377
Views
3
CrossRef citations to date
0
Altmetric
Articles

Chasing Theory with Technology: A Quest to Understand Understanding

References

  • Alexander, P. A., Kulikowich, J. M., & Schulze, S. K. (1994). The influence of topic knowledge, domain knowledge, and interest on the comprehension of scientific exposition. Learning and Individual Differences, 6(4), 379–397. https://doi.org/10.1016/1041-6080(94)90001-9
  • Allen, L. K., Crossley, S. A., Snow, E. L., & McNamara, D. S. (2014a). Game-based writing strategy tutoring for second language learners: Game enjoyment as a key to engagement. Language Learning and Technology, 18(2), 124–150.
  • Allen, L. K., Dascalu, M., McNamara, D. S., Crossley, S. A., & Trausan-Matu, S. (2016a). Modeling individual differences among writers using ReaderBench. In L. G. Chova, A. L. Martínez, & I. C. Torres (Eds.), Proceedings of the 8th annual International Conference on Education and New Learning Technologies (EduLearn) (pp. 5269–5279). Barcelona, Spain: IATED.
  • Allen, L. K., Jacovina, M. E., & McNamara, D. S. (2016b). Computer-based writing instruction. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (2nd ed., pp. 316–329). The Guilford Press.
  • 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. https://doi.org/10.1007/s11145-018-9921-y
  • Allen, L. K., & McNamara, D. S. (2015). You are your words: Modeling students’ vocabulary knowledge with natural language processing. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, & M. Desmarais (Eds.), Proceedings of the 8th international conference on educational data mining (EDM 2015) (pp. 258–265). Madrid, Spain: International Educational Data Mining Society.
  • Allen, L. K., Perret, C. A., & McNamara, D. S. (2016c). 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.
  • Allen, L. K., Snow, E. L., Crossley, S. A., Jackson, G. T., & McNamara, D. S. (2014b). Reading comprehension components and their relation to the writing process. L’année psychologique/Topics in Cognitive Psychology, 114(4), 663–691. https://doi.org/10.4074/S0003503314004047
  • Allen, L. K., Snow, E. L., & McNamara, D. S. (2016d). The narrative waltz: The role of flexibility on writing performance. Journal of Educational Psychology, 108(7), 911–924. https://doi.org/10.1037/edu0000109
  • Anderson, J. R. (1990). The adaptive character of thought. Erlbaum.
  • Bakhtin, M. M. (1981). The dialogic imagination: Four essays ( M. Holquist, Ed., C. Emerson, & M. Holquist, Trans.). University of Texas Press.
  • 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(3), 337–370. https://doi.org/10.1007/s40593-020-00201-7
  • 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(1), 65–83. https://doi.org/10.1097/00011363-200501000-00007
  • Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and self regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221–252. https://doi.org/10.1207/s1532690xci1302_3
  • 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). Springer-Verlag UK.
  • Braasch, J. L. G., Bråten, I., & McCrudden, M. T. (Eds.). (2018). Handbook of multiple source use. Routledge.
  • Braasch, J. L. G., & Kessler, E. D. (2021). Towards a theoretical model of source comprehension in everyday discourse. Discourse Processes, 1–19. ( note this paper is in the same special issue). https://doi.org/10.1080/0163853X.2021.1905393
  • Chi, M. T. H., De Leeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. https://doi.org/10.1207/s15516709cog1803_3
  • 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) (pp. 161–168). Timisoara, Romania: IEEE.
  • Crossley, S. A., Allen, L. K., & McNamara, D. S. (2016a). The Writing Pal: A writing strategy tutor. In S. A. Crossley & D. S. McNamara (Eds.), Adaptive educational technologies for literacy instruction (pp. 204–224). Routledge.
  • 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), lecture notes in computer science (vol. 11625, pp. 84–95). Chicago, IL: Springer.
  • 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), 1–16. https://www.researchgate.net/publication/319604495_To_Aggregate_or_Not_Linguistic_Features_in_Automatic_Essay_Scoring_and_Feedback_Systems
  • Crossley, S. A., Kyle, K., & McNamara, D. S. (2016b). 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. https://doi.org/10.3758/s13428-015-0651-7
  • Crossley, S. A., & McNamara, D. S. (2014). Developing component scores from natural language processing tools to assess human ratings of essay quality. In W. Eberle & C. Boonthum-Denecke (Eds.), Proceedings of the 27th international Florida artificial intelligence research society (FLAIRS) conference (pp. 381–386). Palo Alto, CA: AAAI Press.
  • Crossley, S. A., 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(2), 181–214. https://doi.org/10.1177/0741088314526354
  • 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.
  • Dascalu, M., Crossley, S. A., McNamara, D. S., Dessus, P., & Trausan-Matu, S. (2018a). Please Readerbench this text: A multi-dimensional textual complexity assessment framework. In S. Craig (Ed.), Tutoring and intelligent tutoring systems (pp. 251–271). Nova Science Publishers, Inc. https://research.ou.nl/en/publications/please-readerbench-this-text-a-multi-dimensional-textual-complexi
  • Dascalu, M., McNamara, D. S., Trausan-Matu, S., & Allen, L. K. (2018b). Cohesion network analysis of CSCL participation. Behavior Research Methods, 50(2), 604–619. https://doi.org/10.3758/s13428-017-0888-4
  • 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. https://doi.org/10.1007/s11412-015-9226-y
  • Dascalu, M. D., Ruseti, S., Carabas, M., Dascalu, M., Trausan-Matu, S., & McNamara, D. S. (2020a). 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.
  • Dascalu, M.-D., Ruseti, S., Dascalu, M., McNamara, D. S., & Trausan-Matu, S. (2020b). Multi-document cohesion network analysis: Visualizing intratextual and intertextual links. 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.
  • Doane, S. M., McNamara, D. S., Kintsch, W., Polson, P. G., & Clawson, D. M. (1992). Prompt comprehension in UNIX command production. Memory & Cognition, 20(4), 327–343. https://doi.org/10.3758/BF03210918
  • Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363. https://doi.org/10.1037/0033-295X.100.3.363
  • Graesser, A. C., & McNamara, D. S. (2011). Computational analyses of multilevel discourse comprehension. Topics in Cognitive Science, 3(2), 371–398. https://doi.org/10.1111/j.1756-8765.2010.01081.x
  • Graesser, A. C., McNamara, D. S., & Kulikowich, J. M. (2011). Coh-Metrix: Providing multilevel analyses of text characteristics. Educational Researcher, 40(5), 223–234. https://doi.org/10.3102/0013189X11413260
  • Graesser, A. C., Singer, M., & Trabasso, T. (1994). Constructing inferences during narrative text comprehension. Psychological Review, 101(3), 371–395. https://doi.org/10.1037/0033-295X.101.3.371
  • Graesser, A. C., Wiemer-Hastings, K., Wiemer-Hastings, P., & Kreuz, R., & the Tutoring Research Group. (1999). AutoTutor: A simulation of a human tutor. Cognitive Systems Research, 1(1), 35–51. https://doi.org/10.1016/S1389-0417(99)00005-4
  • Graham, S. (2018). A revised writer(s)-within-community model of writing. Educational Psychologist, 53(4), 258–279. https://doi.org/10.1080/00461520.2018.1481406
  • Graham, S., & Harris, K. R. (2003). Students with learning disabilities and the process of writing: A meta-analysis of SRSD studies. In L. Swanson, K. Harris, & S. Graham (Eds.), Handbook of learning disabilities (pp. 383–402). Guilford Press.
  • Graham, S., & Hebert, M. (2011). Writing-to-read: A meta-analysis of the impact of writing and writing instruction on reading. Harvard Educational Review, 81(4), 710–744. https://doi.org/10.17763/haer.81.4.t2k0m13756113566
  • 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). Academic Press.
  • Healy, A. F., & McNamara, D. S. (1996). Verbal learning and memory: Does the modal model still work? Annual Review of Psychology, 47(1), 143–172. https://doi.org/10.1146/annurev.psych.47.1.143
  • Healy, A. F., Oliver, W. L., & McNamara, T. P. (1987). Detecting letters in continuous text: Effects of display size. Journal of Experimental Psychology Human Perception and Performance, 13(2), 279–290. https://doi.org/10.1037/0096-1523.13.2.279
  • Healy, A. F., Schneider, V. I., & Bourne, L. E., Jr. (2012). Empirically valid principles of training. In A. F. Healy & L. E. Bourne Jr. (Eds.), Training cognition: Optimizing efficiency, durability, and generalizability (pp. 13–39). Psychology Press.
  • Jackson, T. G., Boonthum, C., & McNamara, D. S. (2015). Natural language processing and game-based practice in iSTART. Journal of Interactive Learning Research, 26(2), 189–208. https://www.learntechlib.org/p/42009/
  • Jackson, T. G., & McNamara, D. S. (2013). Motivation and performance in a game-based intelligent tutoring system. Journal of Educational Psychology, 105(4), 1036–1049. https://doi.org/10.1037/a0032580
  • Jackson, T. G., & 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.
  • 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.
  • Johnson, A. M., 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 (pp. 221–250). Nova Science Publishers.
  • Just, M. A., & Varma, S. (2007). The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective and Behavioral Neuroscience, 7(3), 153–191. https://doi.org/10.3758/CABN.7.3.153
  • Kintsch, W. (1988). The role of knowledge in discourse comprehension construction-integration model. Psychological Review, 95(2), 163–182. https://doi.org/10.1037/0033-295X.95.2.163
  • Kintsch, W. (1998). Comprehension: A paradigm for cognition. Cambridge University Press.
  • Landauer, T., McNamara, D. S., Dennis, S., & Kintsch, W. (Eds.). (2007). Handbook of latent semantic analysis. Erlbaum.
  • 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(2), 224–232. https://doi.org/10.3758/BF03193151
  • MacArthur, C. A., Graham, S., & Fitzgerald, J. (Eds.). (2006). Handbook of writing research. Guilford.
  • Magliano, J. P., Todaro, S., Millis, K. 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(2), 185–208. https://doi.org/10.2190/1LN8-7BQE-8TN0-M91L
  • 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(3), 420–438. https://doi.org/10.1007/s40593-018-0164-5
  • McCarthy, K. S., Watanabe, M., Dai, J., & McNamara, D. S. (2020b). Personalized learning in iSTART: Past modifications and future design. Journal of Research on Technology in Education, 52(3), 301–321. https://doi.org/10.1080/15391523.2020.1716201
  • McCrudden, M. T., & McNamara, D. S. (2017). Cognition in education. Routledge.
  • McNamara, D. S. (1995). Effects of prior knowledge on the generation advantage: Calculators versus calculation to learn simple multiplication. Journal of Educational Psychology, 87(2), 307–318. https://doi.org/10.1037/0022-0663.87.2.307
  • McNamara, D. S. (2001). Reading both high and low coherence texts: Effects of text sequence and prior knowledge. Canadian Journal of Experimental Psychology, 55(1), 51–62. https://doi.org/10.1037/h0087352
  • 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.
  • McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38(1), 1–30. https://doi.org/10.1207/s15326950dp3801_1
  • McNamara, D. S. (2009). The importance of teaching reading strategies. Perspectives on Language and Literacy, 35, 34–40. https://www.dropbox.com/s/60hmkbyq82n36zu/152_Teaching%20Reading%20Strategies%20-%20McNamara.pdf?dl=0
  • McNamara, D. S. (2011). Computational methods to extract meaning from text and advance theories of human cognition. Topics in Cognitive Science, 3(1), 3–17. https://doi.org/10.1111/j.1756-8765.2010.01117.x
  • 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(5), 575–592. https://doi.org/10.1177/1461445613501446
  • McNamara, D. S. (2017). Self-explanation and reading strategy training (SERT) improves low-knowledge students’ science course performance. Discourse Processes, 54(7), 479–492. https://doi.org/10.1080/0163853X.2015.1101328
  • McNamara, D. S. (2020). If integration is the keystone of comprehension: Inferencing is the key. Discourse Processes, 58(1), 86–91. https://doi.org/10.1080/0163853X.2020.1788323
  • 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., pp. 362–389). Routledge.
  • 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.
  • McNamara, D. S., Allen, L. K., McCarthy, K. S., & Balyan, R. (2018). NLP: Getting computers to understand discourse. In K. Millis, D. Long, J. Magliano, & K. Wiemer (Eds.), Deep comprehension: Multi-disciplinary approaches (pp. 224–236). Routledge.
  • McNamara, D. S., Boonthum, C., Levinstein, I. B., & Millis, K. (2007a). 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). Erlbaum.
  • McNamara, D. S., Crossley, S. A., & McCarthy, P. M. (2010a). Linguistic features of writing quality. Written Communication, 27(1), 57–86. https://doi.org/10.1177/0741088309351547
  • McNamara, D. S., Crossley, S. A., & Roscoe, R. D. (2013). Natural language processing in an intelligent writing strategy tutoring system. Behavior Research Methods, 45(2), 499–515. https://doi.org/10.3758/s13428-012-0258-1
  • McNamara, D. S., Crossley, S. A., Roscoe, R. D., Allen, L. K., & Dai, J. (2015). A hierarchical classification approach to automated essay scoring. Assessing Writing, 23, 35–59. https://doi.org/10.1016/j.asw.2014.09.002
  • 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). Rowman & Littlefield Education.
  • McNamara, D. S., Graesser, A. C., McCarthy, P., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge: Cambridge University Press.
  • McNamara, D. S., & Healy, A. F. (1995a). 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). Sage.
  • McNamara, D. S., & Healy, A. F. (1995b). 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(3), 399–416. https://doi.org/10.1006/jmla.1995.1018
  • McNamara, D. S., Jackson, G. T., & Graesser, A. C. (2010b). 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). IGI Global.
  • McNamara, D. S., Jacovina, M. E., Snow, E. L., & Allen, L. K. (2015b). From generating in the lab to tutoring systems in classrooms. American Journal of Psychology, 128(2), 159–172. https://doi.org/10.5406/amerjpsyc.128.2.0159
  • McNamara, D. S., & Kendeou, P. (2011). Translating advances in reading comprehension research to educational practice. International Electronic Journal of Elementary Education, 4(1), 33–46. https://www.researchgate.net/publication/281894454_Translating_advances_in_reading_comprehension_research_to_educational_practice
  • McNamara, D. S., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14(1), 1–43. https://doi.org/10.1207/s1532690xci1401_1
  • McNamara, D. S., & Kintsch, W. (1996). Learning from text: Effects of prior knowledge and text coherence. Discourse Processes, 22(3), 247–287. https://doi.org/10.1080/01638539609544975
  • McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy training for active reading and thinking. Behavior Research Methods, Instruments, and Computers, 36(2), 222–233. https://doi.org/10.3758/BF03195567
  • McNamara, D. S., & Magliano, J. P. (2009). Towards a comprehensive model of comprehension. In B. Ross (Ed.), The psychology of learning and motivation (pp. 297–384). Elsevier.
  • 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 (pp. 1–24). Nova Science Publishers, Inc.
  • McNamara, D. S., O’Reilly, T., Best, R., & Ozuru, Y. (2006). Improving adolescent students‘ reading comprehension with Istart. Journal of Educational Computing Research, 34(2), 147–171. https://doi.org/10.2190/1RU5-HDTJ-A5C8-JVWE
  • McNamara, D. S., O’Reilly, T., Rowe, M., Boonthum, C., & Levinstein, I. B. (2007b). 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. 397–421). Erlbaum.
  • 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(1), 229–257. https://www.pegem.net/dosyalar/dokuman/138547-2014010711325-14.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). Erlbaum.
  • Nicula, B., Perret, C. A., Dascalu, 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) (pp. 358–369). Chicago, IL: Springer.
  • 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.
  • 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(2), 121–152. https://doi.org/10.1080/01638530709336895
  • 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(3), 228–242. https://doi.org/10.1016/j.learninstruc.2008.04.003
  • Palincsar, A., & Brown, A. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175. https://doi.org/10.1207/s1532690xci0102_1
  • 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). “Alexandru Ioan Cuza” University Publishing House.
  • 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(5), 481–505. https://doi.org/10.1007/s11423-014-9349-2
  • Raaijmakers, J. G. W., & Shiffrin, R. M. (1980). SAM: A theory of probabilistic search of associative memory. In G. Bower (Ed.), The psychology of learning and motivation (Vol. 14, pp. 207–262). Academic Press.
  • Roscoe, R. D., Allen, L. K., & McNamara, D. S. (2019). Contrasting writing practice formats in a writing strategy tutoring system. Journal of Educational Computing Research, 57(3), 723–754. https://doi.org/10.1177/0735633118763429
  • 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. https://doi.org/10.1016/j.compcom.2014.09.002
  • 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(5), 669–679. https://doi.org/10.1002/acp.3149
  • 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(4), 1010–1025. https://doi.org/10.1037/a0032340
  • Roscoe, R. D., Snow, E. L., Allen, L. K., & McNamara, D. S. (2015b). Automated detection of essay revising patterns: Application for intelligent feedback in a writing tutor. Technology, Instruction, Cognition, and Learning, 10(1), 59–79. https://files.eric.ed.gov/fulltext/ED565460.pdf
  • Roscoe, R. D., Snow, E. L., Brandon, R. D., & McNamara, D. S. (2013a). Educational game enjoyment, perceptions, and features in an intelligent writing tutor. In C. Boonthum-Denecke & G. M. Youngblood (Eds.), Proceedings of the 26th international Florida artificial intelligence research society (FLAIRS) conference (pp. 515–520). Menlo Park, CA: AAAI Press.
  • Roscoe, R. D., Varner (Allen), L. K., Crossley, S. A., & McNamara, D. S. (2013b). Developing pedagogically-guided threshold algorithms for intelligent automated essay feedback. In International journal of learning technology (Vol. 8, pp. 362–381). Int. J. Learning Technology. https://files.eric.ed.gov/fulltext/ED585772.pdf
  • Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching students to generate questions: A review of the intervention studies. Review of Educational Research, 66(2), 181–221. https://doi.org/10.3102/00346543066002181
  • Rouet, J.-F., Britt, M. A., & Durik, A. M. (2017). RESOLV: Readers’ representation of reading contexts and tasks. Educational Psychologist, 52(3), 200–215. https://doi.org/10.1080/00461520.2017.1329015
  • Ruseti, S., Dascalu, M., Johnson, A., McNamara, D. S., Balyan, R., Kopp, K., Crossley, S. A., & Trausan-Matu, S. (2018b). Predicting question quality using recurrent neural networks. 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 I (pp. 491–502). London, UK: Springer.
  • Ruseti, S., Dascalu, M., Johnson, A. M., McNamara, D. S., Balyan, R., McCarthy, K. S., & Trausan-Matu, S. (2018a). Scoring summaries using recurrent neural networks. In R. Nkambou, R. Azevedo, & J. Vassileva (Eds.), Proceedings of the 14th international conference on intelligent tutoring systems (ITS) (pp. 191–201). Montreal, Canada: Springer.
  • Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 97–118). Cambridge University Press.
  • Snow, E. L., Jacovina, M. E., Jackson, G. T., & McNamara, D. S. (2016). iSTART-2: A reading comprehension and strategy instruction tutor. In S. A. Crossley & D. S. McNamara (Eds.), Adaptive educational technologies for literacy instruction (pp. 104–121). Routledge.
  • 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’19) (pp. 161–162). Phoenix, AZ: SOLAR.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.