipulating text structure and providing reading strategy interventions. The goal is to find real-world solutions to help students better understand difficult text. The first solution is to provide relatively cohesive texts, matching as best we can the reader to the text. The second solution is to provide students with reading strategy training that focuses on reading the text actively, attempting to explain the text (while reading) and making text and knowledge-based inferences to support those explanations. This paper discusses these interventions and technology, developed by McNamara and colleagues, designed to evaluate text cohesion and teach active reading strategies. Learning from text: Understanding and learning from written materials is among the most important skills to possess in modern society. The importance of understanding text ranges from being able to decipher the ''three easy steps’ for setting up your computer to understanding the ever-dreaded physiology textbook. Indeed, the ability to comprehend challenging textbooks is one of the most important keys to academic and professional success. However, many students are poor readers, or have difficulty understanding informational texts (Bowen, 1999). In sum, students’ ability to comprehend the challenging textbooks used in classrooms is questionable, particularly when the textbooks involve scientific material (Bowen, 1999; Snow, 2002). This review examines ways in which we can facilitate and enhance reading comprehension. It is based on the premise that reading comprehension can be improved under particular conditions, such as when the reading environment is adapted to the needs of the reader. Of key relevance to this approach is the notion of the "Zone of Proximal Development," (ZPD) which forms a part of Vygotsky’s theory (Vygotsky, 1978). The ZPD refers to the difference between the individual’s capacity to solve problems on their own, and their capacity to solve them with assistance or scaffolding. According to this view, the reading environment can be structured to support comprehension that may not otherwise, under spontaneous reading conditions, have been successful. Importantly, the review takes a constructionist perspective on learning as it advocates the active role of the reader in the comprehension process. According to this view, reading comprehension is facilitated by a combination of what readers bring to the reading situation (e.g., prior knowledge, reading skills and motivation) and the manner in which they interact with the text. Reading contexts are supportive learning environments, particularly when they are adapted to the needs of the reader. For one thing, the way in which texts are written can be modified, so that concepts are presented in a manner and rate that is appropriately suited to the reader’s prior knowledge and aptitudes. Furthermore, readers can be taught techniques for comprehending difficult texts. These take into consideration their reading ability and background knowledge. Moreover, reading contexts have some advantages over oral contexts because the reader can process information at their own pace (e.g., read slowly). The reader also has the option of re-reading, which is likely to be beneficial to the reader's understanding of a text. Interactive Components of Text Comprehension: The recent RAND report on Reading for Understanding (Snow, 2002) documents the pressing need to improve reading comprehension. The RAND report also provides a useful heuristic for conceptualizing reading comprehension, which includes four interactive components: Characteristics of the reader, the text, the comprehension activities, and the sociocultural context (see Figure 1). Accordingly, these four factors rarely operate in isolation and as such, potential interactions between attributes associated with these factors need to be considered to develop a more complete understanding of reading comprehension processes. McNamara’s research in reading comprehension has attempted to discover how the effects of text structure, reader aptitudes, and reading strategies are interdependent. This paper describes two lines of that research. First, we describe the effects of text structure on reading comprehension and how those effects depend on readers’ aptitudes such as prior domain knowledge and reading ability. Second, we describe research regarding the effects of teaching readers to use reading strategies, and how those effects depend on readers’ domain knowledge.  Text Structure A. Text cohesion Text structure plays a crucial role in the ease with which text can be processed, recalled and interpreted. Explicit linguistic elements (e.g., words, features, cues, signals and constituents) and their combinations alter the cohesiveness of a text, making it easier to understand. For example, signaling causal connections, with terms such as because and consequently, help the reader interpret and remember relationships between concepts. Importantly, rewriting poorly written texts in a more cohesive manner provides the reader with information needed for comprehension, which, in turn can aid comprehension (e.g., Beck and McKeown, Sinatra, & Loxterman, 1991; Beyer, 1990; Britton & Gulgoz, 1991; McKeown, Beck, Sinatra, & Loxterman, 1992). Accordingly, one way to help readers comprehend texts is to modify cohesion. This can be done in numerous ways, such as adding low-level information (e.g., identifying anaphoric referents, synonymous terms, connective ties and headers) and supplying background information that was previously unstated in the text. In terms of assessing cohesion, McNamara and colleagues (McNamara, Louwerse & Graesser) are currently building automated measures of text cohesion. Currently the web-based system (called Coh-Metrix) assesses a multitude of text dimensions. This tool will allow readers, writers, editors, educators, researchers, and policy makers to better estimate the appropriateness of a text for their audience, predict comprehension, and pinpoint specific problems with text. Moreover, the Coh-Metrix tool surpasses previous measures of cohesion, such as the Flesch Reading Ease formula and the Flesch-Kincaid Grade Level, which are based on superficial factors such as the number of words in the sentences and the number of letters or syllables per word (i.e., as a reflection of word frequency). B. Reader aptitudes and knowledge Thus far, it has been established that altering text cohesion (i.e., making texts more cohesive), may do well in facilitating comprehension. However, it is important to bear in mind that comprehension does not reside within the text, but rather in the reader. Accordingly, the benefits of text cohesion are related to reader-specific factors (e.g., McNamara, Kintsch, Songer, & Kintsch, 1996), such as prior knowledge of a domain and word specific knowledge. The knowledge a reader constructs about a text is related to what they already know (e.g., Afflerbach, 1986; Chi, Feltovich, & Glaser, 1981; Chiesi, Spilich, & Voss, 1979; Lundeberg, 1987; Means & Voss, 1985). According to the Construction-Integration model of text comprehension (e.g., Kintsch, 1988), readers with more knowledge about the topic are able to form a more coherent situation (mental) model understanding of the text. The situation model perspective is that deeper understanding of the text results from integrating the textbase with prior knowledge. A good textbase understanding relies on a cohesive and well-structured representation of the text. In contrast, a good situation model relies on different processes, primarily on the active use of long-term memory, or world knowledge, during reading.  McNamara’s research has shown that text comprehension is mediated by both text cohesiveness and the reader’s prior knowledge. McNamara et al. (1996) examined the effects of text cohesion and prior knowledge for middle-school students’ comprehension of a science text about heart disease. It was established that text cohesion benefited low-knowledge readers across all measures of comprehension. Figure 2 shows the results for bridging-inference questions (which tapped the situation model understanding) and text-based questions. Low-knowledge readers cannot easily fill in gaps in a low-cohesion text because they do not have the knowledge to generate the necessary inferences. Therefore, they need high-cohesion text to understand and remember the content. In contrast, high-knowledge readers benefited from a low-cohesion text, but only according to the situation model measures of comprehension. Conversely, high-knowledge readers bridge gaps in the text by making knowledge-based inferences. They use information from their world knowledge which, in turn, results in the integration of text information with long-term memory. In sum, for a good situational understanding, a single text cannot be optimal for every reader: Low-knowledge readers benefit more from an easier, cohesive text, whereas high-knowledge readers should be allowed to make their own inferences with a more challenging, less cohesive text (see also, McNamara & Kintsch, 1996). Further evidence for the reversed cohesion effect (comprehension facilitated by low cohesion texts for high-knowledge readers) was found in the McNamara (2001) study. In this study, adult participants read both high and low cohesion versions of a text about cell mitosis. Comprehension was enhanced only for participants who read the low-cohesion version, followed by the high-cohesion version. This result showed that the low-cohesion text induced gap-filling inferences while the participant was reading the text, and it was this on-line active processing that enhanced comprehension. When the reader was exposed to the high-cohesion version first, and thus was not induced to generate their own inferences to bridge the gaps left by low cohesion texts, these benefits did not appear. These results further demonstrated that the amount of material read is not a factor that can explain the reversed cohesion effect. That is, the readers were all exposed to the same information, and thus the same amount of information – only the order of presentation differed. Overall, a particular level of cohesion may lead to a coherent mental representation for one reader, but an incoherent representation for another. While cohesion is generally beneficial, less-skilled, low-knowledge participants may understand little from difficult science texts, regardless of cohesion. High-knowledge readers benefit from low-cohesion text because they do not actively process highly cohesive text. However, if they are skilled readers, and naturally read more actively, high-knowledge readers do not need low-cohesion text to promote active processing. Therefore, to maximize comprehension, it is important to select texts to read that are appropriate to the reader’s abilities and background knowledge. C. Reading Strategy Training We have seen that high-knowledge readers are less affected by text structure if they have sufficient reading skills. However, what can we do for the low-knowledge, or less skilled reader? It is particularly important to be concerned with these readers given the prevalence of low-cohesion texts. This section focuses on the importance of teaching reading strategies to improve text comprehension. In a discussion of what may help less skilled readers become better comprehenders it is useful first to consider what makes a skilled reader. Foremost, skilled readers are more likely to make inferences and more actively process written material than less-skilled readers (e.g., Long et al., 1994; Magliano & Millis, in press; Magliano, Wiemer-Hastings, Millis, Muñoz, & McNamara, 2002; Oakhill, 1984; Oakhill & Yuill, 1996). In addition, they are more likely to engage in comprehension monitoring and active reading strategies than are less-skilled readers (Brown, 1982; Long et al., 1994; Magliano, Millis, Miller, & Schleich, 1999; Oakhill, 1984; Oakhill & Yuill, 1996). Comprehension monitoring and metacognitive reading strategies are critical to successful, skilled reading. This is because readers better understand and learn more from written material when they pay careful attention to whether they understood and used active reading strategies to understand the content, such as predicting what the text will talk about and drawing from background knowledge. Thus, less skilled readers are likely to benefit from utilizing similar reading techniques adopted by more proficient readers. Indeed, there is empirical evidence to show that providing readers with instruction to use metacognitive reading strategies improves reading comprehension skills (Baker, 1996; Baumann, Seifert-Kessell, & Jones, 1992; Bereiter & Bird, 1985; Bielaczyc et al., 1995; Davey, 1983; Dewitz, Carr, & Patberg, 1987; Hansen & Pearson, 1983; Palinscar & Brown, 1984; Yuill & Oakhill, 1988). One successful reading and learning technique is self-explanation (Chi, Bassok, Lewis, Reimann, & Glaser, 1989). Self explanation refers to the process of explaining text while reading. This process involves actively processing the text, understanding the relationships between separate ideas in the text, and relating the ideas to knowledge already possessed by the reader. In a laboratory setting, self-explanation involves reading and explaining sentences or sections from a text aloud. Readers who explain the text either spontaneously or when prompted to do so, understand more from the text and construct better mental models of the content (Chi et al., 1989; Chi & VanLehn, 1991; Chi, de Leeuw, Chiu, & LaVancher, 1994; Magliano, Trabasso, & Graesser, 1999; Trabasso & Magliano, 1996; VanLehn, Jones, & Chi, 1992). However, some readers do not naturally self-explain text, and they may self-explain poorly, when prompted to do so. McNamara’s research has focused on helping readers self-explain texts with the intention of improving self-explanations and enhancing comprehension. McNamara (2003) examined whether self-explanation and reading strategy training (SERT) would help readers better understand text, particularly low-cohesion text. SERT is much like techniques based on thinking aloud, in which the reader verbalizes their thoughts and knowledge about a text (Baumann et al., 1992; Davey, 1983; Coté, Goldman, & Saul, 1998). However, SERT places greater emphasis on the use of active reading strategies to explain text than previous think-aloud interventions. It was hypothesized that reading strategy instruction would help improve participants’ self explanations. In turn, the external nature of self-explanation was intended to help readers become more aware of, and learn to use, reading strategies. SERT training can be administered to a small group of students in about two hours. It begins with a brief instruction including definitions and examples of self-explanation and reading strategies. Self-explanation is described as reading text aloud and explaining what the text means, and several examples are provided. Six reading strategies are introduced to the students as a means for improving self-explanation: (a) comprehension monitoring, being aware of understanding; (b) paraphrasing, or restating the text in different words; (c) elaboration, using prior knowledge or experiences to understand the text (i.e., domain-specific knowledge based inferences); (d) logic, or common sense to understand the text (i.e., domain-general knowledge based inferences); (e) predictions, speculating as to what the text will say next; and (f) bridging, understanding the relations between separate sentences of the text. For each strategy, a description of the strategy and examples of self-explanations using the strategies are provided. Comprehension monitoring is presented as a strategy that should be used all of the time. Paraphrasing is presented as a basis or jumpstart for self-explanation, but not as means for self-explaining the text. The remaining strategies are various forms of inferences (i.e., domain specific, domain-general, predictive, and bridging) that are predicted most likely to enhance comprehension and explanation. After the introduction, students read a science text and watch a video of a student engaging in the process of self-explaining the text. The video is paused at certain points and the students are asked to identify the strategies used by the student in the video. They then discuss these strategies as a group. It is important that all of the students are asked to write down what strategies were used. In this way, they are all more likely to discuss their answers while also constructing a better understanding of the strategies and self-explanation techniques. The students then work with partners to practice the strategies, taking turns in reading orally and sharing thoughts. Instructors are present to assist and monitor the students throughout the training. McNamara (2003) first examined the effects of SERT with 42 adult readers, half of whom received training. In contrast to the training approach described above, however, each participant received individual training, practicing with four texts and watching four videos of a student self-explaining those texts. After training, all participants self-explained a low-cohesion cell mitosis text (used in McNamara, 2001). Few benefits were expected for high-knowledge readers as these readers automatically use their knowledge to bridge conceptual gaps in the text. There were two possible predictions regarding the low-knowledge readers. On the one hand, cohesion gaps may be only surmountable with sufficient prior knowledge. On the other hand, reading strategy training may help the low-knowledge reader to use logic and common sense rather than domain-relevant prior knowledge to fill in conceptual gaps. It was hypothesized that improved reading strategy knowledge would compensate for a reader’s knowledge gaps. While prior knowledge may be the most direct and natural way to resolve cohesion gaps, the reader may be able to ''work harder’ to understand the text by generating more logic-based and text-based inferences. If that is the case, however, benefits of strategy training should only appear on the more text-based measures of comprehension, as opposed to the more knowledge-demanding situation model comprehension questions. That is, the development of a coherent situation model, or deep understanding, of a text is highly dependent on having sufficient prior knowledge.  Figure 3 shows comprehension accuracy on text-based and bridging inference questions for high and low-knowledge readers after self-explaining the cell mitosis science text (McNamara, 2003). According to bridging inference questions, only prior domain knowledge helped the readers to make the necessary inferences. In contrast, text-based questions revealed an effect of training for the low-knowledge readers. SERT was most effective for students who had less prior knowledge about the text domain. This training provided students with strategies they could use while reading, which effectively compensated for their lack of domain knowledge. In addition, protocol analyses indicated these readers relied on their common sense and logic to understand the text. Three subsequent experiments have shown that SERT training not only improves text comprehension, but also improves undergraduate students’ exam performance in science courses. Across almost 1000 students in five classrooms, consistent benefits have been found for SERT. Reliable advantages on exam scores for students who received SERT training in comparison to control students, who did not receive training, ranged between 5 and 14 percent. In addition, prior knowledge of scientific facts generally showed the strongest correlations with exam performance, whereas prior reading skill showed the lowest correlations (which were generally non-significant). Most importantly, training generally had the greatest benefits for those students with less prior knowledge about science. Unfortunately, within all of these experiments, low-knowledge students who did not receive training, often left the science course without a passing grade. On the basis of these findings, McNamara and colleagues are developing an automated reading strategy training program called Interactive Strategy Trainer for Active Reading and Thinking (iSTART). The first module developed within iSTART is an automated version of SERT training. iSTART begins with an introduction to self-explanation and reading strategies delivered by three automated agents, a teacher-agent and two student-agents. The human student watches while the teacher-agent interacts with the student-agents to teach them the reading strategies. As in SERT, the reading strategies include comprehension monitoring, paraphrasing, prediction, elaboration, and bridging. One difference in comparison to SERT is that the Logic and Common Sense strategy was explained in the context of elaboration as using general knowledge rather than domain knowledge. This was done because it was very difficult for students to discriminate between domain knowledge-based elaboration and general knowledge elaboration. For each reading strategy, the strategy itself is defined, and then examples are provided. At the end of each section, the student takes a quiz to assess their understanding of the strategy. Each quiz includes four multiple-choice questions which cover the basic definitions of the strategies and assess the student’s ability to choose explanations that exemplify the strategy. After the introduction, the student progresses to the demonstration section in which two new agents, Merlin and Genie, demonstrate the strategies while self-explaining a text. The student then identifies what strategies are being used in the examples. In the last section, the student practices self-explaining science texts and receives feedback from Merlin. Accordingly, the computerized version scaffolds students’ self-explanations based on the quality of the explanation provided. To provide feedback to the student, the iSTART system must assess the self-explanations on a number of dimensions, primarily based on the type of self-explanation produced by the student. It first assesses whether the self-explanation is too short or simply a close repetition of the sentence. It then assesses whether the self-explanation is relevant to the topic of the sentence text by comparing it to a set of associated words. Finally, it assesses the quality of the self-explanation in terms of the number of words and the number of associations (as opposed to words directly from the sentence). According to these assessments, Merlin provides the student with appropriate requests (e.g., to add more information) or feedback (e.g., Ok, Very Good, Excellent). Merlin also asks the student which strategies he or she used in the self-explanation and in some cases asks the student to use other strategies if the student has used only paraphrasing or comprehension monitoring. Several preliminary examinations of this system have been run and the results of these experiments are being examined. The preliminary analysis indicates that students generally appreciate and enjoy the system, and it is as effective as live SERT training. Our immediate goal is to test this system with high-school students enrolled in science courses. Our long-term goal is for iSTART to successfully provide reading strategy training to a wide array of students, including those with reading comprehension difficulties. Conclusions: The issues faced in the realm of learning from text are similar to those faced for general knowledge and skill acquisition. These include the development of interventions which promote deep-level understandings that are retained over time and facilitate learning among a diverse population of students. The research discussed here highlights the importance of identifying individual differences and the need to adapt interventions on the basis of students’ abilities. It has been illustrated that adapting the learning environment to the level of the student is fundamental to promoting a deeper-level of comprehension. We have identified two ways of enhancing comprehension from text. The first involves increasing text cohesion which, for low knowledge readers, is particularly useful for enhancing the benefits of learning from a text. This is because low knowledge readers sometimes struggle to make inferences from low cohesion texts, thus increasing cohesion is likely to aid deeper-level processing. The second method involves teaching low knowledge readers strategies for comprehending difficult texts. SERT training encourages students to actively engage with the text in an effort to (a) identify what they do and do not understand and (b) self-explain information they read, which includes connecting pre-existing knowledge with information cited in the text, with the goal being to enhance understanding. McNamara’s computerized tools provide a revolutionary means to implementing the two methods of promoting reading comprehension outlined above. In particular, they provide real-world solutions to promoting comprehension in educational environments, such as the classroom. Additionally, we expect to improve iSTART and modify future versions so they are tailored to special populations, such as children experiencing language comprehension difficulties or younger, elementary school children. Finally, we envisage that the strategy trainer is a tool with substantial diversity – the program may be adapted to teach a wide variety of language-related skills. References: Afflerbach, P. (1986). The influence of prior knowledge on expert readers’ importance assignment process. In J.A. Niles & R.V. Lalik (Eds.), National reading conference yearbook, 35. Solving problems in literacy: Learners, teachers and researchers 30-40. Rochester, NY: National Reading Conference. Baker, L., (1996). Social influences on metacognitive development in reading. In C. Cornoldi and J. Oakhill (Eds.), Reading comprehension difficulties 331-352. Mahwah, NJ: Erlbaum. Baumann, J. F., Seifert-Kessell, N. & Jones, L. A. (1992). Effect of think-aloud instruction on elementary students' comprehension monitoring abilities. Journal of Reading Behavior, 24, 143-172. Beck, I. L., McKeown, M. G., Sinatra, G. M., & Loxterman, J. A. (1991). Revising social studies text from a text-processing perspective: Evidence of improved comprehensibility. Reading Research Quarterly, 26, 251-276. Bereiter, C., & Bird, M. (1985). Use of thinking aloud in identification and teaching of reading comprehension strategies. Cognition and Instruction, 2, 131-156. Beyer, R. (1990). Psychologische Untersuchungen zur Gestaltung von Instruktionstexten [Psychological studies concerning the construction of instructional texts]. Mathematisch-Naturwissenschaftliche Reihe, 39, 69-75. (Scientific journal published by Humboldt University, Berlin) Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and regulation strategies: Investigating the effects of knowledge aquisition activities on problem solving. Cognition and Instruction, 13, 221-252. Bowen, B. A. (1999). Four puzzles in adult literacy: Reflections on the national adult literacy survey. Journal of Adolescent and Adult Literacy, 42, 314-323. Britton, B. K., & Gulgoz, S. (1991). Using Kintsch’s computational model to improve instructional text: Effects of repairing inference calls on recall and cognitive structures. Journal of Educational Psychology, 83, 329-345. Brown, A. (1982). Learning how to learn from reading. In J. A. Langer and M. T. Smith-Burke (Eds), Reader meets author: Bridging the gap, 26-54. Newark, DE: International Reading Association. Chi, M., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152. Chi, M. T. H., & VanLehn, K. A. (1991). The content of physics self-explanations. The Journal of the Learning Sciences, 1 (1), 69-105. Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989).Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145-182. Chi, M. T. H., de Leeuw, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. Chiesi, H. I., Spilich, G. J., & Voss, J. F. (1979). Acquisition of domain-related information in relation to high and low domain knowledge. Journal of Verbal Learning and Verbal Behavior, 18, 275-290. Coté, N., Goldman, S. R., & Saul, E. U. (1998). Students making sense of informational text: Relations between processing and representation. Discourse Processes, 25, 1-53. Davey, B. (1983). Think aloud: Modeling the cognitive processes of reading comprehension. Journal of Reading, 27, 44-47. Dewitz, P. Carr, E., & Patberg, J. (1987). Effects of interference training on comprehension and comprehension monitoring. Reading Research Quarterly, 22, 99-121. Hansen, J., & Pearson, P. (1983). An instructional study: Improving the inferential comprehension of good and poor fourth-grade readers. Journal of Educational Psychology, 75, 821-829. Kintsch, W. (1988). Comprehension: A paradigm for cognition. Cambridge, England: Cambridge University Press. Long, D. L., Oppy, B. J., & Seely, M. R. (1994). Individual differences in the time course of inferential processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 1456-1470. Lundeberg, M. (1987). Metacognitive aspects of reading comprehension: Studying understanding in legal case analysis. Reading Research Quarterly, 22, 407-432. Magliano, J. P., & Millis, K.K. (in press). Assessing reading skill with a think-aloud procedure. Cognition and Instruction. Magliano, J. P., Millis, K. K., Miller, J., & Schleich, M. C. (1999, August). Revealing differences between good and poor readers based on thinking aloud. Paper presented at the Society for Text and Discourse, Vancouver, BC. Magliano, J. P., Trabasso, T., & Graesser, A. C. (1999). Strategic processes during comprehension. Journal of Educational Psychology, 91, 615-629. Magliano, J. P., Wiemer-Hastings, K., Millis, K. K., Muñoz, B. D., & McNamara, D. S. (2002). Using latent semantic analysis to assess reader strategies. Behavior Research Methods, Instruments, and Computers. McKeown, M. G., Beck, I. L., Sinatra, G. M., & Loxterman, J. A. (1992). The contribution of prior knowledge and coherent text to comprehension. Reading Research Quarterly, 27, 79-93. McNamara, D., Kintsch, E., Songer, N., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14, 1-43. McNamara, D. S. (2001). Reading both high-coherence and low coherence texts: Effects of text sequence and prior knowledge. Canadian Journal of Experimental Psychology, 55, 51-62. McNamara, D. S. (2003). SERT: Self-explanation reading training. Submitted to Discourse Processes. McNamara, D. S., & Kintsch, W. (1996). Learning from texts: Effects of prior knowledge and text coherence. Discourse Processes, 22, 247-288. Means, M. L., & Voss, J. F. (1985). Star Wars: A developmental study of expert and novice knowledge structures. Journal of Memory and Language, 24, 746-757. Oakhill, J. (1984). Inferential and memory skills in children's comprehension of stories. British Journal of Educational Psychology, 54, 31-39. Oakhill, J., & Yuill, N. (1996). Higher order factors in comprehension disability: Processes and remediation. In C. Cornaldi & J. Oakhill (Eds.), Reading Comprehension Difficulties: Processes and Intervention. Mahwah, NJ: Erlbaum. Palinscar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and monitoring activities. Cognition and Instruction, 2, 117-175. Snow, C. (2002). Reading for understanding: Toward an R &D program in reading comprehension. Santa Monica, CA: RAND Corporation. Trabasso, T., & Magliano, J. P. (1996). How do children understand what they read and what can we do to help them? In M Graves, P. van den Broek, & B. Taylor (Eds.), The first R: A right of all children, 160-188. NY: Columbia University Press. Yuill, N., & Oakhill, J. (1988). Understanding of anaphoric relations in skilled and less skilled comprehenders. British Journal of Psychology, 79, 173-186. VanLehn, K., Jones, R. M., Chi. M. T. H. (1992). A model of the self-explanation effect. Journal of the Learning Sciences, 2, 1-59. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: MIT Press. | |