Publications: 2015/2016

Books (authored, edited)

Sottilare, R., Graesser, A.C., Hu, X., & Brawner, K. (Eds.)(2015), Design Recommendations for Intelligent Tutoring Systems: Authoring Tools (Vol.3). Orlando, FL: Army Research Laboratory. Link to Book

Sottilare, R., Graesser, A.C., Hu, X., Olney, A., Nye, B., & Sinatra, A. (Eds.) (2016). Design Recommendations for Intelligent Tutoring Systems: Domain Modeling (Vol. 4). Orlando, FL: U.S. Army Research Laboratory. Link to Book

Refereed Journal Publications (Does not include book chapters)

Dowell, N. M., & Graesser, A. C. (2015). Modeling learners’ cognitive, affective, and social processes through language and discourse. Journal of Learning Analytics, 1(3). 183-186. Link to PDF

Dowell, N. M., Graesser, A. C., Cai, Z. (2016). Language and discourse analysis with Coh-Metrix: Applications from educational material to learning environments at scale. Journal of Learning Analytics, 3(3) 72-95. Link to PDF

Dowell, N., Windsor, L., & Graesser, A. (2015). Computational linguistics analysis of leaders during crises in authoritarian regimes. Dynamics of Asymmetric Conflict, 8(3), 1-12. doi:10.1080/17467586.2015.1038286. Link to PDF

El Masri, Y.H., Baird, J-A., & Graesser, A.C. (2016). Language effects in international testing: The case of PISA 2006 science items. Assessment in Education: Principles, Policy, & Practice, 23, 427-455.

Fulmer, S. M., D'Mello, S. K., Strain, A., Graesser, A. C. (2015). Interest-based text preference moderates the effect of text difficulty on engagement and learning. Contemporary Educational Psychology, 41, 98-110. Link to PDF

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

Graesser, A.C. (2015). Deeper learning with advances in discourse science and technology. Policy Insights from Behavioral and Brain Sciences, 2, 42-50. Link to PDF

Li, H., & Graesser, A. C. (2016). Formality of the Chinese collective leadership. Behavior Research Methods, 48, 922-935. DOI: 10.3758/s13428-016-0775-4. Link to PDF

Li, H., Graesser, A. C., Conley, M., Cai, Z., Pavlik, P., & Pennebaker, J. W. (2015). A new measure of text formality: An analysis of discourse of Mao Zedong. Discourse Processes, 52(1), 1-28. doi:10.1080/0163853X.2015.1010191 Link to PDF

Medimorecc, M.A., Pavlik, P., Olney, A., Graesser, A.C., & Risko, E.F. (2015). The language of instruction: Compensating for challenge in lectures. Journal of Educational Psychology, 107, 971-990. http://doi.org/10.1037/edu0000024. Link to PDF

Rubin, D. C., Deffler, S. A., Ogle, C. M., Dowell, N. M., & Graesser, A. C. (2016). Participant, Rater, and Computer Measures of Coherence in Posttraumatic Stress Disorder. Journal of Abnormal Psychology, 125,(1), 11-25. doi: http://dx.doi.org/10.1037/ abn0000126 Link to PDF

Book Chapters

Cai, Z., Graesser, A.C., & Hu, X. (2015). ASAT: AutoTutor script authoring tool. In. R. Sottilare, A.C. Graesser, X. Hu, & K. Brawner (Eds.), Design Recommendations for Intelligent Tutoring Systems: Authoring Tools (Vol.3)(pp.199-210). Orlando, FL: Army Research Laboratory. Link to PDF

D’Mello, S. K. & Graesser, A. C. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In Calvo, R. A., D’Mello, S. K., Gratch, J., & Kappas, A. (Eds.). Handbook of Affective Computing (pp. 419-434). Oxford University Press. Link to PDF

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. Link to PDF

Graesser, A.C., Cai, Z., Baer, W.O., Olney, A.M., Hu, X., Reed, M., & Greenberg, D. (2016). Reading comprehension lessons in AutoTutor for the Center for the Study of Adult Literacy. In S.A. Crossley and D.S. McNamara (Eds.). Adaptive educational technologies for literacy instruction (pp. 288-293). New York: Taylor & Francis Routledge. Link to Book

Graesser, A.C., Li, H., Feng, S. (2015). Constructing inferences in naturalistic reading contexts. In E. O’Brien, A. Cook, and R. Lorch, (Eds.), Inferences during Reading (pp. 290-320). Cambridge: Cambridge University Press. Link to PDF

Graesser, A.C., Forsyth, C.M., & Foltz, P. (2016). Assessing conversation quality, reasoning, and problem solving performance with computer agents. In B. Csapo, J. Funke, and A. Schleicher (Eds.), On the nature of problem solving: A look behind PISA 2012 problem solving assessment (pp. 275-297). Heidelberg, Germany: OECD Series. Link to PDF

Graesser, A.C., Hu, X., Nye, B., 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). Routledge: Abingdon, Oxon, UK. Link to PDF

Graesser, A. C., Millis, K., D’Mello, S. K., & Hu, X. (2015). Conversational agents can help humans identify flaws in the science reported in digital media. In D. Rapp & J. Braasch (Eds.) Processing Inaccurate Information: Theoretical and Applied Perspectives from Cognitive Science and the Educational Sciences (pp. 139-158). MIT Press: Cambridge, MA. Link to PDF

Lehman, B., & Graesser, A.C. (2016). Arguing your way out of confusion. In F. Paglieri (Ed.), The Psychology of argument: Cognitive approaches to argumentation and persuasion. London: College Publications. Link to Book

Li, H., Shubeck, K., & 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. 281-298). London, UK: Bloomsbury Academic. Link to PDF

Olney, A., Risko, E. F., D’Mello, S. K., & Graesser, A. C. (2015). Attention in Educational Contexts: The Role of the Learning Task in Guiding Attention. In J. Fawcett, E. F. Risko & A. Kingstone (Eds.). The Handbook of Attention (pp. 623-642). MIT Press: Cambridge, MA. Link to PDF

Shaffer, D.W., Ruis, A.R., & Graesser, A.C. (2015). Authoring networked learner models in complex domains. In. R. Sottilare, A.C. Graesser, X. Hu, & K. Brawner (Eds.), Design Recommendations for Intelligent Tutoring Systems: Authoring Tools (Vol.3)(pp.179-192). Orlando, FL: Army Research Laboratory. Link to PDF

Refereed Conference Publications and Abstracts

Dowell, N. M., Oleksandra, S., Joksimović, S., Graesser, A. C., Dawson, S., Gašević, S., Hennis, T., de Vries, P., & Kovanović, V. (2015). Modeling learners’ social centrality and performance through language and discourse. In O. Santos, J. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, and M. Desmarais (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (pp. 250-257). International Educational Data Mining Society. Link to PDF

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 9^th International Conference on Educational Data Mining (pp. 577-578). Raleigh, North Carolina: EDM Society. Link to PDF

Feng, S., Stewart, J., Clewley, D., & Graesser, A. C.(2015). Emotional, epistemic, and neutral feedback in AutoTutor trialogues to improve reading comprehension. In C. Conati, N. Heffernan, A. Mitrovic, M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (pp. 570-573). Cham: Springer. Link to PDF

Forsyth, C.M., Graesser,A.C., Olney, A.M., Millis, K., Walker, B. & Cai,C. (2015). Moody agents: Affect and discourse during learning in a serious game. In C. Conati, N. Heffernan, A. Mitrovic, M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education (pp. 135-144). Cham: Springer. Link to PDF

Goedecke, P., Dong, D., Shi, G., Feng, S., Risko, E., Olney, A., D'Mello, S., & Graesser, A. (2015). Breaking off engagement: Readers’ cognitive decoupling as a function of reader and text characteristics. In O. Santos, J. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, and M. Desmarais (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (pp. 448-451). International Educational Data Mining Society. Link to PDF

Joksimović, S., Dowell, N. M., Oleksandra, S., Kovanović, V., Gašević, D., Dawson, S., & Graesser, A. C. (2015). How do you connect? Analysis of social capital accumulation in connectivist MOOCs. In J. Baron, & G. Lynch (Eds.), Proceedings of the 5th International Conference on Learning Analytics and Knowledge (pp. 64-68). New York: ACM. doi: 10.1145/2723576.2723604 Link to PDF

Lehman, B., & Graesser A. (2015). To resolve or not to resolve? That is the big question about confusion. In C. Conati, N. Heffernan, A. Mitrovic, M. F. Verdejo (Eds.), Proceedings of 17th International Conference on Artificial Intelligence in Education (AIED2015) (pp. 216-225). Cham: Springer. Link to PDF

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 17^th International Conference on Artificial Intelligence in Education (pp. 694–697). Cham: Springer. Link to PDF

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 9^th International Conference on Educational Data Mining (pp. 430-435). Raleigh, North Carolina: EDM Society. Link to PDF

Li, H., Cheng, C., & Graesser, A. C. (2015). A measure of text formality as a human construct. In I. Russel & B. Eberle (Eds.), Proceedings of the Twenty-eighth International Florida Artificial Intelligence Research Society Conference (pp. 175–180). Palo Alto, California: AAAI Press. Link to PDF

Nixon, T., & Dowell, N. M. (2016). Coh-Metrix in the Cloud: lessons from implementing a web-scale text analytics platform. In R. Ferguson, M. Sharkey, & N. Mirriahi (Eds.), Practitioner Track Proceedings of the 6th International Learning Analytics & Knowledge Conference (LAK16) (pp. 45–46). University of Edinburgh, Edinburgh, UK: SoLAR. Link to PDF

Samei, B., Olney, A., Kelly, S., Nystrand, M., D’Mello, S., Blanchard, N., & Graesser, A. (2015). Modeling classroom discourse: Do models of predicting dialogic instruction properties generalize across populations? In O. Santos, J. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, and M. Desmarais (Eds.), Proceedings of the 8th International Conference on Educational Data Mining (pp. 444-447). International Educational Data Mining Society. Link to PDF

Swartout, W., Nye, B.D., Hartholt, A., Reilly, A., Graesser, A.C., VanLehn, K., Wetzel, J., Liewer, M., Morbini, F., Morgan, B., Wang, L., Benn, G., & Rosenberg, M. (2016). Designing a Personal Assistant for Life Long Learning (PAL3). In Z. Markov and I Russel (Eds.), Proceedings of the 29th International Florida Artificial Intelligence Research Society Conference (pp. 491-496). Palo Alto, CA: Association for the Advancement of Artificial Intelligence. Link to PDF