Guru: A Computer Tutor that Models Expert Human Tutors

Products: The intervention will consist of an expert computer tutor designed to support student achievement in biology.

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.

Cai, Z., Graesser, A.C., Hu, X., & Cockroft, J. L. (2019). Self-improving components in conversational intelligent tutoring systems. In A. Sinatra, A.C. Graesser, X. Hu, K. Brawner and V. Rus (Eds.), Design Recommendations for Intelligent Tutoring Systems: Self-improving systems (Vol.7) (pp. 119-126). Orlando, FL: Army Research Laboratory.

Graesser, A.C., and D'Mello, S.K. (2012). Emotions During the Learning of Difficult Material. In B.H. Ross (Ed.), The Psychology of Learning and Motivation, Volume 57 (pp. 183-225). San Diego: Elsevier Academic Press.

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., Baer, W., Feng, S., Walker, B., Clewley, D., Hays, D.P., Greenberg, D. (2015). Emotions in adaptive computer technologies for adults improving reading. In S. Tettegah and M. Gartmeier (Eds.), Emotions, Technology, Design, and Learning (pp. 3-25). New York: Elsevier.

Graesser, A.C., Keshtkar, F., and Li, H. (2014). The Role of Natural Language and Discourse Processing in Advanced Tutoring Systems. In T. Holtgraves (Ed.), The Oxford Handbook of Language and Social Psychology (pp. 491-509). New York: Oxford Handbooks Online.

Graesser, A.C., Feng, S., & Cai, Z. (2017). Two technologies to help adults with reading difficulties improve their comprehension. In E. Segers and P. Van den Broek (Eds.), Developmental perspectives in written language and literacy. In honor of Ludo Verhoeven (pp. 295-313). John Benjamin Publishing Company.

Graesser, A. C., Lippert, A. M., & Hampton, A. J. (2017). Successes and failures in building learning environments to promote deep learning: The value of conversational agents. In J. Buder & F. W. Hesse (Eds.), Informational Environments: Effects of Use, Effective Designs (pp. 273-298). Springer International Publishing. https://doi.org/10.1007/978-3-319-64274-1_12

Graesser, A.C., Millis, K., D'Mello, S.K., and Hu, X. (2014). Conversational Agents can Help Humans Identify Flaws in the Science Reported in Digital Media. In D. Rapp, and J. Braasch (Eds.), Processing Inaccurate Information: Theoretical and Applied Perspectives From Applied Perspectives From Cognitive Science and the Educational Sciences (pp. 139-159). Cambridge, MA: MIT Press.

Li, H., Shubeck, K. and Graesser, A. C. (2016). Using Technology in Language Assessment. In D., Tsagari, & J. V. Banerjee. (Eds.), Contemporary Second Language Assessment: Contemporary Applied Linguistics (Vol. 4, pp. 281-297). London, UK: Bloomsbury Academic.

Journal article, monograph, or newsletter

Baker, R.S., D'Mello, S.K., Rodrigo, M.T., and Graesser, A.C. (2010). Better to be Frustrated Than Bored: The Incidence, Persistence, and Impact of Learners' Cognitive-Affective States During Interactions With Three Different Computer-Based Learning Environments. International Journal of Human-Computer Studies, 68: 223-241.

D'Mello, S., and Graesser, A.C. (2010). Multimodal Semi-Automated Affect Detection From Conversational Cues, Gross Body Language, and Facial Features. User Modeling and User-Adapted Interaction, 20(2): 147-187.

D'Mello, S., Craig, S., and Graesser, A. (2009). Multi-Method Assessment of Affective Experience and Expression During Deep Learning. International Journal of Learning Technology, 4(3): 165-187.

D'Mello, S., King, B., Chipman, P., and Graesser, A.C. (2010). Towards Spoken Human-Computer Tutorial Dialogues. Human Computer Interaction, 23: 289-323.

D'Mello, S.K., and Graesser, A.C. (2009). Automatic Detection of Learner's Affect From Gross Body Language. Applied Artificial Intelligence, 23: 123-150.

D'Mello, S.K., Lehman, B., and Person, N. (2010). Monitoring Affect States During Effortful Problem Solving Activities. International Journal of Artificial Intelligence in Education, 20(4): 361-389. doi:10.3233/JAI-2010-012

D'Mello, S.K., Olney, A., and Person, N. (2010). Mining Collaborative Patterns in Tutorial Dialogues. Journal of Educational Data Mining, 2(1): 1-37.

D'Mello, S.K, and Graesser, A.C. (2012). Dynamics of Affective States During Complex Learning. Learning and Instruction, 22(2): 145-157.

Gholson, B., Witherspoon, A., Morgan, B., Brittingham, J.K., Coles, R., Graesser, A.C., Sullins, J., and Craig, S.D. (2009). Exploring the Deep-Level Reasoning Questions Effect During Vicarious Learning Among Eighth to Eleventh Graders in the Domains of Computer Literacy and Newtonian Physics. Instructional Science, 37(5): 487-493.

Graesser, A.C. (2009). Cognitive Scientists Prefer Theories and Testable Principles With Teeth. Educational Psychologist, 44(3): 193-197.

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

Graesser, A.C. (2016). Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26, 124 -132. Full text

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., Cai, Z., Morgan, B., & Wang, L. (2017). Assessment with computer agents that engage in conversational dialogues and trialogues with learners. Computers in Human Behavior, 76, 607-616. Full text

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. Full text

Graesser, A.C., Forsyth, C., and Lehman, B. (2017). Two Heads May be Better Than One: Learning From Agents in Conversational Trialogues. Teacher College Record, 119(3). Full text

Kopp, K., Britt, A., Millis, K., and Graesser, A. (2012). Improving the Efficiency of Dialogue in Tutoring. Learning and Instruction, 22(5): 320-330.

Lippert, A., Shubeck, K., Morgan, B., Hampton, A, & Graesser, A.C. (2020) Multiple Agent Designs in Conversational Intelligent Tutoring Systems. Technology, Knowledge and Learning, v25 n3 p443-463.

Louwerse, M.M., Graesser, A.C., McNamara, D.S., and Lu, S. (2009). Embodied Conversational Agents as Conversational Partners. Applied Cognitive Psychology, 23(9): 1244-1255.

Nye, B.D., Graesser, A.C., and Hu, X. (2014). AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring. International Journal of Artificial Intelligence in Education, 24(4): 427-469. Full text

Olney, A. (2011). Large-Scale Latent Semantic Analysis. Behavior Research Methods, 43(2): 414-423.

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.

Wiley, J., Goldman, S.R., Graesser, A.C., Sanchez, C.A., Ash, I.K., and Hemmerich, J.A. (2009). Source Evaluation, Comprehension, and Learning in Internet Science Inquiry Tasks. American Educational Research Journal, 46(4): 1060-1106.

Proceeding

Cai, Z., Li, H., Hu, X., & Graesser A. C. (2016). Can word probabilities from LDA be simply added up to represent documents? In T. Barnes, M. Chi, & M. Feng (Eds.), In Proceedings of the 9th International Conference on Educational Data Mining (pp. 577-578). Raleigh, North Carolina: EDM Society. Full text

Cai, Z., Siebert-Evernston, A., Eagan, B., Shaffer, D.W., Hu, X., & Graesser, A.C. (2019). nCoder+: A Semantic Tool for Improving Recall of nCoder Coding. In B. Eagan, M. Misfeldt, A. Siebert-Evenstone (Eds.), Advances in Quantitative Ethnography Proceedings of the First International Conference on Quantitative Ethnography (pp.41-54). Madison, WI: Springer, Cham.

Li, H., Cai, Z., & Graesser A. C. (2016). How good is popularity? Summary grading in crowdsourcing. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (pp. 430-435). Raleigh, North Carolina: EDM Society. Full text

Li, H., Cheng, C., Yu, Q., & Graesser A. C. (2015). The role of peer agent's learning competency in trialogue-based reading intelligent systems. In C. Conati, N. Heffernan, A. Mitrovic, M. F. Verdejo (Eds.), Proceedings of 17th International Conference on Artificial Intelligence in Education (pp. 694-697). Cham: Springer. Full text