Books McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge, M.A.: Cambridge University Press. Link to Book Sottilare, R., Graesser, A., Hu, X., Holden, H. (Eds.)(2013). Design Recommendations for Intelligent Tutoring Systems: Learner Modeling (Vol.1). Orlando, FL: Army Research Laboratory. Link to Book Sottilare, R., Graesser, A.C., Hu, X., & Goldberg, B. (Eds.)(2014), Design Recommendations for Intelligent Tutoring Systems: Instructional Management (Vol.2). Orlando, FL: Army Research Laboratory. Link to PDF Refereed Journal Publications (Does not include book chapters) Craig, S. D., Hu, X., Graesser, A. C., Bargagliotti A. E., Sterbinsky, A., Cheney, K. R., & Okwumabua, T. (2013). The impact of a technology-based mathematics after-school program using ALEKS on student's knowledge and behaviors. Computers & Education, 68, 495-504. doi:10.1016/j.compedu.2013.06.010 Link to PDF D’Mello, S. K., Dowell, N. & Graesser, A. C. (2014). Unimodal and multimodal human perception of naturalistic non-basic affective states during human-computer interactions. IEEE Transactions on Affective Computing, 4(4), 452-465. doi:10.1109/T-AFFC.2013.19 Link to PDF D’Mello, S. K., & Graesser, A. C. (2014). Confusion and its dynamics during device comprehension with breakdown scenarios. Acta Psychologica, 151, 106-116. doi:10.1016/j.actpsy.2014.06.005 Link to PDF D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A.C. (2014). Confusion can be beneficial for learning. Learning
and Instruction. 29(3), 153-170. doi:10.1016/j.learninstruc.2012.05.003 Link to PDF Feng, S., D’Mello, S. K., & Graesser, A. (2013). Mind wandering while reading easy and difficult texts. Psychonomic Bulletin & Review, 20, 586-592. doi:10.3758/s13423-012-0367-y Link to PDF Forsyth, C.M., Graesser,A.C. Pavlik, P., Cai, Z., Butler,H., Halpern, D.F., & Millis, K.(2013). OperationARIES! methods, mystery and mixed models: Discourse features predict affect in a serious game. Journal of Educational Data Mining, 5(1), 147-189. Link to PDF Graesser, A.C. (2013). Evolution of advanced learning technologies in the 21st Century. Theory Into Practice, 52(1), 93-101. doi:10.1080/00405841.2013.795446 Link to PDF
Graesser, A. C., Li, H., & Forsyth, C.
(2014). Learning by communicating in natural language with conversational
agents. Current Directions in Psychological Science, 23(5), 374-380. doi: 10.1177/0963721414540680 Link to PDF 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,
115(2), 210–229. doi:10.1086/678293 Link to PDF Greiff, S., Wüstenberg, S., Csapó, B.,
Demetriou, A., Hautamäki, J., Graesser, A. C., & Martin, R. (2014).
Domain-general problem solving skills and education in the 21st century. Educational
Research Review, 13, 74–83. doi:10.1016/j.edurev.2014.10.002 Link to PDF Kacewicz, E., Pennebaker, J. W., Davis, M., Jeon, M., & Graesser, A. C. (2014). Pronoun Use Reflects Standings in Social Hierarchies. Journal of Language and Social Psychology, 33(2), 125–143. doi:10.1177/0261927X13502654 Link to PDF Lehman, B., D’Mello, S. K., Strain, A., Mills, C., Gross, M., Dobbins, A., Wallace, P., Millis, K., & Graesser, A. C. (2013). Inducing and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence Special Issue: Best of AIED 2011, 22(2), 71-93. Link to PDF Nye, B.D., Graesser, A.C., & 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. doi:10.1007/s40593-014-0029-5 Link to PDF Rus, V., D’Mello, S. K., Hu, X., & Graesser, A. C. (2013). Recent advances in intelligent tutoring systems with conversational dialogue. AI Magazine, 34, 42-54. Link to PDF Sullins, J., &
Graesser, A.C. (2014). The relationship
between cognitive disequilibrium, emotions, and individual differences on
student question generation. International
Journal of Learning Technology, 9, 221-247. Link to PDF Windsor, L., Dowell, N., & Graesser, A. (2014).
The language of autocrats: Leaders' language in natural crises. Risk,
Hazards & Crisis in Public Policy, 5(4), 446-467. doi:10.1002/rhc3.12068 Link to PDF
Book Chapters Brawner, K., & Graesser, A. (2014). Natural language, discourse, and conversational dialogues within intelligent tutoring systems: A review. In R. Sottilare, A.C. Graesser, X. Hu, & B. Goldberg (Eds.), Design Recommendations for Intelligent Tutoring Systems: Adaptive Instructional Strategies (Vol.2)(pp 189-204). Orlando, FL: Army Research Laboratory. Link to PDF Cai, Z., Feng, S., Baer, W., & Graesser, A. (2014). Instructional strategies in trialog-based intelligent tutoring systems. In R. Sottilare, A.C. Graesser, X. Hu, & B. Goldberg (Eds.), Design Recommendations for Intelligent Tutoring Systems: Adaptive Instructional Strategies (Vol.2)(pp. 225-235). Orlando, FL: Army Research Laboratory. Link to PDF D’Mello, S. K. & Graesser, A. C. (2013). Design of dialog-based intelligent tutoring systems to simulate human-to-human tutoring. In A. Neustein & J. Markowitz (Eds.) Where humans meet machines: Innovative solutions to knotty natural language problems (pp. 233-270). Springer Verlag, Heidelberg/New York. Link to PDF D’Mello, S. K. & Graesser, A. C. (2014). Confusion. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), Handbook of emotions and education (289-310). New York, NY: Taylor & Francis. Link to PDF D’Mello, S. K., Strain, A. C., Olney, A., & Graesser, A. C. (2013). Affect, Meta-affect, and Affect Regulation during Complex Learning. In R. Azevedo and V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 669-681). Springer: New York. Link to PDF Graesser, A.C. (2013). A guide to understanding learner models. In R. Sottilare, A.C. Graesser, X. Hu, & H. Holden (Eds.), Design Recommendations for Intelligent Tutoring Systems: Learner Modeling (Vol.1)(pp. 3-6). Orlando, FL: Army Research Laboratory. Link to PDF Graesser, A.C. (2014). Guided instruction and scaffolding. In R. Sottilare, A.C. Graesser, X. Hu, & B. Goldberg (Eds.), Design Recommendations for Intelligent Tutoring Systems: Adaptive Instructional Strategies (Vol.2). Orlando, FL: Army Research Laboratory. Link to PDF Graesser, A. C., D’Mello, S. K, & Strain, A. (2014). Emotions in advanced learning technologies. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), Handbook of emotions and education (pp. 473-493). New York, NY: Taylor & Francis. Link to PDF Graesser, A.C., & Forsyth, C. (2013). Discourse comprehension. In D. Reisberg (Ed.), Oxford handbook of cognitive psychology (475-491). Oxford, UK: Oxford University Press. Link to PDF Graesser, A. C., Keshtkar, F., & Li, H. (2014). The role of natural language and discourse processing in advanced tutoring systems. In T. Holtgraves (Ed.), Oxford Handbook of Language and social psychology (pp. 491-509). New York: Oxford University Press. Link to PDF Graesser, A. C., & Li, H. (2013). How might comprehension deficits be explained by the constraints of text and multilevel discourse processes? In B. Miller, L.E. Cutting, & P. McCardle (Eds.), Unraveling reading comprehension: Behavioral, neurobiological, and genetic components (pp, 33–42). Baltimore: Paul Brookes Publishing. Link to PDF D’Mello, S., & Graesser, A. (2013). Design of dialog-based intelligent tutoring systems to simulate human-to-human tutoring. In Where Humans Meet Machines (pp. 233-269). Springer New York. Link to PDF D’Mello, S. K. & Graesser, A. C. (2014). 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 Book Graesser, A. C., Millis, K., D’Mello, S. K., & Hu, X. (2014). 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. MIT Press: Cambridge, MA.
Link to PDF Keshtkar, F., Burkett, C., Li, H., & Graesser, A. C. (2014). Using Data Mining Techniques to Detect the Personality of Players in an Educational Game. In A. Pena-Ayala (Ed.), Educational data mining: Applications and trends (pp. 125-150). New York: Springer. Link to PDF Lesgold, A. Graesser, A. (2013). Important
considerations for learner models: Transfer potnential and pedagogical content
knowledge . In R. Sottilare, A.C.
Graesser, X. Hu, & H. Holden (Eds.), Design Recommendations for Intelligent
Tutoring Systems: Learner Modeling (Vol.1)(pp. 15-22). Orlando, FL: Army Research Laboratory. Link to PDF Millis, K., Graesser, A.C., & Halpern, D.F. (2014). Operation ARA: A serious game that combines intelligent tutoring and learning principles to teach science. In V.A. Benassi, C.E. Overson, and C.M. Hakala (Eds.), Applying science of learning in education: Infusing psychological science into the curriculum (pp. 169-183). Washington, D.C.: Society for the Teaching of Psychology Series. Link to PDF Nye, B.D., Graesser, A.C., & Hu, X. (2014). Multimedia learning with intelligent tutoring systems. In R. Mayer (Ed.). Cambridge handbook of multimedia learning (3rd Ed.) (pp. 705-728). Cambridge: Cambridge University Press. Link to PDF Rus, R., Baggett, W., Gire, E.,
Franceschetti, D., Conley, M., & Graesser, A. (2013). Toward learner models
based on learning progressions (LPs) in DeepTutor . In R. Sottilare, A.C. Graesser, X. Hu, & H. Holden (Eds.),
Design Recommendations for Intelligent Tutoring Systems: Learner
Modeling (Vol.1)(pp.
183-192). Orlando, FL: Army Research Laboratory. Link to PDF Rus, V., Graesser, A.C., & Conley, M. (2014). The DENDROGRAM model of instruction. In R. Sottilare, A.C. Graesser, X. Hu, & H. Holden (Eds.), Design Recommendations for Intelligent Tutoring Systems: Adaptive Instructional Strategies (Vol.1) (pp. 311-325). Orlando, FL: Army Research Lab. Link to PDF Rus, V., Niraula, N., Lintean, M., & Graesser, A.C. (2013). An Overview of Dialogue and Semantic Processing in Educational Technologies. C. Forascu, A. Ionita, D. Tufis, D. Cristea, & V. Rus (Eds.), Language Technologies in Romanian and Diaspora Research & Development (pp. 311-325). Romania: Cuza Publishing House. Link to PDF Refereed Conference Publications and Abstracts Cade, W. L., Dowell, N. M., Tausczik, Y. R., Pennebaker, J. W., & Graesser, A. C. (2014). Modeling students socioaffective responses to group interactions in a collaborative online chat environment. In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014) (pp. 399-400). Berlin: Springer. Link to PDF Cheng, Q., Cheng, K., Li, H., Cai, Z., Hu, X., & Graesser, A. C. (2013). AutoTutor 2013: Conversation-based Online Intelligent Tutoring System with Rich Media. In S. Trausan-Matu, K. Boyer, M. Crosby, & K. Panou (Eds.), Proceedings of the 16th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 930 - 931). Berlin: Springer. Link to PDF Dowell, N., Cade, W., Tausczik, Y., Pennebaker, J., & Graesser, A. (2014). What works: Creating adaptive and intelligent systems for collaborative learning support. In S. Trausan-Matu, K. Boyer, M. Crosby, & K. Panou (Eds.), Proceedings of the 16th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 124 - 133). Berlin: Springer. Link to PDF Dowell, N., Cai, Z., & Graesser, A. C. (2014).
Analyzing language and discourse with Coh-Metrix. Workshop presented at 2nd
Learning Analytics Summer Institutes (LASI 2014), Cambridge, MA. Link to PDF Duan, Y., Dowell, N. M., Graesser, A. C., & Li, H. (2014). Linguistic style and social historical context: An automated linguistic analysis of Mao Zedong’s speeches. In W. Eberle & C. Boonthum-Denecke (Eds.) Proceedings of 27th Florida Artificial Intelligence Research Society Conference (FLAIRS 2014) (pp. 43-46). Menlo Park, CA: AAAI Press. Link to PDF Forsyth, C.M., Graesser,A.C., Pavlik, P., Millis, K., & Samei, B. (2014). Discovering theoretically grounded predictors of shallow vs. deep- level learning. In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014) (pp. 229-232). International Educational Data Mining Society. Link to PDF Huang, X., Xie, J., Craig,
S., Graesser, A., & Hu, X. (2014). The effects of a computerized tutor on the
mathematics attitudes of sixth grade students in an after-school program. In W. Eberle, C. Boonthum-Denecke, I. Russell, &
G.M. Youngblood (Eds.), Proceedings of the Twenty-seventh International
Florida Artificial Intelligence Research Society Conference. (pp. 522-523). Menlo Park, CA: AAAI Press. Link to PDF Jin, W., Li, H., Cai, Z., Keshtkar, F., Graesser, A. C., & Shaffer, W. D. (2013). AutoMentor: Artificial Intelligent Mentor in Educational Game. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 940 - 941). Berlin: Springer. Link to PDF Keshtkar, F., Samei, B., Morgan, B., & Graesser, A. C. (2014). A data mining approach to construct production rules in an educational game. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 11-14). Berlin: Springer. Link to PDF Lehman, B., D'Mello, S., & Graesser, A. (2013). Who benefits from confusion induction during learning? An individual differences cluster analysis. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 51-60). Berlin: Springer. Link to PDF Lehman, B., & Graesser, A. (2014). Impact of agent role on confusion induction and learning. In S. Trausan-Matu, K. Boyer, M. Crosby, & K. Panou (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 45 - 54). Berlin: Springer. Link to PDF Li, H., Cheng, Q., Graesser, A.C. (2014). Evaluation of
Google translation: Topics. In W. Eberle, C. Boonthum-Denecke, I. Russell,
& G.M. Youngblood (Eds.), Proceedings of the Twenty-seventh
International Florida Artificial Intelligence Research Society Conference. (pp. 523) Palo Alto, California: AAAI Press. Link to PDF Li, H., Duan, Y., Clewley,
D., Morgan, B., Graesser, A. C., Shaffer, D. W., & Saucerman, J. (2014). Question asking during collaborative problem solving
in an online game environment. In S. Trausan-Matu, K. Boyer, M. Crosby, & K. Panou
(Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (pp. 617-618). Springer-Verlag. Link to PDF Li, H. & Graesser, A.C. (2013). Does Chinese Political Leaders’ Language Formality Decrease with Aging? In W. Eberle & C. Boonthum-Denecke (Eds.), Proceedings of 26th Florida Artificial Intelligence Research Society Conference (FLAIRS 2014). (pp. 653-654). Palo Alto, California: AAAI Press. Link to PDF Li, H., Graesser, A.C., & Cai, Z. (2013). Comparing two measures of formality. In W. Eberle & C. Boonthum-Denecke (Eds.), Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference. (pp. 220-225). Palo Alto, California: AAAI Press. Link to PDF Li, H., Graesser, A.C., & Cai, Z. (2014). Comparison of
Google translation with human translation. In W. Eberle, C. Boonthum-Denecke,
I. Russell, & G.M. Youngblood (Eds.), Proceedings of the Twenty-seventh
International Florida Artificial Intelligence Research Society Conference. (pp. 190-195). Palo Alto, California: AAAI Press. Link to PDF Li, H., Graesser, A. C., & Cai, Z. (2013).
Component model in discourse analysis. In S. K. D’Mello, R. A. Calvo, & A.
Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (pp. 326-327). International Educational Data Mining Society. Link to PDF Mills, C., Bosch, N., Graesser, A.C., & D’Mello, S. (2014). To quit or not to quit: Predicting future behavioral disengagement
from reading patterns. In S. Trausan-Matu, K. Boyer, M. Crosby, & K. Panou (Eds.),
Proceedings of the Twelfth International Conference on
Intelligent Tutoring Systems (pp. 19-28). Switzerland: Springer International Publishing. Link to PDF Mills, C., D’Mello, S., Lehman, B., Bosch, N., Strain, A., & Graesser, A. (2013). What makes learning fun? Exploring the influence of choice and difficulty on mind wandering and engagement during learning. In K. Yacef, C. Lane, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (pp. 71-80). Berlin: Springer. Link to PDF Mintz, L., Stefanescu, D., Feng, S., D'Mello, S.,
& Graesser, A. (2014). Automatic
assessment of student reading comprehension from short summaries. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M.
McLaren, (Eds.) Proceedings of the 7th International Conference on Educational
Data Mining (EDM 2014) (pp. 333-334). Link to PDF Morgan, B., Keshtkar, F., Graesser, A., & Shaffer, D. W. (2013). Automating the mentor in a serious game: A discourse analysis using finite state machines. In C. Stephanidis (Ed.), Proceedings of the 15th International Conference on Human-Computer Interaction (HCI International). (pp. 591-595). Berlin: Springer. Link to PDF Rus, V., Graesser, A.C.,
Baggett, W., Franceschetti, D., Stefanescu, D., Niraula, N. (2014). Macro-adaptivity
in conversational intelligent tutoring matters. Automated response to questions with production rules. In S. Trausan-Matu, K. Boyer, M.
Crosby, & K. Panou (Eds.), Proceedings of the Twelfth
International Conference on Intelligent Tutoring Systems. (pp.
242–247). Switzerland: Springer International Publishing. Link to PDF Rus, V., Shala, L., & Graesser, A. (2014). A bilingual
analysis of cohesion in a corpus of leader speeches. In W. Eberle, C. Boonthum-Denecke, I.
Russell, & G.M. Youngblood (Eds.), Proceedings of the Twenty-seventh
International Florida Artificial Intelligence Research Society Conference. (pp. 225–230). Palo Alto, California: AAAI Press. Link to PDF Rus, V., Stefanescu, D., Niraula, N., Graesser, A.C. (2014). DeepTutor:
towards macro- and micro-adaptive conversational intelligent tutoring at scale. In M Sahami, A. Fox, M. A. Hearst, and M. T.H. Chi (Eds.), Proceedings of the Learning at Scale
Conference (pp. 209-210). New York: ACM. Link to PDF Samei, B., Keshtkar, F., Graesser, A., & Rus, V. (2013). A tool for speech act classification using interactive machine learning. In D’Mello, S. K., Calvo, R. A., and Olney, A. (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013). (pp. 406-407). Link to PDF Samei, B., Li, H., Keshtkar, F., Rus, V., & Graesser, A. (2014) Context-based Speech Act Classification in Intelligent Tutoring Systems. In S. Trausan-Matu, K. Boyer, M. Crosby, & K. Panou (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 236 - 241. Berlin: Springer. Link to PDF Samei, B., Olney, A., Kelly, S., Nystrand, M., D'Mello, S., Blanchard,
Sun, X., Glaus, M., & Graesser, A. (2014). Domain independent
assessment of dialogic properties of classroom discourse. In Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren,
(Eds.) Proceedings of the 7th International Conference on Educational Data
Mining (EDM 2014) (pp. 233-236). International
Educational Data Mining Society. Link to PDF Ştefănescu, D., Rus, V., & Graesser, A. (2014). Assessing
students’ prior knowledge from tutorial dialogue. In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (Eds), Proceedings of the 7th International Conference on
Educational Data Mining (pp. 197-200). International Educational Data Mining
Society. Link to PDF Vega, B., Feng, S., Lehman, B., Graesser, A., & D’Mello, S. (2013). Reading into the text: Investigating the influence of text complexity on cognitive engagement. In D’Mello, S. K., Calvo, R. A., and Olney, A. (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013) (pp. 296-299). International Educational Data Mining Society. Link to PDF Xie, J., Craig, S., Huang,
X., Graesser, A., & Hu, X. (2014).
The effects
of math ability on students’ math learning behaviors in a computer-based tutoring
system. In W. Eberle, C. Boonthum-Denecke, I. Russell, & G.M. Youngblood
(Eds.), Proceedings of the Twenty-seventh International Florida Artificial
Intelligence Research Society Conference (pp. 526-527). Palo Alto, California: AAAI
Press. Link to PDF
Xie, J., Huang, X., Hua,
H., Wang, J., Tang, Q., Craig, S. D., Graeser, A.C., Lin, K., & Hu, X.
(2013). Discovering the Relationship between Student Effort and Ability for
Predicting the Performance of Technology-Assisted Learning in a Mathematics
After-School Program. Learning. In S. K. D’Mello, R. A. Calvo, & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data
Mining (pp. 354-355). Worcester, MA: International Educational Data Mining
Society. Link to PDF |
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