Chapter published in:
Developmental Perspectives in Written Language and Literacy: In honor of Ludo Verhoeven
Edited by Eliane Segers and Paul van den Broek
[Not in series 206] 2017

Two technologies to help adults with reading difficulties improve their comprehension

| | University of Memphis, Memphis, USA
|
|
A proficient reader is skilled at interpreting and comprehending text at multiple levels of language and discourse. This chapter describes two technologies that are designed to help adult readers who have reading difficulties at various levels. One technology (called AutoTutor) has two computer agents (a tutor and peer) that engage the adult reader in conversational trialogues designed to improve reading comprehension skills at multiple levels of language and discourse. A second technology (called Coh-Metrix) automatically scales texts on discourse formality as well as more specific levels, such as word abstractness, syntactic complexity, discourse cohesion, and narrativity (versus informational discourse). Scaling texts on difficulty is important for adults to read texts at an appropriate level of difficulty – not too easy or difficult.
Keywords: AutoTutor, Coh-Metrix, conversational agents, discourse processing, intelligent tutoring systems, trialogues
Published online: 21 December 2017
https://doi.org/10.1075/z.206.18gra
References

References

Biber, D.
(1988) Variation across speech and writing. Cambridge, UK: Cambridge University Press. CrossrefGoogle Scholar
Cai, Z., Feng, S., Baer, W., & Graesser, A.
(2014) Instructional strategies in trialog-based intelligent tutoring systems. In R. Sottilare, A. C. Graesser, X. Hu, & B. Goldberg (Eds.), Design recommendations for intelligent tutoring systems: Adaptive instructional strategies (Vol. 2, pp. 225–235). Orlando, FL: Army Research Laboratory.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 W. Chen & S. Li (Eds.), Proceedings of the 3rd IEEE International Conference on Intelligent Computing and Intelligent Systems (pp. 429–433). Guangzhou: IEEE Press.Google Scholar
Clark, H. H.
(1996) Using language. Cambridge: Cambridge University Press, hardbound. CrossrefGoogle Scholar
Deane, P., Sheehan, K. M., Sabatini, J., Futagi, Y., & Kostin, I.
(2006) Differences in text structure and its implications for assessment of struggling readers. Scientific Studies of Reading, 10, 257–275. CrossrefGoogle 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, 515–528. CrossrefGoogle Scholar
Goldman, S. R., Braasch, J. L. G., Wiley, J., Graesser, A. C., & Brodowinska, K.
(2012) Comprehension and learning from internet sources: Processing patterns of better and poorer learners. Reading Research Quartely, 47(4), 356–381.Google Scholar
Graesser, A. C.
(2011) Learning, thinking, and emoting with discourse technologies. American Psychologist, 66, 743–757. CrossrefGoogle Scholar
(2016) Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26, 124–132. CrossrefGoogle Scholar
Graesser, A. C., Cai, Z., Baer, W. O., Olney, A. M., Hu, X., Reed, M., & Greenberg, D.
(2016) Reading comprehension lessons in AutoTutor for the Center for the Study of Adult Literacy. In S. A. Crossley & D. S. McNamara (Eds.), Adaptive educational technologies for literacy instruction (pp. 288–293. New York: Taylor & Francis Routledge.Google Scholar
Graesser, A. C., Forsyth, C., & Lehman, B.
in press). Two heads are better than one: Learning from agents in conversational trialogues. Teachers College Record.
Graesser, A., Jeon, M., & Duffy, D.
(2008) Agent technologies designed to facilitate interactive knowledge construction. Discourse Processes, 45(4–5), 298–322. CrossrefGoogle Scholar
Graesser, A. C., Li, H., & Forsyth, C.
(2014) Learning by communicating in natural language with conversational agents. Current Directions in Psychological Science, 23, 374–380. CrossrefGoogle Scholar
Graesser, A. C., & McNamara, D. S.
(2011) Computational analyses of multilevel discourse comprehension. Topics in Cognitive Science, 3, 371–398. CrossrefGoogle Scholar
(2012) Automated analysis of essays and open-ended verbal responses. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology, Vol 1: Foundations, planning, measures, and psychometrics (pp. 307–325). Washington, DC: American Psychological Association. CrossrefGoogle Scholar
Graesser, A. C., McNamara, D. S., & Kulikowich, J.
(2011) Coh-Metrix: Providing multilevel analyses of text characteristics. Educational Researcher, 40, 223–234. CrossrefGoogle Scholar
Graesser, A. C., McNamara, D. S., Cai, Z., Conley, M., Li, H., & Pennebaker, J.
(2014) Coh-Metrix measures text characteristics at multiple levels of language and discourse. Elementary School Journal, 115, 210–229. CrossrefGoogle Scholar
Graesser, A. C., Singer, M., & Trabasso, T.
(1994) Constructing inferences during narrative text comprehension. Psychological Review, 101(3), 371–395. CrossrefGoogle Scholar
Greenberg, D.
(2008) The challenges facing adult literacy programs. Community Literacy Journal, 3, 39–54.Google Scholar
Haberlandt, K. F., & Graesser, A. C.
(1985) Component processes in text comprehension and some of their interactions. Journal of Experimental Psychology: General, 114(3), 357–374. CrossrefGoogle Scholar
Halliday, M. A. K., & Hasan, R.
(1976) Cohesion in English. London: Longman Group Ltd.Google Scholar
Haviland, S. E., & Clark, H. H.
(1974) What’s new? Acquiring new information as a process in comprehension. Journal of Verbal Learning and Verbal Behavior, 13, 512–521. CrossrefGoogle Scholar
Hiebert, E. H., & Mesmer, H. A.
(2013) Upping the ante of text complexity in the Common Core State Standards: Examining its potential impact on young readers. Educational Researcher, 42(1), 44–51. CrossrefGoogle Scholar
Jackson, G. T., & McNamara, D. S.
(2013) Motivation and performance in a game-based intelligent tutoring system. Journal of Educational Psychology, 105, 1036–1049. CrossrefGoogle 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
Just, M. A., & Carpenter, P. A.
(1987) The psychology of reading and language comprehension. Newton, MA: Allyn & Bacon.Google Scholar
Kintsch, W.
(1998) Comprehension: A paradigm for cognition. Cambridge, UK: Cambridge University Press.Google Scholar
Landauer, T. K., Kireyev, K., & Panaccione, C.
(2011) Word maturity: A new metric for word knowledge. Scientific Studies of Reading, 15(1), 92–108. CrossrefGoogle Scholar
Landauer, T., McNamara, D. S., Dennis, S., Kintsch, W.
(Eds.) (2007) Handbook of latent semantic analysis. Mahwah, NJ: Erlbaum.Google Scholar
Lovett, M. W., Lacerenza, L., De Palma, M., & Frijters, J. C.
(2012) Evaluating the efficacy of remediation for struggling readers in high school. Journal of Learning Disabilities, 45, 151–169. CrossrefGoogle Scholar
Magliano, J. P., & Graesser, A. C.
(2012) Computer-based assessment of student-constructed responses. Behavioral Research Methods, 44, 608–621. 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 T. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of latent semantic analysis (pp. 227–241). 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, MA: Cambridge University Press. CrossrefGoogle Scholar
McNamara, D. S., & Kintsch, W.
(1996) Learning from text: Effects of prior knowledge and text coherence. Discourse Processes, 22, 247–288. 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, 292–330. CrossrefGoogle Scholar
Meyer, B. F., Wijekumar, K., Middlemiss, W., Higley, K., Lei, P., & Meier, C., & Spielvogel, J.
(2010) Web-based tutoring of the structure strategy with or without elaborated feedback or choice for fifth- and seventh-grade readers. Reading Research Quarterly, 45(1), 62–92. CrossrefGoogle Scholar
National Research Council [NRC]
(2011) Improving adult literacy instruction: Options for practice and research. Washington, DC: The National Academies Press.Google Scholar
Nelson, J., Perfetti, C., Liben, D., & Liben, M.
(2011) Measures of text difficulty: Testing their predictive value for grade levels and student performance. New York, NY: Student Achievement Partners.Google Scholar
Nye, B. D., Graesser, A. C., & Hu, X.
(2014) AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469. CrossrefGoogle Scholar
OECD
(2013) Time for the U.S. to Reskill?: What the survey of adult skills says. OECD Skills Studies, OECD Publishing. CrossrefGoogle 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, 1200–1210. CrossrefGoogle Scholar
Olney, A., D’Mello, S. K., Person, N., Cade, W., Hays, P., Williams, C., Lehman, B., & Graesser, A. C.
(2012) Guru: A computer tutor that models expert human tutors. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2012 (pp. 256–261). Berlin, Germany: Springer.Google Scholar
Perfetti, C. A.
(1999) Comprehending written language: A blueprint of the reader. In C. M. Brown & P. Hagoort (Eds.), The neurocognition of language (pp. 167–208). Oxford: Oxford University Press.Google Scholar
Perfetti, C.
(2007) Reading ability: Lexical quality to comprehension. Scientific Studies of Reading, 11(4), 357–383. CrossrefGoogle Scholar
Rapp, D. N., van den Broek, P., McMaster, K. L., Kendeou, P., & Espin, C. A.
(2007) Higher-order comprehension processes in struggling readers: A perspective for research and intervention. Scientific studies of reading, 11, 4, 289–312. CrossrefGoogle Scholar
Rouet, J.
(2006) The skills of document use: From text comprehension to Web-based learning. Mahwah, NJ: Erlbaum.Google Scholar
Rus, V., D’Mello, S., Hu, X., & Graesser, A. C.
(2013) Recent advances in intelligent systems with conversational dialogue. AI Magazine, 34, 42–54. CrossrefGoogle Scholar
Sabatini, J. P., & Albro, E.
(2012) Assessing reading in the 21st century: Aligning and applying advances in the reading and measurement sciences. Lanham, MD: R&L Education.Google Scholar
Sheehan, K. M., Kostin, I., Napolitano, D., & Flor, M.
(2014) The TextEvaluator Tool: Helping teachers and test developers select texts for use in instruction and assessment. Elementary School Journal, 115(2), 184–209. CrossrefGoogle Scholar
Snow, C.
(2002) Reading for understanding: Toward an R&D program in reading comprehension. Santa Monica, CA: RAND Corporation.Google Scholar
Van den Broek, P. W., White, M. J., Kendeou, P., & Carlson, S.
(2009) Reading between the lines. Developmental and individual differences in cognitive processes in reading comprehension. In R. K. Wagner, C. Schatschneider, & C. Phythian-Sence (Eds.), Beyond decoding. The behavioral and biological foundations of reading comprehension (pp. 107–123). New York: The Guilford Press.Google Scholar
van Dijk, T. A., & Kintsch, W.
(1983) Strategies of discourse comprehension. New York: Academic Press.Google Scholar
VanLehn, K.
(2011) The relative effectiveness of human tutoring, intelligent tutoring systems and other tutoring systems. Educational Psychologist, 46, 197–221. 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, 3–62. CrossrefGoogle Scholar
Verhoeven, L., & Graesser, A. C.
(2008) Introduction: Cognitive and linguistic factors in interactive knowledge construction. Discourse Processes, 45, 289–297. CrossrefGoogle Scholar
Verhoeven, L.
(1994) Transfer in bilingual development: The linguistic interdependence hypothesis revisited. Language Learning, 44(3), 381–415. CrossrefGoogle Scholar
(2000) Components in early second language reading and spelling. Scientific Studies of Reading, 4(4), 313–330. CrossrefGoogle Scholar
Verhoeven, L., & van Elsacker, W.
(2016) Home and school predictors of reading achievement in linguistically diverse learners in the intermediate primary grades. Written and Spoken Language Development across the Lifespan, 11, 65–76. CrossrefGoogle Scholar
Verhoeven, L., & van Leeuwe, J.
(2008) Prediction of the development of reading comprehension: A longitudinal study. Applied Cognitive Psychology, 22(3), 407–423. CrossrefGoogle Scholar
Wiley, J., Goldman, S., Graesser, A., Sanchez, C., Ash, I., & Hemmerich, J.
(2009) Source evaluation, comprehension, and learning in internet science inquiry tasks. American Educational Research Journal, 46, 1060–1106. CrossrefGoogle Scholar
Williamson, G. L., Fitzgerald, J., & Stenner, A. J.
(2014) Student reading growth illuminates the Common Core text-complexity standard: Raising both bars. Elementary School Journal, 115, 230–254. CrossrefGoogle 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), 1–20. CrossrefGoogle Scholar
Zapata-Rivera, D., Jackson, T., & Katz, I. R.
(2015) Authoring conversation-based assessment scenarios. In R. Sottilare, A. C. Graesser, X. Hu, & K. Brawner (Eds.), Design recommendations for intelligent tutoring systems: Authoring tools (Vol. 3, pp. 191–200). Orlando, FL: Army Research Laboratory.Google Scholar
Zwaan, R. A., & Radvansky, G. A.
(1998) Situation models in language comprehension and memory. Psychological Bulletin, 123, 162–185. CrossrefGoogle Scholar