A Multidimensional Investigation of Deep-level and Surface-level Processing

REFERENCES

• Afflerbach, P.P. (1990). The influence of prior knowledge on expert readers’ main idea construction strategies. Reading Research Quarterly, 25, 31–46.
   Web of Science ®Google Scholar
• Aiken, L.S., & West, S.G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.
   Google Scholar
• Alexander, P.A. (1997). Mapping the multidimensional nature of domain learning: The interplay of cognitive, motivational, and strategic forces. In M.L. Maehr & P.R. Pintrich (Eds.), Advances in motivation and achievement (Vol., pp. 213–250). Greenwich, CT: JAI Press.
   Google Scholar
• Alexander, P.A. (2003). The development of expertise: The journey from acclimation to proficiency. Educational Researcher, 32, 10–14.
   Google Scholar
• Alexander, P.A. (2004). A model of domain learning: Reinterpreting expertise as a multidimensional, multistage process. In D.Y. Dai & R.J. Sternberg (Eds.), Motivation, emotion, and cognition: Integrative perspectives on intellectual functioning and development (pp. 273–298). Mahwah, NJ: Erlbaum.
   Google Scholar
• Alexander, P.A., Dinsmore, D.L., Parkinson, M.M., & Winters, F.I. (2011). Self-regulated learning in academic domains. In B. Zimmerman & D. Schunk (Eds.), Handbook of self-regulation of learning and performance. New York, NY: Routledge.
   Google Scholar
• Alexander, P.A., & Judy, J.E. (1988). The interaction of domain-specific and strategic knowledge in academic performance. Review of Educational Research, 58, 375–404.
   Web of Science ®Google Scholar
• Alexander, P.A., Kulikowich, J.M., & Schulze, S.K. (1994). How subject-matter knowledge affects recall and interest. American Educational Research Journal, 31, 313–337.
   Web of Science ®Google Scholar
• Alexander, P.A., Murphy, P.K., & Kulikowich, J.M. (1998). What responses to domain-specific analogy problems reveal about emerging competence: A new perspective on an old acquaintance. Journal of Educational Psychology, 90, 397–406.
   Web of Science ®Google Scholar
• Alexander, P.A., Murphy, P.K., Woods, B.S., Duhon, K.E., & Parker, D. (1997). College instruction and concomitant changes in students’ knowledge, interest, and strategy use: A study of domain learning. Contemporary Educational Psychology, 22, 125–146.
   Web of Science ®Google Scholar
• Alexander, P.A., Schallert, D.L., & Reynolds, R.E. (2009). What is learning anyway? A topographical perspective considered. Educational Psychologist, 44, 176–192.
   Web of Science ®Google Scholar
• Alexander, P.A., Sperl, C.T., Buehl, M.M., Fives, H., & Chiu, S. (2004). Modeling domain learning: Profiles from the field of special education. Journal of Educational Psychology, 96, 545–557.
   Web of Science ®Google Scholar
• Ally, B.A., Gold, C.A., & Budson, A.E. (2009). An evaluation of recollection and familiarity in Alzheimer's disease and mild cognitive impairment using receiver operating characteristics. Brain and Cognition, 69, 504–513.
   PubMed Web of Science ®Google Scholar
• Bates, M. (1975). The lady lives on blood. In A. Ternes (Ed.), Ants, Indians, and little dinosaurs (pp. 74–82). New York, NY: Scribner & Sons.
   Google Scholar
• Bentler, P.M. (2006). EQS 6 structural equations program manual. Encino, CA: Multivariate Software, Inc.
   Google Scholar
• Biggs, J.B. (1978). Individual and group differences in study processes. British Journal of Educational Psychology, 48, 266–279.
   Web of Science ®Google Scholar
• Biggs, J.B., & Collis, K.F. (1982). Evaluating the quality of learning: The SOLO taxonomy (Structure of the Observed Learning Outcome). New York, NY: Academic Press.
   Google Scholar
• Block, R.A. (2009). Intent to remember briefly presented human faces and other pictorial stimuli enhances recognition memory. Memory & Cognition, 37, 667–678.
   PubMed Web of Science ®Google Scholar
• Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., & Karthwohl D.R. (1956). Taxonomy of educational objectives: the classification of educational goals; Handbook I: Cognitive domain. New York, NY: Longmans.
   Google Scholar
• Boekaerts, M. (2002). Bringing about change in the classroom: Strengths and weaknesses of the self-regulated learning approach—EARLI Presidential Address, 2001. Learning and Instruction, 12, 589–604.
   Web of Science ®Google Scholar
• Buehl, M.M., Alexander, P.A., Murphy, P.K., & Sperl, C.T. (2001). Profiling persuasion: The role of beliefs, knowledge, and interest in the processing of persuasive texts that vary by argument structure. Journal of Literacy Research, 33, 269–301.
   Web of Science ®Google Scholar
• Burgess, M.C. R., & Weaver, G.E. (2003). Interest and attention in facial recognition. Perceptual and Motor Skills, 96, 467–480.
   PubMed Web of Science ®Google Scholar
• Byrne, B.M. (1994). Structural equation modeling with EQS and EQS/Windows: Basic concepts, applications, and programming. Thousand Oaks, CA: Sage.
   Google Scholar
• Campbell, D.T., & Fiske, D.W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105.
   PubMed Web of Science ®Google Scholar
• Cano, F. (2007). Approaches to learning and study orchestrations in high school students. European Journal of Psychology of Education, 22, 131–151.
   Web of Science ®Google Scholar
• Carrell, P.L., & Connor, U. (1991). Reading and writing descriptive and persuasive texts. The Modern Language Journal, 75, 314–324.
   Web of Science ®Google Scholar
• Chamorro-Premuzic, T., & Furnham, A. (2008). Personality, intelligence and approaches to learning as predictors of academic performance. Personality and Individual Differences, 44, 1596–1603.
   Web of Science ®Google Scholar
• Chi, M.T., Glaser, R., & Rees, E. (1981). Expertise in problem solving. Pittsburgh, PA: Learning Research and Development Center, University of Pittsburgh.
   Google Scholar
• Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2003). Applied multiple regression/correlation for the behavioral sciences. Hillsdale, NJ: Erlbaum.
   Google Scholar
• Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Orlando, FL: Holt, Rinehart and Winston.
   Google Scholar
• Dinsmore, D.L., & Alexander, P.A. (2012). A critical discussion of deep and surface processing: What it means, how it is measured, the role of context, and model specification. Educational Psychology Review, 24, 499–567.
   Web of Science ®Google Scholar
• Dinsmore, D.L., Loughlin, S.M., Parkinson, M.M., & Alexander, P.A. (in press). Effects of reading persuasive text and thinking aloud on metacognition. Journal of Learning and Individual Differences.
   Google Scholar
• Ericsson, K.A. (2006). Protocol analysis and expert thought: Concurrent verbalizations of thinking during experts’ performance on representative tasks. In K.A. Ericsson, N. Charness, P.J. Feltovich, & R.R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 223–241). Cambridge, UK: Cambridge University Press.
   Google Scholar
• Finney, S.J., & DiStefano, C. (2006). Nonnormal and categorical data in structural equation modeling. In G.R. Hancock & R.O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269–314). Greenwich CT: Information Age.
   Google Scholar
• Fleiss, J.L. (1981). Balanced incomplete block designs for inter-rater reliability studies. Applied Psychological Measurement, 5, 105–112.
   Web of Science ®Google Scholar
• Fox, E., Dinsmore, D.L., & Alexander, P.A. (2007, April). Situational success at reading challenging texts: Exposing the fragile understanding of college students. In P.A. Alexander (Chair), Fragile understanding: When good ideas go bad. Symposium conducted at the annual meeting of the American Educational Research Association, Chicago, IL.
   Google Scholar
• Fox, E., Dinsmore, D.L., Maggioni, L., & Alexander, P.A. (2008, March). Undergraduates’ independent and scaffolded reading of course texts: Further evidence of fragile understanding. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
   Google Scholar
• Frederickson, N. (1990). Systems approaches in EP practice: A re-evaluation. In N. Jones & N. Frederickson (Eds.), Refocusing educational psychology (pp. 130–164). Oxford, UK: Palmer.
   Google 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, 247–269.
   Google Scholar
• Greene, J.A., & Azevedo, R. (2009). A macro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemporary Educational Psychology, 34, 18–29.
   Web of Science ®Google Scholar
• Hancock, G.R. (2006). Power analysis in covariance structure modeling. In G.R. Hancock & R.O. Mueller (Eds.), Structural equation modeling: A second course (pp. 69–115). Greenwich, CT: Information Age.
   Google Scholar
• Hancock, G.R., & Mueller, R.O. (2001). Rethinking construct reliability within latent variable systems. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future—A festschrift in honor of Karl Jöreskog. Lincolnwood, IL: Scientific Software International.
   Google Scholar
• Hancock, G.R., & Mueller, R.O. (2006). Introduction. In G.R. Hancock & R.O. Mueller (Eds.), Structural equation modeling: A second course (pp. 1–12). Greenwich CT: Information Age.
   Google Scholar
• Hidi, S., & Renninger, K.A. (2006). The four-phase model of interest development. Educational Psychologist, 41, 111–127.
   Web of Science ®Google Scholar
• Hotchkiss, L. (1976). A technique for comparing path models between subgroups. Sociological Methods & Research, 5, 53–76.
   Web of Science ®Google Scholar
• Hu, L-T, & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
   Web of Science ®Google Scholar
• Jones, B.W. (2004). Life in the solar system and beyond. New York, NY: Springer.
   Google Scholar
• Kamalski, J., Sanders, T., & Lentz, L. (2008). Coherence marking, prior knowledge, and comprehension of informative and persuasive texts: Sorting things out. Discourse Processes, 45, 323–345.
   Web of Science ®Google Scholar
• Kardash, C.M., & Howell, K.L. (2000). Effects of epistemological beliefs and topic-specific beliefs on undergraduates’ cognitive and strategic processing of dual-positional text. Journal of Educational Psychology, 92, 524–535.
   Web of Science ®Google Scholar
• Kelderman, H. (1996). Multidimensional Rasch models for partial-credit scoring. Applied Psychological Measurement, 20, 155–168.
   Web of Science ®Google Scholar
• Kellogg, R.T. (2001). Competition for working memory among writing processes. American Journal of Psychology, 114, 175–191.
   PubMed Web of Science ®Google Scholar
• Kendeou, P., & van den Broek, P. (2007). The effects of prior knowledge and text structure on comprehension processes during reading of scientific texts. Memory & Cognition, 35, 1567–1577.
   PubMed Web of Science ®Google Scholar
• Kintsch, W. (1988). The role of knowledge in discourse comprehension: A construction-integration model. Psychological Review, 95, 163–182.
   PubMed Web of Science ®Google Scholar
• Kintsch, W. (1998). Comprehension: A paradigm for cognition. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Kintsch, W. (2004). The construction-integration model of text comprehension and its implications for instruction. In R.B. Ruddell & N.J. Unrau (Eds.), Theoretical models and processes of reading (5th ed., pp. 1270–1362). Newark, DE: International Reading Association.
   Google Scholar
• Kintsch, W., & Welsch, D.M. (1991). The construction-integration model: A framework for studying memory for text. In W.E. Hockley & S. Lewandowsky (Eds.), Relating theory and data: Essays on human memory in honor of Bennet B. Murdock (pp. 367–385). Hillsdale, NJ: Erlbaum.
   Google Scholar
• Kwan, J.L. Y., & Chan, W. (2011). Comparing standardized coefficients in structural equation modeling: A model reparmeterization approach. Behavior Research Methods, 43, 730–754.
   PubMed Web of Science ®Google Scholar
• Laxman, K. (2010). A conceptual framework mapping the application of information search strategies to well and ill-structured problem solving. Computers & Education, 55, 513–526.
   Web of Science ®Google Scholar
• Lehman, S., & Schraw, G. (2002). Effects of coherence and relevance on shallow and deep text processing. Journal of Educational Psychology, 94, 738–750.
   Web of Science ®Google Scholar
• Lodewyk, K.R., Winne, P.H., & Jamieson-Noel, D.L. (2009). Implications of task structure on self-regulated learning and achievement. Educational Psychology, 29, 1–25.
   Web of Science ®Google Scholar
• Lunine, J.I. (2005). Astrobiology: A multidisciplinary approach. San Francisco, CA: Pearson.
   Google Scholar
• Marsh, H.W. (1989). Confirmatory factor analyses of multitrait-multimethod data: Many problems and few solutions. Applied Psychological Measurement, 13, 335–361.
   Web of Science ®Google Scholar
• Marsh, H.W., Wen, Z., Hau, K.-T., Little, T.D., Bovaird, J.A., & Widamin, K.F. (2007). Unconstrained structural equation models of latent variables: Contrasting residual- and mean-centered approaches. Structural Equation Modeling, 14, 570–580.
   Web of Science ®Google Scholar
• Marton, F., & Saljo, R. (1976a). On qualitative differences in learning: I. Outcome and process. British Journal of Educational Psychology, 46, 4–11.
   Web of Science ®Google Scholar
• Marton, F., & Saljo, R. (1976b). On qualitative differences in learning: II. Outcome as a function of the learner's conception of the task. British Journal of Educational Psychology, 46, 115–127.
   Web of Science ®Google Scholar
• McNamara, D.S., & Kintsch, W. (1996). Learning from texts: Effects of prior knowledge and text coherence. Discourse Processes, 22, 247–288.
   Web of Science ®Google Scholar
• Mikkilä-Erdmann, M. (2001). Improving conceptual change concerning photosynthesis through text design. Learning and Instruction, 11, 241–257.
   Web of Science ®Google Scholar
• Muis, K.R., & Franco, G.M. (2009). Epistemic beliefs: Setting the standards for self-regulated learning. Contemporary Educational Psychology, 34, 306–318.
   Web of Science ®Google Scholar
• Murphy, P.K., & Alexander, P.A. (2002). What counts? The predictive powers of subject-matter knowledge, strategic processing, and interest in domain-specific performance. The Journal of Experimental Education, 70, 197–214.
   Web of Science ®Google Scholar
• Murphy, P.K., Long, J.F., Holleran, T.A., & Esterly, E. (2003). Persuasion online or on paper: A new take on an old issue. Learning and Instruction, 13, 511–532.
   Web of Science ®Google Scholar
• NASA Astrobiology Institute (2010). About NAI. Retrieved from: https://astrobiology.nasa.gov/nai/about/
   Google Scholar
• Nolen, S.B., & Haladyna, T.M. (1990). Personal and environmental influences on students’ beliefs about effective study strategies. Contemporary Educational Psychology, 15, 116–130.
   Web of Science ®Google Scholar
• Pearson, P.D. (1978). Some practical applications of a psycholinguistic model of reading,” pp. 84–87. In S.J. Samuels (Ed.), What research has to say about reading instruction (pp. 84–87). Newark, DE: International Reading Association.
   Google Scholar
• Pedhazur, E.J. (1997). Multiple regression in behavioral research: Explanation and prediction. New York, NY: Holt, Rinehart, and Winston.
   Google Scholar
• Perfetti, C.A., Beck, I., Bell, L.C., & Hughes, C. (1987). Phonemic knowledge and learning to read are reciprocal: A longitudinal study of first grade children. Merrill-Palmer Quarterly, 33, 283–319.
   Web of Science ®Google Scholar
• Phan, H.P. (2008). Unifying different theories of learning: Theoretical framework and empirical evidence. Educational Psychology, 28, 325–340.
   Web of Science ®Google Scholar
• Pressley, M., & Afflerbach, P. (1995). Verbal protocols of reading: The nature of constructively responsive reading. Hillsdale, NJ: Erlbaum.
   Google Scholar
• Rodgers, J.L. (2010). The epistemology of mathematical and statistical modeling: A quiet methodological revolution. American Psychologist, 65, 1–12.
   PubMed Web of Science ®Google Scholar
• Sadoski, M., Goetz, E.T., & Rodriguez, M. (2000). Engaging texts: Effects of concreteness on comprehensibility, interest, and recall in four text types. Journal of Educational Psychology, 92, 85–95.
   Web of Science ®Google Scholar
• Schiefele, U. (1999). Interesting and learning from text. Scientific Studies of Reading, 3, 257–279.
   Google Scholar
• Shostak, G.S., & Barnett, A. (2003). Cosmic company: The search for life in the universe. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Simon, H.A. (1978). Information-processing theory of human problem solving. In W.K. Estes (Ed.), Handbook of learning and cognitive processes (pp. 271–295). Hillsdale, NJ: Erlbaum.
   Google Scholar
• Sullivan, Y.M., & Cantwell, R.H. (1999). The planning behaviours of musicians engaging traditional and non-traditional scores. Psychology of Music, 27, 245–266.
   Google Scholar
• Vermetten, Y.J., Vermunt, J.D., & Lodewijks, H.G. (2002). Powerful learning environments? How university students differ in their response to instructional measures. Learning and Instruction, 12, 263–284.
   Web of Science ®Google Scholar
• Vermunt, J.D. (1998). The regulation of constructive learning processes. British Journal of Educational Psychology, 68, 149–171.
   Web of Science ®Google Scholar
• Webb, S. (2002). If the universe is teeming with aliens…Where is everybody? Fifty solutions to the Fermi paradox and the problem of extraterrestrial life. New York, NY: Copernicus Books.
   Google Scholar
• Williams, J.P., Stafford, K.B., Lauer, K.D., Hall, K.M., & Pollini, S. (2009). Embedding reading comprehension training in content-area instruction. Journal of Educational Psychology, 101, 1–20.
   Web of Science ®Google Scholar
• Wolfe, M.B. (2005). Memory for narrative and expository text: independent influences of semantic associations and text organization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 359–364.
   PubMed Web of Science ®Google Scholar