Publications‎ > ‎

2013/2014



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


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 Psychology33(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

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

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 PublishingLink 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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