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IES Grant

Title: The Writing Pal: An Intelligent Tutoring System that Provides Interactive Writing Strategy Training
Center: NCER Year: 2008
Principal Investigator: McNamara, Danielle Awardee: University of Memphis
Program: Education Technology      [Program Details]
Award Period: 3 years Award Amount: $2,015,456
Goal: Development and Innovation Award Number: R305A080589
Description:

Purpose: Writing well is an essential skill for success in school and beyond, but learning to write is a complex process that depends on changes in students' strategic behavior, knowledge about writing, and motivation. Substantial numbers of American students are not performing at or above proficient levels in writing. The purpose of this project is to develop an automated, intelligent writing tutor—The Writing Pal—that provides writing strategy instruction and writing skill development. The system will be interactive and provide adaptive strategies to users, delivering scaffolded and progressive instruction to teach and guide writers through the writing process, with the goal of writers being able to implement these skills independently.

Project Activities: The goal of the project is to develop and test an affordable, high-quality tool that harnesses computer technology to provide interactive, adaptive tutoring to improve student writing that very closely mimics human one-on-one tutoring interaction. The Writing Pal modules will be iteratively refined based on the results of the usability and module evaluation studies. In order to ascertain the feasibility of using The Writing Pal in typical high school classrooms, teacher questionnaires about system use and observations of student interaction will be gathered. These results, along with theories of writing, technology design, and pedagogy, will guide revisions to the integrated system. The project culminates with a feasibility study examining the usability and the effectiveness of the integrated system.

Products: The products of this project include The Writing Pal, an adaptive, intelligent tutor of the writing process, as well as published reports.

Structured Abstract

Setting: The research setting will be high schools in Tennessee.

Population: Participants will include approximately 350 students, primarily those enrolled in high school (grades 9–12) but also some enrolled in remedial English college courses. It is expected that a substantial portion of the population will be African-American students who qualify for free and/or reduced price lunches.

Intervention: The proposed computer tool, driven theoretically in part by Flower and Hayes' (1981) cognitive process theory of writing, consists of two principle components: Strategy Training and Essay Writing. The Strategy Training component contains three sections, emphasizing different strategies related to composing an effective essay: prewriting (free-writing and essay planning), drafting (generating an introduction and conclusion), and reviewing (evaluating how well the essay meets the assignment's requirements). The Essay Writing component scaffolds the writing of complete compositions as students work through strategy modules and providing feedback.

Research Design and Methods: The research plan is to develop multiple modules independently through an iterative process and then integrate them into a coherent system. Initially, multiple prototypes of each module are generated and tested for physical and logistical usability by the designers and potential users, thereby allowing for comparisons among prototypes. Additionally, algorithms for understanding the computational language, which will guide feedback, will be designed.

Teachers will inform interface design through interviews and shared sample lesson plans. Student interaction with the system will be evaluated using a variety of performance and attitudinal measures. During the iterative development process, the team will first examine performance with each module separately before incorporating the modules into the Tutor. The project will then culminate with a feasibility study, testing about 60 students across 9 pre/post-test sessions and 7 training sessions.

Key Measures: The test sessions will include 25-minute essay writing assessments (similar to SAT/ACT essay questions), the Kaufman Test of Educational Achievement-2 Written Expression subtest to provide norm-referenced scores, and the Daly-Miller Writing Apprehension Test to assess students' comfort level in writing. Key measures include those of performance (such as time on task, success/failure rates, error rates) and attitudes (such as impressions of likeability and helpfulness of the modules), as well as pre/post-test measures of reading skill and vocabulary knowledge (Gates-MacGinitie test), written expression (Kaufman Test of Educational Achievement), and writing apprehension (Daly-Miller Writing Apprehension Test).

Data Analytic Strategy: The researchers will apply a variety of statistical analyses, including descriptive statistics, correlation and regression analyses, analysis of variance, and analysis of covariance. They will examine the effect of The Writing Pal on the above measures, with prior writing and reading abilities as factors.

Related IES Projects: Exploration of Automated Writing Strategy Instruction for Adolescent Writings Using The Writing Pal (R305A120707) and Center for the Study of Adult Literacy (CSAL): Developing Instructional Approaches Suited to the Cognitive and Motivational Needs for Struggling Adults (R305C120001)

Publications

Book

McNamara, D.S., Graesser, A.C., McCarthy, P.M., and Cai, Z. (2014). Automated Evaluation of Text and Discourse With Coh-Metrix. Boston, MA: Cambridge University Press.

McNamara, D.S., Graesser, A.C., McCarthy, P.M., and Cai, Z. (2014). Automated Evaluation of Text and Discourse With Coh-Metrix. Cambridge, MA: Cambridge University Press.

Book chapter

Brawner, K., and Graesser, A. (2014). Natural Language, Discourse, and Conversational Dialogues Within Intelligent Tutoring Systems: A Review. In R. Sottilare, A.C. Graesser, X. Hu, and B. Goldberg (Eds.), Design Recommendations for Intelligent Tutoring Systems: Instructional Management, Volume 2 (pp. 189–204). Orlando, FL: Army Research Laboratory.

Cai, Z., Feng, S., Baer, W., and Graesser, A. (2014). Instructional Strategies in Trialogue-Based Intelligent Tutoring Systems. In R. Sottilare, A.C. Graesser, X. Hu, and B. Goldberg (Eds.), Design Recommendations for Intelligent Tutoring Systems: Instructional Management, Volume 2 (pp. 225–235). Orlando, FL: Army Research Laboratory.

Crossley, S.A. and McNamara, D.S. (2017). Educational Technologies and Literacy Development. Adaptive Educational Technologies for Literacy Instruction. Taylor & Francis eBooks.

D'Mello, S.K., and Graesser, A.C. (in press). Cohesion Relationships in Tutorial Dialogues as Predictors of Learners' Affective States. In P. McCarthy, and C. Boonthum (Eds.), Applied Natural Language Processing and Content Analysis: Identification, Investigation, and Resolution. Hershey, PA: IGI Global.

Graesser, A.C. and McNamara, D.S. (in press). Use of Computers to Analyze and Score Essays and Open-Ended Verbal Responses. In H. Cooper, P. Camic, R. Gonzalez, D. Long, and A. Panter (Eds.), APA Handbook of Research Methods in Psychology. Washington, DC: American Psychological Association.

Graesser, A.C., and D'Mello, S.K. (2011). Theoretical Perspectives on Affect and Deep Learning. In R. Calvo, and S. D'Mello (Eds.), New Perspectives on Affect and Learning Technologies (pp. 11–22). New York: Springer.

Graesser, A.C., and Forsyth, C.M. (2013). Discourse Comprehension. In D. Reisberg (Ed.), The Oxford Handbook of Cognitive Psychology (pp. 475–491). New York: Oxford University Press.

Graesser, A.C., and Lehman, B. (2012). Questions Drive Comprehension of Text and Multimedia. In M.T. McCrudden, J. Magliano, and G. Schraw (Eds.), Text Relevance and Learning From Text (pp. 53–74). Greenwich, CT: Information Age Publishing.

Graesser, A.C., and McNamara, D.S. (2012). Automated Analysis of Essays and Open-Ended Verbal Responses. In H. Cooper, P.M. Camic, D.L. Long, A.T. Panter, D. Rindskopf, and K.J. Sher (Eds.), APA Handbook of Research Methods in Psychology, Volume 1: Foundations, Planning, Measures, and Psychometrics (pp. 307–325). Washington, DC: American Psychological Association.

Graesser, A.C., and McNamara, D.S. (in press). Technologies That Support Reading Comprehension. In C. Dede, and J. Richards (Eds.), Digital Teaching Platforms. New York: Teachers College Press.

Graesser, A.C., and Millis, K.K. (2011). Discourse and Cognition. In T. Van Dijk (Ed.), Discourse Studies (pp. 126–142). Los Angeles: Sage.

Graesser, A.C., Conley, M., and Olney, A. (2012). Intelligent Tutoring Systems. In K.R. Harris, S. Graham, and T. Urdan (Eds.), APA Educational Psychology Handbook: Vol. 3. Applications to Learning and Teaching (pp. 451–473). Washington, DC: American Psychological Association.

Graesser, A.C., D'Mello, S.K., and Cade, W. (2011). Instruction Based on Tutoring. In R.E. Mayer, and P.A. Alexander (Eds.), Handbook of Research on Learning and Instruction (pp. 408–426). New York: Routledge Press.

Graesser, A.C., Franceschetti, D., Gholson, B., and Craig, S. (2011). Learning Newtonian Physics With Conversational Agents and Interactive Simulation. In N.L. Stein, and S. Raudenbush (Eds.), Developmental Cognitive Science Goes to School (pp. 157–172). New York: Routledge.

Graesser, A.C., Hu, X., Nye, B., and Sottilare, R. (2016). Intelligent Tutoring Systems, Serious Games, and the Generalized Intelligent Framework for Tutoring (GIFT). In H.F. O'Neil, E.L. Baker, and R.S. Perez (Eds.), Using Games and Simulation for Teaching and Assessment (pp. 58–79). New York: Routledge.

Graesser, A.C., Lin, D., and D'Mello, S. (2010). Computer Learning Environments That Support Deep Comprehension. In M.T. Banich, and D. Caccamise (Eds.), Generalization of Knowledge (pp. 201–224). Mahwah, NJ: Erlbaum.

Graesser, A.C., McNamara, D.S., and Louwerse, M.M. (2009). Methods of Automated Text Analysis. In M.L. Kamil, P.D. Pearson, E.B. Moje, and P. Afflerbach (Eds.), Handbook of Reading Research: Volume IV (pp. 34–53). Mahwah, NJ: Erlbaum.

Graesser, A.C., McNamara, D.S., and Rus, V. (2011). Computational Modeling of Discourse and Conversation. In M. Spivey, M. Joanisse, and K. McRae (Eds.), The Cambridge Handbook of Psycholinguistics (pp. 558–572). Cambridge, U.K.: Cambridge University Press.

Mavrikis, M., D'Mello, S.K., Porayska-Pomsta, K., Cocea, M., and Graesser, A.C. (2010). Modeling Affect by Mining Students Interactions With Learning Environments. In C. Romero, S. Ventura, M. Pechenizkiy, and R.S. Baker (Eds.), Handbook of Educational Data Mining (pp. 231–244). New York: CRC Press.

McCarthy, P.M., and Graesser, A.C. (in press). The Writing-Pal: Natural Language Algorithms to Support Intelligent Tutoring on Writing Strategies. In P.M. McCarthy, and C. Boonthum (Eds.), Applied Natural Language Processing and Content Analysis: Identification, Investigation, and Resolution. Hershey, PA: IGI Global.

McCarthy, P.M., Dufty, D., Hempelman, C., Cai, Z., Graesser, A.C., and McNamara, D.S. (2011). Evaluating Givenness/Newness. In P.M. McCarthy, and C. Boonthum (Eds.), Applied Natural Language Processing and Content Analysis: Identification, Investigation, and Resolution (pp. 231–244). Hershey, PA: IGI Global.

McNamara, D.S., and Graesser, A.C. (2012). Coh-Metrix: An Automated Tool for Theoretical and Applied Natural Language Processing. In P.M. McCarthy, and C. Boonthum (Eds.), Applied Natural Language Processing: Identification, Investigation and Resolution (pp. 188–205). Hershey, PA: IGI Global.

McNamara, D.S., and Magliano, J.P. (2009). Towards a Comprehensive Model of Comprehension. In B. Ross (Ed.), The Psychology of Learning and Motivation, Volume 51 (pp. 297–384). San Diego: Elsevier Academic Press.

McNamara, D.S., Graesser, A.C, and Louwerse, M.M. (in press). Sources of Text Difficulty: Across the Ages and Genres. In J.P. Sabatini, and E. Albro (Eds.), Assessing Reading in the 21st Century: Aligning and Applying Advances in the Reading and Measurement Sciences. Lanham, MD: R&L Education.

McNamara, D.S., Jackson, G.T., and 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.

Millis, K., Forsyth, C., Butler, H., Wallace, P., Graesser, A., and Halpern, D.F. (2011). Operation ARIES!: A Serious Game for Teaching Scientific Inquiry. In M.M.A. Oikonomou, and L. Jain (Eds.), Serious Games and Edutainment Applications (pp. 169–195). UK: Springer-Verlag.

Snow, E.L., Jacovina, M.E., Jackson, G.T., and McNamara, D.S. (2017). iSTART-2: A Reading Comprehension and Strategy Instruction Tutor. Adaptive Educational Technologies for Literacy Instruction. Jul 2016: Taylor & Francis eBooks.

Varner, L.K., Jackson, G.T., Snow, E.L., and McNamara, D.S. (2013). Linguistic Content Analysis as a Tool for Improving Adaptive Instruction. Artificial Intelligence in Education (pp. 692–695). Berlin Heidelberg: Springer.

Weston, J., Crossley, S.A., and McNamara, D.S. (2010). Computationally Assessing Expert Judgments of Freewriting Quality. In P.M. McCarthy, and C. Boonthum (Eds.), Applied Natural Language Processing and Content Analysis: Identification, Investigation, and Resolution (pp. 365–382). Hershey, PA: IGI Global.

Journal article, monograph, or newsletter

Crossley, S.A., and McNamara, D.S. (2009). Computational Assessment of Lexical Differences in L1 and L2 Writing. Journal of Second Language Writing, 18(2): 119–135.

Crossley, S.A., and McNamara, D.S. (2011). Shared Features of L2 Writing: Intergroup Homogeneity and Text Classification. Journal of Second Language Writing, 20(4): 271–285.

Crossley, S.A., and McNamara, D.S. (2011). Understanding Expert Ratings of Essay Quality: Coh-Metrix Analyses of First and Second Language Writing. IJCEELL, 21: 170–191.

Crossley, S.A., and McNamara, D.S. (2012). Predicting Second Language Writing Proficiency: The Role of Cohesion, Readability, and Lexical Difficulty. Journal of Research in Reading, 35(2), 115–135.

Crossley, S.A., and Salsbury, T. (2011). The Development of Lexical Bundle Accuracy and Production in English Second Language Speakers. International Review of Applied Linguistics in Language Teaching (IRAL), 49(1): 1–26.

Crossley, S.A., Greenfield, J., and McNamara, D.S. (2008). Assessing Text Readability Using Cognitively Based Indices. TESOL Quarterly, 42(3): 475–493.

Crossley, S.A., McNamara, D.S., Weston, J., and McLain-Sullivan, S.T. (2011). The Development of Writing Proficiency as a Function of Grade Level: A Linguistic Analysis. Written Communication, 28(3): 282–311.

Crossley, S.A., Salsbury, T., and McNamara, D.S. (2009). Measuring L2 Lexical Growth Using Hypernymic Relationships. Language Learning, 59(2): 307–334.

Crossley, S.A., Salsbury, T., and McNamara, D.S. (2012). Predicting the Proficiency Level of Language Learners Using Lexical Indices. Language Testing, 29(2): 243–263.

Crossley, S.A., Salsbury, T., McNamara, D.S., and Jarvis, S. (2011). Predicting Lexical Proficiency in Language Learner Texts Using Computational Indices. Language Testing, 28(4): 561–580.

D'Mello, S., Dowell, N., and Graesser, A.C. (2011). Does it Really Matter Whether Students' Contributions are Spoken Versus Typed in an Intelligent Tutoring System With Natural Language?. Journal of Experimental Psychology: Applied, 17(1): 1–17.

D'Mello, S.K., Graesser, A.C., and King, B. (2010). Toward Spoken Human-Computer Tutorial Dialogues. Human Computer Interaction, 25: 289–323.

Duran, N.D., Hall, C., McCarthy, P.M., and McNamara, D.S. (2010). The Linguistic Correlates of Conversational Deception: Comparing Natural Language Processing Technologies. Applied PsycholinguisticsR305A080589. EJ892958. , 31(3): 439–462.

Graesser, A.C. (2011). Improving Learning. Monitor on Psychology, 42(7): 2–8.

Graesser, A.C. (2011). Learning, Thinking, and Emoting With Discourse Technologies. American Psychologist, 66(8): 746–757.

Graesser, A.C., and Hu, X. (2011). Commentary on Causal Prescriptive Statements. Educational Psychology Review, 23(2): 279–285.

Graesser, A.C., and McNamara, D.S. (2010). Self-Regulated Learning in Learning Environments With Pedagogical Agents That Interact in Natural Language. Educational Psychologist, 45(4): 234–244.

Graesser, A.C., and McNamara, D.S. (2011). Computational Analyses of Multilevel Discourse Comprehension. Topics in Cognitive Science, 3(2): 371–398.

Graesser, A.C., Dowell, N., and Moldovan, C. (2011). A Computer's Understanding of Literature. Scientific Studies of Literature, 1(1): 24–33.

Graesser, A.C., Li, H., and Forsyth, C. (2014). Learning by Communicating in Natural Language With Conversational Agents. Current Directions in Psychological Science, 23(5): 374–380.

Graesser, A.C., McNamara, D.S., and Kulikowich, J. (2011). Coh-Metrix: Providing Multilevel Analyses of Text Characteristics. Educational Researcher, 40(5): 223–234.

Graesser, A.C., McNamara, D.S., Cai, Z., Conley, M., Li, H., and Pennebaker, J. (2014). Coh-Metrix Measures Text Characteristics at Multiple Levels of Language and Discourse. Elementary School Journal, 115(2): 210–229.

Hancock, J.T., Beaver, D.I., Chung, C.K., Frazee, J., Pennebaker, J.W., Graesser, A., and Cai, Z. (2010). Social Language Processing: A Framework for Analyzing the Communication of Terrorists and Authoritarian Regimes. Behavioral Sciences of Terrorism and Political Aggression, 2: 108–132.

Jackson, G.T., Guess, R.H., and McNamara, D.S. (2010). Assessing Cognitively Complex Strategy use in an Untrained Domain. Topics in Cognitive Science, 2(1): 127–137.

McCarthy, P.M. (2009). Are Three Words all we Need? A Psychological and Computational Study of Genre Recognition. Journal for Computational Linguistics and Language Technology, 1: 23–57.

McCarthy, P.M., and Jarvis, S. (2010). MTLD, Vocd-D, and HD-D: A Validation Study of Sophisticated Approaches to Lexical Diversity Assessment. Behavior Research Methods, 42(2): 381–392.

McCarthy, P.M., Guess, R., and McNamara, D.S. (2009). The Components of Paraphrase Evaluations. Behavior Research Methods, 41(3): 682–690.

McNamara, D.S. (2010). Strategies to Read and Learn: Overcoming Learning by Consumption. Medical Education, 44(4): 340–346.

McNamara, D.S. (2011). Measuring Deep, Reflective Comprehension and Learning Strategies: Challenges and Successes. Metacognition and Learning, 6(2): 195–203.

McNamara, D.S., Crossley, S.A., and McCarthy, P.M. (2010). Linguistic Features of Writing Quality. Written Communication, 27(1): 57–86.

McNamara, D.S., Louwerse, M.M., McCarthy, P.M., and Graesser, A.C. (2010). Coh-Metrix: Capturing Linguistic Features of Cohesion. Discourse Processes, 47(4): 292–330.

Myers, J.C., McCarthy, P.M., Duran, N.D., and McNamara, D.S. (2011). The bit in the Middle and why it's Important: A Computational Analysis of the Linguistic Features of Body Paragraphs. Behavior Research Methods, 43(1): 201–209.

Rus, V., McCarthy, P.M., McNamara, D.S., and Graesser, A.C. (2009). Identification of Sentence-to-Sentence Relations Using a Text Entailer. Research on Language and Computation, 7(2): 371–398.

Proceeding

Crossley, S.A., and McNamara, D.S. (2010). Cohesion, Coherence, and Expert Evaluations of Writing Proficiency. In Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 984–989). Austin, TX: Cognitive Science Society.

Crossley, S.A., and McNamara, D.S. (2011). Text Coherence and Judgments of Essay Quality: Models of Quality and Coherence. In Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Expanding the Space of Cognitive Science (pp. 1236–1241). Boston, MA: Cognitive Science Society.

Crossley, S.A., Roscoe, R.D., and McNamara, D.S. (2011). Predicting Human Scores of Essay Quality Using Computational Indices of Linguistic and Textual Features. In Proceedings of the 15th International Conference on Artificial Intelligence in Education (AIED 11) (pp. 438–440). Heidelberg, Germany: Springer.

Dempsey, K.B., McCarthy, P.M., Myers, J.C., Weston, J., and McNamara, D.S. (2009). Determining Paragraph Type From Paragraph Position. In Proceedings of the 22nd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 33–38). Menlo Park, CA: The AAAI Press.

Duran, N.D., Crossley, S.A., Hall, C., McCarthy, P.M., and McNamara D.S. (in press). Using Coh-Metrix to Analyze Deception With Linguistic Indices. In Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference. Menlo Park, CA: The AAAI Press.

Feng, S., Cai, Z., Crossley, S.A., and McNamara, D.S. (2011). Simulating Human Ratings on Word Concreteness. In Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 245–250). Menlo Park, CA: AAAI Press.

Healy, S.J., Weintraub, J.D., McCarthy, P.M., Hall, C. and McNamara, D.S. (2009). Assessment of LDAT as a Grammatical Diversity Assessment Tool. In Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference (pp. 249–253). Menlo Park, CA: The AAAI Press.

Lintean, M., and Rus, V. (2011). Dissimilarity Kernels for Paraphrase Identification. In Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 263–268). Menlo Park, CA: AAAI Press.

Lintean, M., Moldovan, C., Rus, V., and McNamara, D.S. (2010). The Role of Local and Global Weighting in Assessing the Semantic Similarity of Texts Using Latent Semantic Analysis. In Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 235–240). Menlo Park, CA: The AAAI Press.

McCarthy, P.M. (2010). GPAT: A Genre Purity Assessment Tool. In Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 241–246). Menlo Park, CA: The AAAI Press.

McCarthy, P.M., Cai, Z., and McNamara D.S. (2009). Computational Replication of Human Assessments of Paraphrase. In Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference. Menlo Park, CA: The AAAI Press.

Min, H.C., and McCarthy, P.M. (2010). Identifying Varietals in the Discourse of American and Korean Scientists: A Contrastive Corpus Analysis Using The Gramulator. In Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 247–252). Menlo Park, CA: The AAAI Press.

Renner, A.M., McCarthy, P.M., and McNamara, D.S. (2009). Computational Considerations in Correcting User-Language. In Proceedings of the Twenty-Second International FLAIRS Conference (pp. 278–283). Menlo Park, CA: The AAAI Press.

Roscoe, R.D., Crossley, S.A., Weston, J.L., and McNamara, D.S. (2011). Automated Assessment of Paragraph Quality: Introductions, Body, and Conclusion Paragraphs. In Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 281–286). Menlo Park, CA: AAAI.

Roscoe, R.D., Varner, L.K., Cai, Z., Crossley, S.A., and McNamara, D. (2011). Internal Usability Testing of Automated Essay Feedback in an Intelligent Writing Tutor. In Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 543–548). Menlo Park, CA: AAAI Press.

Rus, V., Feng, S., Brandon, R., Crossley, S., and McNamara, D.S. (2011). A Linguistic Analysis of Student Generated Paraphrases. In Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference (pp. 293–298). Palm Beach, FL: AAAI Press.

Weston, J. Crossley, S.A., and McNamara, D.S. (2010). Towards a Computational Assessment of Freewriting Quality. In Proceedings of the 23rd International Florida Artificial Intelligence Research Society (FLAIRS) Conference. Menlo Park, CA: The AAAI Press.


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