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13 - Constructing inferences in naturalistic reading contexts

Published online by Cambridge University Press:  05 May 2015

Edward J. O'Brien
Affiliation:
University of New Hampshire
Anne E. Cook
Affiliation:
University of Utah
Robert F. Lorch, Jr
Affiliation:
University of Kentucky
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Publisher: Cambridge University Press
Print publication year: 2015

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References

Attali, Y., & Burstein, J. (2006). Automated essay scoring with e-rater R V.2. Journal of Technology, Learning and Assessment, 4, 130.Google Scholar
Azevedo, R., Moos, D., Johnson, A., & Chauncey, A. (2010). Measuring cognitive and metacognitive regulatory processes used during hypermedia learning: issues and challenges. Educational Psychologist, 45, 210–23.CrossRefGoogle Scholar
Baker, L. (1985). Differences in standards used by college students to evaluate their comprehension of expository prose. Reading Research Quarterly, 20, 298313.CrossRefGoogle Scholar
Baker, R. S., D'Mello, S. K., Rodrigo, M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: the incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68, 223–41.CrossRefGoogle Scholar
Barth, C. M., & Funke, J. (2010). Negative affective environments improve complex solving performance. Cognition and Emotion, 24, 1259–68.CrossRefGoogle Scholar
Biber, D., Conrad, S., & Reppen, R. (1998). Corpus Linguistics: Investigating Language Structure and Use. Cambridge University Press.CrossRefGoogle Scholar
Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology-Enhanced Learning, 5, 123–52.CrossRefGoogle Scholar
Blanc, N., Kendeou, P., van den Broek, P., & Brouillet, D. (2008). Updating situation models during reading of news reports: evidence from empirical data and simulations. Discourse Processes, 45, 103–21.CrossRefGoogle Scholar
Braasch, J. L., Rouet, J. F., Vibert, N., & Britt, M. A. (2012). Readers’ use of source information in text comprehension. Memory & Cognition, 40, 450–65.CrossRefGoogle ScholarPubMed
Bransford, J. D., Brown, A. L., & Cocking, R. R. (eds.). (2000). How People Learn (expanded edn.). Washington, DC: National Academy Press.Google Scholar
Bråten, I., Strømsø, H. I., & Britt, M. A. (2009). Trust matters: examining the role of source evaluation in students’ construction of meaning within and across multiple texts. Reading Research Quarterly, 44, 628.CrossRefGoogle Scholar
Bråten, I., & Strømsø, H. (2006). Constructing meaning from multiple information sources as a function of personal epistemology. Information Design Journal, 14, 5667.CrossRefGoogle Scholar
Briner, S. W., Virtue, S., & Kurby, C. A. (in review). Forward and backward causal relations in narrative text. Discourse Processes.Google Scholar
Britt, M. A., & Aglinskas, C. (2002). Improving students’ ability to identify and use source information. Cognition and Instruction, 20, 485522.CrossRefGoogle Scholar
Britt, M. A., & Rouet, J. F. (2012). Learning with multiple documents: component skills and their acquisition. In Lawson, M. J. & Kirby, J. R. (eds.), The Quality of Learning: Dispositions, Instruction, and Mental Structures (pp. 385404). Cambridge University Press.Google Scholar
Britton, B. K., & Graesser, A. C. (1996) (eds.). Models of Understanding Text. Mahwah, NJ: Erlbaum.Google Scholar
Cai, Z., Graesser, A. C., Forsyth, C., Burkett, C., Millis, K., Wallace, P., Halpern, D., & Butler, H. (2011). Trialog in ARIES: user input assessment in an intelligent tutoring system. In Chen, W. & Li, S. (eds.), Proceedings of the Third IEEE International Conference on Intelligent Computing and Intelligent Systems (pp. 429–33). Guangzhou: IEEE Press.Google Scholar
Calvo, R. A., & D'Mello, S. K. (2010). Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 1837.CrossRefGoogle Scholar
Chi, M. T. H., de Leeuw, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–77.Google Scholar
Cook, A. E., Halleran, J. G., & O'Brien, E. J. (1998). What is readily available during reading? A memory-based view of text processing. Discourse Processes, 26(2–3), 109–29.CrossRefGoogle Scholar
Coté, N., Goldman, S. R., & Saul, E. U. (1998). Students making sense of informational text: relations between processing and representation. Discourse Processes, 25, 153.CrossRefGoogle Scholar
D'Mello, S. K., Dowell, N., & Graesser, A. C. (2011). Does it really matter whether students’ contributions are spoken versus typed in an intelligent tutoring system with natural language? Journal of Experimental Psychology: Applied, 17, 117.Google Scholar
D'Mello, S. K., Dowell, N., & Graesser, A. C. (2012). Unimodal and multimodal human perception of naturalistic non-basic affective states during human-computer interactions. IEEE Transactions on Affective Computing, 4, 452–65.Google Scholar
D'Mello, S. K., & Graesser, A. C. (2010). Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-adapted Interaction, 20, 147–87.Google Scholar
D'Mello, S. K., & Graesser, A. C. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22, 145–57.Google Scholar
D'Mello, S. K., & Graesser, A. C. (in press). Confusion. In Pekrun, R. & Linnenbrink-Garcia, L. (eds.), Handbook of Emotions and Education. New York: Taylor & Francis.Google Scholar
D'Mello, S., Lehman, B., Pekrun, R., & Graesser, A. C. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–70.Google Scholar
Eason, S. H., Goldberg, L. F., Young, K. M., Geist, M. C., & Cutting, L. E. (2012). Reader–text interactions: how differential text and question types influence cognitive skills needed for reading comprehension. Journal of Educational Psychology, 104, 515–28.CrossRefGoogle ScholarPubMed
Feng, S., D'Mello, S. K., & Graesser, A. (2013). Mind wandering while reading easy and difficult texts, Psychonomic Bulletin & Review, 20, 586–92.CrossRefGoogle ScholarPubMed
Festinger, L. (1957). A Theory of Cognitive Dissonance. Stanford, CA: Stanford University Press.CrossRefGoogle Scholar
Forsyth, C. M., Graesser, A. C. Pavlik, P., Cai, Z., Butler, H., Halpern, D.F., & Millis, K. (2013). Operation ARIES! methods, mystery and mixed models: discourse features predict affect in a serious game. Journal of Educational Data Mining, 5, 147–89.Google Scholar
Franklin, M. S., Smallwood, J., & Schooler, J. W. (2011). Catching the mind in flight: using behavioral indices to detect mind wandering in real time. Psychonomic Bulletin & Review, 18, 992–97.CrossRefGoogle ScholarPubMed
Gernsbacher, M. A. (ed.). (1990). Language Comprehension as Structure Building. Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Glenberg, A. M. (1997). What memory is for. Behavior and Brain Sciences, 20, 155.CrossRefGoogle ScholarPubMed
Glenberg, A. M., & Robertson, D. A. (1999). Indexical understanding of instructions. Discourse Processes, 28, 126.CrossRefGoogle Scholar
Goldman, S. R., Braasch, J. L. G., Wiley, J., Graesser, A. C., & Brodowinska, K. (2012). Comprehending and learning from internet sources: processing patterns of better and poorer learners. Reading Research Quarterly, 47, 356–81.CrossRefGoogle Scholar
Goldman, S. R., Graesser, A. C., & van den Broek, P. W. (1999). Reflections. In Goldman, S. R., Graesser, A. C., & van den Broek, P. W. (eds.), Narrative Comprehension, Causality, and Coherence: Essays in Honor of Tom Trabasso (pp. 115). Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Goldman, S. R., & van Oostendorp, H. (1999). Conclusions, conundrums and challenges for the future. In van Oostendorp, H. & Goldman, S. R. (eds.), The Construction of Mental Representations during Reading (pp. 367–76). Mahwah, NJ: Erlbaum.Google Scholar
Graesser, A. C. (1981). Prose Comprehension beyond the Word. New York: Springer-Verlag.CrossRefGoogle Scholar
Graesser, A. C., Baggett, W. B., & Williams, K. (1996). Question-driven explanatory reasoning. Applied Cognitive Psychology, 10, 1731.3.0.CO;2-7>CrossRefGoogle Scholar
Graesser, A. C., & Bertus, E. L. (1998). The construction of causal inferences while reading expository texts on science and technology. Scientific Studies of Reading, 2, 247–69.CrossRefGoogle Scholar
Graesser, A. C., & Bower, G. H. (eds.). (1990). The Psychology of Learning and Motivation: Inferences and Text Comprehension. New York: Academic Press.Google Scholar
Graesser, A. C., Cai, Z., Louwerse, M. M., & Daniel, F. (2006). Question understanding aid (QUAID): a web facility that tests question comprehensibility. Public Opinion Quarterly, 70(1), 322.CrossRefGoogle Scholar
Graesser, A. C., Conley, M., & Olney, A. (2012). Intelligent tutoring systems. In Harris, K. R., Graham, S., and Urdan, T. (eds.), APA Educational Psychology Handbook: Vol. III. Applications to Learning and Teaching (pp. 451–73). Washington, DC: American Psychological Association.Google Scholar
Graesser, A. C., & D'Mello, S. (2012). Emotions during the learning of difficult material. In Ross, B. (eds.), The Psychology of Learning and Motivation, Vol. LVII (183225). New York: Elsevier.CrossRefGoogle Scholar
Graesser, A. C., D'Mello, S. K., Hu, X., Cai, Z., Olney, A., & Morgan, B. (2012). AutoTutor. In McCarthy, P. and Boonthum-Denecke, C. (eds.), Applied Natural Language Processing: Identification, Investigation, and Resolution (pp. 169–87). Hershey, PA: IGI Global.Google Scholar
Graesser, A. C., Jeon, M., & Dufty, D. (2008). Agent technologies designed to facilitate interactive knowledge construction. Discourse Processes, 45, 298322.CrossRefGoogle Scholar
Graesser, A. C., & Lehman, B. (2012). Questions drive comprehension of text and multimedia. In McCrudden, M. T., Magliano, J., & Schraw, G. (eds.), Text Relevance and Learning from Text (pp. 5374). Greenwich, CT: Information Age Publishing.Google Scholar
Graesser, A. C., & Li, H. (2013). How might comprehension deficits be explained by the constraints of text and multilevel discourse processes? In Miller, B., Cutting, L. E., & McCardle, P. (eds.), Unraveling Reading Comprehension: Behavioral, Neurobiological, and Genetic Components (pp. 3342). Baltimore: Paul Brooks Publishing.Google Scholar
Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H., Ventura, M., Olney, A., & Louwerse, M. M. (2004a). AutoTutor: a tutor with dialogue in natural language. Behavioral Research Methods, Instruments, and Computers, 36, 180–93.CrossRefGoogle ScholarPubMed
Graesser, A. C., Lu, S., Olde, B. A., Cooper-Pye, E., & Whitten, S. (2005). Question asking and eye tracking during cognitive disequilibrium: comprehending illustrated texts on devices when the devices break down. Memory & Cognition, 33, 1235–47.CrossRefGoogle ScholarPubMed
Graesser, A. C., & McMahen, C. L. (1993). Anomalous information triggers questions when adults solve problems and comprehend stories. Journal of Educational Psychology, 85, 136–51.CrossRefGoogle Scholar
Graesser, A. C., & McNamara, D. S. (2011). Computational analyses of multilevel discourse comprehension. Topics in Cognitive Science, 3, 371–98.CrossRefGoogle ScholarPubMed
Graesser, A. C., & McNamara, D. S. (2012). Automated analysis of essays and open-ended verbal responses. In Cooper, H., Camic, P. M., Long, D. L., Panter, A. T., Rindskopf, D., & Sher, K. J. (eds.), APA Handbook of Research Methods in Psychology, Vol. I: Foundations, Planning, Measures, and Psychometrics (pp. 307–25). Washington, DC: American Psychological Association.Google Scholar
Graesser, A. C., McNamara, D. S., & Kulikowich, J. (2011). Coh-Metrix: providing multilevel analyses of text characteristics. Educational Researcher, 40, 223–34.CrossRefGoogle Scholar
Graesser, A. C., McNamara, D. S., Louwerse, M. M., & Cai, Z. (2004b). Coh-Metrix: analysis of text on cohesion and language. Behavioral Research Methods, Instruments, and Computers, 36, 193202.CrossRefGoogle ScholarPubMed
Graesser, A. C., Singer, M., & Trabasso, T (1994). Constructing inferences during narrative text comprehension. Psychological Review, 101, 371–95.CrossRefGoogle ScholarPubMed
Graesser, A. C., Wiley, J., Goldman, S. R., O'Reilly, T., Jeon, M., & McDaniel, B. (2007). SEEK Web tutor: fostering a critical stance while exploring the causes of volcanic eruption. Metacognition and Learning, 2, 89105.CrossRefGoogle Scholar
Halpern, D. F., Millis, K., Graesser, A. C., Butler, H., Forsyth, C., & Cai, Z. (2012). Operation ARA: a computerized learning game that teaches critical thinking and scientific reasoning. Thinking Skills and Creativity, 7, 93100.CrossRefGoogle Scholar
Hiebert, E. H., & Mesmer, H. A. E. (2013). Upping the ante of text complexity in the Common Core State Standards examining its potential impact on young readers. Educational Researcher, 42, 4451.CrossRefGoogle Scholar
Hyönä, J., Lorch, R. F. Jr., & Rinck, M. (2003). Eye movement measures to study global text processing. In Hyönä, J., Radach, R., & Deubel, H. (eds.), The Mind's Eye: Cognitive and Applied Aspects of Eye Movement Research (pp. 313–34). Amsterdam: Elsevier.Google Scholar
Johnson, L. W., & Valente, A. (2008). Tactical language and culture training systems: using artificial intelligence to teach foreign languages and cultures. In Goker, M. & Haigh, K. (eds.) Proceedings of the Twentieth Conference on Innovative Applications of Artificial Intelligence (pp. 1632–39). Palo Alto, CA: AAAI Press.Google Scholar
Jurafsky, D., & Martin, J. H. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Kendeou, P., Papadopoulos, T. C., & Spanoudis, G. (2012). Processing demands of reading comprehension tests in young readers. Learning and Instruction, 22, 354–67.CrossRefGoogle Scholar
Kendeou, P., Smith, E. R., & O'Brien, E. J. (2013). Updating during reading comprehension: Why causality matters. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 854–65.Google ScholarPubMed
Kintsch, W. (1998). Comprehension: A Paradigm for Cognition. Cambridge University Press.Google Scholar
Kurby, C. A., & Zacks, J. M. (2013). The activation of modality-specific representations during discourse processing. Brain and Language, 126, 338–49.CrossRefGoogle ScholarPubMed
Landauer, T. K., Laham, R. D., & Foltz, P. W. (2003). Automated scoring and annotation of essays with the intelligent essay assessor. In Shermis, M. & Bernstein, J. (eds.). Automated Essay Scoring: A Cross-disciplinary Perspective. Mahwah, NJ: Erlbaum.Google Scholar
Landauer, T., McNamara, D. S., Dennis, S., & Kintsch, W. (eds.). (2007). Handbook of Latent Semantic Analysis. Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Leacock, C., & Chodorow, M. (2003). C-rater: automated scoring of short-answer questions. Computers and the Humanities, 37, 389405.CrossRefGoogle Scholar
Lehman, B., D'Mello, S. K., & Graesser, A. C. (2012). Confusion and complex learning during interactions with computer learning environments. Internet and Higher Education, 15, 184–94.CrossRefGoogle Scholar
Lehman, B., D'Mello, S. K., Strain, A., Mills, C., Gross, M., Dobbins, A., Wallace, P., Millis, K., & Graesser, A. C. (2013). Inducing and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence in Education, 22, 85105.Google Scholar
Lewis, M. R., & Mensink, M. C. (2012). Prereading questions and online text comprehension. Discourse Processes, 49, 367–90.CrossRefGoogle Scholar
Lorch, R. F. Jr, & O'Brien, E. J. (eds.). (1995). Sources of Coherence in Reading. Mahwah, NJ: Erlbaum.Google Scholar
Macaulay, D. (1988). The Way Things Work. Boston: Houghton Mifflin.Google Scholar
Magliano, J. P., & Graesser, A. C. (2012). Computer-based assessment of student constructed responses. Behavioral Research Methods, 44, 608–21.CrossRefGoogle ScholarPubMed
Magliano, J. P., & Millis, K. K. (2003). Assessing reading skill with a think-aloud procedure and latent semantic analysis. Cognition and Instruction, 21, 251–83.CrossRefGoogle Scholar
Magliano, J. P., Millis, K. K., Levinstein, I., & Boonthum, C. (2011). Assessing comprehension during reading with the reading strategy assessment Tool (RSAT). Metacognition and Learning, 6, 131–54.CrossRefGoogle ScholarPubMed
Magliano, J. P., Trabasso, T., & Graesser, A. C. (1999). Strategic processing during comprehension. Journal of Educational Psychology, 91, 615–29.CrossRefGoogle Scholar
Maier, J., & Richter, T. (2013). Text belief consistency effects in the comprehension of multiple texts with conflicting information. Cognition and Instruction, 31, 151–75.CrossRefGoogle Scholar
McCrudden, M. T., Schraw, G., & Kambe, G. (2005). The effect of relevance instructions on reading time and learning. Journal of Educational Psychology, 97, 88102.CrossRefGoogle Scholar
McKoon, G., & Ratcliff, R. (1992). Spreading activation versus compound cue accounts of priming: mediated priming revisited. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 1155–72.Google ScholarPubMed
McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38, 130.CrossRefGoogle Scholar
McNamara, D. S., Boonthum, C., Levinstein, I. B., & Millis, K. (2007). Evaluating self-explanations in iSTART: comparing word-based and LSA algorithms. In Landauer, T., McNamara, D.S., Dennis, S., & Kintsch, W. (eds.), Handbook of Latent Semantic Analysis (pp. 227–41). Mahwah, NJ: Erlbaum.Google Scholar
McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated Evaluation of Text and Discourse with Coh-Metrix. Cambridge University Press.CrossRefGoogle Scholar
McNamara, D. S., Louwerse, M. M., McCarthy, P. M., & Graesser, A. C. (2010). Coh-Metrix: capturing linguistic features of cohesion. Discourse Processes, 47, 292330.CrossRefGoogle Scholar
McNamara, D. S., & Magliano, J. (2009). Toward a comprehensive model of comprehension. In Ross, B. (ed.), The Psychology of Learning and Motivation (pp. 297383). Oxford: Elsevier.CrossRefGoogle Scholar
McNamara, D. S., O'Reilly, T., Best, R., & Ozuru, Y. (2006). Improving adolescent students’ reading comprehension with iSTART. Journal of Educational Computing Research, 34, 147–71.CrossRefGoogle Scholar
McNamara, D. S., O'Reilly, T., Rowe, M., Boonthum, C., & Levinstein, I. B. (2007). iSTART: a web-based tutor that teaches self-explanation and metacognitive reading strategies. 3In McNamara, D. S. (ed.), Reading Comprehension Strategies: Theories, Interventions, and Technologies (pp. 397421). Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Millis, K., Forsyth, C., Butler, H., Wallace, P., Graesser, A. C., & Halpern, D. (2011). Operation ARIES! A serious game for teaching scientific inquiry. In Ma, M., Oikonomou, A., & Lakhmi, J. (eds.), Serious Games and Edutainment Applications (pp. 169–96). London: Springer-Verlag.Google Scholar
Millis, K., & Graesser, A. C. (1994). The time-course of constructing knowledge-based inferences for scientific texts. Journal of Memory and Language, 33, 583–99.CrossRefGoogle Scholar
Myers, J. L., & O'Brien, E. J. (1998). Accessing the discourse representation during reading. Discourse Processes, 26, 131–57.CrossRefGoogle Scholar
Myers, J. L., O'Brien, E. J., Albrecht, J. E., & Mason, R. A. (1994). Maintaining global coherence during reading. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 876–86.Google Scholar
Narvaez, D., van den Broek, P., & Ruiz, A. B. (1999). The influence of reading purpose on inference generation and comprehension in reading. Journal of Educational Psychology, 91, 488–96.CrossRefGoogle Scholar
Nelson, J., Perfetti, C., Liben, D., & Liben, M. (2012). Measures of Text Difficulty: Testing Their Predictive Value for Grade Levels and Student Performance. New York: Student Achievement Partners.Google Scholar
O'Brien, E. J., Rizzella, M. L., Albrecht, J. E., & Halleran, J. G. (1998). Updating a situation model: a memory-based text processing view. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1200–10.Google Scholar
Otero, J., & Graesser, A. C. (2001). PREG: elements of a model of question asking. Cognition and Instruction, 19, 143–75.CrossRefGoogle Scholar
Perfetti, C. A., Rouet, J. F., & Britt, M. A. (1999). Toward a theory of documents representation. In van Oostendorp, H. & Goldman, S. R. (eds.),The Construction of Mental Representations during Reading (pp. 99122). Mahwah, NJ: Erlbaum.Google Scholar
Rapp, D. N. (2008). How do readers handle incorrect information during reading? Memory & Cognition, 36, 688701.CrossRefGoogle ScholarPubMed
Reynolds, R. E., & Anderson, R. C. (1982). Influence of questions on the allocation of attention during reading. Journal of Educational Psychology, 74, 623–32.CrossRefGoogle ScholarPubMed
Rothkopf, E. Z., & Billington, M. J. (1979). Goal-guided learning from text: inferring a descriptive processing model from inspection times and eye movements. Journal of Educational Psychology, 71, 310–27.CrossRefGoogle ScholarPubMed
Rouet, J. (2006). The Skills of Document Use: From Text Comprehension to Web-based Learning. Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Rowe, J., Shores, L., Mott, B., & Lester, J. (2010). Integrating learning and engagement in narrative-centered learning environments. In Aleven, V., Kay, J., & Mostow, J. (eds.), Proceedings of the Tenth International Conference on Intelligent Tutoring Systems (pp. 166–77). Pittsburgh, PA.Google Scholar
Shermis, M. D., Burstein, J., Higgins, D., & Zechner, K. (2010). Automated essay scoring: writing assessment and instruction. In Baker, E., McGaw, B., & Petersen, N. S. (eds.), International Encyclopedia of Education (pp. 2026). Oxford: Elsevier.CrossRefGoogle Scholar
Singer, M., Andruslak, P., Reisdorf, P., & Black, N. L. (1992). Individual differences in bridging inference processes. Memory & Cognition, 20, 539–48.CrossRefGoogle ScholarPubMed
Singer, M., Graesser, A. C., & Trabasso, T. (1994). Minimal or global inference during reading. Journal of Memory and Language, 33, 421–41.CrossRefGoogle Scholar
Snow, C. (2002). Reading for Understanding: Toward an R&D Program in Reading Comprehension. Santa Monica, CA: RAND Corporation.Google Scholar
Stadtler, M., Scharrer, L., Brummernhenrich, B., & Bromme, R. (2013). Dealing with uncertainty: readers’ memory for and use of conflicting information from science texts as function of presentation format and source expertise. Cognition and Instruction, 31, 130–50.CrossRefGoogle Scholar
Van den Broek, P., Rapp, D. N., & Kendeou, P. (2005). Integrating memory-based and constructionist processes in accounts of reading comprehension. Discourse Processes, 39, 299316.CrossRefGoogle Scholar
Van den Broek, P., Risden, K., Fletcher, C. R., & Thurlow, R. (1996). A “landscape” view of reading: fluctuating patterns of activation and the construction of a stable memory representation. In Britton, B. K. & Graesser, A. C. (eds.), Models of Understanding Text (pp. 165–87). Mahwah, NJ: Erlbaum.Google Scholar
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems and other tutoring systems. Educational Psychologist, 46, 4, 197221.CrossRefGoogle Scholar
VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31, 362.CrossRefGoogle ScholarPubMed
Van Oostendorp, H. (ed.). (2003). Cognition in a Digital World. Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Vega, B., Feng, S., Lehman, B., Graesser, A., & D'Mello, S. (2013). Reading into the text: investigating the influence of text complexity on cognitive engagement. In D'Mello, S. K., Calvo, R. A., & Olney, A. (eds.), Proceedings of the Sixth International Conference on Educational Data Mining (pp. 296–99).Google Scholar
Ward, W., Cole, R., Bolaños, D., Buchenroth-Martin, C., Svirsky, E., Van Vuuren, S. Weston, T., Zheng, J., & Becker, L. (2011). My science tutor: a conversational multimedia virtual tutor for elementary school science. ACM Transactions of Speech and Language Processing, 13, 416.Google Scholar
Weaver, C. A., Mannes, S., & Fletcher, C. R. (eds.). (1995). Discourse Comprehension: Strategies and Processing Revisited. Mahwah, NJ: Erlbaum.Google Scholar
Wiley, J., Goldman, S. R., Graesser, A. C., Sanchez, C. A., Ash, I. K., & Hemmerich, J. A. (2009). Source evaluation, comprehension, and learning in Internet science inquiry tasks. American Educational Research Journal, 46, 1060–106.CrossRefGoogle Scholar
Zwaan, R. A., Magliano, J. P., & Graesser, A. C. (1995). Dimensions of situation model construction in narrative comprehension. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 386–97.Google Scholar
Zwaan, R. A., & Radvansky, G. A. (1998). Situation models in language comprehension and memory. Psychological Bulletin, 123, 162–85.CrossRefGoogle ScholarPubMed
Zwaan, R. A., & van Oostendorp, H. (1993). Do readers construct spatial representations in naturalistic story comprehension? Discourse Processes, 16, 125–43.CrossRefGoogle Scholar

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