Co-Principal Investigators: Art Graesser, Max Louwerse

This research team plans to develop two automated tools (Coh-Metrix and Coh-GIT) that will enable writers, editors, and educators to more accurately estimate the appropriateness of a text for their audience, and to pinpoint specific problems with the text. Using these tools, the researchers will experimentally examine the effects of text cohesion on reading comprehension with respect to reader aptitudes (e.g., prior knowledge, reading ability, and motivation) in 3rd to 5th grade students and college undergraduates.

Structured Abstract

Purpose: Texts are at the core of our educational system. Successful schools require high quality texts that are appropriate for particular populations of readers. Unfortunately, the tools used to determine the readability of texts are severely limited and may paradoxically aggravate comprehension difficulties. This research team plans to develop two automated tools (Coh-Metrix and Coh-GIT) that will enable writers, editors, and educators to more accurately estimate the appropriateness of a text for their audience, and to pinpoint specific problems with the text. At the conclusion of this research, authors will have tools to assist them in writing more readable texts and in ensuring that students read texts that both support and challenge them as readers.

Research Design and Methods: Using these tools, the researchers will experimentally examine the effects of text cohesion on reading comprehension with respect to reader aptitudes (e.g., prior knowledge, reading ability, and motivation) in 3rd to 5th grade students and college undergraduates.

Project Website: http://CohMetrix.Memphis.edu

Publications from this project:

Best, R.M., Floyd, R.G., and McNamara, D.S. (2008). Differential Competencies Contributing to Children's Comprehension of Narrative and Expository Texts. Reading Psychology, 29: 137–164.

Bruss, M., Albers, M., and McNamara, D.S. (2004). Changes in Scientific Articles Over Two Hundred Years: A Coh-Metrix Analysis. Proceedings of the 22nd Annual International Conference on Computer Documentation. (pp. 104–109). Memphis, TN: ACM Press.

Cai, Z., McNamara, D.S., Louwerse, M., Hu, X., Rowe, M., and Graesser, A.C. (2004). NLS: Non-Latent Similarity Algorithm. In K. Forbus, D. Gentner, T. Regier (Eds.), Proceedings of the 26th Annual Meeting of the Cognitive Science Society (pp. 180–185). Mahwah, NJ: Erlbaum.

Crossley, S.A., McCarthy, P.M., Louwerse, M., and McNamara, D.S. (2007). Linguistic Analysis of Simplified and Authentic Texts. Modern Language Journal, 91: 15–30.

Dufty, D.F., McNamara, D., Louwerse, M., Cai, Z., Graesser, A.C. (2004). Automated Evaluation of Aspects of Document Quality. Proceedings of the 22nd Annual International Conference on Documentation. Memphis,TN: ACM Press.

Hempelmann, C.F., Dufty, D., McCarthy, P.M., Graesser, A.C., Cai, Z., and McNamara, D.S. (2005). Using LSA to Automatically Identify Givenness and Newness of Noun Phrases in Written Discourse. Proceedings of the 27th Annual Meeting of the Cognitive Science Society. (pp. 941–946). Mahwah, NJ: Erlbaum.

Hempelmann, C.F., Rus, V., Graesser, A.C., and McNamara, D.S. (2006). Evaluating State-Of-The-Art Treebank-Style Parsers for Coh-Metrix and Other Learning Technology Environments. Natural Language Engineering Special Issue: Building Educational Applications Using Natural Language Processing, 12 (2): 131–144.

Graesser, A.C., Hu, X., and McNamara, D.S. (2005). Computerized Learning Environments that Incoporate Research in Discourse Psychology, Cognitive Science, and Computational Linguistics. In A.F. Healy (Ed.), Experimental Cognitive Psychology and Its Applications: Festschrift in Honor of Lyle Bourne, Walter Kintsch, and Thomas Landauer (pp. 183–194). Washington, D.C.: American Psychological Association.

Graesser, A.C., and Petschonek, S. (2005). Automated Systems that Analyze Text and Discourse: QUAID, Coh-Metrix, and Autotutor. In W.R. Lenderking and D. Revicki (Eds.), Advancing Health Outcomes Research Methods and Clinical Applications. Mclean, VA: Degnon Associates.

Graesser, A., Louwerse, M., McNamara, D., Olney, A., Cai, Z., and Mitchell, H. (2007). Inference Generation and Cohesion in the Construction of Situation Models: Some Connections With Computational Linguistics. In F. Schmalhofer and C.A. Perfetti (Eds.), Higher Level Language Processes in the Brain: Inference and Comprehension Processes (pp. 289–310). Mahwah, NJ: Erlbaum.

Graesser, A.C., McNamara, D.S., Louwerse, M.M., and Cai, Z. (2004). Coh-Metrix: Analysis of Text on Cohesion and Language. Behavioral Research Methods, Instruments and Computers, 36: 193–202.

Graesser, A.C., Jeon, M., Yan, Y., and Cai, Z. (2007). Discourse Cohesion in Text and Tutorial Dialogue. Information Design Journal Special Issue: Discourse, Cognition and Communication, 15: 199–213.

Duran, N.D.; McCarthy, P.M.; Graesser, A.C., and McNamara, D. (2007). Using Temporal Cohesion to Predict Temporal Coherence in Narrative and Expository Texts. Behavior Research Methods, 39: 212–223.

Louwerse, M.M., McCarthy, P.M., McNamara, D.S., and Graesser, A.C. (2004). Variation in Language and Cohesion Across Written and Spoken Registers. In K. Forbus, D. Gentner, T. Regier (Eds.), Proceedings of the 26th Annual Meeting of the Cognitive Science Society (pp. 843–848). Mahwah, NJ: Erlbaum.

McNamara, D.S., Ozuru, Y., Graesser, A.C., and Louwerse, M. (2006). Validating Coh-Metrix. In R. Sun and N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (p. 573). Mahwah, NJ: Eribaum.

McNamara, D.S., Cai, Z., and Louwerse, M.M. (2007). Optimizing LSA Measures of Cohesion. In T. Landauer, D.S., McNamara, S. Dennis, and W. Kintsch (Eds.), Handbook of Latent Semantic Analysis (pp. 379–400). Mahwah, NJ: Erlbaum.

McNamara, D.S., Floyd, R.G., Best, R., and Louwerse, M. (2004). World Knowledge Driving Young Readers' Comprehension Difficulties. In Y.B. Yasmin, W.A., Sandoval, N. Enyedy, A.S. Nixon, and F. Herrera (Eds.), Proceedings of the Sixth International Conference of the Learning Sciences: Embracing Diversity in the Learning Sciences (pp. 326–333). Mahwah, NJ: Erlbaum.

O'Reilly, T., and McNamara, D.S. (2007). Reversing the Reverse Cohesion Effect: Good Texts Can Be Better for Strategic, High Knowledge Readers. Discourse Processes, 47 (2): 121–152.

Ozuru, Y., Dempsey, K., Sayroo, J., and McNamara, D.S. (2005). Effect of Text Cohesion on Comprehension of Biology Texts. Proceedings of the 27th Annual Meeting of the Cognitive Science Society (pp. 1696–1701). Hillsdale, NJ: Erlbaum.

Rus, V., Hempelmann, C., Graesser, A.C., McNamara, D.S. (2006). Evaluating State-Of-The-Art Treebank-Style Parsers for Coh-Metrix and Other Learning Technology Environments. Natural Language Engineering, 12: 1–14.