Natural language processing 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

Doane, S.M., Sohn, Y.W., McNamara, D.S., & Adams, D. (2000). Comprehension-based skill acquisition. Cognitive Science, 24, 1-52. [

Shapiro, A.M., & McNamara, D.S. (2000). The use of latent semantic analysis as a tool for the quantitative assessment of understanding and knowledge. Journal of Educational Computing Research, 22, 1-36.

Millis, K.K, Magliano, J.P., Wiemer-Hastings, K., & McNamara, D.S. (2001). Using LSA in a computer-based test of reading comprehension. In J.D. Moore, C. Luckhardt-Redfield, & W.L. Johnson (Eds.), Artificial intelligence in education: AI-ED in the wired and wireless future: Vol. 68. Frontiers in artificial intelligence and applications (pp. 583-585). Amsterdam, The Netherlands: IOS Press. [

Magliano, J.P., Wiemer-Hastings, K., Millis, K.K., Muñoz, B.D., & McNamara, D.S. (2002). Using latent semantic analysis to assess reader strategies. Behavior Research Methods, Instruments, & Computers, 34, 181-188.

Hu, X., Cai, Z., Franceschetti, D., Penumatsa, P., Graesser, A.C., Louwerse, M.M., McNamara, D.S., & the Tutoring Research Group (2003). LSA: First dimension and dimensional weighting. In R. Alterman & D. Hirsh (Eds.), Proceedings of the 25th Annual Conference of the Cognitive Science Society (pp. 587-592). Mahwah, NJ: Erlbaum. [PDF]

Kurby, C.A., Wiemer-Hastings, K., Ganduri, N., Magliano, J.P., Millis, K.K., & McNamara, D.S. (2003). Computerizing reading training: Evaluation of a latent semantic analysis space for science text. Behavior Research Methods, Instruments, & Computers, 35, 244-250.

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.

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

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.

Millis, K., Kim, H.J., Todaro, S., Magliano, J.P., Wiemer-Hastings, K., & McNamara, D.S. (2004). Identifying reading strategies using latent semantic analysis: Comparing semantic benchmarks. Behavior Research Methods, Instruments, & Computers, 36, 213-221.

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.

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.

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.

Boonthum, C., Levinstein, I., & McNamara, D.S. (2007). Evaluating self-explanations in iSTART: Word matching, latent semantic analysis, and topic models. In A. Kao & S. Poteet (Eds.), Natural Language Processing and Text Mining (pp. 91-106). London: Springer-Verlag UK.

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.

Hu, X., Cai, Z., Wiemer-Hasting, P., Graesser, A., & McNamara, D.S. (2007). Strengths, limitations, and extensions of LSA. In T. Landauer, D.S., McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis (pp. 401-425). Mahwah, NJ: Erlbaum.

Kintsch, W., McNamara, D.S. Dennis, S., & Landauer, T.K. (2007). LSA and meaning: In theory and application. In T. Landauer, D.s., McNamara, S. Dennis, & W.Kintsch (Eds.),Handbook of Latent Semantic Analysis (pp. 467-479). Mahwah, NJ: Erlbaum.

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.

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.

McCarthy, P.M., & McNamara, D.S. (2007). Are seven words all we need? Recognizing genre at the sub-sentential level. In D.S. McNamara and G. Trafton (Eds.), Proceedings of the 29th annual conference of the Cognitive Science Society(pp. 1295-1300). Austin, TX: Cognitive Science Society.

McCarthy, P.M., Rus, V., Crossley, S.A., Bigham, S.C., Graesser, A.C., & McNamara, D.S. (2007). Assessing entailer with a corpus of natural language. In D. Wilson & G. Sutcliffe (Eds.), Proceedings of the twentieth International Florida Artificial Intelligence Research Society Conference (pp. 247-252). Menlo Park, California: The AAAI Press.

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.

McNamara, D.S., Cai, Z., & Louwerse, M.M. (2007). Optimizing LSA measures of cohesion. In T. Landauer, D.S. McNamara, S. Dennis, & W. Kintch (Eds.), Handbook of Latent Semantic Analysis (pp. 379-400). Mahwah, NJ: Erlbaum.

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.

Rus, V., McCarthy, P.M., Lintean, M.C., Graesser, A.C., & McNamara, D.S. (2007). Assessing student self-explanations in an Intelligent Tutoring System. In D.S. McNamara & G. Trafton (Eds.), Proceedings of the 29th annual conference of the Cognitive Science Society (pp. 623-628). Austin, TX: Cognitive Science Society.

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.

McCarthy, P.M., Rus, V., Crossley, S., Graesser, A.C., & McNamara, D.S. (2008). Assessing forward-, reverse-, and average-entailment indices on natural language input from the intelligent tutoring system, iSTART. In D. Wilson & G. Sutcliffe (Eds.), Proceedings of the 21st International Florida Artificial Intelligence Research Society Conference (pp. 165-170).

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., Lintean, M., McCarthy, P.M., McNamara, D.S., & Graesser, A.C. (2008). Paraphrase identification with lexico-syntactic graph subsumption. In D. Wilson & G. Sutcliffe (Eds.), Proceedings of the 21st International Florida Artificial Intelligence Research Society Conference (pp. 201-206). Menlo Park, CA: The AAAI Press.

Rus, V., McCarthy, P.M., McNamara, D.S., & Graesser, A.C. (2008). A study of textual entailment.International Journal of Artifacial Intelligence Tools, 17, 659-685.

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.

Jackson, G.T., Guess, R.H., & McNamara, D.S. (2009). Assessing cognitively complex strategy use in an untrained domain. In N.A. Taatgen, H. van Rijn, L. Schomaker, & J. Nerbonne (Eds.), Proceedings of the 31st Annual Meeting of the Cognitive Science Society (pp. 2164-2169). Amsterdam, The Netherlands: Cognitive Science Society. [paper awarded the Cognition and Student Learning Prize and subsequently published in Topics journal, 2010]

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.

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.

Renner, A. M., McCarthy, P. M., Boonthum, C., & McNamara, D. S. (2009). Speling mistacks and typeos: Can your ITS handle them? In P. Dessus, S. Trausan-Matu, P. van Rosmalen, & F. Wild (Eds.), Proceedings of the Workshop on Natural Language Processing in Support of Learning; Metrics, Feedback, & Connectivity at the 14th International Conference on Artificial Intelligence in Education(pp. 26-33). Brighton, UK: AIED.

Renner, A.M., McCarthy, P.M., McNamara, D.S. (2009). Computational considerations in correcting user-language. In C.H. Lane & H.W. Guesgen (Eds.), Proceedings of the 22nd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 278-283). Menlo Park, CA: The AAAI Press.

Rus, V., Lintean, M., Graesser, A.C., McNamara, D.S. (2009). Assessing student paraphrases using lexical semantics and word weighting. In V. Dimitrova, R. Mizoguchi, B. du Boulay, & A.C. Graesser (Eds.),Artificial intelligence in education; Building learning systems that care; From knowledge representation to affective modeling(pp. 165-172). Amsterdam, The Netherlands: IOS Press.

Rus, V., McCarthy, P.M., Graesser, A.C., & McNamara, D.S. (2009). Identification of sentence-to-sentence relations using a textual entailer. Research on Language and Computation, 7, 1-21.

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. (2010) Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educational Psychologist, 45, 234-244.

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.

Jackson, G.T., Dempsey K.B., & McNamara, D.S. (2010). The evolution of an automated reading strategy tutor: From classroom to a game-enhanced automated system. In M.S. Khine & I.M. Saleh (Eds.), New Science of learning: Cognition, computers and collaboration in education (pp. 283-306). New York, NY:Springer.

Jackson, G.T., Guess, R.H., & McNamara, D.S. (2010). Assessing cognitively complex strategy use in an untrained domain. Topics in Cognitive Science, 2, 127-137.

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.

Boonthum, C., McCarthy, P.M., Lamkin, T., Jackson, G.T., Magliano, J., & McNamara, D.S. (2011). Automatic natural language processing and the detection of reading skills and reading comprehension. In R. C. Murray & P. M. McCarthy (Eds.), Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 234-239). Menlo Park, CA: 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

O’Rourke, S., Calvo, R. A., & McNamara, D. S. (2011). Visualizing topic flow in students’ essays. Journal of Educational Technology & Society, 14, 4-15.

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.

Roscoe, R.D., Varner (Allen), L.K., Cai, Z., Weston, J.L., Crossley, S.A., & McNamara, D.S. (2011). Internal usability testing of automated essay feedback in an intelligent writing tutor. In R. C. Murray & P. M. McCarthy (Eds.), Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 543-548). 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.

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., 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.

Proske, A., Narciss, S., & McNamara, D. S. (2012). Computer-based scaffolding to facilitate students’ development of expertise in academic writing. Journal of Research in Reading, 35, 136-152.

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., 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

Allen, L. K., Snow, E. L., & McNamara, D. S. (2014). Now we’re talking: Leveraging the power of natural language processing to inform ITS development. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (pp. 401-402). London, UK: International Data Educational Data Mining Society.

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., & McNamara, D. S. (2014). The importance of grammar and mechanics in writing assessment and instruction: Evidence from data mining. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (pp. 300-303). London, UK.

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., 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.

Crossley, S. A., Salsbury, T., Titak, A., & McNamara, D. S. (2014). Frequency effects and second language lexical acquisition: Word types, word tokens, and word production. International Journal of Corpus Linguistics, 19, 301-332.

Crossley, S. A., Sung, H., & McNamara, D. S. (2014). What’s so simple about simplified texts? A computational and psycholinguistic investigation of text comprehension and text processing. Reading in a Foreign Language, 26, 92-113.

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., 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.

Allen, L. K. (2015). Who do you think I am? Modeling individual differences for more adaptive and effective instruction. 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.), Doctoral Consortium within the Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), (pp.659-661). Madrid, Spain.

Allen, L. K., Crossley, S. A., Snow, E. L., Jacovina, M. E., Perret, C. A., & McNamara, D. S. (2015). Am I wrong or am I right? Gains in monitoring accuracy in an Intelligent Tutoring System for writing. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education(AIED 2015), (pp. 533-536). Madrid, Spain.

Allen, L. K., & McNamara, D. S. (2015). You are your words: Modeling students’ vocabulary knowledge with natural language processing. 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.258-265). Madrid, Spain: International Educational Data Mining Society.

Allen, L. K., Snow, E. L., & McNamara, D. S. (2015). Are you reading my mind? Modeling students’ reading comprehension skills with Natural Language Processing techniques. 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. 246-254). Poughkeepsie, NY: ACM.

Brown, A., Lynch, C. F., Eagle, M., Albert, J., Barnes, T., Baker, R., Bergner, Y., & McNamara, D. S. (2015). Good communities and bad communities: Does membership affect performance? 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. 612-613). Madrid, Spain: International Educational Data Mining Society.

Brown, R., Lynch, C. F., Wang, Y., Eagle, M., Albert, J., Barnes, T., Baker, R., Bergner, Y., & McNamara, D. S. (2015). Communities of performance & communities of preference. 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 Graph Analytics Workshop at the 8th International Educational Data Mining Conference (EDM 2015), (pp. 12-19). Madrid, Spain.

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.

Crossley, S. A., Kyle, K., & McNamara, D. S. (2015). To aggregate or not? Linguistic features in automatic essay scoring and feedback systems. The Journal of Writing Assessment, 8(1).[LINK]

Dascalu, M., Stavarache, L.L., Dessus, P., Trausan-Matu, S., McNamara, D.S., & Bianco, M. (2015). Predicting comprehension from students’ summaries. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), (pp. 95–104). Madrid, Spain: Springer.

Dascalu, M., Stavarache, L. L., Trausan-Matu, S., Dessus, P., Bianco, M., & McNamara, D.S. (2015). ReaderBench: An integrated tool supporting both individual and collaborative learning. 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; Tool Demonstration), (pp. 436-437). 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., Dessus, P., Bianco, M., & McNamara, D.S. (2015). Dialogism: A framework for CSCL and a signature of collaboration. In O. Lindwall, P. Häkkinen, T. Koschmann, P. Tchounikine & S. Ludvigsen (Eds.), Proceedings of the 11th International Conference on Computer-Supported Collaborative Learning (CSCL 2015), (pp. 86-93). Gothenburg, Sweden: ISLS.

Dascalu, M., Trausan-Matu, S., Dessus, P., & McNamara, D.S. (2015). Discourse cohesion: A signature of collaboration. 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. 350-354). Poughkeepsie, NY: ACM.

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. 

Jackson, T. G., Boonthum, C., & McNamara, D. S. (2015). Natural Language Processing and game-based practice in iSTART. Journal of Interactive Learning Research, 26, 189-208.

Jacovina, M. E., Snow, E. L., Dai, J., & McNamara, D. S. (2015). Authoring tools for ill-defined domains in intelligent tutoring systems: Flexibility and stealth profiling. In R. Sottilare, A. Graesser, X. Hu, & K. Brawner, (Eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems: Authoring Tools(Volume 3, pp. 109-121). Orlando, FL: U.S. Army Research Laboratory.

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.

Kyle, K., Crossley, S. A., & McNamara, D. S. (2015). Construct validity in TOEFL iBT speaking tasks: Insights from natural language processing. Language Testing, 21. DOI: 10.1177/0265532215587391

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]

Paraschiv, I. C., Dascalu, M., Dessus, P., Trausan-Matu, S., & McNamara, D. S. (2015). A paper recommendation system with ReaderBench: The graphical visualization of semantically related papers and concepts. In E. Popescu & S. Graf (Eds.), Proceedings of the 8th International Workshop on Social and Personal Computing for Web-Supported Learning Communities at the International Conference on Smart Learning Environments. Sinaia, Romania: Springer.

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

Roscoe, R. D., Snow, E. L., Allen, L. K., & McNamara, D. S. (2015). Automated detection of essay revising patterns: Application for intelligent feedback in a writing tutor. Technology, Instruction, Cognition, and Learning, 10(1), 59-79.

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.

Snow, E. L., Allen, L. K., Jacovina, M. E., Crossley, S. A., Perret, C. A., & McNamara, D. S. (2015). Keys to Detecting Writing Flexibility Over Time: Entropy and Natural Language Processing Journal of Learning Analytics, 2(3), 40-54.

Snow, E. L., Allen, L. K., Jacovina, M. E., Perret, C. A., & McNamara, D. S. (2015). You’ve got style!: Detecting writing flexibility across time. 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. 194-202). Poughkeepsie, NY: ACM.

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., Dascalu, M., McNamara, D. S., Crossley, S., & Trausan-Matu, S. (2016). Modeling individual differences among writers using ReaderBench. In EduLearn (pp. 5269-5279). Barcelona, Spain: IATED.

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.

Allen, L. K., Jacovina, M. E., & McNamara, D. S. (2016). Cohesive features of deep text comprehension processes. In J. Trueswell, A. Papafragou, D. Grodner, & D. Mirman (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society in Philadelphia, PA, (pp. 2681-2686). Austin, TX: Cognitive Science Society.

Allen, L. K., Jacovina, M. E., & McNamara, D.S. (2016). Computer-based writing instruction. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of Writing Research, (2nd ed.), (pp. 316-329). New York: The Guilford Press.

Allen, L. K., Mills, C., Jacovina, M. E., Crossley, S., D’Mello, S., & McNamara, D. S. (2016). Investigating boredom and engagement during writing using multiple sources of information: The essay, the writer, and keystrokes. In D. Gašević, G. Lynch, S. Dawson, H. Drachsler, & C. P. Rosé (Eds.), Proceedings of the 6th International Learning Analytics & Knowledge Conference, Edinburgh, United Kingdom (LAK’16), (pp. 114-123). New York, NY: ACM.

Allen, L. K., Snow, E. L., & McNamara, D. S. (2016). The narrative waltz: The role of flexibility on writing performance. Journal of Educational Psychology. doi: 10.1037/edu0000109 

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., Allen, L., Snow E., & McNamara, D. S. (2016). Incorporating learning characteristics into automatic essay scoring models: What individual differences and linguistic features tell us about writing quality. Journal of Educational Data Mining.

Crossley, S. A., Dascalu, M., Trausan-Matu, S., Allen, L. K., & McNamara, D. S. (2016). Document cohesion flow: Striving towards coherence. In J. Trueswell, A. Papafragou, D. Grodner, & D. Mirman (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society in Philadelphia, PA, (pp.764-769). Austin, TX: Cognitive Science Society.

Crossley, S., Kyle, K., Davenport, J., & McNamara, D. S. (2016). Automatic assessment of constructed response data in a chemistry tutor. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016), (pp.336-340). Raleigh, NC: International Educational Data Mining Society.

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). Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social order analysis. Behavior Research Methods.

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.

Crossley, S. A., Paquette, L., Dascalu, M., McNamara, D. S., & Baker, R. (2016). Combining click-stream data with NLP tools to better understand MOOC completion. In D. Gašević, G. Lynch, S. Dawson, H. Drachsler, & C. P. Rosé (Eds.), Proceedings of the 6th International Learning Analytics & Knowledge Conference, Edinburgh, United Kingdom (LAK’16), (pp. 6-14). New York, NY: ACM.

Crossley, S. A., Rose, D. F., Danekes, C., Rose, C. W., & McNamara, D. S. (2016). That noun phrase may be beneficial and this may not be: discourse cohesion in reading and writing. Reading and Writing, 1-21.

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.

Paraschiv, I. C., Dascalu, M., McNamara, D.S., & Trausan-Matu, S. (2016). Finding the needle in a haystack: Who are the most central authors within a domain?. In K. Verbert, M. Sharples & T. Klobucar (Eds.), 11th European Conference on Technology Enhanced Learning (EC-TEL 2016), (pp. 632–635). Lyon, France: Springer.

Shum, S. B., Knight, S. McNamara, D. S., Allen, L. K., Bektik, D., & Crossley, S. A. (2016). Critical perspectives on writing analytics. In D. Gašević, G. Lynch, S. Dawson, H. Drachsler, & C. P. Rosé (Eds.), Workshop Proceedings of the 6th International Learning Analytics and Knowledge Conference (LAK’16), (pp. 481-483). New York, NY: ACM.

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.

Allen, L. K., Perret, C. A., Likens, A., & McNamara, D. S. (2017). What’d you say again? Recurrence quantification analysis as a method for analyzing the dynamics of discourse in a reading strategy tutor. In A. Wise, P. Winne, G. Lynch, X. Ochoa, I. Molenaar, & S. Dawson (Eds.), Proceedings of the 7th International Conference on Learning Analytics & Knowledge (LAK 17) in Vancouver, BC, Canada, (pp. 373-382). New York, NY: ACM.

Allen, L. K., & McNamara, D. S. (2017). Five building blocks for comprehension strategy instruction. In J.A. Leon & I. Escudero (Eds.), Reading Comprehension in Educational Settings (pp.125-144). Philadelphia, PA: John Benjamins.

Balyan, R., McCarthy, K. S., & McNamara, D. S. (2017). Combining machine learning and natural language processing to assess literary text comprehension. In X. Hu, T. Barnes, A. Hershkovitz & L. Paquette (Eds.), Proceedings of the 10th International Conference on Educational Data Mining (EDM), . (pp.244-249). Wuhan, China: International Educational Data Mining Society.

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.

Crossley, S. A., Dascalu, M., & McNamara, D. S. (2017). How important is size? An investigation of corpus size and meaning in both Latent Semantic Analysis and Latent Dirichlet Allocation. . In Z. Markov & V. Rus (Eds.), Proceedings of the 30th Annual Florida Artificial Intelligence Research Society International Conference (FLAIRS).. (pp. 293-296). Marco Island, FL: AAAI Press.

Crossley, S. A., Liu, R., & McNamara, D. S. (2017). Predicting math performance using natural language processing tools. In A. Wise, P. Winne, G. Lynch, X. Ochoa, I. Molenaar, & S. Dawson (Eds.), Proceedings of the 7th International Conference on Learning Analytics & Knowledge (LAK 17) in Vancouver, BC, Canada, (pp.339-347). New York, NY: ACM.

Crossley, S. A., Skalicky, S., Dascalu, M., McNamara, D., & Kyle, K. (2017). Predicting text comprehension, processing, and familiarity in adult readers: New approaches to readability formulas. Discourse Processes. 1-20

Dascalu, M., Allen, K. A., McNamara, D. S., Trausan-Matu, S., & Crossley, S. A. (2017). Modeling comprehension processes via automated analyses of dialogism. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Meeting of the Cognitive Science Society (CogSci 2017). (pp. 1884-1889). London, UK: Cognitive Science Society.

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.

McNamara, D. S., Allen, L. K., Crossley, S. A., Dascalu, M., & Perret, C. A. (2017). Natural language processing and learning analytics. In G. Siemens & C. Lang (Eds.), Handbook of Learning Analytics and Educational Data Mining (pp.93-104). Society for Learning Analytics Research. [LINK]

Paraschiv, I. C., Dascalu, M., McNamara, D .S., Trausan-Matu, S., & Banica, C. K. (2017). Exploring the LAK Dataset using Cohesion Network Analysis. In D. Trandabat & D. Gifu (Eds.), 3rd Workshop on Social Media and the Web of Linked Data (RUMOUR 2017), in conjunction with the Joint Conference on Digital Libraries (JCLD 2017) (pp. 17–21). Toronto, Canada: “Alexandru Ioan Cuza” University Publishing House.

Paraschiv, I.C., Dascalu, M., Trausan-Matu, S., Nistor, N., Montes de Oca, A. M., & McNamara, D. S. (2017). Semantic similarity versus co-authorship networks: A detailed comparison. In I. Dumitrache & A. Florea (Eds.), Proceedings of the 3rd International Workshop on Design and Spontaneity in Computer-Supported Collaborative Learning (DS-CSCL-2015), in conjunction with the 21th Int. Conference on Control Systems and Computer Science (CSCS21). Bucharest, Romania: IEEE.

Schillinger, D., McNamara, D. S., Crossley, S. A., Sarkar, H. M. U., Duran, N., Allen, J., Liu, J. Oryn, D., Karter, A. J., & Lyles, C. R., (2017). The next frontier in communication and the Eclippse study: Bridging the linguistic divide in secure messaging. Journal of Diabetes Research. 1-9

Skalicky, S., Crossley, S. A., McNamara, D. S., & Muldner, K. (2017). Identifying creativity during problem solving using linguistic features. Creativity Research Journal. 27(4), 343-353.

Crossley, S., Wan, Q., Allen, L., & McNamara, D. S. (2023). Source inclusion in synthesis writing: An NLP approach to understanding argumentation, sourcing, and essay quality. Reading and writing, 36(4), 1053-1083. [LINK] [PDF]