Coh-Metrix publications

Books and proceedings

Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch, W. (Eds.). (2011). Handbook of Latent Semantic Analysis. Routledge.

McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated Evaluation of Text and Discourse with Coh-Metrix. Cambridge University Press. https://doi.org/10.1017/cbo9780511894664

Crossley, S. A., & McNamara, D. S. (Eds.). (2016). Adaptive educational technologies for literacy instruction. Routledge. https://doi.org/10.4324/9781315647500

Journal articles, book chapters, proceedings, encyclopedia articles, and book reviews

Bruss, M., Albers, M.J., & McNamara, D. (2004). Changes in scientific articles over two hundred years: A Coh-Metrix analysis. In S. Tilley & S. Huang (Eds.), Proceedings of the 22nd Annual International Conference on Design of Communication: the Engineering of Quality Documentation (pp. 104-109). New York: ACM Press.

Dufty, D.F., McNamara, D., Louwerse, M., Cai, Z., & Graesser, A.C. (2004). Automatic evaluation of aspects of document quality. In S. Tilley & S. Huang (Eds.), Proceedings of the 22nd Annual International Conference on Design of Communication: the Engineering of Quality Documentation (pp. 14-16). New York: ACM Press.

Graesser, A.C., McNamara, D.S., Louwerse, M., & Cai, Z. (2004). Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, & Computers, 36, 193-202.

Louwerse, M.M., McCarthy, P.M., McNamara, D.S., & 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 Cognitive Science Society (pp.843-848). Mahwah, NJ: Erlbaum.

Best, R.M., Rowe, M., Ozuru, Y., & McNamara, D.S. (2005). Deep-level comprehension of science texts: The role of the reader and the text. Topics in Language Disorders, 25, 65-83.

Graesser, A.C., Hu, X., & McNamara, D.S. (2005). Computerized learning environments that incorporate 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.

Hempelmann, C.F., Dufty, D., McCarthy, P.M., Graesser, A.C., Cai, Z., & McNamara, D.S. (2005). Using LSA to automatically identify givenness and newness of noun phrases in written discourse. In B. G. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 941-946). Mahwah, NJ: Erlbaum.

Hempelmann, C.F., Rus, V., Graesser, A.C., & McNamara, D.S. (2005). Evaluating state-of-the-art treebank-style parsers for Coh-Metrix and other learning technology environments. In Proceedings of the Second Workshop on Building Educational Applications using Natural Language Processing and Computational Linguistics (pp. 69-76). New Brunswick, NJ: ACL.

McNamara, D.S., & Shapiro, A.M. (2005). Multimedia and hypermedia solutions for promoting metacognitive engagement, coherence, and learning. Journal of Educational Computing Research, 33, 1-29.

Bell, C.M., McCarthy, P.M., & McNamara, D.S. (2006). Variations in language use across gender: Biological versus sociological theories. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1009). Mahwah, NJ: Erlbaum.

Dufty, D.F., Graesser, A.C., Louwerse, M., & McNamara, D.S. (2006). Assigning grade level to textbooks: Is it just readability? In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1251-1256). Austin, TX: Cognitive Science Society.

Duran, N., McCarthy, P.M., Graesser, A.C., & McNamara, D.S. (2006). Using Coh-Metrix temporal indices to predict psychological measures of time. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 190-195). Austin, TX: Cognitive Science Society.

Hempelmann, C.F., Rus V., 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, 131-144.

Louwerse, M.M., Graesser, A.C., McNamara, D.S., Jeuniaux, P., & Yang, F. (2006). Coherence is also in the eye of the beholder. In Silva, M. & Cox, A. (Eds), Proceedings of the Cognitive Science Workshop “What have eye movements told us so far, and what is next?” London, University College London.

Louwerse, M.M., McNamara, D.S., Graesser, A.C., Lewis, G. & Zirnstein, M. (2006). An eye for an eye, and for other modalities. In Silva, M. & Cox, A. (Eds.), Proceedings of the Cognitive Science Workshop “What have eye movements told us so far, and what is next?” London, University College London.

McCarthy, P.M., Lewis, G.A., Dufty, D.F., & McNamara, D.S. (2006). Analyzing writing styles with Coh-Metrix. In Proceedings of the Florida Artificial Intelligence Research Society International Conference (FLAIRS) (pp. 764-770).

McNamara, D.S., Ozuru, Y., Graesser, A.C., & Louwerse, M. (2006). Validating Coh-Metrix. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 573-578). Austin, TX: Cognitive Science Society.

Rowe, M., Ozuru, Y., & McNamara, D.S. (2006). An analysis of a standardized reading ability test: what do questions actually measure? In S.A. Barab, K.E. Hay, D. T. Hickey (Eds.), Proceedings of the Seventh International Conference of the Learning Sciences (p. 627-633). Mahwah, NJ: Erlbaum.

Bellissens, C., Jeuniaux, P., Duran, N., & McNamara, D. (2007). Towards a textual cohesion model that predicts self-explanations inference generation as a function of text structure and readers’ knowledge levels. Proceedings of the 29th Annual Meeting of the Cognitive Science Society(pp. 233-238). Austin, TX: Cognitive Science Society.

Briner, S., Kurby, C., McNamara, D.S. (2007). Individual differences and the impact of forward and backward causal relations on the online processing of narratives. Proceedings of the 29th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Briner, S.W., McCarthy, P.M., & McNamara, D.S. (2007).  Assessing AutoProp: An automated propositionalization tool. Coyote Papers: Psycholinguistic and Computational Perspectives.  University of Arizona Working Papers in Linguistics, 15,1-17.

Crossley, S.A., Dufty, D.F., McCarthy, P.M., & McNamara, D.S. (2007). Toward a new readability: A mixed model approach. In D.S. McNamara and G. Trafton (Eds.), Proceedings of the 29th annual conference of the Cognitive Science Society(pp. 197-202). Austin, TX: Cognitive Science Society.

Crossley, S.A., Louwerse, M., McCarthy, P.M., & McNamara, D.S. (2007).  A linguistic analysis of simplified and authentic texts. Modern Language Journal, 91, 15-30.

Crossley, S.A., McCarthy, P.M. & McNamara, D.S. (2007). Discriminating between second language learning text-types. In D. Wilson & G. Sutcliffe (Eds.), Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference (pp. 205-210). Menlo Park, California: The AAAI Press.

Dempsey, K.B., McCarthy, P.M., & McNamara, D.S. (2007). Using phrasal verbs as an index to distinguish text genres. In D. Wilson and G. Sutcliffe (Eds.), Proceedings of the twentieth International Florida Artificial Intelligence Research Society Conference (pp. 217-222). Menlo Park, California: The AAAI Press.

Duran, N., Bellissens, C., Taylor, R., & McNamara, D. (2007). Qualifying text difficulty with automated indices of cohesion and semantics. In D.S. McNamara and G. Trafton (Eds.), Proceedings of the 29th Annual Meeting of the Cognitive Science Society(pp. 233-238). Austin, TX: Cognitive Science Society.

Duran, N.D., McCarthy, P.M., Graesser, A.C., & McNamara, D.S. (2007). Using temporal cohesion to predict temporal coherence in narrative and expository texts. Behavior Research Methods, 39, 212-223.

Graesser, A., Louwerse, M., McNamara, D.S., Olney, A., Cai, Z., & Mitchell, H. (2007) Inference generation and cohesion in the construction of situation models: Some connections with computational linguistics. In F. Schmalhofer & 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., & Rus, V. (2007). Computational modeling of discourse and conversation. In M. Spivey, M. Joanisse, & K. McRae (Eds.), Cambridge handbook of psycholinguistics (pp.). Cambridge, UK: Cambridge University Press.

Hall, C., McCarthy, P.M., Lewis, G.A., Lee, D.S., & McNamara, D.S. (2007).  A Coh-Metrix assessment of American and English/Welsh Legal English.  Coyote Papers: Psycholinguistic and Computational Perspectives.  University of Arizona Working Papers in Linguistics, 15, 40-54.

Lightman, E.J., McCarthy, P.M., Dufty, D.F., & McNamara, D.S. (2007). The structural organization of high school educational texts. In D. Wilson & G. Sutcliffe (Eds.), Proceedings of the twentieth International Florida Artificial Intelligence Research Society Conference (pp. 235-240). Menlo Park, California: The AAAI Press.

Lightman, E.J., McCarthy, P.M., Dufty, D.F., & McNamara, D.S. (2007). Using computation text analysis tool to compare the lyrics of suicidal and non-suicidal song-writers. In D.S. McNamara and G. Trafton (Eds.), Proceedings of the 29th annual conference of the Cognitive Science Society(pp. 1217-1222).Austin, TX: Cognitive Science Society.

McCarthy, P.M., Briner, S.W., Rus, V., & McNamara, D.S. (2007). Textual signatures: Identifying text-types using latent semantic analysis to measure the cohesion of text structures. In A. Kao, & S. Poteet (Eds.), Natural language processing and text mining (pp. 107-122) . London: Springer-Verlag UK. [PDF]

McCarthy, P.M., Lehenbauer, B.M., Hall, C., Duran, N.D., Fujiwara, Y., & McNamara, D.S. (2007). A Coh-Metrix analysis of discourse variation in the texts of Japanese, American, and British Scientists. Foreign Languages for Specific Purposes, 6, 46-77.

McNamara, D.S. (2007). IIS: A marriage of computational linguistics, psychology, and educational technologies. In D. Wilson & G. Sutcliffe (Eds.), Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference (pp. 15-20). Menlo Park, California: The AAAI Press.

Millis, K., Magliano, J., Wiemer-Hastings, K., Todaro, S., & McNamara, D.S. (2007). Assessing and improving comprehension with Latent Semantic Analysis. In T. Landauer, D.S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis (pp. 207-225). Mahwah, NJ: Erlbaum.

Boonthum-Denecke, C., Levinstein, I.B., McNamara, D.S., Magliano, J.P., & Millis, K.K. (2008). NLP techniques for intelligent tutoring systems. In J. Rabuñal, J. Dorado & A. Pazos (Eds), Encyclopedia of Artificial Intelligence (pp. 1253-1258). Hershey, PA: Idea Group, Inc.

Crossley, S.A., Greenfield, J., & McNamara, D.S. (2008). Assessing text readability using cognitively based indices. TESOL Quarterly, 42, 475-493.

Crossley, S.A. & McNamara, D.S. (2008). Assessing second language reading texts at the intermediate level: An approximate replication of Crossley, Louwerse, McCarthy, and McNamara (2007). Language Teaching, 41, 229-409.

Crossley, S.A., Salsbury, T. McCarthy, P.M., & McNamara, D.S. (2008), LSA as a measure of coherence in second language natural discourse. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th annual conference of the Cognitive Science Society (pp.1906-1911). Washington, D.C.: Cognitive Science Society.

Crossley, S.A., Salsbury, T., McCarthy, P.M., & McNamara, D.S. (2008). Using latent semantic analysis to explore second language lexical development. In D. Wilson & G. Sutcliffe (Eds.), Proceedings of the 21st International Florida Artificial Intelligence Research Society Conference (pp. 136-141). Menlo Park, CA: The AAAI Press.

Graesser, A.C., Jeon, M., Cai, Z., & McNamara, D.S. (2008). Automatic analyses of language, discourse, and situation models. In W. van Peer & J. Auracher (Eds.), New Beginnings for Study of Literature. New Castle: Cambridge Scholars Publications.

McCarthy, P.M., Renner, A.M., Duncan, M.G., Duran, N.D., Lightman, E.J., & McNamara, D.S. (2008). Identifying topic sentencehood. Behavior Research and Methods, 40, 647-664.

Ozuru, Y., Rowe, M., O’Reilly, T., & McNamara, D.S. (2008). Where’s the difficulty in standardized reading tests: The passage or the question? Behavior Research Methods. 40, 1001-1015. 

Rus, V., McCarthy, P.M., McNamara, D.S., & Graesser, A.C. (2008). Natural language understanding and assessment. In J.R. Rabuñal, J. Dorado, & A. Pazos (Eds.). Encyclopedia of Artificial Intelligence (pp. 1179-1184). Hershey, PA: Idea Group, Inc.

Crossley, S.A., Louwerse, M., & McNamara, D.S. (2009). Identifying linguistic cues that distinguish text types: A comparison of first and second language speakers. Language Research, 42, 361-381.

Crossley, S.A. & McNamara, D.S. (2009). Computational assessment of lexical differences in L1 and L2 writing. Journal of Second Language Writing, 18, 119-135.

Crossley, S.A., Salsbury, T., & McNamara, D.S. (2009). Measuring L2 lexical growth using hypernymic relationships. Language Learning, 59, 307-334.

Dempsey, K.B., McCarthy, P.M., Myers, J.C., Weston, J., & McNamara, D.S. (2009). Determining paragraph type from paragraph position. In C.H. Lane & H.W. Guesgen (Eds.), Proceedings of the 22nd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 33-38) Menlo Park, CA: The AAAI Press.

Duran, N.D., Crossley, S.A., Hall, C., McCarthy, P.M., & McNamara, D.S. (2009). Expanding a catalogue of deceptive linguistic features with NLP technologies. In C.H. Lane & H.W. Guesgen (Eds.), Proceedings of the 22nd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 243-248). Menlo Park, CA: The AAAI Press.

Healy, S.L., Weintraub, J.D., McCarthy, P.M., Hall, C., & McNamara, D.S. (2009). Assessment of LDAT as a grammatical diversity assessment tool. In C. H. Lane & H. W. Guesgen (Eds.), Proceedings of the 22nd International Florida Artificial Intelligence Research Society (FLAIRS) International Conference (pp. 249-253). Menlo Park, CA: The AAAI Press.

McCarthy, P.M., Cai, Z., & McNamara, D.S. (2009). Computational replication of human assessments of paraphrase. In C.H. Lane & H.W. Guesgen (Eds.), Proceedings of the 22nd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 266-271). Menlo Park, CA: The AAAI Press.

McCarthy, P.M., Guess, R.H., & McNamara, D.S. (2009). The components of paraphrase evaluations. Behavioral Research Methods, 41, 682-690. [PDF]

McCarthy, P.M., Hall, C., Duran, N.D., Doiuchi, M., Duncan, B., Fujiwara, Y., & McNamara, D.S. (2009). Analyzing journal abstracts written by Japanese, American, and British scientists using Coh-Metrix and the Gramulator. The ESPecialist, 30, 141-173.

McCarthy, P.M., Myers, J.C., Briner, S.W., Graesser, A.C., & McNamara, D.S. (2009). Are three words all we need? A psychological and computational study of sub-sentential genre recognition. Journal for Language Technology and Computational Linguistics, 24, 23-55.

Bellissens, C., Jeuniaux, P., Duran, N.D., McNamara, D.S. (2010). A text relatedness and dependency computational model: Using Latent Semantic Analysis and Coh-Metrix to predict self-explanation quality. Studia Informatica Universalis, 8(1), 85-125.

Crossley, S.A. & McNamara, D.S. (2010). Cohesion, coherence, and expert evaluations of writing proficiency. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 984-989). Austin, TX: Cognitive Science Society.

Crossley, S. A., & McNamara, D. S. (2010). Interlanguage talk: What can breadth of knowledge features tell us about input and output differences? In H. W. Guesgen & C. Murray (Eds.), Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 229-234). Menlo Park, CA: The AAAI Press.

Crossley, S. A., Salsbury, T., & McNamara, D. S. (2010). The development of polysemy and frequency use in English second language speakers. Language Learning, 60, 573-605. [Awarded most outstanding article of the year in Language Learning for 2010.]

Crossley, S. A., Salsbury, T., & McNamara, D. S. (2010). The development of semantic relations in second language speakers. A case for Latent Semantic Analysis. Vigo International Journal of Applied Linguistics, 7, 55-74.

Crossley, S. A., Salsbury, T., & McNamara, D. S. (2010). The role of lexical cohesive devices in triggering negotiations for meaning. Issues in Applied Linguistics, 18, 55-80.

Duran, N.D., Hall, C., McCarthy, P.M., & McNamara, D.S. (2010). The linguistic correlates of conversational deception: Comparing natural language processing technologies. Applied Psycholinguistics.

Graesser, A. C., McNamara, D. S., & Louwerse, M. M. (2010). Methods of automated text analysis. In M. L. Kamil, P. D. Pearson, E. B. Moje, & P. Afflerbach (Eds.), Handbook of reading research: Volume IV(pp. 34-53). Mahwah, NJ: Erlbaum.

Lintean, M., Moldovan, C., Rus, V., & McNamara, D.S. (2010). The role of local and global weighting in assessing the semantic similarity of texts using Latent Semantic Analysis. In H.W. Guesgen & C. Murray (Eds.), Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 235-240). Menlo Park, CA: The AAAI Press.

McNamara, D.S., Crossley, S.A., & McCarthy, P.M. (2010). Linguistic features of writing quality. Written Communication, 27, 57-86.

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. [LINK]

Myers, J.C., McCarthy, P.M., Duran, N.D., & McNamara, D.S. (2010). The bit in the middle and why it’s important: A computational analysis of the linguistic features of body paragraphs. Behavior Research Methods, 41, 201-209.

Weston, J., Crossley, S.A., & McNamara, D.S. (2010). Towards a computational assessment of freewriting quality. In H.W. Guesgen & C. Murray (Eds.), Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 283-288). Menlo Park, CA: The AAAI Press.

Crossley, S.A., Allen, D., & McNamara, D.S. (2011). Text readability and intuitive simplification: A comparison of readability formulas. Reading in a Foreign Language, 23, 84-102.

Crossley, S. A., Dempsey, K., & McNamara, D. S. (2011). Classifying paragraph types using linguistic features: Is paragraph positioning important? Journal of Writing Research, 3, 119-143.

Crossley, S. A., & McNamara, D. S. (2011). Shared features of L2 writing: Intergroup homogeneity and text classification. Journal of Second Language Writing, doi: 10.1016/j.jslw2011.05.007.

Crossley, S.A., & McNamara, D.S. (2011). Text coherence and judgments of essay quality: Models of quality and coherence. In L. Carlson, C. Hoelscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. (pp. 1236-1231). Austin, TX: Cognitive Science Society.

Crossley, S.A., & McNamara, D.S. (2011). Understanding expert ratings of essay quality: Coh-Metrix analyses of first and second language writing. IJCEELL, 21, 170-191.

Crossley, S. A., Roscoe, R., Graesser, A., & McNamara, D. S. (2011). Predicting human scores of essay quality using computational indices of linguistic and textual features. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education. (pp. 438-440). Auckland, New Zealand: AIED.

Crossley, S. A., Salsbury, T., McNamara, D. S., & Jarvis, S. (2011). Predicting lexical proficiency in language learner texts using computational indices. Language Testing, 28, 562-580.

Crossley, S.A., Salsbury, T., McNamara, D.S., & Jarvis, S. (2011). What is lexical proficiency? Some answers from computational models of speech data. TESOL Quarterly, 45, 182-193.

Crossley, S.A., Weston, J., McLain Sullivan, S.T., & McNamara, D.S.(2011). The development of writing proficiency as a function of grade level: A linguistic analysis. Written Communication, 28, 282-311.

Feng, S., Cai, Z., Crossley, S.A., & McNamara, D.S. (2011). Simulating human ratings on word concreteness. In R. C. Murray & P. M. McCarthy (Eds.), Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 245-250). Menlo Park, CA: AAAI Press.

Graesser, A.C. & McNamara, D.S. (2011). Computational analyses of multilevel discourse comprehension. Topics in Cognitive Science, 2, 371-398.

Graesser, A.C., McNamara, D.S., & Kulikowich, J.M. (2011). Coh Metrix: Providing multilevel analyses of text characteristics. Educational Researcher, 40, 223-234.

McNamara, D. S. (Ed.). (2011). Computational methods to extract meaning from text and advance theories of human cognition. Topics in Cognitive Science, 3 (1), 3-17. https://doi.org/10.1111/j.1756-8765.2010.01117.x

Roscoe, R.D., Crossley, S.A., Weston, J.L., & McNamara, D.S. (2011). Automated assessment of paragraph quality: Introductions, body, and conclusion paragraphs. In R. C. Murray & P. M. McCarthy (Eds.), Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 281-286). Menlo Park, CA: AAAI Press.

Rus, V., Feng, S., Brandon, R., Crossley, S.A., & McNamara, D.S. (2011). A linguistic analysis of student-generated paraphrases. In R. C. Murray & P. M. McCarthy (Eds.), Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 293-298). Menlo Park, CA: AAAI Press.

Salsbury, T., Crossley, S. A., & McNamara, D. S. (2011). Psycholinguistic word information in second language oral discourse. Second Language Research, 27, 343-360.

Weston, J.L., Crossley, S.A., McCarthy, P.M., & McNamara, D.S. (2011). Number of words versus number of ideas: Finding a better predictor of writing quality. In R. C. Murray & P. M. McCarthy (Eds.), Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 335-340). Menlo Park, CA: AAAI Press.

Bell, C., McCarthy, P.M., & McNamara, D.S. (2012). Using LIWC and Coh-Metrix to investigate gender differences in linguistic styles. In P.M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 545-556).Hershey, PA: IGI Global.

Brandon, R., Crossley, S. A., & McNamara, D. S. (2012). A linguistic analysis of expert-generated paraphrases. In P. M. McCarthy & G. M. Youngblood (Eds.), Proceedings of the 25th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 268-271). Menlo Park, CA: The AAAI Press.

Crossley, S. A., Cai, Z., & McNamara, D. S. (2012). Syntagmatic, paradigmatic, and automatic n-gram approaches to assessing essay quality. In P. M. McCarthy & G. M. Youngblood (Eds.), Proceedings of the 25th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 214-219). Menlo Park, CA: The AAAI Press.

Crossley, S. A., & McNamara, D. S. (2012). Detecting the first language of second language writers using automated indices of cohesion, lexical sophistication, syntactic complexity and conceptual knowledge. In S. Jarvis & S. A. Crossley (Eds.), Approaching language transfer through text classification: Explorations in the detection-based approach (pp. 106-126). Bristol, UK: Multilingual Matters.

Crossley, S.A., & McNamara, D. S. (2012). Interlanguage talk: A computational analysis of non-native speakers’ lexical production and exposure. In P. M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 425-437). Hershey, PA: IGI Global.

Crossley, S.A., & McNamara, D.S. (2012). Predicting second language writing proficiency: The roles of cohesion and linguistic sophistication. Journal of Research in Reading, 53, 115-136. 

Crossley, S.A., Salsbury, T., & McNamara, D. S. (2012). Predicting the proficiency level of language learners using lexical indices. Language Testing, 29, 240-260.

Graesser, A. C., & McNamara, D. S. (2012). Automated analysis of essays and open-ended verbal responses. In H. Cooper, P. Camic, R. Gonzalez, D. Long, & A. Panter (Eds.), APA handbook of research methods in psychology: Foundations, planning, measures, and psychometrics. Washington, DC: American Psychological Association.

Graesser, A. C., & McNamara, D. S. (2012). Reading instruction: Technology-based supports for classroom instruction. In C. Dede & J. Richards (Eds.), Digital teaching platforms: Customizing classroom learning for each student (pp. 71-87). New York: Teachers College Press.

Jackson, G.T., & McNamara, D.S. (2012). Applying NLP metrics to students’ self explanations. In P.M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 261-275). Hershey, PA: IGI Global.

Jarvis, S., Bestgen, Y., Crossley, S. A., Granger, S., Paquot, M., Thewissen, J., & McNamara, D. S. (2012). The comparative and combined contributions of n-grams, Coh-Metrix indices and error types in the L1 classification of learner texts. In S. Jarvis & S. A. Crossley (Eds.), Approaching language transfer through text classification: Explorations in the detection-based approach (pp. 154-177). Bristol, UK: Multilingual Matters.

McCarthy, P.M., Dufty, D., Hempelman, C., Cai, Z., Graesser, A.C., & McNamara, D.S. (2012). Newness and givenness of information: Automated identification in written discourse. In P.M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 457-478). Hershey, PA: IGI Global.

McNamara, D.S., & Graesser, A.C. (2012). Coh-Metrix: An automated tool for theoretical and applied natural language processing. In P.M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 188-205). Hershey, PA: IGI Global.

McNamara, D.S., Graesser, A.C., & Louwerse, M.M. (2012). Sources of text difficulty: Across genres and grades. In J.P. Sabatini, E. Albro, & T. O’Reilly (Eds.), Measuring up: Advances in how we assess reading ability (pp. 89-116). Lanham, MD: R&L Education. [

McNamara, D.S., Raine, R., Roscoe, R., Crossley, S., Jackson, G.T., Dai, J., Cai, Z., Renner, A., Brandon, R., Weston, J., Dempsey, K., Carney, D., Sullivan, S., Kim, L., Rus, V., Floyd, R., McCarthy, P.M., & Graesser, A.C. (2012). The Writing-Pal: Natural language algorithms to support intelligent tutoring on writing strategies. In P.M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 298-311). Hershey, P.A.: IGI Global.

Renner, A., McCarthy, P.M., Boonthum, C., & McNamara, D.S. (2012). Maximizing ANLP evaluation: Harmonizing flawed input. In P.M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 438-456). Hershey, PA: IGI Global.

Roscoe, R., Kugler, D., Crossley, S., Weston, J., & McNamara, D. S. (2012). Developing pedagogically-guided threshold algorithms for intelligent automated essay feedback. In P. M. McCarthy & G. M. Youngblood (Eds.), Proceedings of the 25th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 466-471). Menlo Park, CA: The AAAI Press.

Rus, V., Lintean, M., Graesser, A. C., & McNamara, D. S. (2012). Text-to-text similarity of sentences. In P.M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 110-121). Hershey, PA: IGI Global.

Weston, J., Crossley, S. A., & McNamara, D. S. (2012). Computationally assessing expert judgments of freewriting quality. In P.M. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing and content analysis: Identification, investigation, and resolution (pp. 365-382). Hershey, PA: IGI Global.

Crossley, S. A., Cobb, T., & McNamara, D. S. (2013). Comparing count-based and band-based indices of word frequency: Implications for active vocabulary research and pedagogical applications. System, 41, 965-981.

Crossley, S. A., Defore, C., Kyle, K., Dai, J., & McNamara, D. S. (2013). Paragraph specific n-gram approaches to automatically assessing essay quality. In S. K. D’Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (pp. 216-219). Heidelberg, Berlin, Germany: Springer.

Crossley, S. A., & McNamara, D. S. (2013). Text analysis tools for spoken response grading. Language Learning and Technology, 17, 171-192.

Crossley, S. A., Roscoe, R. D., & McNamara, D. S. (2013). Using automatic scoring models to detect changes in student writing in an intelligent tutoring system. In C. Boonthum-Denecke & G. M. Youngblood (Eds.), Proceedings of the 26th International Flordia Artificial Intelligence Research Society (FLAIRS) Conference (pp. 208-213). Menlo Park, CA: The AAAI Press.

Crossley, S. A., Salsbury, T., & McNamara, D. S. (2013). Validating lexical measures using human scores of lexical proficiency. In M. Daller & S. Jarvis (Eds.), Vocabulary knowledge: Human ratings and automated measures (pp. 105-134). Philadelphia, PA: John Benjamins.

Crossley, S. A., Varner (Allen), L. K., & McNamara, D. S. (2013). Cohesion-based prompt effects in argumentative writing. In C. Boonthum-Denecke & G. M. Youngblood (Eds.), Proceedings of the 26th Annual Flordia Artificial Intelligence Research Society (FLAIRS) Conference (pp. 202-207). Menlo Park, CA: The AAAI Press.

Crossley, S. A., Varner (Allen), L. K., Roscoe, R. D., & McNamara, D. S. (2013). Using automated cohesion indices as a measure of writing growth in intelligent tutoring systems and automated essay writing systems. In K. Yacef et al. (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED), (pp. 269-278). Heidelberg, Berlin: Springer.

Guo, L., Crossley, S. A., & McNamara, D. S. (2013). Predicting human judgments of essay quality in both integrated and independent second language writing samples: A comparison study. Assessing Writing, 18, 218-238.

Kyle, K., Crossley, S. A., Dai, J., & McNamara, D. S. (2013). Native language identification: A key ngrams approach. In Proceedings of the North American Association for Computational Linguistics (NAACL)(pp. 242).

McNamara, D. S. (2013). The epistemic stance between the author and reader: A driving force in the cohesion of text and writing. Discourse Studies, 15, 575-592.

McNamara, D. S., Crossley, S. A., & Roscoe, R. D. (2013). Natural language processing in an intelligent writing strategy tutoring system. Behavior Research Methods, 45, 499-515.

Roscoe, R. D., Varner (Allen), L. K., Crossley, S. A., & McNamara, D. S. (2013). Developing pedagogically-guided threshold algorithms for intelligent automated essay feedback. International Journal of Learning Technology, 8, 362-381.

Varner (Allen), L. K., Jackson, G. T., Snow, E. L., & McNamara, D. S. (2013). Are you committed? Investigating interactions among reading commitment, natural language input, and students’ learning outcomes. In S. K. D’Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (pp. 368-369). Heidelberg, Berlin, Germany: Springer.

Varner (Allen), L. K., Jackson, G. T., Snow, E. L., & McNamara, D. S. (2013). Does size matter? Investigating user input at a larger bandwidth. In C. Boonthum-Denecke & G. M. Youngblood (Eds.),Proceedings of the 26th International Flordia Artificial Intelligence Research Society (FLAIRS) Conference (pp. 546-549). Menlo Park, CA: AAAI Press.

Varner (Allen), L. K., Jackson, G. T., Snow, E. L., & McNamara, D. S. (2013). Linguistic content analysis as a tool for improving adaptive instruction. In K. Yacef et al. (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED), (pp. 692-695). Heidelberg, Berlin: Springer.

Varner (Allen), L. K., Roscoe, R. D., & McNamara, D. S. (2013). Evaluative misalignment of 10th-grade student and teacher criteria for essay quality: An automated textual analysis. Journal of Writing Research, 5, 35-59. doi: 10.17239/jowr-2013.05.01.2

Crossley, S. A., Allen, L. K., & McNamara, D. S. (2014). A multidimensional analysis of essay writing: What linguistic features tell us about situational parameters and the effects of language functions on judgments of quality. In T. B. Sardinha and M. V. Pinto (Eds.), Multi-dimensional analysis, 25 years on: A tribute to Douglas Biber (pp. 197-237). Philadelphia, PA: John Benjamins.

Crossley, S. A., Allen, L. K., & McNamara, D. S. (2014). Analyzing discourse processing using a simple natural language processing tool (SiNLP). Discourse Processes, 51, 511-534. [LINK]

Crossley, S. A., Feng, S., Cai, Z., & McNamara, D. S. (2014). Computer simulations of MRC and Psycholinguistics Database word properties: Concreteness, familiarity, and imageability. In M. Daller & S. Jarvis (Eds.), Vocabulary knowledge: Human ratings and automated measures (pp. 135-156). Philadelphia, PA: John Benjamins.

Crossley, S. A., Kyle, K., Allen, L. K., Guo, L., & McNamara, D. S. (2014). Linguistic microfeatures to predict L2 writing proficiency: A case study in automated writing evaluation. Journal of Writing Assessment. [LINK]

Crossley, S. A., & McNamara, D. S. (2014). Developing component scores from natural language processing tools to assess human ratings of essay quality. In W. Eberle & C. Boonthum-Denecke (Eds.),Proceedings of the 27th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 381-386). Palo Alto, CA: AAAI Press.

Crossley, S. A., & McNamara, D. S. (2014). Does writing development equal writing quality? A computational investigation of syntactic complexity in L2 learners. Journal of Second Language Writing, 26, 66-79.

Crossley, S. A., Roscoe, R. D., & McNamara, D. S. (2014). What is successful writing? An investigation into the multiple ways writers can write high quality essays. Written Communication, 31, 181-214. Awarded John R. Hayes Award for Excellence in Research for 2014.

Crossley, S. A., Salsbury, T., & McNamara, D. S. (2014). Assessing lexical proficiency using analytic ratings: A case for collocation accuracy. Applied Linguistics. Advance online publication. doi: 10.1093/applin/amt056.

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. The Elementary School Journal, 114, 210-229.

McNamara, D. S. & Schober, M. F. (Eds.). (2014). Society for Text and Discourse Annual Meeting 2013: Introduction to the Special Issue [Special issue]. Discourse Processes, 51(5-6), 357-358. https://doi.org/10.1080/0163853X.2014.915378 [PDF]

Roscoe, R. D., Allen, L. K., Weston, J. L., Crossley, S. A., & McNamara, D. S. (2014). The Writing Pal intelligent tutoring system: Usability testing and development. Computers and Composition, 34, 39-59. [PDF]

Roscoe, R. D., Crossley, S. A., Snow, E. L., Varner (Allen), L. K., & McNamara, D. S. (2014). Writing quality, knowledge, and comprehension correlates of human and automated essay scoring. In W. Eberle & C. Boonthum-Denecke (Eds.), Proceedings of the 27th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 393-398). Palo Alto, CA: AAAI Press.

Crossley, S. A., Allen, L. K., Snow, E. L., & McNamara, D. S. (2015). Pssst…Textual features…There is more to Automatic Essay Scoring than just you!. In J. Baron, G. Lynch, N. Maziarz, P. Blikstein, A. Merceron, & G. Siemens (Eds.), Proceedings of the 5th International Learning Analytics & Knowledge Conference (LAK’15), (pp. 203-207). Poughkeepsie, NY: ACM.

Dascalu, M., Stavarache, L. L., Dessus, P., Trausan-Matu, S., McNamara, D. S., & Maryse, B. (2015). ReaderBench: An integrated cohesion-centered framework. In C. Rensing, T. Klobucar, & G. Conole (Eds.), Proceedings of the 10th European Conference on Technology Enhanced Learning (ECTEL), (pp. 505-508). Toledo, Spain: Springer.

Dascalu, M., Trausan-Matu, S., McNamara, D. S., & Dessus, P. (2015). ReaderBench – Automated evaluation of collaboration based on cohesion and dialogism. International Journal of Computer-Supported Collaborative Learning, 10(4), 395-423.

Higgs, K., Magliano, J. P., Vidal-Abarca, E., Martínez, T., & McNamara D. S. (2015). Bridging skill and task-oriented reading. Discourse Processes.

Jung, Y., Crossley, S. A., & McNamara, D. S. (2015). Linguistic features in MELAB writing task performances. CaMLA Working Papers, No 2015-5, 1-17. Retrieved from Cambridge Michigan Language Assessments website: http://www.cambridgemichigan.org/wp-content/uploads/2015/04/CWP-2015-05.pdf.

McNamara, D. S. (2015). Self-Explanation and Reading Strategy Training (SERT) Improves low-knowledge students’ science course performance. Discourse Processes. [LINK]

McNamara, D. S., Crossley, S. A., Roscoe, R. D., Allen, L. K., & Dai, J. (2015). Hierarchical classification approach to automated essay scoring. Assessing Writing, 23, 35-59.

McNamara, D. S. & Schober, M. F. (Eds.). (2015). 2014 Society for Text and Discourse Annual Meeting: Introduction to the Special Issue [Special issue]. Discourse Processes, 52(5-6), 335-336. https://doi.org/10.1080/0163853X.2015.1045802 [PDF]

Roscoe, R. D., Jacovina, M. E., Harry, D., Russell, D. G., & McNamara, D. S. (2015). Partial verbal redundancy in multimedia presentations for writing strategy instruction. Applied Cognitive Psychology, 29, 669–679. doi: 10.1002/acp.3149

San Pedro, M. O., Snow, E. L., McNamara, D. S., Baker. R. S., & Heffernan, N. (2015) Exploring dynamical assessments of affect, behavior, and cognition and math state test achievement. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, & M. Desmarais (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), (pp. 85-92). Madrid, Spain.

Sullins, J., McNamara, D. S., Acuff, S., Neely, D., Hildebrand, E., Stewart, G., & Hu, X. (2015). Are you asking the right questions: The use of animated agents to teach learners to become better question askers. In C. Boonthum-Denecke, I. Russell, & W. Eberle (Eds.), Proceedings of the 28th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 479-481). Hollywood, FL: AAAI Press.

Allen, L. K., Jacovina, M. E., Dascalu, M., Roscoe, R. D., Kent, K. M., Likens, A. D., & McNamara, D. S. (2016). {ENTER}ing the time series {SPACE}: Uncovering the writing process through keystroke analysis. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining, Raleigh, NC (EDM 2016), (pp.22-29). Raleigh, NC: International Educational Data Mining Society.

Crossley, S. A., Allen, L. K., & McNamara, D. S. (2016). The Writing Pal: A writing strategy tutor. In S. A. Crossley & D. S. McNamara (Eds.) Adaptive educational technologies for literacy instruction (pp. 204-224). New York: Taylor & Francis, Routledge.

Crossley, S. A., Kyle, K., & McNamara, D. S. (2016). The development and use of cohesive devices in L2 writing and their relations to judgments of essay quality. The Journal of Second Language Writing, 32, 1-16.

Crossley, S. A., Kyle, K., & McNamara, D. S. (2016). The Tool for the Automatic Analysis of Text Cohesion (TAACO): Automatic assessment of local, global, and text cohesion. Behavior Research Methods, 48(4), 1227-1237.

Crossley, S. A. & McNamara, D. S. (2016). Educational technologies and literacy development. In S. A. Crossley & D. S. McNamara (Eds.) Adaptive educational technologies for literacy instruction (pp. 1-12). New York: Taylor & Francis, Routledge.

Crossley, S. A., & McNamara, D. S. (2016). Say more and be more coherent: How elaboration and cohesion can increase writing quality. The Journal of Writing Research, 7(3), 351-370.

Crossley, S. A., Muldner, K., & McNamara, D. S. (2016). Idea generation in student writing: Computer assessments and links to successful writing. Written Communication, 1-27.

Dascalu, M., McNamara, D. S., Crossley, S. A., & Trausan-Matu, S. (2016). Age of exposure: A model of word learning. In S. Zilberstein, D. Schuurmans, & M. Wellman (Eds.), Proceedings of the 30th Annual Meeting of the Association for the Advancement of Artificial Intelligence (AAAI’16). Phoenix, AZ: AAAI Press.

Jackson, G. T., Allen, L. K., & McNamara, D. S. (2016). Common Core TERA: Text Ease and Readability Assessor. In D. S. McNamara & S. A. Crossley (Eds.) Adaptive educational technologies for literacy instruction (pp.49-68). New York: Taylor & Francis, Routledge.

Liu, Z., Brown, R., Lynch, C.F., Barnes, T., Baker, R., Bergner, Y., & McNamara, D. S. (2016). MOOC learner behaviors by country and culture; an exploratory analysis. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016), (pp.127-134). Raleigh, NC: International Educational Data Mining Society.

Roscoe, R. D., Jacovina, M. E., Allen, L. K., Johnson, A. C., & McNamara, D. S. (2016). Toward revision-sensitive feedback in automated writing evaluation. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016), (pp.628-629). Raleigh, NC: International Educational Data Mining Society.

Skalicky, S., Berger, C. M., Crossley, S. A., & McNamara, D. S. (2016). Linguistic features of humor in academic writing. Advances in Language and Literacy Studies, 7(3), 248-259.

Snow, E. L., Likens, A. D., Allen, L. K., & McNamara, D. S. (2016). Taking control: Stealth assessment of deterministic behaviors within a game-based system. International Journal of Artificial Intelligence in Education, 26, 1011-1032.

Crossley, S. A., Barnes, T., Lynch, C., & McNamara, D. S. (2017). Linking language to math success in blended course. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings of the 10th International Conference on Educational Data Mining (EDM) (pp. 180-185), Wuhan, China: International Educational Data Mining Society.

Crossley, S. A., Dascalu, M., Baker, M., McNamara, D. S., & Trausan-Matu, S. (2017). Predicting Success in Massive Open Online Courses (MOOC) Using Cohesion Network Analysis. In B. K. Smith, M. Borge, K. Y. Lim, & E. Mercier (Eds.), In Proceedings of the 12th International Conference on Computer-Supported Collaborative Learning (CSCL 2017). (pp. 103-110). Philadelphia, PA: ISLS.

Dascalu, M., Gutu, G., Paraschiv, I., Ruseti, s., Dessus, P., McNamara, D. S., Crossley, S. A., & Trausan-Matu, S. (2017). Cohesion-centered analysis of CSCL environments using ReaderBench. In B. Boulay, R.Baker, & E. Andre (Eds.),18th International Conference on Artificial Intelligence in Education (AIED 2017). (pp. 485–489). Wuhan, China: Springer.

Dascalu, M., McNamara, D. S., Trausan-Matu, S., & Allen, L. K., (2017). Cohesion network analysis of CSCL participation. Behavior Research Methods, 1-16.

Jackson, G. T. & McNamara, D. S. (2017). The motivation and mastery cycle framework: Predicting long-term benefits of educational games. In Y. Baek (Ed.), Game-based learning: Theory, strategies and performance outcomes. (pp. 97 – 122). Nova Science Publishers: New York.

Likens, A. D., Allen, L. K., & McNamara, D. S. (2017). Keystroke dynamics predict essay quality. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Meeting of the Cognitive Science Society (CogSci 2017), (pp.2573-2578). London, UK: Cognitive Science Society.