All publications

Books and proceedings

Landauer, T., McNamara, D. S., Dennis, S., & Kintsch, W. (Eds.). (2007). Handbook of Latent Semantic Analysis. Mahwah, NJ: Erlbaum.

McNamara, D. S. (Ed.). (2007). Reading comprehension strategies: Theory, interventions, and technologies. Mahwah, NJ: Erlbaum.

McNamara, D. S., & Trafton, J. G. (Eds.). (2007). Proceedings of the 29th Annual Cognitive Science Society. Austin, TX: Cognitive Science Society.

McNamara, D. S., Graesser, A. C., McCarthy, P., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge: Cambridge University Press.

Crossley, S. A., & McNamara, D. S. (Eds.). (2016). Adaptive educational technologies for literacy instruction. New York: Taylor & Francis, Routledge.

McCrudden, M. T., & McNamara, D. S. (2017). Cognition in Education. New York: Taylor & Francis, Routledge.

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

Doane, S.M., McNamara, D.S., Kintsch, W., Polson, P.G., Dungca, R.G., & Clawson, D.M. (1991). Action planning: The role of prompts in UNIX command production. In K. J. Hammon and D. Getner (Eds.), Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society (pp. 682-687). Hillsdale, NJ: Erlbaum.

Meissen, G.J., Mastromauro, C.A., Kiely, D.K., McNamara, D.S., & Meyers, R.H. (1991). Understanding the decision to take the predictive test for Huntington Disease. American Journal of Medical Genetics, 39, 404-410. [PDF]

Doane, S.M., McNamara, D.S., Kintsch, W., Polson, P.G., & Clawson, D.M. (1992). Prompt comprehension in UNIX command production. Memory and Cognition, 20, 327-343. [LINK]

Turner, M.L., Johnson, S.K., McNamara, D.S., & Engle, R.W. (1992). Effects of same-modality interference on immediate serial recall of auditory and visual information. The Journal of General Psychology, 119, 247-263. [PDF]

Healy, A.F., Clawson, D.M., McNamara, D.S., Marmie, W.R., Schneider, V.I., Rickard, T.C., Crutcher, R.J., King, C., Ericsson, K.A., & Bourne, L.E., Jr. (1993). The long-term retention of knowledge and skills. In D. Medin (Ed.), The Psychology of Learning and Motivation (pp. 135-164). New York: Academic Press. [PDF]

Doane, S.M., Sohn, Y.W., Adams, D., & McNamara, D.S. (1994). Learning from instruction: A comprehension-based approach. In A. Ram & K. Eiselt (Eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society(pp. 254-259). Hillsdale, NJ: Erlbaum. [PDF]

McNamara, D.S. (1995). Effects of prior knowledge on the generation advantage: Calculators versus calculation to learn simple multiplication. Journal of Educational Psychology, 87, 307-318. [PDF]

McNamara, D.S., & Healy, A.F. (1995). A generation advantage for multiplication skill and nonword vocabulary acquisition. In A.F. Healy & L.E. Bourne, Jr. (Eds.), Learning and Memory of Knowledge and Skills (pp. 132-169). Thousand Oaks, CA: Sage. [LINK]

McNamara, D.S., & Healy, A.F. (1995). A procedural explanation of the generation effect: The use of an operand retrieval strategy for multiplication and addition problems. Journal of Memory and Language, 34, 399-416. [PDF]

Healy, A.F., & McNamara, D.S. (1996). Verbal learning and memory: Does the modal model still work? Annual Review of Psychology, 47, 143-172. [PDF]

McNamara, D.S., & Kintsch, W. (1996). Learning from text: Effects of prior knowledge and text coherence. Discourse Processes, 22, 247-288. [PDF]

McNamara, D.S., & Kintsch, W. (1996). Working memory in text comprehension: Interrupting difficult text. In G.W. Cottrell (Ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society (pp. 104-109). Hillsdale, NJ: Erlbaum. [PDF]

McNamara, D.S., Kintsch, E., Songer, N.B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14, 1-43. [PDF]

McNamara, D.S. (1997). Comprehension skill: A knowledge-based account. In M.G. Shafto & P. Langley (Eds.), Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society (pp. 508-513). Hillsdale, NJ: Erlbaum. [PDF]

McNamara, D.S., & Scott, J.L. (1999). Training reading strategies. In M. Hahn & S.C. Stoness (Eds.), Proceedings of the Twenty First Annual Conference of the Cognitive Science Society (pp. 387-392). Hillsdale, NJ: Erlbaum. [PDF]

McNamara, D.S., & Scott, J.L. (1999). Training self-explanation and reading strategies. In Proceedings of the Human Factors and Ergonomics Society Forty-third Annual Meeting. Houston, TX: Human Factors & Ergonomics Society. [PDF]

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

McNamara, D. S. (2000). Book review: Reading Comprehension Difficulties: Processes and Intervention, C. Cornoldi & J. Oakhill (Eds.). Journal of Pragmatics, 33, 943-956. Mahwah, NJ: Erlbaum.[PDF]

McNamara, D.S., & Healy, A.F. (2000). A procedural explanation of the generation effect for simple and difficult multiplication problems and answers. Journal of Memory and Language, 43, 652-679. [PDF]

McNamara, D.S., Scott, J.L., & Bess, T. (2000). Building blocks of knowledge: Constructivism from a cognitive perspective. G. McAuliffe, C. Lovell, & K. Eriksen (Eds.), Preparing Counselors and Therapists: Creating Constructive and Developmental Programs (pp. 62-75). Virginia Beach, VA: The Donning Company. [PDF]

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. [PDF]

McNamara, D.S. (2001). Reading both high-coherence and low-coherence texts: Effects of text sequence and prior knowledge. Canadian Journal of Experimental Psychology, 55, 51-62. [PDF]

McNamara, D.S. (2001). Speed reading. In N.J. Smelser & P.B. Bates (Eds.), International Encyclopedia of the Social & Behavioral Sciences, New York, NY: Elsevier.[PDF]

McNamara, D.S., & Scott, J.L. (2001). Working memory capacity and strategy use. Memory & Cognition, 29, 10-17. [LINK]

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. [PDF]

Cottrell, K.G., & McNamara, D.S. (2002). Cognitive precursors to science comprehension. In W.D. Gray & C.D. Schunn (Eds.), Proceedings of the Twenty-fourth Annual Meeting of the Cognitive Science Society(pp. 244-249). Mawah, NJ: Erlbaum. [PDF]

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. [LINK]

McNamara, D.S., & O’Reilly, T. (2002). Learning: Knowledge acquisition, representation, and organization. J.W. Guthrie et al. (Eds.), The Encyclopedia of Education. New York: Macmillan Reference.[PDF]

O’Reilly, T., McNamara, D.S., & The Strategies Lab (2002). What’s a science student to do? In W.D. Gray & C.D. Schunn (Eds.), Proceedings of the Twenty-fourth Annual Conference of the Cognitive Science Society (pp. 726-731). Mawah, NJ: Erlbaum. [PDF]

Risser, M.R., McNamara, D.S., Baldwin, C.L., Scerbo, M.W., Barshi, I. (2002). Interference while hearing or reading information: Considerations for ATC communication. In Proceedings of the Human Factors and Ergonomics Society Forty-sixth Annual Meeting (pp. 392-396). [PDF]

Graesser, A.C., McNamara, D.S., & Louwerse, M.M (2003). What do readers need to learn in order to process coherence relations in narrative and expository text. In A.P. Sweet and C.E. Snow (Eds.), Rethinking reading comprehension. New York: Guilford Publications.[PDF]

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. [LINK]

Levinstein, I.B., McNamara, D.S., Boonthum, C., Pillarisetti, S.P., Yadavalli, K. (2003). Web-based intervention for higher-order reading skills. In D. Lassner & C. McNaught (Eds.), Proceedings of ED-MEDIA 2003: World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 835-841). [PDF]

McNamara, D.S., Best, R., & Castellano, C. (2003). Learning from text: Facilitating and enhancing comprehension. www.speechpathology.com. [PDF]

Risser, M.R., Scerbo, M.W., Baldwin, C.L., & McNamara, D.S. (2003). ATC commands executed in speech and text formats: Effects of task interference. In Proceedings of the 12th Biennial International Symposium on Aviation Psychology (pp. 999-1004). [PDF]

Scerbo, M.W., Risser, M.R., Baldwin, C.L., & McNamara, D.S. (2003). Implementing speech and simulated data link commands: The role of task interference and message length. Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting (pp. 95-99). Houston, TX: Human Factors & Ergonomics Society. [PDF]

Best, R., Dockrell, J.E., & McNamara, D.S. (2004). Children’s semantic representation of a science term. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the 26th Annual Cognitive Science Society(pp. 1525). Mahwah, NJ: Erlbaum. [PDF]

Best, R., Ozuru, Y., & McNamara, D.S. (2004). Self-explaining science texts: Strategies, knowledge, and reading skill. In Y. B. Kafai, W. A. Sandoval, N. Enyedy, A. S. Nixon, & F. Herrera (Eds.), Proceedings of the Sixth International Conference of the Learning Sciences: Embracing Diversity in the Learning Sciences (pp. 89-96). Mahwah, NJ: Erlbaum. [LINK]

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. [PDF]

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. [PDF]

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. [PDF]

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. [LINK]

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. [PDF]

McNamara, D.S. (2004). Aprender del texto: efectos de la estructura textual y las estrategias del lector. Revista Signos, 37, 19-30. [PDF]

McNamara, D.S. (2004). Review of Précis of thoughts on thought by E. Hunt.Contemporary Psychology, 49, 617-619. [PDF]

McNamara, D.S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38. [LINK]

McNamara, D.S., Floyd, R.G., Best, R., & Louwerse, M. (2004). World knowledge driving young readers’ comprehension difficulties. In Y. B. Kafai, W. A., Sandoval, N. Enyedy, A. S. Nixon & F. Herrera (Eds.),Proceedings of the Sixth International Conference of the Learning Sciences: Embracing Diversity in the Learning Sciences (pp. 326-333). Mahwah, NJ: Erlbaum. [LINK]

McNamara, D.S., Levinstein, I.B., & Boonthum, C. (2004). iSTART: Interactive strategy trainer for active reading and thinking.Behavioral Research Methods, Instruments, & Computers, 36, 222-233. [LINK]

McNamara, D.S., & McDaniel, M.A. (2004). Suppressing irrelevant information: Knowledge activation or inhibition? Journal of Experimental Psychology: Learning, Memory, & Cognition, 30, 465-482. [PDF]

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. [LINK]

O’Reilly, T., Best, R., & McNamara, D.S. (2004). Self-explanation reading training: Effects for low-knowledge readers. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the 26th Annual Cognitive Science Society (pp. 1053-1058). Mahwah, NJ: Erlbaum. [PDF]

O’Reilly, T., Sinclair, G.P., & McNamara, D.S. (2004a). iSTART: A web-based reading strategy intervention that improves students’ science comprehension. In Kinshuk, D. G. Sampson, & P. Isaías (Eds.), Proceedings of the IADIS International Conference Cognition and Exploratory Learning in Digital Age: CELDA 2004 (pp. 173-180). Lisbon, Portugal: IADIS Press. [PDF]

O’Reilly, T.P., Sinclair, G.P., & McNamara, D.S. (2004b). Reading strategy training: Automated versus live. In K. Forbus, D. Gentner & T. Regier (Eds.), Proceedings of the 26th Annual Cognitive Science Society (pp. 1059-1064). Mahwah, NJ: Erlbaum. [PDF]

Ozuru, Y., Best, R., & McNamara, D.S. (2004). Contribution of reading skill to learning from expository texts. In K. Forbus, D. Gentner & T. Regier (Eds.), Proceedings of the 26th Annual Cognitive Science Society (pp. 1071-1076). Mahwah, NJ: Erlbaum. [PDF]

Risser, M.R., Scerbo, M.W., Baldwin, C.L., & McNamara, D.S. (2004). Implementing voice and datalink commands under task Interference during simulated flight. In Proceedings of the 5th HPSAA II Conference, Human Performance, Situation Awareness and Automation Technology. Daytona Beach, FL. [PDF]

Todaro, S.A., Magliano, J.P., Millis, K.K., McNamara, D.S., & Kurby, C.C. (2004). Intra-clause constraints in think-aloud protocols. In K. Forbus, D. Gentner & T. Regier (Eds.), Proceedings of the 26th Annual Cognitive Science Society (pp. 1642). Mahwah, NJ: Erlbaum. [PDF]

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. [PDF]

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.[PDF]

Graesser, A.C., McNamara, D.S., & VanLehn, K. (2005). Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART. Educational Psychologist, 40, 225-234. [PDF]

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. [PDF]

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. [PDF]

Magliano, J.P., Todaro, S. Millis, K., Wiemer-Hastings, K., Kim, H.J., & McNamara, D.S. (2005). Changes in reading strategies as a function of reading training: A comparison of live and computerized training. Journal of Educational Computing Research, 32, 185-208. [PDF]

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. [PDF]

Ozuru, Y., Dempsey, K., Sayroo, J., & McNamara, D.S. (2005). Effect of text cohesion on comprehension of biology texts. In B. G. Bara, L. Barsalou & M. Bucciarelli (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 1696-1701). Mahwah, NJ: Erlbaum. [PDF]

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. [PDF]

Best, R., Ozuru, Y., Floyd., R., & McNamara, D.S. (2006). Children’s text comprehension: Effects of genre, knowledge, and text cohesion. In S. A. Barab, K. E. Hay, D. T. Hickey (Eds.), Proceedings of the Seventh International Conference of the Learning Sciences (pp. 37-42). Mahwah, NJ: Erlbaum. [LINK]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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). [PDF]

McNamara, D.S. (2006). Bringing cognitive science into education and back again: The value of interdisciplinary research. Invited editorial in Cognitive Science, 30, 1-4.[PDF]

McNamara, D.S., O’Reilly, T., Best, R., & Ozuru, Y. (2006). Improving adolescent students’ reading comprehension with iSTART. Journal of Educational Computing Research, 34, 147-171. [PDF]

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.[PDF]

Muñoz, B., Magliano, J.P., Sheridan, R., & McNamara, D.S. (2006). Typing versus thinking aloud when reading: Implications for computer-based assessment and training tools. Behavior Research Methods, Instruments, & Computers, 38, 211-217. [LINK]

O’Reilly, T., Taylor, R.S., & McNamara, D.S. (2006). Classroom based reading strategy training: Self-explanation vs. reading control. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1887). Mahwah, NJ: Erlbaum. [PDF]

Risser, M.R., Scerbo, M.W., Baldwin, C.L., & McNamara, D.S. (2006). Interference timing and acknowledgement response with voice and datalink ATC commands. Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting. San Francisco, CA. [PDF]

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. [LINK]

Taylor, R.S., O’Reilly, T., Rowe, M., & McNamara, D.S. (2006). Improving understanding of science texts: iSTART strategy training vs. web design control task. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 2234-2239). Mahwah, NJ: Erlbaum. [PDF]

Taylor, R., O’Reilly, T., Sinclair, G., & McNamara, D.S. (2006). Enhancing learning of expository science texts in a remedial reading classroom via iSTART. Proceedings of the 7th International Conference of Learning Sciences (pp. 765-770), Bloomington, Indiana. [LINK]

Bell, C., & McNamara, D.S. (2007). Integrating iSTART into a high school curriculum. Proceedings of the 29th Annual Meeting of the Cognitive Science Society(pp. 809-814). Austin, TX: Cognitive Science Society. [PDF]

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. [PDF]

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. [LINK]

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. [LINK]

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. [LINK]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [LINK]

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. [PDF ]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

Kurby, C.A., Ozuru, Y., & McNamara, D.S. (2007). Individual differences in comprehension monitoring ability during reading. In D.S. McNamara and G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society(pp. 413-418). Austin, TX: Cognitive Science Society. [PDF]

Levinstein, I.B., Boonthum, C., Pillarisetti, S.P., Bell, C., & McNamara, D.S. (2007). iSTART 2: Improvements for efficiency and effectiveness. Behavior Research Methods, 39, 224-232. [LINK]

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. [PDF]

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. [PDF]

Magliano, J. P., Millis, K. K., Ozuru, Y., & McNamara, D.S. (2007). A multidimensional framework to evaluate reading assessment tools. In D.S. McNamara (Ed.), Reading comprehension strategies: Theories, interventions, and technologies (pp. 107-136). Mahwah, NJ: Erlbaum. [PDF]

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 U.K. [LINK]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

McNamara, D.S., de Vega, M., & O’Reilly, T. (2007). Comprehension skill, inference making, and the role of knowledge. In F. Schmalhofer & C.A. Perfetti (Eds.), Higher level language processes in the brain: Inference and comprehension processes (pp. 233-251). Mahwah, NJ: Erlbaum. [PDF]

McNamara, D.S., O’Reilly, T., Rowe, M., Boonthum, C., & Levinstein, I.B. (2007). iSTART: A web-based tutor that teaches self-explanation and metacognitive reading strategies. In D.S. McNamara (Ed.), Reading comprehension strategies: Theories, interventions, and technologies (pp. 397-420). Mahwah, NJ: Erlbaum. [PDF]

McNamara, D. S., Ozuru, Y., Best, R., & O’Reilly, T. (2007). The 4-pronged comprehension strategy framework. In D.S. McNamara (Ed.), Reading comprehension strategies: Theories, interventions, and technologies (pp. 465-496). Mahwah, NJ: Erlbaum. [PDF]

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. [PDF]

O’Reilly, T., & McNamara, D.S. (2007). Reversing the reverse cohesion effect: good texts can be better for strategic, high-knowledge readers. Discourse Processes, 43, 121-152. [LINK] [PDF]

O’Reilly, T., & McNamara, D.S. (2007). The impact of science knowledge, reading skill, and reading strategy knowledge on more traditional “High-Stakes” measures of high school students’ science achievement. American Educational Research Journal, 44, 161-196. [PDF]

Ozuru, Y., Best, R., Bell, C., Witherspoon, A., & McNamara, D.S. (2007). Influence of question format and text availability on assessment of expository text comprehension. Cognition & Instruction, 25, 399-438. [PDF]

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. [PDF]

VanderVeen, A., Huff, K., Gierl, M., McNamara, D.S., Louwerse, M., & Graesser, A.C. (2007). Developing and validating instructionally relevant reading competency profiles measured by the critical reading sections of the SAT. In D.S. McNamara (Ed.), Reading comprehension strategies: Theories, interventions, and technologies (pp. 137-172). Mahwah, NJ: Erlbaum. [PDF]

Best, R.M., Floyd, R.G., & McNamara, D.S. (2008). Differential competencies contributing to children’s comprehension of narrative and expository texts. Reading Psychology, 29, 137-164. [PDF]

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. [PDF]

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

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

McCarthy, P.M., Briner, S.W., Myers, J.C., Graesser, A.C., & McNamara, D.S. (2008). Are three words all we need? Recognizing genre at the sub-sentential level. In. V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th annual conference of the Cognitive Science Society (pp. 613-618). Washington, D.C.: Cognitive Science Society. [PDF]

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. [LINK]

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). [PDF]

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. [LINK]

Rowe, M., & McNamara, D.S. (2008). Inhibition needs no negativity: Negativity links in the construction-integration model. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th annual conference of the Cognitive Science Society (pp. 1777-1782). Washington, D.C.: Cognitive Science Society. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

Todaro, S., Magliano, J. P., Millis, K., McNamara, D. S., & Kurby, C. (2008). Assessing the structure of verbal protocols. In V. Sloutsky, B. Love, & K.McRae (Eds.), Proceedings of the 30th annual conference of the Cognitive Science Society (pp.607-612). Washington, D.C.: Cognitive Science Society. [PDF]

Azevedo, R., Witherspoon, A., Graesser, A.C., McNamara, D.S., Chauncey, A., Siler, E., Cai, Z., Rus, V., & Lintean, M. (2009). MetaTutor: Analyzing self-regulated learning in a tutoring system for biology. 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. 635-637). Amsterdam, The Netherlands: IOS Press. [PDF]

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. [LINK]

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. [PDF]

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

Dempsey, K.B., Brunelle, J.F., Jackson, G.T., Boonthum, C., Levinstein, I.B., & McNamara, D.S. (2009). MiBoard: Multiplayer interactive board game. In H.C. Lane, A. Ogan, & V. Shute (Eds.), Proceedings of the Workshop on Intelligent Educational Games at the 14th Annual Conference on Artificial Intelligence in Education (pp. 113-116). Brighton, UK: AIED. [LINK]

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. [PDF]

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. [PDF]

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. [PDF]

Jackson, G.T., Boonthum, C., & McNamara, D.S. (2009). iSTART-ME: Situating extended learning within a game-based environment. In H.C. Lane, A. Ogan, & V. Shute (Eds.), Proceedings of the Workshop on Intelligent Educational Games at the 14th Annual Conference on Artificial Intelligence in Education (pp. 59-68). Brighton, UK: AIED. [PDF]

Jackson, G.T., Graesser, A.C., & McNamara, D.S. (2009). What students expect may have more impact than what they know or feel. 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. 73-80). Amsterdam, The Netherlands: IOS Press. [PDF]

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] [PDF]

Louwerse, M.M., Graesser, A.C., McNamara, D.S., & Lu, S. (2009). Embodied conversational agents as conversational partners. Applied Cognitive Psychology, 23, 1244-1255. [PDF]

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. [PDF]

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

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. [PDF]

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. [PDF]

McNamara, D.S. (2009). The importance of teaching reading strategies. Perspectives on Language and Literacy, 35, 34-40. [To be republished in Joshi, M. & Moats, L. (Eds.). (2011). Expert perspectives on intervention with reading disabilities: An anthology from publications of the international dylexia association.] [PDF]

McNamara, D.S., Boonthum, C., Kurby, C.A., Magliano, J., Pillarisetti, S., & Bellissens, C. (2009). Interactive paraphrasing training: The development and testing of an iSTART module. 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. 181-188). Amsterdam, The Netherlands: IOS Press. [PDF]

McNamara, D.S., Jackson, G.T., & Graesser, A.C. (2009). Intelligent tutoring and games (iTaG). In H.C. Lane, A. Ogan, & V. Shute (Eds.), Proceedings of the Workshop on Intelligent Educational Games at the 14th Annual Conference on Artificial Intelligence in Education(pp. 1-10). Brighton, UK: AIED. [PDF]

McNamara, D.S., & Magliano, J.P. (2009). Self-explanation and metacognition: The dynamics of reading. In J.D. Hacker, J. Dunlosky, & A.C. Graesser (Eds.), Handbook of Metacognition in Education (pp. 60-81). Mahwah, NJ: Erlbaum. [PDF]

McNamara, D.S., & Magliano, J.P. (2009). Towards a comprehensive model of comprehension. In B. Ross (Ed.), The psychology of learning and motivation. New York, NY: Elsevier Science. [PDF]

McNamara, D.S., & O’Reilly, T. (2009). Theories of comprehension skill: Knowledge and strategies versus capacity and suppression. In A. M. Columbus (Ed.), Advances in Psychology Research, 62, (pp.). Hauppauge, NY: Nova Science Publishers, Inc. [PDF]

Ozuru, Y., Dempsey, K., & McNamara, D.S. (2009). Prior knowledge, reading skill, and text cohesion in the comprehension of science texts. Learning and Instruction, 19, 228-242. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

Brunelle, J.F., Jackson, G.T., Dempsey, K., Boonthum, C., Levenstein, I.B., & McNamara, D.S. (2010). Game-based iSTART practice: From MiBoard to self-explanation showdown. In H.W. Guesgen & C. Murray (Eds.), Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 480-485). Menlo Park, CA: The AAAI Press. [LINK]

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. [LINK]

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. [LINK]

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.] [PDF]

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. [LINK]

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. [LINK]

Dempsey, K., Jackson, G.T., Brunelle, J.F., Rowe, M.P., & McNamara, D.S. (2010). MiBoard: A digital game from a physical world. In H.W. Guesgen & C. Murray (Eds.), Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 498-503). Menlo Park, CA: The AAAI Press. [LINK]

Dempsey, K. B., Jackson, G. T., & McNamara, D. S. (2010). MiBoard: Creating a virtual environment from a physical environment. In J. Kay & V. Aleven (Eds.), Proceedings of the 10th International Conference on Intelligent Tutoring Systems (pp. 294-296). Berlin/Heidelberg: Springer. [LINK]

Duran, N.D., Dale, R., & McNamara, D.S. (2010). The action dynamics of overcoming the truth. Psychonomic Bulletin & Review, 17, 486-419. [LINK]

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. [PDF]

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. [PDF]

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. [PDF]

Jackson, G.T., Boonthum, C., & McNamara, D.S. (2010). The efficacy of iSTART extended practice: Low ability students catch up. In J. Kay & V. Aleven (Eds.), Proceedings of the 10th International Conference on Intelligent Tutoring Systems (pp. 349-351). Berlin/Heidelberg: Springer. [LINK]

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. [PDF]

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. [LINK]

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. [LINK]

McNamara, D.S. (2010). Strategies to read and learn: Overcoming learning by consumption. Medical Education, 44, 340-346. [LINK]

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

McNamara, D.S., Jackson, G.T., & Graesser, A.C. (2010). Intelligent tutoring and games (ITaG). In Y.K. Baek (Ed.), Gaming for classroom-based learning: Digital role-playing as a motivator of study (pp. 44-65). Hershey, PA: IGI Global. [PDF]

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] [PDF]

Moss, J., Schunn, C.D., Schneider, W., McNamara, D.S., & VanLehn, K. (2010). An fMRI study of strategic reading comprehension. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 1319-1324). Austin, TX: Cognitive Science Society. [PDF]

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. [LINK]

Ozuru, Y., Briner, S., Best, R., & McNamara, D.S. (2010). Contributions of self-explanation to comprehension of high and low cohesion texts. Discourse Processes, 47, 641-667. [LINK] [PDF]

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. [LINK]

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. [LINK]

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. [LINK]

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. [LINK]

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. [PDF]

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. [LINK]

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. [PDF]

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. [LINK]

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.[PDF]

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. [PDF]

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. [PDF]

Dai, J., Raine, R.B., Roscoe, R., Cai, Z., & McNamara, D.S. (2011). The Writing-Pal tutoring system: Development and design. Journal of Engineering and Computer Innovations, 2, 1-11. [LINK]

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. [LINK]

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

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

He, X. Y., Yang, H., Li, H. J., Wei, Y. B., & McNamara, D. S. (2011). Updating the spatial situation model: Effects of cognitive style. Acta Psychologica Sinica, 43, 1247-1262. [PDF]

Jackson, G. T., Davis, N. L., Graesser, A. C., & McNamara, D. S. (2011). Students’ enjoyment of a game-based tutoring system. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education (pp. 475-477). Auckland, New Zealand, AIED. [LINK]

Jackson, G. T., Dempsey, K. B., Graesser, A. C., & McNamara, D. S. (2011). Short and long term benefits of enjoyment and learning within a serious game. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education (pp. 139-146). Auckland, New Zealand: AIED. [LINK]

Jackson, G.T., & McNamara, D.S. (2011). Motivational impacts of a game-based intelligent tutoring system. In R. C. Murray & P. M. McCarthy (Eds.), Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 519-524). Menlo Park, CA: AAAI Press. [LINK]

McNamara, D.S. (2011). Computational methods to extract meaning from text and advance theories of human cognition. Topics in Cognitive Science, 2, 1-15. [LINK]

McNamara, D. S. (2011). Measuring deep, reflective comprehension and learning strategies: Challenges and successes. Megacognition and Learning, 3, 1-11. [PDF]

McNamara, D. S., & Dempsey, K. (2011). Reader expectations of question formats and difficulty: Targeting the zone. In M. McCrudden, J. Magliano, & G. Schraw (Eds.), Text relevance and learning from text. Charlotte, NC: Information Age Publishing, Inc. [PDF]

McNamara, D. S., & Kendeou, P. (2011). Translating advances in reading comprehension research to educational practice. International Electronic Journal of Elementary Education, 4, 33-46. [PDF]

McNamara, D. S., Ozuru, Y., & Floyd, R. G. (2011). Comprehension challenges in the fourth grade: The roles of text cohesion, text genre, and readers’ prior knowledge. International Electronic Journal of Elementary Education, 4, 229-257. [LINK]

Moss, J., Schunn, C.D., Schneider, W., & McNamara, D.S. (2011). An fMRI study of zoning out during strategic reading comprehension. In L. Carlson, C. Hoelscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 1218-1223). Austin, TX: Cognitive Science Society. [LINK]

Moss, J., Schunn, C. D., Schneider, W., McNamara, D. S., & VanLehn, K. (2011). The neural correlates of strategic reading comprehension: Cognitive control and discourse comprehension. NeuroImage, 58, 675-686. [PDF]

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

Raine, R.B., Mintz, L., Crossley, S.A., Dai, J., & McNamara, D.S. (2011). Text box size, skill, and iterative practice in a writing task. In R. C. Murray & P. M. McCarthy (Eds.), Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 537-542). Menlo Park, CA: AAAI Press. [LINK]

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. [LINK]

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. [LINK]

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. [LINK]

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

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. [LINK]

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. [PDF]

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. [PDF]

Crossley, S.A., Allen, D., & McNamara, D.S. (2012). Text simplification and comprehensive input: A case for intuitive approach. Language Teaching and Research, 16, 89-108. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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.  [PDF]

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

Di Sano, S., La Caprara, K., Rosa, A., Raine, R. B., & McNamara, D. S. (2012). Learning to write persuasive essays: A preliminary study on the effectiveness of an intelligent tutorial system with high school students. In C. Gelati, B. Arfe, & L. Mason (Eds.), Issues in writing research (pp. 214-220). Padua: CLEUP. [PDF]

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, D.C.: American Psychological Association. [PDF]

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. [PDF]

Jackson, G. T., Boonthum-Denecke, C. B., & McNamara, D. S. (2012). A comparison of gains between educational games and a traditional ITS. In P. M. McCarthy & G. M. Youngblood (Eds.), Proceedings of the 25th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 444-449)Menlo Park, CA: The AAAI Press. [PDF]

Jackson, G. T., Dempsey, K. B., & McNamara, D. S. (2012). Game-based practice in a reading strategy tutoring system: Showdown in iSTART-ME. In H. Reinders (Ed.), Computer Games (pp. 115-138)Bristol, UK: Multilingual Matters. [LINK]

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. [PDF]

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. [PDF]

Kurby, C. A., Magliano, J. P., Dandotkar, S., Woehrle, J., Gilliam, S., & McNamara, D. S. (2012). Changing how students process and comprehend texts with computer-based self-explanation training. Journal of Educational Computing Research, 47, 429-459. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

McNamara, D. S., Jackson, G. T., Boonthum, C., Deng, Y., & Xiangyou, H. (2012). iSTART-ME: A natural language game-enhanced comprehension strategy tutor. Journal of South China Normal University, 6. [PDF]

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. [PDF]

Ozuru, Y., Kurby, C. A., & McNamara, D. S. (2012). The effect of judgment task on metacomprehension judgments and its accuracy. Metacognition and Learning, 7, 113-131. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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

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. [LINK]

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. [PDF]

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. [LINK]

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. [PDF]

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. [PDF]

Jackson, G. T., & McNamara, D. S. (2013). Motivation and performance in a game-based intelligent tutoring system. Journal of Educational Psychology, 105, 1036-1049. [PDF]

Jackson, G. T., Snow, E. L., Varner (Allen), L. K., & McNamara, D. S. (2013). Game performance as a measure of comprehension and skill transfer. In C. Boonthum-Denecke & G. M. Youngblood (Eds.),Proceedings of the 26th Annual Flordia Artificial Intelligence Research Society (FLAIRS) Conference (pp. 497-502). Menlo Park, CA: The AAAI Press. [LINK]

Jackson, G. T., Varner (Allen), L. K., Boonthum-Denecke, C., & McNamara, D. S. (2013). The impact of individual differences on learning with an educational game and a traditional ITS. International Journal of Learning Technology, 8, 315-336. [PDF]

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). [PDF]

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. [PDF]

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. [PDF]

Moss, J., Schunn, C. D., Schneider, W., & McNamara, D. S. (2013). The nature of mind wandering during reading varies with the cognitive control demands of the reading strategy. Brain Research, 1539, 48-60. [PDF]

Ozuru, Y., Briner, S., Kurby, C. A., & McNamara, D. S. (2013). Comparing text comprehension measured by multiple-choice and open-ended questions. Canadian Journal of Experimental Psychology, 67, 215-227. [PDF]

Roscoe, R. D., Brandon, R., Snow, E., & McNamara, D. S. (2013). Game-based writing strategy practice with the Writing Pal. In K. Pytash & R. Ferdig (Eds.), Exploring Technology for Writing and Writing Instruction (pp. 1-20). Hershey, PA: IGI Global. [PDF]

Roscoe, R. D., & McNamara, D. S. (2013). Writing Pal: Feasibility of an intelligent writing strategy tutor in the high school classroom. Journal of Educational Psychology, ,105, 1010-1025. [PDF]

Roscoe, R. D., Snow, E. L., Brandon, R. D, & McNamara, D. S. (2013). Educational game enjoyment, perceptions, and features in an intelligent writing tutor. In C. Boonthum-Denecke & G. M. Youngblood (Eds.), Proceedings of the 26th International Flordia Artificial Intelligence Research Society (FLAIRS) Conference (pp. 515-520). Menlo Park, CA: AAAI Press. [LINK]

Roscoe, R. D., Snow, E. L., & McNamara, D. S. (2013). Feedback and revising in an intelligent tutoring system for writing strategies. In K. Yacef et al. (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED), (pp. 259-268). Heidelberg, Berlin: Springer. [PDF]

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. [PDF]

Snow, E. L., Jackson, G. T., Varner (Allen), L. K., & McNamara, D. S. (2013). Expectations of technology: A factor to consider in game-based learning environments. In K. Yacef et al. (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED), (pp. 359-368). Heidelberg, Berlin: Springer. [PDF]

Snow, E. L., Jackson, G. T., Varner (Allen), L. K., & McNamara, D. S. (2013). Investigating the effects of off-task personalization on system performance and attitudes within a game-based environment. In S. K. D’Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (pp. 272-275). Heidelberg, Berlin, Germany: Springer. [PDF]

Snow, E. L., Jackson, G. T., Varner (Allen), L. K., & McNamara, D. S. (2013). The impact of performance orientation on students’ interactions and achievements in an ITS. In C. Boonthum-Denecke & G. M. Youngblood (Eds.), Proceedings of the 26th International Flordia Artificial Intelligence Research Society (FLAIRS) Conference (pp. 521-526). Menlo Park, CA: AAAI Press. [PDF]

Snow, E. L., Jackson, G. T., Varner (Allen), L. K., & McNamara, D. S. (2013). The impact of system interactions on motivation and performance. In Proceedings of the 15th International Conference on Human-Computer Interaction (HCII). (pp. 103-107). Heidelberg, Berlin, Germany: Springer. [PDF]

Snow, E. L., Likens, A., Jackson, G. T., & McNamara, D. S. (2013). Students’ walk through tutoring: Using a random walk analysis to profile students. In S. K. D’Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (pp. 276-279). Heidelberg, Berlin, Germany: Springer. [PDF]

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. [PDF]

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. [LINK]

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. [PDF]

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 [PDF]

Weston, J. L., & McNamara, D. S. (2013). Using multi-level models to assess data from an intelligent tutoring system. In S. K. D’Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (pp. 370-371). Heidelberg, Berlin, Germany: Springer. [LINK]

Allen, L. K., Crossley, S. A., Snow, E. L., & McNamara, D. S. (2014). Game-based writing strategy tutoring for second language learners: Game enjoyment as a key to engagement. Language Learning and Technology, 18, 124-150. [PDF]

Allen, L. K., Snow, E. L., Crossley, S. A., Jackson, G. T., & McNamara, D. S. (2014). Reading comprehension components and their relation to the writing process. L’année psychologique/Topics in Cognitive Psychology, 114, 663-691. [PDF]

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. [LINK]

Allen, L. K., Snow, E. L., & McNamara, D. S. (2014). The long and winding road: Investigating the differential writing patterns of high and low skilled writers. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (pp. 304-307). London, UK: International Educational Data Mining Society. [PDF]

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. [PDF]

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] [PDF]

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. [PDF]

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. [LINK]

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. [LINK]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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. [LINK]

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. [PDF]

McNamara, D. S., & Schober, M. F. (2014). Society for Text and Discourse Annual Meeting 2013: Introduction to the Special Issue. Discourse Processes, 51(5-6), 357-358. [LINK]

Proske, A., Roscoe, R. D., & McNamara, D. S. (2014). Game-based practice versus traditional practice in computer-based writing strategy training: Effects on motivation and achievement. Education Technology Research Development, 62, 481-505. [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. [LINK]

Roscoe, R. D., Varner(Allen), L. K., Snow, E. L., & McNamara, D. S. (2014). Designing usable automated formative feedback for intelligent tutoring of writing. In J. L. Polman, E. A. Kyza, D. K. O’Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O’Connor, T. Lee, & L. D’Amico (Eds.), Proceedings of the 11th International Conference of the Learning Sciences (ICLS), Volume 3, (pp. 1423-1425). Boulder, CO. [PDF]

Snow, E. L., Allen, L. K., Jackson, G. T., & McNamara, D. S. (2014). Tracking choices: Computational analysis of learning trajectories. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (pp. 316-319). London, UK. [PDF]

Snow, E. L., Allen, L. K., Russel, D. G., & McNamara, D. S. (2014). Who’s in control?: Categorizing nuanced patterns of behaviors within a game-based intelligent tutoring system. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (pp. 185-192). London, UK: International Educational Data Mining Society. [PDF]

Snow, E. L., Jackson, G. T., & McNamara, D. S. (2014). Emergent behaviors in computer-based learning environments: Computational signals of catching up. Computers in Human Behavior, 41, 62-70. [PDF]

Snow, E. L., Jackson, G. T., & McNamara, D. S. (2014). What do they do?: Tracing students’ patterns of interactions within a game-based Intelligent Tutoring System. In J. L. Polman, E. A. Kyza, D. K. O’Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O’Connor, T. Lee, & L. D’Amico (Eds.), Proceedings of the 11th International Conference of the Learning Sciences (ICLS), Volume 3, (pp. 1481-1482). Boulder, CO: ISLS. [LINK]

Snow, E. L., Jacovina, M. E., Allen, L. K., Dai, J., & McNamara, D. S. (2014). Entropy: A stealth assessment of agency in learning environments. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining, (pp. 241-244). London, UK. [PDF]

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. [LINK]

Allen, L. K., Crossley, & McNamara, D. S. (2015). Predicting misalignment between teachers’ and students’ essay scores using Natural Language Processing tools. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education(AIED 2015), (pp. 529-532). Madrid, Spain: Springer. [PDF]

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. [PDF]

Allen, L. K., & McNamara, D. S., (2015). Promoting self-regulated learning in an Intelligent Tutoring System for writing. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Doctoral Consortium within the Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), (pp. 827-830). Madrid, Spain. [PDF]

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. [LINK]

Allen, L. K., McNamara, D. S., & McCrudden, M. T. (2015). Change your mind: Investigating the effects of self-explanation in the resolution of misconceptions. In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. Maglio, (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (Cog Sci 2015), (pp. 78-83). Pasadena, CA: Cognitive Science Society. [LINK]

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. [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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]

Crossley, S. A., McNamara, D. S., Baker, R., Wang, Y., Paquette, L., Barnes, T., & Bergner, Y. (2015). Language to completion: Success in an educational data mining massive open online class. 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. 388-391). Madrid, Spain: Springer. [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. [PDF]

Dascalu, M., Stavarache, L.L., Dessus, P., Trausan-Matu, S., McNamara, D.S., & Bianco, M. (2015). ReaderBench: The learning companion. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), (pp. 915–916). Madrid, Spain: Springer. [LINK]

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. [PDF]

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. [PDF]

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. [LINK]

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. [PDF]

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. [PDF]

Higgs, K., Magliano, J. P., Vidal-Abarca, E., Martínez, T., & McNamara D. S. (2015). Bridging skill and task-oriented reading. Discourse Processes, 54(1), 19-39.  [PDF]

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. [PDF]

Jacovina, M. E., Snow, E. L., Allen, L. K., Roscoe, R. D., Weston, J. L., Dai, J., & McNamara, D. S. (2015). How to visualize success: Presenting complex data in a writing strategy tutor. 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. 594-595). Madrid, Spain: International Educational Data Mining Society. [LINK]

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. [PDF]

Jacovina, M. E., Snow, E. L., Jackson, G. T., & McNamara, D. S. (2015). Game features and individual differences: Interactive effects on motivation and performance. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), (pp. 642-645). Madrid, Spain: Springer. [PDF]

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. [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 [PDF]

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. [PDF]

McNamara, D. S., Jacovina, M. E., & Allen, L. K. (2015). Higher order thinking in comprehension. In P. Afflerbach (Ed.), Handbook of individual differences in reading: Text and context, (pp. 164-176). Taylor & Francis, Routledge: NY. [PDF]

McNamara, D. S., Jacovina, M. E., Snow, E. L., & Allen, L. K. (2015). From generating in the lab to tutoring systems in classrooms. American Journal of Psychology, 128(2), 159-172. [PDF]

McNamara, D. S., & Schober, M. F. (2015). 2014 Society for Text and Discourse Annual Meeting: Introduction to the Special Issue. Discourse Processes, (ahead-of-print), 1-2. [LINK] [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. [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 [PDF]

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. [LINK]

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. [LINK]

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. [PDF]

Snow, E. L., Allen, L. K., Jacovina, M. E., & McNamara, D. S. (2015). Does agency matter?: Exploring the impact of controlled behaviors within a game-based environment. Computers & Education, 26, 378-392. [PDF]

Snow, E. L., Allen, L. K., Jackson, G. T., & McNamara, D. S. (2015). Spendency: Students’ propensity to use system currency. International Journal of Artificial Intelligence in Education, 25, 1-21. [PDF]

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. [PDF]

Snow, E. L., Allen, L. K., & McNamara, D. S. (2015). The dynamical analysis of log-data within educational games. In Loh & Sheng (Eds.). Serious Games Analytics: Methodologies for Performance Measurement, Assessment, and Improvement (pp. 81-100). Springer International Publishing. [PDF]

Snow, E. L., Jacovina, M. E., & McNamara, D. S., (2015). Promoting metacognition within a game-based environment. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), (pp.864-867). Madrid, Spain: Springer. [PDF]

Snow, E. L & McNamara, D. S. (2015). Dynamic user modeling within a game-based ITS. 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. 639-641). Madrid, Spain: International Educational Data Mining Society. [LINK]

Snow, E. L., McNamara, D. S., Jacovina, M. E., Allen, L. K., Johnson, A. M., Perret, C. A., Dai, J., Jackson, G. T., Likens, A. D., Russell, D. G., & Weston, J. L. (2015). Promoting metacognitive awareness within a game-based intelligent tutoring system. In A. Mitrovic, F. Verdejo, C. Conati, & N. Heffernan (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), (pp.786-789). Madrid, Spain: Springer. [PDF]

Snow, E. L., San Pedro, M. O., Jacovina, M. E., McNamara, D. S., & Baker. R. S. (2015). Achievement versus experience: Predicting students’ choices during gameplay. 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.564-565). Madrid, Spain: International Educational Data Mining Society. [PDF]

Soto, C. M., McNamara, D. S., Jacovina, M. E., Snow, E. L., Dai, J., Allen, L. K., Perret, C. A., Johnson, A. M., & Russell, D. G. (2015). iSTART-E: Desarrollando un tutor inteligente para la comprensión lectora de estudiantes de habla hispana. In M. García (Ed.), Proceedings of the 15th Annual Colloquium on Peninsular and Spanish American Literature, Linguistics and Culture. Orlando, FL. [PDF]

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. [LINK]

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. [PDF]

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. [LINK]

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. [LINK]

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. [PDF]

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. [LINK]

Allen, L. K., Perret, C. A., & McNamara, D. S. (2016). Linguistic signatures of cognitive processes during writing. 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. 2483-2488). Austin, TX: Cognitive Science Society. [PDF]

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  [PDF]

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. [PDF]

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. [LINK]

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. [LINK]

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. [LINK]

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.[PDF]

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. [PDF]

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. [PDF]

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. [LINK]

Crossley, S. A., & McNamara, D. S. (2016). Text-based recall and extra-textual generations resulting from simplified and authentic texts. Reading in a Foreign Language, 28(1),1-19.[PDF]

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.[PDF]

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. [LINK]

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. [PDF]

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. [PDF]

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.

Jacovina, M. E., Jackson, G. T., Snow, E. L., & McNamara, D. S. (2016). Timing game-based practice in a reading comprehension strategy tutor. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Proceedings of the 13th International Conference on Intelligent Tutoring Systems (ITS 2016) (pp. 80-89). Zagreb, Croatia: Spinger. [PDF]

Johnson, A. M., Jacovina, M. E., Jackson, G. T., Tighe, E. L., & McNamara, D.S. (2016) Lost in Springdale: An interactive narrative for adult literacy learners. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Proceedings of the 13th International Conference on Intelligent Tutoring Systems (ITS 2016) (pp. 494-495). Zagreb, Croatia: Springer. [PDF]

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. [LINK]

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. [PDF]

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. [LINK]

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. [PDF]

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. [LINK]

Snow, E. L., Jacovina, M. E., Jackson, G. T., & McNamara, D. S. (2016). iSTART-2: A reading comprehension and strategy instruction tutor. In D.S. McNamara & S. A. Crossley (Eds.) Adaptive educational technologies for literacy instruction (pp.104-121). Taylor & Francis, Routledge: NY. [PDF]

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. [PDF]

Weston-Sementelli, J. L., Allen, L. K., & McNamara, D. S. (2016). Comprehension and writing strategy training improves performance on content-specific source-based writing tasks. International Journal of Artificial Intelligence in Education, 1-32. [PDF]

Allen, L. K., Likens, A., & McNamara, D. S. (2017). Recurrence quantification analysis: A technique for the dynamical analysis of student writing. In Z. Markov & V. Rus (Eds.), Proceedings of the 30th Annual Florida Artificial Intelligence Research Society International Conference (FLAIRS), (pp. 240-245). Marco Island, FL: AAAI Press. [LINK]

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), (pp. 373-382). Vancouver, Canada: ACM. [LINK]

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. [PDF]

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. [LINK]

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. [LINK]

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.),Proceedings of the 12th International Conference on Computer-Supported Collaborative Learning (CSCL 2017), (pp. 103-110). Philadelphia, PA: ISLS. [LINK]

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. [LINK]

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. [LINK]

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 [PDF]

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. [LINK]

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. [PDF]

Dascalu, M., Jacovina, M., Soto, C. M., Allen, L. K., Dai, J., Guerrero, T. A., & McNamara, D. S. (2017). Teaching iSTART to understand Spanish. In B. Boulay, R. Baker, & E. Andre (Eds.), Proceedings of the 18th International Conference on Artificial Intelligence in Education (AIED 2017). (pp. 485–489). Wuhan, China: Springer. [PDF]

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

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. [LINK]

Johnson, A. M., Guerrero, T. A., Tighe, E. L., & McNamara, D. S. (2017). iSTART-ALL: Confronting adult low literacy with intelligent tutoring for reading comprehension. In B. Boulay, R. Baker & E. Andre (Eds.), Proceedings of the 18th International Conference on Artificial Intelligence in Education (AIED), (pp. 125-136). Wuhan, China: Springer.[PDF]

Johnson, A., McCarthy, K. S., Kopp, K., Perret, C. A., & McNamara, D. S. (2017). Adaptive reading and writing instruction in iSTART and W-Pal. In Z. Markov & V. Rus (Eds.), Proceedings of the 30th Annual Florida Artificial Intelligence Research Society International Conference (FLAIRS), (pp. 561-566). Marco Island, FL: AAAI Press. [LINK]

Johnson, A. M., Tighe, E. L. & McNamara, D. S. (2017). Development of an interactive narrative for adult literacy learners. In R. Roscoe, S. Craig, & I. Douglas (Eds.),  End-user considerations in educational technology design, (pp. 238-262). IGI Global. [PDF]

Kopp, K. J., Johnson, A. M., Crossley, S. A., & McNamara, D. S. (2017). Assessing question quality using natural language processing. In B. Boulay, R. Baker & E. Andre (Eds.),  Proceedings of the 18th International Conference on Artificial Intelligence in Education (AIED), (pp.201-211). Wuhan, China: Springer. [PDF]

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. [LINK]

McCarthy, K. S., Jacovina, M. E., Snow, E. L. Guerrero, T. A., & McNamara, D. S. (2017). iSTART therefore I understand: But metacognitive supports did not enhance comprehension gains. In B. Boulay, R. Baker, & E. Andre (Eds.),  Proceedings of the 18th International Conference on Artificial Intelligence in Education (AIED), (pp. 201-211). Wuhan, China: Springer. [PDF]

McCarthy, K. S., Johnson, A. M., Likens, A. D., Martin, Z., & McNamara, D. S. (2017). Metacognitive prompt overdose: Positive and negative effects of prompts in iSTART. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.),  Proceedings of the 10th International Conference on Educational Data Mining (EDM), (pp. 404-405). Wuhan, China: International Educational Data Mining Society. [LINK]

Allen, L. K., Likens, A., & McNamara, D. S. (2017). Recurrence quantification Analysis: A technique for the dynamical analysis of student writing. In Z. Markov & V. Rus (Eds.),  Proceedings of the 30th Annual Florida Artificial Intelligence Research Society International Conference (FLAIRS), (pp. 240-245). Marco Island, FL: AAAI Press. [LINK]

McNamara, D. S. (2017). Self-explanation and reading strategy training (SERT) improves low-knowledge students’ science course performance. Discourse Processes, 54(7), 479-492. [LINK] [PDF]

McNamara, D. S. & Allen, L. K. (2017). Toward an integrated perspective of writing as a discourse process. In M. Schober, A. Britt, & D. N. Rapp (Eds.), Handbook of discourse processes (2nd ed.). New York, NY: Routledge. [PDF]

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] [PDF]

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. [PDF]

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. [PDF]

Perret, C. A., Johnson, A. M., McCarthy, K. S., Guerrero, T. A., & McNamara, D. S. (2017). StairStepper: An adaptive remedial iSTART module. In B. Boulay, R. Baker & E. Andre (Eds.), Proceedings of the 18th International Conference on Artificial Intelligence in Education (AIED), (pp.557-560). Wuhan, China: Springer. [PDF]

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 [PDF]

Skalicky, S., Crossley, S. A., McNamara, D. S., & Muldner, K. (2017). Automatically identifying humorous and persuasive language produced during a creative problem-solving task. In Z. Markov & V. Rus (Eds.), Proceedings of the 30th Annual Florida Artificial Intelligence Research Society International Conference (FLAIRS), (pp. 282-287). Marco Island, FL: AAAI Press. [LINK]

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. [PDF]

Allen, L. K., Likens, A., & McNamara, D. S. (2018). A multi-dimensional analysis of writing flexibility in an automated writing evaluation system. In Buckingham Shum, S., Ferguson, R., Merceron, A., & Ochoa, X. (Eds), Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK’18), (pp. 380-388). Sydney, Australia: SOLAR. [LINK]

Balyan, R., McCarthy, K. S., & McNamara, D. S. (2018). Comparing machine learning classification approaches for predicting expository text difficulty. In K. Brawner & V. Rus (Eds.), Proceedings of the 31st Annual Florida Artificial Intelligence Research Society (FLAIRS-31), (pp. 412-426). Melbourne, FL: AAAI Press. [PDF]

Crossley, S. A., Sirbu, M.-D., Dascalu, M., Barnes, T., Lynch, C. F., & McNamara, D. S. (2018). Modeling math success using Cohesion Network Analysis. In C. P. Rosé, R. Martínez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren & B. d. Boulay (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018), Part II (pp. 63-67). London, UK: Springer. [PDF]

Dascalu, M., Crossley, S. A., McNamara, D. S., Dessus, P., & Trausan-Matu, S. (2018). Please Readerbench this text: A multi-dimensional textual complexity assessment framework. In Craig, S. (Ed.), Tutoring and Intelligent Tutoring Systems (pp. 251-271). Hauppauge, NY: Nova Science Publishers. [LINK]

Likens, A. D., McCarthy, K. S., Allen, L. K., & McNamara, D. S. (2018). Recurrence quantification analysis as a method for studying text comprehension dynamics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK’18). Sydney, Australia. [PDF]

McCarthy, K. S., Likens, A. D., Kopp, K. J., Watanabe, M., Perret, C. A., & McNamara, D. S. (2018). The “LO”-down on grit: Non-cognitive trait assessments fail to predict learning gains in iSTART and W-Pal. In Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK’18). Sydney, Australia. [LINK]

McCarthy, K. S., Soto, C., Malibran, C., Fonesca, L., Simian, M., & McNamara, D. S. (2018). iSTART-E: Reading comprehension strategy training for Spanish speakers. In C. P. Rosé, R. Martinez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren & B. d. Boulay (Eds.),  Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018),  Part II (pp. 215-219). London, UK: Springer. [PDF]

Stone, M., Kent, K. M., Roscoe, R. D., Corley, K. M., Allen, L. K., & McNamara, D. S. (2018). The design implementation framework: Iterative design from the lab to the classroom. In R. Roscoe, S. D. Craig, & I. Douglas (Eds.),  End-user considerations in educational technology design (pp.76-98). Hershey, PA: IGI Global. [PDF]

Baker, R.S. Wang, Y., Paquette, L., Aleven, V., Popsecu, O., Sewall, J., Rose, C., Tomar, G., Ferschke, O., Zhang, J., Cennamo, M., Ogden, S., Condit, T., Diaz, J., Crossley, S., McNamara, D., Comer, D., Lynch, C., Brown, R., Barnes, T., Bergner, Y. (2016). Educational data mining: A MOOC experience. In ElAtia, S., Ipperciel, D., & Zaiane, O.R. (Eds.),  Data mining and learning analytics: Applications in educational research. Wiley. [PDF]

Jacovina, M., & McNamara, D. S. (2017). Intelligent tutoring systems for literacy: Existing technologies and continuing challenges. In R. Atkinson (Ed.),  Intelligent tutoring systems: Structure, applications and challenges.  Nova Science Publishers Inc. [LINK]

Johnson, A., Perret, C. A., Watanabe, M., Kopp, K., McCarthy, K. S., & McNamara, D. S. (2018). Adaptive literacy instruction in iSTART and W-Pal: Implementing the outer loop. In S. Craig (Ed.),  Tutoring and intelligent tutoring systems.  [PDF]

McCarthy, K. S., Kopp, K. J., Allen, L. K., & McNamara, D. S. (2018). Methods of studying text: Memory, comprehension, and learning. In H. Otani & B. Schwarz (Eds.),  Research methods in human memory (pp. 104-124). Routledge.

McCarthy, K. S., Likens, A. D., Johnson, A. M., Guerrero, T. A., & McNamara, D. S. (2018). Metacognitive overload!: Positive and negative effects of metacognitive prompts in an intelligent tutoring system. International Journal of Artificial Intelligence in Education, 28, 420-438.  [LINK]

McNamara, D., Allen, L. K., McCarthy, S. & Balyan, R. (2018). NLP: getting computers to understand discourse. In K. Millis, Long, D., Magliano, J. & Wiemer, K (Eds.), Deep learning: Multi-disciplinary approaches. Routledge.

Panaite, M., Dascalu, M., Johnson, A. M., Balyan, R., Dai, J., McNamara, D. S., & Trausan-Matu, S. (2018). Bring it on! Challenges encountered while building a comprehension tutoring system using ReaderBench. In C. P. Rosé, R. Martinez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren & B. d. Boulay (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018),  Part I (pp. 409-419). London, UK: Springer.  [PDF]

Ruseti, S., Dascalu, M., Johnson, A. M., McNamara, D. S., Balyan, R., McCarthy, K. S., & Trausan-Matu, S. (2018). Scoring summaries using recurrent neural networks. In Nkambou, R., Azevedo, R., Vassileva, J. (Eds.), Proceedings of the 14th International Conference on Intelligent Tutoring Systems (ITS) in Montreal, Canada (pp. 191-201). London, UK: Springer.  [PDF]

Ruseti, S., Dascalu, M., Johnson, A., McNamara, D. S., Balyan, R., Kopp, K., Crossley, S. A., & Trausan-Matu, S. (2018). Predicting question quality using recurrent neural networks. In C. P. Rosé, R. Martinez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren & B. d. Boulay (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018),  Part I (pp. 491-502). London, UK: Springer. [PDF]

Sirbu, D., Dascalu, M., Crossley, S. A., McNamara, D. S., Barnes, T., Lynch, C., & Trausan-Matu, S. (2018). Exploring online course sociograms using cohesion network analysis. In C. P. Rosé, R. Martinez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren & B. d. Boulay (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (AIED 2018), Part II (pp. 337-342). London, UK: Springer. [PDF]

Allen, L. K., Likens, A. D., & McNamara, D. S. (2019). Modeling the dissemination of misinformation through discourse dynamics. In P. Kendeou, D. H. Robinson, & M. T. McCrudden (Eds.), Misinformation and fake news in education, (pp. 159-186). Information age Publishing. [LINK]

Allen, L. K., Likens, A. D., & McNamara, D. S. (2019). Writing flexibility in argumentative essays: a multidimensional analysis. Reading and Writing, 32(6), 1607-1634. [PDF]

Allen, L. K., Mills, C., Perret, C. A., & McNamara, D. S. (2019). Are you talking to me? Multidimensional language analysis of explanations during reading. In S. Hsiao, J. Cunningham, K. McCarthy, G. Lynch, N. Hoover, C. Brooks, R. Ferguson, & U. Hoppe (Eds.). Proceedings of the 19th International Conference on Learning Analytics and Knowledge (LAK ’18) (pp. 116-120). Phoenix, AZ, USA: SOLAR. [PDF]

Allen, L. K., Zachary, L. C., & McNamara, D. S. (2019). Learning with text: Toward a multidimensional perspective on text-based communication. In S. Hsiao, J. Cunningham, K. McCarthy, G. Lynch, N. Hoover, C. Brooks, R. Ferguson, & U. Hoppe (Eds.), Companion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK’18) (pp. 252). Phoenix, AZ, USA: SOLAR. [PDF]

Balyan, R., Crossley, S. A., Brown, W., Karter, A. J., McNamara, D. S., Liu, J. Y., Lyles, C. R., & Schillinger, D. (2019). Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE Study.  PLOS ONE, 14(2). [PDF]

Crossley, S. A., Kim, M., Allen, L. K., & McNamara, D. S. (2019). Automated Summarization Evaluation (ASE) using natural language processing tools. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Proceedings of the 20th International Conference of Artificial Intelligence in Education (AIED) in Chicago, IL (pp. 84-95). Cham, Switzerland: Springer. [PDF]

Dascalu, M. Paraschiv, I. C., McNamara, D. S., & Trausan-Matu, S. (2019). Towards an automated model of comprehension (AMoC). In V. Pammer-Schnidler, M. Scheffel, & J. Broisin (Eds.), Proceedings of the 14th European Conference on Technology Enhanced Learning (EC-TEL 2019). Leeds, UK: Springer. [PDF]

Green, C. S., Bavelier, D., Kramer, A. F., Vinogradov, S., Ansorge, U., Ball, K. K., Bingel, U., Chien, J. M., Colzato, L. S., Edwards, J. D., Facoetti, A., Gazzaley, A., Gathercole, S. E., Ghisletta, P., Gori, S., Granic., I., Hillman, C. H., Hommel, B., Jaeggi, S. M., Kanske, P., Karbach, J., Kingstone, A., Kliegel, M., Klingberg, T., Kuhn, S., Levi, D. M., Mayer, R. E., McLaughlin, A. C., McNamara, D. S., Morris, M., C., Nahum, M., Newcombe, N. S., Panizzutti, R., Prakash, R. S., Rizzo, A., Schubert, T., Seitz, A. R., Short, S. J., Singh, I., Slotta, J. D., Strobach, T., Thomas, M. S. C., Tipton, E., Tong, X., Vlach, H. A., Wetherell, J. L., Wexler, A., & Witt, C. M. (2019). Improving methodological standards in behavioral interventions for cognitive enhancement. Journal of Cognitive Enhancement, 3(1), 2-29. [PDF]

Jung, J., Crossley, S. A., & McNamara, D. S. (2019). Predicting second language writing proficiency in learner texts using computational tools.  The Journal of Asia TEFL, 16(1), 37-52. [PDF]

Liu, R., Stamper, J., Davenport, J., Crossley, S., McNamara, D. S., Nzinga, K., & Sherin, B. (2019). Learning linkages: Integrating data streams of multiple modalities and timescales. Journal of Computer Assisted Learning, 35(1), 99-109. [PDF]

McCarthy, K. S., Roscoe, R. D., & McNamara, D. S. (2019). Checking it twice: Does adding spelling and grammar checkers improve essay quality in an automated writing tutor?. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Proceedings of the 20th International Conference of Artificial Intelligence in Education (AIED) in Chicago, IL (pp. 270-282). Cham, Switzerland: Springer. [LINK]

McNamara, D. S., Allen, L. K., & Crossley, S. A. (2019). WAT: Writing assessment tool. In S. Hsiao, J. Cunningham, K. McCarthy, G. Lynch, N. Hoover, C. Brooks, R. Ferguson, & U. Hoppe (Eds.), Companion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK’18) (pp. 250). Phoenix, AZ, USA: SOLAR. [PDF]

McNamara, D. S., Roscoe, R., Allen, L., & Balyan, R., McCarthy, K. S. (2019). Literacy: From the perspective of text and discourse theory. Journal of Language and Education, 5(3), 56-69. [PDF]

Nicula, B., Perret, C. A., Dascula, M., & McNamara, D. S. (2019). Predicting multi-document comprehension: Cohesion network analysis. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Proceedings of the 20th International Conference of Artificial Intelligence in Education (AIED) in Chicago, IL (pp. 358-369). Cham, Switzerland: Springer. [PDF]

Panaite, M., Ruseti, S., Dascalu, M., Balyan, R., McNamara, D. S., & Trausan-Matu, S. (2019). Automated scoring of self-explanations using recurrent neural networks. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou & J. Schneider (Eds.), Proceedings of the 14th European Conference on Technology Enhanced Learning (EC-TEL 2019) (pp. 659–663). Delft, Netherlands: Springer. [PDF]

Semere, W., Crossley, S. A., Karter, A. J., Lyles, C. R., Brown III, W., Reed, M., McNamara, D. S., Liu, J. L., & Schillinger, D. (2019). Secure messaging with physicians by proxies for patients with diabetes: Findings from the ECLIPPSE study. Journal of General Internal Medicine. [LINK]

Sirbu, M. D., Dascalu, M., Crossley, S., McNamara, D. S., & Trausan-Matu, S. (2019). Longitudinal analysis of participation in online courses powered by cohesion network analysis. In Lund, K., Niccolai, G., Lavoué, E., Hmelo-Silver, C., Gweon, G., and Baker, M. (Eds.), Proceedings of the 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019, Volume 2 (pp. 640-643). Lyon, France: International Society of the Learning Sciences. [LINK]

Skalicky, S., Crossley, S. A., McNamara, D. S., & Muldner, K. (2019). Measuring creative ability in spoken bilingual text: The role of language proficiency and linguistic features. In A. Goel, C. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Meeting of the Cognitive Science Society.  Montreal, Canada: Cognitive Science Society. [LINK]

Solovyev, V., Solnyshkina, M., Gafiyatova, E., McNamara, D. S., & Ivanov, V. (2019). Sentiment in academic texts. In S. Balandin, V. Deart, T. Tyutina, & Y. Koucheryavy (Eds.), Proceedings of the 24th Conference of Open Innovations Association (FRUCT) in Moscow, Russia, (pp. 408-414). Piscataway, NJ: IEEE. [PDF]

Soto, C., de Blume, A. P. G., Jacovina, M., McNamara, D. S., Benson, N., & Riffo, B. (2019). Reading comprehension and metacognition: The importance of inferential skills. Cogent Education, 6(1), DOI: 10.1080/2331186X.2019.1565067 [LINK]

Watanabe, M., McCarthy, K., & McNamara, D. S. (2019). Examining the effects of adaptive task selection on students’ motivation in an intelligent tutoring system. In S. Hsiao, J. Cunningham, K. McCarthy, G. Lynch, N. Hoover, C. Brooks, R. Ferguson, & U. Hoppe (Eds.), Companion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK’18) (pp. 161-62). Phoenix, AZ, USA: SOLAR. [PDF]

Zachary, L. C., Allen, L. K., Poulos, M. C., McNamara, D. S., & Crossley, S. A. (2019). Modeling vocabulary knowledge using a computational text analysis of student’s source-based writing. In M. Desmarais, R. Nkambou, C. Lynch, & A. Merceron (Eds.), Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019). Montreal, Canada.

McNamara, D. S., & Allen, L. K. (2019). Writing. In L. Zhang, M. T. & McCrudden (Eds.). Oxford Research Encyclopedia of Educational Psychology. Oxford University Press. [PDF]

Roscoe, R., Allen, L. K., & McNamara, D. S. (2019). Contrasting writing practice formats in a writing strategy tutoring system. Journal of Educational Computing Research. [PDF]

Allen, L. K., & McNamara, D. S. (2020). Defining deep reading comprehension for diverse readers. In P. Afflerbach, E. Birr Moje, P. Enciso & N. K. Lesaux (Eds.), Handbook of Reading Research, Volume V (pp. 261-276). New York: Routledge. [PDF]

Balyan, R., McCarthy, K. S., & McNamara, D. S. (2020). Applying natural language processing and hierarchical machine learning approaches to text difficulty classification. International Journal of Artificial Intelligence in Education (IJAIED), 30, 337-370. https://doi.org/10.1007/s40593-020-00201-7. [PDF]

Botarleanu, R. M., Dascalu, M., Crossley, S. A., & McNamara, D. S. (2020). Sequence-to-sequence models for automated text simplification. In R. Luckin, V. Cavalli-Sforza, I. Ibert Bittencourt, M. Cukorova, & K. Muldner (Eds.), Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED 2020). Ifrane, Morocco: Springer. [LINK]

Cemballi, A. G., Karter, A. J., Schillinger, D., Liu, J. Y., McNamara, D. S., Brown III, W., Crossley, S., Semere, W., Reed, M., Allen, J., & Lyles, C. R. (2020). Descriptive examination of secure messaging in a longitudinal cohort of diabetes patients in the ECLIPPSE study. Journal of the American Medical Informatics Association, 28(6), 1252-1258. [LINK]

Cioaca, V., Dascalu, M., & McNamara, D. S. (2020). Extractive summarization using cohesion network analysis and submodular set functions. In 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2020). Timisoara, Romania: IEEE. [PDF]

Crossley, S. A., Balyan, R., Liu, J., Karter, A. J., McNamara, D. S., & Schillinger, D. (2020). Developing and testing automatic models of patient communicative health literacy using linguistic features. Health Communication, 36(8), 1018-1028. https://doi.org/10.1080/10410236.2020.1731781. [PDF]

Crossley, S. A., Balyan, R., Liu, J., Karter, A., McNamara, D. S., & Schillinger, D. (2020). Predicting the readability of physicians’ secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study. Journal of Communication in Healthcare: Strategies, Media and Engagement in Global Health, 13(4), 344-356. [LINK]

Dascalu, M. D., Ruseti, S., Carabas, M., Dascalu, M., Trausan-Matu, S., & McNamara, D. S. (2020). Cohesion network analysis: Predicting course grades and generating sociograms for a Romanian moodle course. In 16th International Conference on Intelligent Tutoring Systems (ITS 2020). Athens, Greece: Springer. [PDF]

Dascalu, M. D., Ruseti, S., Dascalu, M., McNamara, D. S., & Trausan-Matu, S. (2020). Multi-document cohesion network analysis: Visualizing intratextual and intertextual links. In R. Luckin, V. Cavalli-Sforza, I. Ibert Bittencourt, M. Cukorova, & K. Muldner (Eds.), Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED 2020). Ifrane, Morocco: Springer. [PDF]

McCarthy, K. S., Soto, C. M., Gutierrez de Blume, A. P., Palma D., González, J. I., & McNamara, D. S. (2020). Improving reading comprehension in Spanish using iSTART-E: A pilot study. International Journal of Computer-Assisted Language Learning and Teaching, 10(4), 66-82. [PDF]

McCarthy, K. S., Watanabe, M., & McNamara, D. S. (2020). The design implementation framework: Guiding principles for the redesign of a reading comprehension intelligent tutoring system. In M. Schmidt, A. Tawfik, Y. Earnshaw, & I. Jahnke (Eds.), Learner and user experience research: An introduction for the field of learning design & technology. EdTech Books. [LINK]

McCarthy, K. S., Watanabe, M., & McNamara, D. S. (2020). Personalized learning in iSTART: Past modifications and future design. Journal of Research on Technology in Education, 52(3), 301-321. [LINK]

McNamara, D. S. (2020). If integration is the keystone of comprehension: Inferencing is the key. Discourse Processes, 58(1), 86-91. DOI: 10.1080/0163853X.2020.1788323. [LINK]

Nicula, B., Perret, C. A., Dascalu, M., & McNamara, D. S. (2020). Extended multi-document cohesion network analysis centered on comprehension prediction. In I. Ibert Bittencourt, M. Cukorova, K. Muldner, E. Millan, & R. Luckin (Eds.), Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED 2020). Ifrane, Morocco: Springer. [PDF]

Nicula, B. Perret, C. A., Dasalu, M., & Mcnamara, D. S. (2020). Multi-document cohesion network analysis: Automated prediction of inferencing across multiple documents. Proceedings of the 32nd International Conference on Tools with Artificial Intelligence (ICTAI 2020). Online: IEEE. [PDF]

Schillinger, D., Balyan, R., Crossley, S. A., McNamara, D. S., Liu, J. Y., & Karter, A. J. (2020). Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study. Health Services Research, 56(1), 132-144. [LINK]

Wan, Q., Crossley, S. A., Allen, L. K., & McNamara, D. S. (2020). Claim detection and relationship with writing quality. In V. Cavalli-Sforza, C. Romero, A. Rafferty, & J. R. Whitehill (Eds.), Proceedings of the 13th International Conference on Educational Data Mining (EDM). Virtual Conference: International Educational Data Mining Society. [LINK]

Arner, T., McCarthy, K. S., & McNamara, D. S. (2021). iSTART StairStepper – Using comprehension strategy training to game the test. Computers, 10(4), 48. [LINK]

Botarleanu, R.-M., Dascalu, M., Allen, L. K., Crossley, S. A., & McNamara, D.S. (2021). Automated summary scoring with ReaderBench. In 17th International Conference on Intelligent Tutoring Systems (ITS 2021). Athens, Greece: Springer. [PDF]

Botarleanu, R.-M., Dascalu, M., Watanabe, M., McNamara, D. S., & Crossley, S. A. (2021). Multilingual age of exposure. In 22nd International Conference on Artificial Intelligence in Education (AIED 2021) (pp. 77-87). Utrecht, Netherlands (Online): Springer. [PDF]

Brown III, Balyan, R., Karter, A. J., Crossley, S., Semere, W., Duran, N. D., Lyles, C., Liu, J., Moffet, H. H., Daniels, R., McNamara, D. S., & Schillinger, D. (2021). Challenges and solutions to employing natural language processing and machine learning to measure patients’ health literacy and physician writing complexity: The ECLIPPSE study. Journal of Biomedical Informatics, 113, 103658. [LINK]

Butterfuss, R., Arner, T., McNamara, D. S., & Allen, L. K. (2021). Social media spillover: Attitude-inconsistent tweets reduce memory for subsequent information. In T. Fitch, C. Lamm, H. Leder, & K. Tessmar (Eds.), Proceedings of the 43rd Annual Conference of the Cognitive Science Society. Vienna Austria: Cognitive Science Society. [LINK]

Corlatescu, D., Dascalu, M., & McNamara, D. S. (2021). The automated model of comprehension v2.0. In 22nd International Conference on Artificial Intelligence in Education (AIED 2021). Utrecht, Netherlands (Online): Springer. [PDF]

Crossley, S., Wan, Q., Allen, L., & McNamara, D. S. (2021).  Source inclusion in synthesis writing: An NLP approach to understanding argumentation, sourcing, and essay quality.  Reading and Writing. [LINK]

Dascalu, M.-D., Ruseti, S., Dascalu, M., McNamara, D. S., Carabas, M., Rebedea, T., & Trausan-Matu, S. (2021). Before and during COVID-19: A cohesion network analysis of students’ online participation in moodle courses. Computers in Human Behavior, 121, 106780. [LINK]

Fang, Y., Li, T., Roscoe, R. D., & McNamara, D. S. (2021). Predicting literacy skills via stealth assessment in a simple vocabulary game. In R. A. Sottilare, & J. Schwarz (Eds.), Proceedings of the 23rd Human-computer Interaction International Conference. Washington, D.C. (Online): Springer. [PDF]

Flynn, L. E., McCarthy, K. S., McNamara, D. S., Magliano, J. P., & Allen, L. K. (2021).  The appearance of coherence: Using cohesive properties of readers’ constructed responses to predict individual differences. Revista Signos: Estudios de Lingüística, 54(107), 1061-1088. [PDF]

McCarthy, K. S., Magliano, J. P., Snyder, J. O., Kenney, E. A., Newton, N. N., Perret, C. A., Knezevic, M., Allen, L. K., & McNamara, D. S. (2021). Quantified qualitative analysis: Rubric development and inter-rater reliability as iterative design. In E. de Vries, J. Ahn, & Y. Hod (Eds.), 15th International Conference of the Learning Sciences – ICLS 2021. Bochum, Germany (Online): International Society of the Learning Sciences. [LINK]

McCarthy, K. S., & McNamara, D. S. (2021). The multidimensional knowledge in text comprehension framework. Educational Psychologist. DOI: 10.1080/00461520.2021.1872379. [LINK]

McNamara, D. S. (2021). Chasing theory with technology: A quest to understand understanding. Discourse Processes, 58(5-6), 442-448.  [LINK]

Nicula, B., Dascalu, M., Newton, N. N., Orcutt, E., & McNamara, D. S. (2021). Automated paraphrase quality assessment using recurrent neural networks and language models (SP). In 17th International Conference on Intelligent Tutoring Systems (ITS 2021) (pp. 333-340). Athens, Greece: Springer. [PDF]

Öncel, P., Flynn, L. E., Sonia, A. N., Barker, K., Lindsay, G. C., McClure, C. M., McNamara, D. S., & Allen, L. K. (2021).  Automatic student writing evaluation: Investigating the impact of individual differences on source-based writing. In M. Scheffel, N. Dowell, S. Joksimovic, & G. Siemens (Eds.), Proceedings of the 11th International Learning Analytics & Knowledge Conference, (pp. 620-625).  New York, NY : Association for Computing Machinery. [LINK

Ruseti, S., Dascalu, M-D., Corlatescu, D., Dascalu, M., Trausan-Matu, S., & McNamara, D. S. (2021). Exploring dialogism using language models. In 22nd International Conference on Artificial Intelligence in Education (AIED 2021) (pp. 296-301). Utrecht, Netherlands (Online): Springer. [PDF]

Schillinger, D., Balyan, R., Crossley, S., McNamara, D., & Karter, A. (2021). Validity of a computational linguistics-derived automated health literacy measure across race/ethnicity: Findings from the ECLIPPSE project. Journal of Health Care for the Poor and Underserved, 32(2), 347-365. [LINK]

Schillinger, D., Duran, N, Crossley, S., Balyan, R., McNamara, D. S., & Karter, A. J. (2021).  Precision communication: Physicians’ linguistic adaptation to patients’ health literacy.  Sciences Advances, 7(51). [LINK]

Shin, J., Balyan, R., Banawan, M., Leite, W. L., & McNamara, D. S. (2021). Pedagogical communication language in video lectures: Empirical findings from Algebra Nation. In E. de Vries, J. Ahn, & Y. Hod (Eds.), 15th International Conference of the Learning Sciences – ICLS 2021. Bochum, Germany (Online): International Society of the Learning Sciences. [LINK]

Wan, Q., Crossley, S. A., Banawan, M., Balyan, R., McNamara, D. S., & Allen, L. K. (2021). Automated claim identification using NLP features in student argumentative essays. In F. Bourchet, J. Vie, S Hsiao, & S. Sahebi (Eds.), Proceedings of the 14th International Conference on Educational Data Mining (EDM-2021). Paris, France: International Educational Data Mining Society. [LINK]

Wang, Z., O’Reilly, T., Sabatini, J., McCarthy, K. S., & McNamara, D. S. (2021). A tale of two tests: The role of topic and general academic knowledge in traditional versus contemporary scenario-based reading. Learning and Instruction, 73, 101462. [LINK]

McNamara, D. S., Arner, T., Butterfuss, R., Mallick, D. B., Lan, A. S., Roscoe, R. D., Roedinger III, H. L., Baraniuk, R. G., (2022).  Situating AI (and big data) in the learning sciences: Moving toward large-scale learning sciences.  In A. Alavi, & B. McLaren (Eds.), Artificial intelligence in STEM education: The paradigmatic shifts in research, education, and technology. CRC Press.

Butterfuss, R., Roscoe, R. D., Allen, L. K., McCarthy, K. S. & McNamara, D. S. (in press). Strategy uptake in W-Pal: Adaptive feedback and instruction. Journal of Educational Computing Research.

Technical reports

Doane, S.M., Kintsch, W., Polson, P.G., & McNamara, D.S. (1991). Producing UNIX commands: What experts must know (Learning Series Rep. No. UIUC-BI-CS-91-20). Urbana, IL: Beckman Institute of Cognitive Science.

Kintsch, E., McNamara, D.S., Kintsch, W., & Songer, N.B. (1992). Revising the coherence of science texts to improve comprehension and learning: Traits of mammals (ICS Rep. No. 92-3). Boulder, CO.

McNamara, D.S. (1992). The generation effect: A detailed analysis of the role of semantic processing(ICS Rep. No. 92-2). Boulder, CO.

McNamara, D.S. (1994). Revising the coherence of instructional texts improve comprehension and learning (Project JSMF 93-12).Progress report submitted to James S. McDonnell Foundation Program in Cognitive Studies for Educational Practice.

McNamara, D.S. (1995). Revising the coherence of instructional texts improve comprehension and learning (Project JSMF 93-12). Final report submitted to James S. McDonnell Foundation Program in Cognitive Studies for Educational Practice.

Doane, S.M., Sohn, Y.W., McNamara, D.S., & Adams, D. (1997). Comprehension-based skill acquisition(Rep. No. UIUC-BI-CS-97-01). Urbana, IL: Beckman Institute of Cognitive Science.

McNamara, D.S. (1997, 1998). Background knowledge assessment as a key to improving learning from text (Project JSMF 95-56). Progress report submitted to James S. McDonnell Foundation Program in Cognitive Studies for Educational Practice.

McNamara, D.S. (1999). A preliminary analysis of photoreading (Project NAG 2-1319). National Aeronautics and Space Administration. [PDF]

McNamara, D.S. (2000). Background knowledge assessment as a key to improving learning from text (Project JSMF 95-56). Final report submitted to James S. McDonnell Foundation Program in Cognitive Studies for Educational Practice.

McNamara, D.S. (2000). Promoting active reading strategies to improve undergraduate students’ understanding of science. Final report submitted to the ODU College of Sciences and ODU Office of Academic Affairs.

McNamara, D.S., & the Strategies Lab (2001). Promoting active reading strategies to improve undergraduate students’ understanding of science. Annual project report submitted to the National Science Foundation IERI. [Activities], [Findings]

McNamara, D.S., & the Strategies Lab (2002). PPromoting active reading strategies to improve undergraduate students’ understanding of science. Annual project report submitted to the National Science Foundation IERI. [Progress Report]

McNamara, D.S., & the CSEP lab (2003). Coh-Metrix: Automated cohesion and coherence scores to predict text readability and facilitate comprehension. Annual project report submitted to the Institute of Education Sciences.

McNamara, D.S., & the CSEP lab (2003). Promoting active reading strategies to improve undergraduate students’ understanding of science. Annual project report submitted to the National Science Foundation IERI. [Activities], [Findings]

McNamara, D.S., & the CSEP lab (2004). Coh-Metrix: Automated cohesion and coherence scores to predict text readability and facilitate comprehension. Annual project report submitted to the Institute of Education Sciences.

McNamara, D.S., & the CSEP lab (2004). Promoting active reading strategies to improve undergraduate students’ understanding of science. Annual project report submitted to the National Science Foundation IERI. [Activities], [Findings]

McNamara, D.S., & the CSEP lab (2005). Coh-Metrix: Automated cohesion and coherence scores to predict text readability and facilitate comprehension. Annual project report submitted to the Institute of Education Sciences

McNamara, D.S., & the CSEP lab (2005). iSTART: Interactive Strategy Training for Active Reading and Thinking. Annual project report submitted to the Institute of Education Sciences. [Findings]

McNamara, D.S., & the CSEP lab (2005). Promoting active reading strategies to improve undergraduate students’ understanding of science. Annual project report submitted to the National Science Foundation IERI.

McNamara, D.S., & the CSEP lab (2006). iSTART: Interactive Strategy Training for Active Reading and Thinking. Annual project report submitted to the Institute of Education Sciences.

McNamara, D.S., & the CSEP Lab. (2006). iSTART: A classroom study. Technical Report. University of Memphis. [PDF]

McNamara, D.S., & the CSEP Lab. (2006). iSTART: Benefits and Effects of Extended Practice. Technical Report. University of Memphis. [PDF]

McNamara, D.S. & the CSEP lab (2006). Promoting active reading strategies to improve undergraduate students’ understanding of science. Final project report submitted to the National Science Foundation IERI.

McNamara, D.S., & the CSEP lab (2007). iSTART: Interactive Strategy Training for Active Reading and Thinking. Annual project report submitted to the Institute of Education Sciences (IES). [PDF]

McNamara, D.S. & the CSEP Lab (2008). iSTART: Interactive Strategy Training for Active Reading and Thinking. Annual Project report submitted to the Institute of Education Sciences (IES).

McNamara, D.S. & the CSEP Lab (2008). W-Pal: Writing Pal. Annual project report submitted to the Institute of Education Sciences (IES).

Rowe, M. P. (2008). Alternate forms of reading comprehension strategy practice: Computer and game-based practice methods (Doctoral Dissertation). University of Memphis, Memphis, TN. [PDF]

McNamara, D.S. & the CSEP Lab (2009). iSTART: Interactive Strategy Training for Active Reading and Thinking. Annual Project report submitted to the National Science Foundation (NSF).

McNamara, D.S. & the CSEP Lab (2009). W-Pal: Writing Pal. Annual Project report submitted to the Institute of Education Sciences (IES).

Davis, N., Jackson, G. T., McNamara, D. S. (2010). Game-based features: Not a cure-all band-aid for learning and motivation. Technical Report. University of Memphis. [PDF]

McNamara, D.S. & the CSEP Lab (2010). iSTART-ME: A game-based reading strategy tutoring environment. Annual project report submitted to the National Science Foundation (NSF).

McNamara, D.S. & the CSEP Lab (2010). W-Pal: Writing Pal. Annual project report submitted to the Institute of Education Sciences (IES).

Dempsey, K.B. (2011). The effects of games on engagement and performance in intelligent tutoring systems (Doctoral dissertation). [PDF]

Jackson, G.T., & McNamara, D.S. (2011). Natural language assessment within game-based practice.American Educational Research Association (AERA) Proceedings. [PDF]

McNamara, D.S., Graesser, A.C., Cai, Z., & Kulikowich, J.M. (2011). Coh-Metrix easability components: Aligning text difficulty with theories of text comprehension. American Educational Research Association (AERA) Proceedings. [PDF]

McNamara, D.S. & the CSEP Lab (2011). iSTART-ME: A game-based reading strategy tutoring environment. Annual project report submitted to the National Science Foundation (NSF). [Findings]

McNamara, D.S. & the CSEP Lab (2011). W-Pal: Writing Pal. Annual project report submitted to the Institute of Education Sciences (IES).

McNamara, D.S. & the CSEP Lab (2012). iSTART-ME: A game-based reading strategy tutoring environment. Annual project report submitted to the National Science Foundation (NSF).

McNamara, D.S. & the CSEP Lab (2012). W-Pal: Writing Pal. Annual project report submitted to the Institute of Education Sciences (IES).

McNamara, D.S. & the SoLET Lab (2012). iSTART-ME: A game-based reading strategy tutoring environment. Final project report submitted to the National Science Foundation (NSF). [PDF]

McNamara, D.S. & the SoLET Lab (2013). Exploration of Automated Writing Strategy Instruction for Adolescent Writers Using the Writing Pal. Annual project report submitted to the Institute of Educational Sciences (IES). [PDF]

McNamara, D.S. & the SoLET Lab (2014). W-Pal: Writing Pal. Final project report submitted to the Institute of Education Sciences (IES). [PDF]

McNamara, D.S. & the SoLET Lab (2014). Exploration of Automated Writing Strategy Instruction for Adolescent Writers Using the Writing Pal Annual project report submitted to the Institute of Educational Sciences (IES). [PDF]

McNamara, D.S. & the SoLET Lab (2014). Exploring the Educational Game Landscape through Focused Studies and Ecological Interventions Annual project report submitted to the Institute of Educational Sciences (IES). [PDF]

McNamara, D.S. & the SoLET Lab (2015). Exploration of Automated Writing Strategy Instruction for Adolescent Writers Using the Writing Pal Annual project report submitted to the Institute of Educational Sciences (IES). [PDF]

McNamara, D.S. & the SoLET Lab (2015). Exploring the Educational Game Landscape through Focused Studies and Ecological Interventions Annual project report submitted to the Institute of Educational Sciences (IES). [PDF]

McNamara, D.S. & the SoLET Lab (2016). Exploration of Automated Writing Strategy Instruction for Adolescent Writers Using the Writing Pal. Annual project report submitted to the Institute of Educational Sciences (IES). [PDF]

McNamara, D.S. & the SoLET Lab (2016). Exploring the Educational Game Landscape through Focused Studies and Ecological Interventions Annual project report submitted to the Institute of Educational Sciences (IES). [PDF]

McNamara, D. S., & the SoLET Lab (2017). Exploration of Automated Writing Strategy Instruction for Adolescent Writers Using the Writing Pal: Annual Performance Report R305A120707. Annual project report submitted to the Institute of Educational Sciences (IES).

McNamara, D. S., & the SoLET Lab (2017). Exploring the Educational Game Landscape through Focused Studies and Ecological Interventions: Annual Performance Report R305A130124. Annual project report submitted to the Institute of Educational Sciences (IES).

McNamara, D. S., & the SoLET Lab (2018). Exploration of Automated Writing Strategy Instruction for Adolescent Writers Using the Writing Pal: Final Performance Report r305A120707. Annual project report submitted to the Institute of Educational Sciences (IES).

McNamara, D. S., & the SoLET Lab (2018). Exploring the Educational Game Landscape through Focused Studies and Ecological Interventions: Final Performance Report R305A130124. Annual project report submitted to the Institute of Educational Sciences (IES).

McNamara, D. S., & the SoLET Lab (2019). The Development of the Writing Assessment Tool (WAT): An On-line Platform for the Automated Assessment of Writing Education Technology: Annual Performance Report R305A180261. Annual project report submitted to the Institute of Educational Sciences (IES).

McNamara, D. S., & the SoLET Lab (2019). Developing a Deeper Understanding of the Cognitive Processes that Drive Multiple Document Comprehension: Annual Performance Report R305A180144. Annual project report submitted to the Institute of Educational Sciences (IES).