@String { aaai = {American Association of Artificial Intelligence} } @String { ablex = {Ablex Publishing Corporation} } @String { academic = {Academic Press} } @String { cambridge = {Cambridge University Press} } @String { kap = {Kluwer Academic Publishers} } @String { lea = {Lawrence Erlbaum Associates} } @String { oxford = {Oxford University Press} } @String { springer = {Springer-Verlag} } @InProceedings{Blanchard2015a, author = {Blanchard, Nathaniel and Brady, Michael and Olney, Andrew M. and Glaus, Marci and Sun, Xiaoyi and Nystrand, Martin and Samei, Borhan and Kelly, Sean and D'Mello, Sidney}, title = {A Study of Automatic Speech Recognition in Noisy Classroom Environments for Automated Dialog Analysis}, booktitle = {Artificial Intelligence in Education}, year = {2015}, editor = {Conati, Cristina and Heffernan, Neil and Mitrovic, Antonija and Verdejo, M. Felisa}, language = {English}, volume = {9112}, series = {Lecture Notes in Computer Science}, publisher = {Springer International Publishing}, isbn = {978-3-319-19772-2}, pages = {23-33}, doi = {10.1007/978-3-319-19773-9_3}, comment = {Acceptance rate 29\%}, keywords = {Google Speech; Bing Speech; Sphinx 4; Microsoft Speech; ASR engine evaluation}, } @InProceedings{Blanchard2015, author = {Blanchard, Nathaniel and D'Mello, Sidney and Olney, Andrew M. and Nystrand, Martin}, title = {Automatic Classification of Question \& Answer Discourse Segments from Teacher's Speech in Classrooms}, booktitle = {Proceedings of the 8th International Conference on Educational Data Mining}, year = {2015}, editor = {Olga C. Santos and Jesus G. Boticario and Cristobal Romero and Mykola Pechenizkiy and Agathe Merceron and Piotr Mitros and Jos\'{e} Mar\'{i}a Luna and Cristian Mihaescu and Pablo Moreno and Arnon Hershkovitz and Sebastian Ventura and and Michel Desmarais}, publisher = {International Educational Data Mining Society}, pages = {282--288}, comment = {Acceptance rate 36\%}, owner = {aolney}, timestamp = {2015.07.04}, } @InProceedings{Blanchard2016, Title = {Semi-Automatic Detection of Teacher Questions from Human-Transcripts of Audio in Live Classrooms}, Author = {Nathaniel Blanchard and Patrick J. Donnelly and Andrew M. Olney and Borhan Samei and Brooke Ward and Xiaoyi Sun and Sean Kelly and Martin Nystrand and Sidney K. D'Mello}, Booktitle = {Proceedings of the 9th International Conference on Educational Data Mining}, editor = {Tiffany Barnes and Min Chi and Mingyu Feng}, Year = {2016}, Pages = {288-291}, Comment = {Acceptance rate 52\% as short paper}, Owner = {aolney}, Timestamp = {2016.06.13} } @InProceedings{Cade2011, Title = {Building rapport with a 3D conversational agent}, Author = {Cade, W. and Olney, A. and Hays, D.P. and Lovel, J.}, Booktitle = {Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction}, Year = {2011}, Address = {Berlin}, Editor = {S.K. D'Mello and A. Graesser and B. Schuller and J. Martin}, Pages = {305-306}, Publisher = {Springer-Verlag}, Comment = {Acceptance rate 35\%}, Owner = {aolney}, Timestamp = {2011.10.11} } @InProceedings{Cade2010a, Title = {An Exploration of Off Topic Conversation}, Author = {Cade, Whitney L. and Lehman, Blair A. and Olney, Andrew}, Booktitle = {Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics}, Year = {2010}, Address = {Los Angeles, California}, Month = jun, Pages = {669--672}, Publisher = {Association for Computational Linguistics}, Comment = {Acceptance rate 31\%}, } @InProceedings{Cade2014, Title = {Animated Presentation of Pictorial and Concept Map Media in Biology}, Author = {Cade, Whitney L. and Maass, Jaclyn K. and Hays, Patrick and Olney, Andrew M.}, Booktitle = {Intelligent Tutoring Systems}, Year = {2014}, Editor = {Trausan-Matu, Stefan and Boyer, Kristy Elizabeth and Crosby, Martha and Panourgia, Kitty}, Pages = {416--425}, Publisher = {Springer International Publishing}, Series = {Lecture Notes in Computer Science}, Volume = {8474}, Comment = {Acceptance rate 18\% approx.}, Doi = {10.1007/978-3-319-07221-0_52}, ISBN = {978-3-319-07220-3}, Keywords = {picture; concept map; animated media; static display; Khan Academy; Biology; link; node}, Url = {http://dx.doi.org/10.1007/978-3-319-07221-0_52} } @InProceedings{Cade2010, Title = {Using Topic Models to Bridge Coding Schemes of Differing Granularity}, Author = {Cade, Whitney L. and Olney, Andrew M.}, Booktitle = {Proceedings of the 3rd International Conference on Educational Data Mining}, Year = {2010}, Editor = {Baker, R.S.J.d. and Merceron, A. and Pavlik, P.I. Jr.}, Pages = {281-282}, Comment = {Acceptance rate 43\%}, Owner = {aolney}, Timestamp = {2010.03.11} } @InProceedings{Cade2010b, Title = {Tutor Me {Elmo}: Improving Engagement and Learning Gains in Intelligent Tutoring Systems with a Robotic Interface}, Author = {Cade, Whitney L. and Olney, Andrew M. and Patrick Hays and Person, Natalie K.}, Booktitle = {Early Career Researchers Track Proceedings of the IEEE 3rd International Conference on Digital Game and Intelligent Toy Enhanced Learning}, Year = {2010}, Address = {Jhongli, Taiwan}, Editor = {Biswas, G. and Carr, D. and Chee, Y.S. and Hwang, W.}, Pages = {1-2}, Publisher = {National Central University.}, Comment = {Acceptance rate 61\%}, Owner = {aolney}, Timestamp = {2010.03.11} } @Article{DMello2010b, author = {D'Mello, Sidney K. and Olney, Andrew M. and Natalie Person}, title = {Mining Collaborative Patterns in Tutorial Dialogues}, journal = {Journal of Educational Data Mining}, year = {2010}, volume = {2}, number = {1}, pages = {1-37}, owner = {aolney}, timestamp = {2010.03.11}, } @InProceedings{DMello2010a, author = {D'Mello, Sidney K. and Claire Williams and Patrick Hays and Olney, Andrew M.}, title = {Individual Differences as Predictors of Learning and Engagement}, booktitle = {Proceedings of the 32nd Annual Conference of the Cognitive Science Society}, year = {2010}, editor = {S. Ohlsson and R. Catrambone}, publisher = {Cognitive Science Society}, pages = {308-313}, address = {Austin, TX}, comment = {Acceptance rate 30\%}, owner = {aolney}, timestamp = {2010.03.11}, } @InProceedings{DMello2010, Title = {Collaborative Lecturing by Human and Computer Tutors}, Author = {Sidney K. D'Mello and Patrick Hays and Claire Williams and Whitney Cade and Jennifer Brown and Olney, Andrew M.}, Booktitle = {Intelligent Tutoring Systems}, Year = {2010}, Address = {Berlin}, Pages = {178-187}, Publisher = {Springer}, Series = {Lecture Notes in Computer Science}, Comment = {Acceptance rate 30\%}, Owner = {aolney}, Timestamp = {2010.03.11} } @Article{DMello2012, Title = {Gaze tutor: A gaze-reactive intelligent tutoring system}, Author = {Sidney K. D'Mello and Andrew Olney and Claire Williams and Patrick Hays}, Journal = {International Journal of Human-Computer Studies}, Year = {2012}, Number = {5}, Pages = {377 - 398}, Volume = {70}, Doi = {10.1016/j.ijhcs.2012.01.004}, ISSN = {1071-5819}, Keywords = {Affective computing}, Url = {http://www.sciencedirect.com/science/article/pii/S1071581912000250} } @InCollection{DMello2013a, Title = {Affect, Meta-affect, and Affect Regulation During Complex Learning}, Author = {D'Mello, Sidney K. and Strain, Amber Chauncey and Olney, Andrew and Graesser, Art}, Booktitle = {International Handbook of Metacognition and Learning Technologies}, Publisher = {Springer New York}, Year = {2013}, Editor = {Azevedo, Roger and Aleven, Vincent}, Pages = {669-681}, Series = {Springer International Handbooks of Education}, Volume = {26}, Doi = {10.1007/978-1-4419-5546-3_44}, ISBN = {978-1-4419-5545-6}, Language = {English}, Url = {http://dx.doi.org/10.1007/978-1-4419-5546-3_44} } @InProceedings{Donnelly2016, author = {Donnelly, Patrick J. and Blanchard, Nathan and Samei, Borhan and Olney, Andrew M. and Sun, Xiaoyi and Ward, Brooke and Kelly, Sean and Nystrand, Martin and D'Mello, Sidney K.}, title = {Automatic Teacher Modeling from Live Classroom Audio}, booktitle = {Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization}, year = {2016}, series = {UMAP '16}, publisher = {ACM}, location = {Halifax, Nova Scotia, Canada}, isbn = {978-1-4503-4368-8}, pages = {45--53}, doi = {10.1145/2930238.2930250}, acmid = {2930250}, address = {New York, NY, USA}, comment = {Acceptance rate 24\%}, keywords = {automatic feedback, classroom discourse, dialogic instruction, educational data mining, speech recognition}, numpages = {9}, } @InProceedings{Forsyth2015, Title = {Moody Agents: Affect and Discourse During Learning in a Serious Game}, Author = {Forsyth, Carol M. and Graesser, Arthur and Olney, Andrew M. and Millis, Keith and Walker, Breya and Cai, Zhiqiang}, Booktitle = {Artificial Intelligence in Education}, Year = {2015}, Editor = {Conati, Cristina and Heffernan, Neil and Mitrovic, Antonija and Verdejo, M. Felisa}, Pages = {135-144}, Publisher = {Springer International Publishing}, Series = {Lecture Notes in Computer Science}, Volume = {9112}, Comment = {Acceptance rate 29\%}, Doi = {10.1007/978-3-319-19773-9_14}, ISBN = {978-3-319-19772-2}, Keywords = {Discourse; Serious game; Emotion; Intelligent tutoring systems; Learning}, Language = {English}, Url = {http://dx.doi.org/10.1007/978-3-319-19773-9_14} } @InProceedings{Goedecke2015, Title = {Breaking Off Engagement: Readers' Disengagement as a Function of Reader and Text Characteristics}, Author = {Patricia J. Goedecke and Daqi Dong and Genghu Shi and Shi Feng and Evan Risko and Andrew M. Olney and Sidney K. D'Mello and Arthur C. Graesser}, Booktitle = {Proceedings of the 8th International Conference on Educational Data Mining}, Year = {2015}, Editor = {Olga C. Santos and Jesus G. Boticario and Cristobal Romero and Mykola Pechenizkiy and Agathe Merceron and Piotr Mitros and Jos\'{e} Mar\'{i}a Luna and Cristian Mihaescu and Pablo Moreno and Arnon Hershkovitz and Sebastian Ventura and and Michel Desmarais}, Pages = {448--451}, Publisher = {International Educational Data Mining Society}, Comment = {Acceptance rate 36\% as short paper}, Owner = {aolney}, Timestamp = {2015.07.04} } @InProceedings{Graesser2005c, Title = {{AutoTutor's} Coverage of Expectations During Tutorial Dialogue}, Author = {Graesser, A. and Olney, A. and Ventura, M. and Jackson, G. T.}, Booktitle = {Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference}, Year = {2005}, Address = {Menlo Park, CA}, Pages = {518-523}, Publisher = {AAAI Press}, Comment = {Acceptance rate 49\%} } @InCollection{Graesser2016, author = {Arthur C. Graesser and Zhiqiang Cai and Whitney O. Baer and Andrew M. Olney and Xiangen Hu and Megan Reed and Daphne Greenberg}, booktitle = {Adaptive Educational Technologies for Literacy Instruction.}, title = {Reading Comprehension Lessons in {AutoTutor} for the {Center for the Study of Adult Literacy}}, editor = {Crossley, Scott A. and {McNamara}, Danielle S.}, isbn = {978-1-138-12543-8}, note = {{DOI}: 10.4324/9781315647500 {DOI}: 10.4324/9781315647500}, pages = {288--293}, publisher = {Routledge}, month = jun, owner = {aolney}, pagetotal = {-1}, timestamp = {2016.06.13}, year = {2016}, } @Article{Graesser2005, author = {Graesser, Arthur C. and Chipman, Patrick and Haynes, Brian and Olney, Andrew M.}, title = {{AutoTutor}: An Intelligent Tutoring System with Mixed-Initiative Dialogue}, journal = {IEEE Transactions on Education}, year = {2005}, volume = {48}, number = {4}, month = nov, pages = {612- 618}, owner = {aolney}, timestamp = {2007.05.21}, } @InCollection{Graesser2011, author = {Graesser, Arthur C. and Conley, Mark W. and Olney, Andrew}, title = {Intelligent tutoring systems.}, booktitle = {{APA} educational psychology handbook, Vol 3: Application to teaching and learning}, year = {2011}, editor = {K. R. Harris and S. Graham and T. Urdan and A. G. Bus and S. Major and H. L. Swanson}, publisher = {American Psychological Association}, isbn = {1-4338-0999-0 {(Hardcover);} 978-1-4338-0999-6 {(Hardcover)}}, pages = {451--473}, address = {Washington, {DC}, {US}}, keywords = {human tutoring, intelligent tutoring systems, pedagogical theories}, } @InCollection{Graesser2012a, Title = {{AutoTutor}}, Author = {Graesser, Arthur C. and D'Mello, Sidney K. and Hu, Xiangen and Cai, Zhiquiang and Olney, Andrew and Morgan, Brent}, Booktitle = {Applied Natural Language Processing: Identification, Investigation, and Resolution.}, Publisher = {IGI Global}, Year = {2012}, Address = {Hershey, PA}, Editor = {P. McCarthy and C. Boonthum-Denecke}, Pages = {169-187}, Owner = {aolney}, Timestamp = {2011.03.03} } @InProceedings{Graesser2002, Title = {Implementing Latent Semantic Analysis in Learning Environments with Conversational Agents and Tutorial Dialog}, Author = {Graesser, A. C. and Hu, X. and Olde, B. A. and Ventura, M. and Olney, A. and Louwerse, M. and Franceschetti, D. R. and Person, N. K.}, Booktitle = {Proceedings of the 24rd Annual Conference of the Cognitive Science Society}, Year = {2002}, Address = {Mahwah, NJ}, Editor = {Gray, W. G. and Schunn, C. D.}, Pages = {37}, Publisher = {Erlbaum} } @InProceedings{Graesser2003a, Title = {{Why/AutoTutor}: A Test of Learning Gains from a Physics Tutor with Natural Language Dialog}, Author = {Graesser, A. C. and Jackson, G. T. and Mathews, E. C. and Mitchell, H. H. and Olney, A. and Ventura, M. and Chipman, P. and Franceschetti, D. and Hu, X. and Louwerse, M. M. and Person, N. K. and TRG}, Booktitle = {Proceedings of the 25rd Annual Conference of the Cognitive Science Society}, Year = {2003}, Address = {Boston, MA}, Editor = {Alterman, R. and Hirsh, D.}, Pages = {1-5}, Publisher = {Cognitive Science Society}, Comment = {Acceptance rate 23\%} } @InCollection{Graesser2007, Title = {Inference Generation and Cohesion in the Construction of Situation Models: Some Connections with Computational Linguistics}, Author = {Graesser, A. C. and Louwerse, M. M. and McNamara, D. and Olney, A. and Cai, Z. and Mitchell, H.}, Booktitle = {Higher Level Language Processes in the Brain: Inferences and Comprehension Processes}, Publisher = {Erlbaum}, Year = {2007}, Address = {Mahwah, NJ}, Editor = {Schmalhofer, F. and Perfetti, C.}, Pages = {289-310} } @Article{Graesser2004, Title = {{AutoTutor}: A Tutor with Dialogue in Natural Language}, Author = {Graesser, Arthur C. and Lu, Shulan and Jackson, G. Tanner and Mitchell, Heather and Ventura, Mathew and Olney, Andrew and Louwerse, Max M.}, Journal = {Behavioral Research Methods, Instruments, and Computers}, Year = {2004}, Pages = {180-193}, Volume = {36} } @InProceedings{Graesser2003b, Title = {{AutoTutor} Improves Deep Learning of Computer Literacy: Is It the Dialog or the Talking Head?}, Author = {Graesser, A. C. and Moreno, K. and Marineau, J. and Adcock, A. and Olney, A. and Person, N.}, Booktitle = {Proceedings of Artificial Intelligence in Education}, Year = {2003}, Address = {Amsterdam}, Editor = {Hoppe, U. and Verdejo, F. and Kay, J.}, Pages = {47-54}, Publisher = {IOS Press}, Comment = {Acceptance rate 32\% approx} } @InCollection{Graesser2005b, Title = {{AutoTutor}: A Cognitive System That Simulates a Tutor That Facilitates Learning Through Mixed-Initiaive Dialogue}, Author = {Graesser, Arthur C. and Olney, Andrew M. and Haynes, Brian C. and Chipman, Patrick}, Booktitle = {Cognitive Systems: Human Cognitive Models in Systems Design}, Publisher = {Erlbaum}, Year = {2005}, Address = {Mahwah, NJ}, Editor = {Forsythe, C. and Bernard, M. L. and Goldsmith, T. E.}, Pages = {177-212} } @InProceedings{Hu2003, Title = {A Revised Algorithm for Latent Semantic Analysis}, Author = {Hu, X. and Cai, Z. and Louwerse, M. and Olney, A. and Penumatsa, P. and Graesser, A. C. and TRG}, Booktitle = {Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence}, Year = {2003}, Address = {San Francisco}, Editor = {Gottlob, G. and Walsh, T.}, Pages = {1489-1491}, Publisher = {Morgan Kaufmann}, Comment = {Acceptance rate 28\% as poster} } @InProceedings{Jackson2003, Title = {Modeling Student Performance to Enhance the Pedagogy of {AutoTutor}}, Author = {Jackson, G. T. and Mathews, E. C. and Lin, D. and Olney, A. and Graesser, A. C.}, Booktitle = {User Modeling}, Year = {2003}, Editor = {Brusilovsky, Peter and Corbett, Albert T. and de Rosis, Fiorella}, Pages = {368-372}, Publisher = {Springer}, Comment = {Acceptance rate 25\%} } @InProceedings{Jackson2006, author = {George T. Jackson and Andrew Olney and Arthur C. Graesser and Hyun-Jeong J. Kim}, title = {{AutoTutor 3-D} Simulations: Analyzing Users' Actions and Learning Trends}, booktitle = {Proceedings of the 28th Annual Meetings of the Cognitive Science Society}, year = {2006}, editor = {Ron Sun}, publisher = {Erlbaum}, pages = {1557-1562}, address = {Mahwah, NJ}, comment = {Acceptance rate 71\% as poster}, owner = {aolney}, timestamp = {2011.03.03}, } @InCollection{Jeuniaux2012, Title = {Practical Programming for {NLP}}, Author = {Patrick Jeuniaux and Olney, Andrew M. and Sidney D'Mello}, Booktitle = {Applied Natural Language Processing: Identification, Investigation, and Resolution}, Publisher = {IGI Global}, Year = {2012}, Address = {Hershey, PA}, Editor = {P. McCarthy and C. Boonthum-Denecke}, Pages = {122-156}, Owner = {aolney}, Timestamp = {2010.03.11} } @INPROCEEDINGS{Kim2005, author = {Kim, H. J. and Graesser, A. and Jackson, G. T. and Olney, A. and Chipman, P.}, title = {Computer Simulation as an Instructional Technology in {AutoTutor}}, booktitle = {Artificial Intelligence in Education: Supporting Learning Through Intelligent and Socially Informed Technology}, year = {2005}, editor = {Looi, C. and McCalla, G. and Bredeweg, B. and Breuker, J.}, pages = {845-847}, address = {Amsterdam}, publisher = {IOS Press}, comment = {Acceptance rate 31\%}, owner = {aolney}, timestamp = {2009.03.01} } @InProceedings{Kim2005a, Title = {The Effectiveness of Computer Simulations in a Computer-Based Learning Environment}, Author = {Kim, H. J. and Graesser, A. and Jackson, G. T. and Olney, A. and Chipman, P.}, Booktitle = {Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2005}, Year = {2005}, Address = {Chesapeake, VA}, Editor = {G. Richards}, Pages = {1362--1367}, Publisher = {AACE}, Comment = {Acceptance rate 43\%} } @InProceedings{Lehman2010, Title = {Off Topic Conversation in Expert Tutoring: Waste of Time or Learning Opportunity?}, Author = {Lehman, Blair and Cade, Whitney and Olney, Andrew M.}, Booktitle = {Proceedings of the 3rd International Conference on Educational Data Mining}, Year = {2010}, Editor = {Baker, R.S.J.d. and Merceron, A. and Pavlik, P.I. Jr.}, Pages = {101-110}, Comment = {Acceptance rate 43\%}, Owner = {aolney}, Timestamp = {2010.03.11} } @InProceedings{Li2014, Title = {Question classification in an epistemic game}, Author = {Haiying Li and Borhan Samei and Andrew M. Olney and Arthur C. Graesser and David W. Shaffer}, Booktitle = {International Conference on Intelligent Tutoring Systems}, Year = {2014}, Owner = {aolney}, Timestamp = {2014.06.05} } @InProceedings{Louwerse2002, Title = {Good Computational Manners: Mixed-Initiative Dialog in Conversational Agents}, Author = {Louwerse, M. M. and Graesser, A. C. and Olney, A. and the Tutoring Research Group}, Booktitle = {Etiquette for Human-Computer Work: Papers from the {AAAI} Fall Symposium}, Year = {2002}, Editor = {Miller, C.}, Pages = {71-76}, Publisher = {AAAI Press} } @InProceedings{Marineau2002, Title = {{AutoTutor's} Log Files and Categories of Language and Discourse}, Author = {Marineau, J. and Olney, A. and Louwerse, M. and Person, N. and Olde, B. and Susarla, S. and Chipman, P. and Graesser, A. C. and TRG}, Booktitle = {Workshop Proceedings of Empirical Methods for Tutorial Dialogue Systems at {ITS} 2002}, Year = {2002}, Address = {San Sebastian, Spain}, Editor = {Rose, C. P. and Eleven, V.}, Pages = {85-92}, Comment = {Acceptance rate 32\% approx} } @InProceedings{Mathews2003, Title = {Achieving Domain Independence in {AutoTutor}}, Author = {Mathews, E. C. and Jackson, G. T. and Olney, A. and Chipman, P. and Graesser, A. C.}, Booktitle = {Proceedings of the Seventh World Multiconference on Systemics, Cybernetics, and Informatics}, Year = {2003}, Address = {Orlando}, Editor = {Callaos, N. and Margenstern, M. and Zhang, J. and Castillo, O. and Doberkat, E.}, Pages = {172-176}, Publisher = {IIIS}, Comment = {Acceptance rate 35\% approx} } @Article{Medimorec2015, Title = {The Language of Instruction: Compensating for Challenge in Lectures}, Author = {Srdan Medimorec and Phil Pavlik and Andrew Olney and Art Graesser and Evan F. Risko}, Journal = {Journal of Educational Psychology}, Year = {2015}, Number = {4}, Pages = {971--990}, Volume = {107}, Doi = {10.1037/edu0000024}, Owner = {aolney}, Timestamp = {2014.12.17} } @InProceedings{Mills2015, Title = {Mind Wandering During Learning with an Intelligent Tutoring System}, Author = {Mills, Caitlin and D'Mello, Sidney and Bosch, Nigel and Olney, Andrew M.}, Booktitle = {Artificial Intelligence in Education}, Year = {2015}, Editor = {Conati, Cristina and Heffernan, Neil and Mitrovic, Antonija and Verdejo, M. Felisa}, Pages = {267-276}, Publisher = {Springer International Publishing}, Series = {Lecture Notes in Computer Science}, Volume = {9112}, Comment = {Acceptance rate 29\%}, Doi = {10.1007/978-3-319-19773-9_27}, ISBN = {978-3-319-19772-2}, Keywords = {Mind wandering; Intelligent tutoring; Engagement; Attention}, Language = {English}, Url = {http://dx.doi.org/10.1007/978-3-319-19773-9_27} } @InProceedings{Nye2015, Title = {Evaluating the Effectiveness of Integrating Natural Language Tutoring into an Existing Adaptive Learning System}, Author = {Nye, Benjamin D. and Windsor, Alistair and Pavlik, Phillip and Olney, Andrew and Hajeer, Mustafa and Graesser, Arthur C. and Hu, Xiangen}, Booktitle = {Artificial Intelligence in Education}, Year = {2015}, Editor = {Conati, Cristina and Heffernan, Neil and Mitrovic, Antonija and Verdejo, M. Felisa}, Pages = {743-747}, Publisher = {Springer International Publishing}, Series = {Lecture Notes in Computer Science}, Volume = {9112}, Comment = {Acceptance rate 56\% as short paper}, Doi = {10.1007/978-3-319-19773-9_106}, ISBN = {978-3-319-19772-2}, Keywords = {Intelligent Tutoring Systems; Natural language tutoring; Mathematics education; Worked examples; Isomorphic examples}, Language = {English}, Url = {http://dx.doi.org/10.1007/978-3-319-19773-9_106} } @InProceedings{Olney2010a, Title = {Extraction of Concept Maps from Textbooks for Domain Modeling}, Author = {Olney, Andrew M.}, Booktitle = {Intelligent Tutoring Systems}, Year = {2010}, Editor = {Aleven, Vincent and Kay, Judy and Mostow, Jack}, Pages = {390-392}, Publisher = {Springer Berlin / Heidelberg}, Series = {Lecture Notes in Computer Science}, Volume = {6095}, Affiliation = {University of Memphis Memphis TN 38152 USA}, Comment = {Acceptance rate 32\% approx}, Url = {http://dx.doi.org/10.1007/978-3-642-13437-1\_80} } @InProceedings{Olney2010c, author = {Olney, Andrew M.}, title = {Likability-Based Genres: Analysis and Evaluation of the {Netflix} Dataset}, booktitle = {{Proceedings of the 32nd Annual Conference of the Cognitive Science Society}}, year = {2010}, editor = {S. Ohlsson and R. Catrambone}, publisher = {Cognitive Science Society}, pages = {37--42}, address = {Austin, TX}, comment = {Acceptance rate 30\%}, owner = {aolney}, timestamp = {2010.03.11}, } @InCollection{Olney2014, author = {Olney, Andrew M.}, title = {Scaffolding Made Visible}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Instructional Management}, year = {2014}, editor = {Robert A. Sottilare and Arthur C. Graesser and Xiangen Hu and Benjamin Goldberg}, volume = {2}, series = {Adaptive Tutoring}, publisher = {Army Research Laboratory}, isbn = {978-0-9893923-3-4}, chapter = {26}, pages = {327--340}, address = {Orlando, FL}, owner = {aolney}, timestamp = {2014.06.05}, } @Article{Olney2013, Title = {Predicting film genres with implicit ideals}, Author = {Olney, Andrew McGregor}, Journal = {Frontiers in Psychology}, Year = {2013}, Number = {565}, Volume = {3}, Abstract = {We present a new approach to defining film genre based on implicit ideals. When viewers rate the likability of a film, they indirectly express their ideal of what a film should be. Across six studies we investigate the category structure that emerges from likability ratings and the category structure that emerges from the features of film. We further compare these data-driven category structures with human annotated film genres. We conclude that film genres are structured more around ideals than around features of film. This finding lends experimental support to the notion that film genres are set of shifting, fuzzy, and highly contextualized psychological categories.}, Doi = {10.3389/fpsyg.2012.00565}, ISSN = {1664-1078}, Url = {http://www.frontiersin.org/cognitive_science/10.3389/fpsyg.2012.00565/abstract} } @Article{Olney2013c, Title = {Symbolic, indexical, and iconic communication with domestic dogs}, Author = {Andrew M. Olney}, Journal = {Humana.Mente Journal of Philosophical Studies}, Year = {2013}, Pages = {79-98}, Volume = {24}, Owner = {aolney}, Timestamp = {2013.09.27} } @Article{Olney2011a, Title = {Large Scale Latent Semantic Analysis}, Author = {Andrew M. Olney}, Journal = {Behavior Research Methods}, Year = {2011}, Number = {2}, Pages = {414-423}, Volume = {43}, Owner = {aolney}, Timestamp = {2010.03.11} } @InProceedings{Olney2009, author = {Olney, Andrew M.}, title = {{GnuTutor}: An Open Source Intelligent Tutoring System Based on {AutoTutor}}, booktitle = {Proceedings of the 2009 AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems}, year = {2009}, publisher = {AAAI Press}, month = nov, pages = {70-75}, address = {Washington, DC}, comment = {Acceptance rate 35\% approx}, owner = {aolney}, timestamp = {2010.01.09}, } @InProceedings{Olney2009a, Title = {{GnuTutor}: An open source intelligent tutoring system}, Author = {Olney, Andrew M.}, Booktitle = {Proceedings of the 14th International Conference on Artificial Intelligence in Education}, Year = {2009}, Address = {Brighton UK}, Pages = {803}, Publisher = {Amsterdam: IOS Press}, Comment = {Acceptance rate 32\% approx}, Owner = {aolney}, Timestamp = {2010.01.29} } @InProceedings{Olney2009b, author = {Olney, Andrew M.}, title = {Generalizing Latent Semantic Analysis}, booktitle = {{IEEE International Conference on Semantic Computing}}, year = {2009}, publisher = {IEEE Computer Society}, isbn = {978-0-7695-3800-6}, pages = {40-46}, address = {Los Alamitos, CA, USA}, comment = {Acceptance rate 30\%}, } @InProceedings{Olney2007, Title = {Multi-robot Dispatch}, Author = {Olney, Andrew M.}, Booktitle = {{Proceedings of the International Joint Conference on Artificial Intelligence 5th workshop on Knowledge and Reasoning in Practical Dialogue Systems}}, Year = {2007}, Address = {Hyderabad, India}, Pages = {42-45}, Comment = {Acceptance rate 16\%}, Timestamp = {2006.10.26} } @InProceedings{Olney2007a, Title = {Dialogue Generation for Robotic Portraits}, Author = {Olney, Andrew M.}, Booktitle = {{Proceedings of the International Joint Conference on Artificial Intelligence 5th workshop on Knowledge and Reasoning in Practical Dialogue Systems}}, Year = {2007}, Address = {Hyderabad, India}, Pages = {15-21}, Comment = {Acceptance rate 16\%}, Timestamp = {2006.10.26} } @InProceedings{Olney2007b, Title = {Latent Semantic Grammar Induction: Context, Projectivity, and Prior Distributions}, Author = {Olney, Andrew M}, Booktitle = {{Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms for Natural Language Processing}}, Year = {2007}, Address = {Rochester, NY, USA}, Pages = {45--52}, Publisher = {Association for Computational Linguistics}, Comment = {Acceptance rate 24\%}, Owner = {aolney}, Timestamp = {2009.03.01}, } @InProceedings{Olney2007c, Title = {Semantic heads for grammar induction}, Author = {Olney, A. M.}, Booktitle = {Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition.}, Year = {2007}, Address = {Nashville, TN.}, Pages = {13-15}, Comment = {Acceptance rate 69\% as poster}, Owner = {aolney}, Timestamp = {2010.06.24} } @PhdThesis{Olney2006, Title = {Unsupervised induction of latent semantic grammars with application to parsing}, Author = {Olney, Andrew M.}, School = {University of Memphis}, Year = {2006}, Month = aug, Owner = {aolney}, Timestamp = {2006.10.28} } @InCollection{Olney2015, author = {Andrew M. Olney and Keith Brawner and Phillip Pavlik and Kenneth R. Koedinger}, title = {Emerging Trends in Automated Authoring}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Authoring Tools \& Expert Modeling Techniques}, year = {2015}, editor = {Robert Sottilare and Arthur Graesser and Xiangen Hu and Keith Brawner}, volume = {3}, series = {Adaptive Tutoring}, publisher = {U.S. Army Research Laboratory}, isbn = {978-0-9893923-7-2}, chapter = {19}, pages = {227--242}, address = {Orlando, FL}, owner = {aolney}, timestamp = {2015.02.15}, } @InProceedings{Olney2011, author = {Olney, Andrew M. and Cade, Whitney and Williams, Claire}, title = {Generating Concept Map Exercises from Textbooks}, booktitle = {Proceedings of the {Sixth Workshop on Innovative Use of NLP for Building Educational Applications}}, year = {2011}, publisher = {Association for Computational Linguistics}, month = jun, pages = {111--119}, address = {Portland, Oregon}, comment = {Acceptance rate 26\%}, } @InProceedings{Olney2015a, author = {Olney, Andrew M. and Cade, Whitney L.}, title = {Authoring Intelligent Tutoring Systems Using Human Computation: Designing for Intrinsic Motivation}, booktitle = {Foundations of Augmented Cognition}, year = {2015}, editor = {Schmorrow, Dylan D. and Fidopiastis, Cali M.}, language = {English}, volume = {9183}, series = {Lecture Notes in Computer Science}, publisher = {Springer International Publishing}, isbn = {978-3-319-20815-2}, pages = {628-639}, doi = {10.1007/978-3-319-20816-9_60}, keywords = {Authoring; Intelligent tutoring system; Human computation; Motivation}, } @InCollection{Olney2013a, author = {Andrew M. Olney and Whitney L. Cade}, title = {Matching Learner Models to Instructional Strategies}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Learner Modeling}, year = {2013}, editor = {Robert Sottilare and Arthurt Graesser and Xiangen Hu and Heather Holden}, volume = {1}, series = {Adapive Tutoring}, publisher = {U.S. Army Research Laboratory}, isbn = {978-0-9893923-0-3}, chapter = {4}, pages = {23--38}, address = {Orlando}, owner = {aolney}, timestamp = {2013.09.27}, } @InProceedings{Olney2005, Title = {An Orthonormal Basis for Entailment}, Author = {Olney, Andrew M. and Cai, Zhiqiang}, Booktitle = {{Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference}}, Year = {2005}, Address = {Menlo Park, CA}, Pages = {554-559}, Publisher = {AAAI Press}, Comment = {Acceptance rate 49\%}, Owner = {aolney}, Timestamp = {2009.03.01} } @InProceedings{Olney2005a, Title = {An Orthonormal Basis for Topic Segmentation in Tutorial Dialogue}, Author = {Olney, Andrew M. and Cai, Zhiqiang}, Booktitle = {{Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing}}, Year = {2005}, Address = {Philadelphia}, Pages = {971-978}, Publisher = {Association for Computational Linguistics}, Comment = {Acceptance rate 32\%}, Owner = {aolney}, Timestamp = {2009.03.01} } @InProceedings{Olney2010, Title = {A {DIY} Pressure Sensitive Chair for Intelligent Tutoring Systems}, Author = {Olney, Andrew M. and D'Mello, Sidney}, Booktitle = {Intelligent Tutoring Systems}, Year = {2010}, Editor = {Aleven, Vincent and Kay, Judy and Mostow, Jack}, Pages = {456}, Publisher = {Springer Berlin / Heidelberg}, Series = {Lecture Notes in Computer Science}, Volume = {6095}, Affiliation = {University of Memphis Memphis TN 38152 USA}, Comment = {Acceptance rate 30\%}, Url = {http://dx.doi.org/10.1007/978-3-642-13437-1_113} } @InProceedings{Olney2012a, author = {Olney, Andrew M. and D'Mello, Sidney K. and Person, Natalie and Cade, Whitney and Hays, Patrick and Williams, Claire and Lehman, Blair and Graesser, Arthur}, title = {Guru: A Computer Tutor That Models Expert Human Tutors}, booktitle = {Intelligent Tutoring Systems}, year = {2012}, editor = {Cerri, Stefano and Clancey, William and Papadourakis, Giorgos and Panourgia, Kitty}, volume = {7315}, series = {Lecture Notes in Computer Science}, publisher = {Springer Berlin / Heidelberg}, isbn = {978-3-642-30949-6}, pages = {256-261}, affiliation = {University of Memphis, USA}, comment = {Acceptance rate 44\% as short paper}, keyword = {Computer Science}, owner = {aolney}, timestamp = {2012.05.30}, } @Article{Olney2012c, Title = {The World within {Wikipedia}: An Ecology of Mind}, Author = {Andrew M. Olney and Rick Dale and Sidney D'Mello}, Journal = {Information}, Year = {2012}, Pages = {229-255}, Volume = {3}, Owner = {aolney}, Timestamp = {2012.06.01} } @InCollection{Olney2016, author = {Olney, Andrew M. and Graesser, Arthur}, title = {Design and Construction of Domain Models}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Domain Modeling}, year = {2016}, editor = {Sottilare, Robert. and Graesser, Arthur and Hu, Xiangen and Olney, Andrew and Nye, Benjamin and Sinatra, Anne}, volume = {4}, series = {Adaptive Tutoring}, note = {Available at: https://gifttutoring.org/documents/}, publisher = {U.S. Army Research Laboratory}, isbn = {978-0-9893923-9-6}, chapter = {7}, pages = {93--96}, address = {Orlando, FL}, owner = {aolney}, timestamp = {2016.07.15}, } @InCollection{Olney2010b, Title = {Tutorial Dialog in Natural Language}, Author = {Olney, Andrew M. and Graesser, Arthur C. and Person, Natalie K.}, Booktitle = {Advances in Intelligent Tutoring Systems}, Publisher = {Springer-Verlag}, Year = {2010}, Address = {Berlin}, Editor = {Nkambou, R. and Bourdeau, J. and Mizoguchi, R.}, Pages = {181-206}, Series = {Studies in Computational Intelligence}, Volume = {308}, Owner = {aolney}, Timestamp = {2010.06.24} } @Article{Olney2012b, author = {Olney, Andrew M. and Graesser, Arthur C. and Person, Natalie K.}, title = {Question Generation from Concept Maps}, journal = {Dialogue and Discourse}, year = {2012}, volume = {3}, number = {2}, pages = {75--99}, } @Misc{Olney2012d, Title = {Agentpalooza: Rapid Creation and Deployment of Embodied Conversational Agents}, Author = {Andrew M. Olney and David Patrick Hays and Whitney L. Cade}, HowPublished = {Tutorial at the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)}, Month = {July}, Year = {2012}, Owner = {aolney}, Timestamp = {2012.07.21} } @InProceedings{Olney2013b, Title = {{XNAgent}: Authoring Embodied Conversational Agents for Tutor-User Interfaces}, Author = {Andrew M. Olney and Patrick Hays and Whitney L. Cade}, Booktitle = {Proceedings of the Workshops at the 16th International Conference on Artificial Intelligence in Education ({AIED} 2013)}, Year = {2013}, Editor = {Erin Walker and Chee-Kit Looi}, Month = {July}, Pages = {137--145}, Volume = {7}, Owner = {aolney}, Timestamp = {2013.09.27} } @InProceedings{Olney2003, author = {Olney, Andrew M. and Louwerse, Max and Mathews, Eric and Marineau, Johanna and Hite-Mitchell, Heather and Graesser, Arthur C.}, title = {Utterance Classification in {AutoTutor}}, booktitle = {Proceedings of the {HLT}-{NAACL} 03 Workshop on Building Educational Applications Using Natural Language Processing}, year = {2003}, publisher = {Association for Computational Linguistics}, pages = {1-8}, address = {Philadelphia}, comment = {Acceptance rate 23\%}, } @InCollection{Olney2012, author = {Olney, Andrew M. and Person, Natalie K. and Graesser, Arthur C.}, title = {Guru: Designing a Conversational Expert Intelligent Tutoring System}, booktitle = {Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches}, year = {2012}, editor = {P. McCarthy and C. Boonthum-Denecke and T. Lamkin}, publisher = {IGI Global}, pages = {156-171}, address = {Hershey, PA}, owner = {aolney}, timestamp = {2010.03.11}, } @InProceedings{Olney2002, Title = {{AutoTutor}: A Conversational Tutoring Environment}, Author = {Olney, Andrew M. and Person, Natalie K. and Louwerse, Max and Graesser, Arthur C.}, Booktitle = {Proceedings of the {ACL}-02 Demonstration Session}, Year = {2002}, Address = {Philadelphia}, Pages = {108-109}, Publisher = {Association for Computational Linguistics}, Comment = {Acceptance rate 26\%} } @InCollection{Olney2015b, author = {Andrew M. Olney and Evan F. Risko and Sidney K. D'Mello and Arthur C. Graesser}, title = {Attention in Educational Contexts: The Role of the Learning Task in Guiding Attention}, booktitle = {The Handbook of Attention}, year = {2015}, editor = {Jonathan Fawcett and Evan F. Risko and Alan Kingstone}, publisher = {MIT Press}, pages = {623--642}, owner = {aolney}, timestamp = {2014.06.05}, } @InCollection{Pavlik2013, author = {Pavlik, Jr.,Philip I. and Keith Brawner and Andrew M. Olney and Antonija Mitrovic}, title = {A Review of Student Models Used in Intelligent Tutoring Systems}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Learner Modeling}, year = {2013}, editor = {Robert A. Sottilare and Arthur C. Graesser and Xiangen Hu and Heather Holden}, volume = {1}, series = {Adapive Tutoring}, publisher = {U.S. Army Research Laboratory}, isbn = {978-0-9893923-0-3}, chapter = {5}, pages = {39--68}, address = {Orlando}, owner = {aolney}, timestamp = {2013.09.27}, } @InProceedings{Pavlik2012, Title = {Facilitating Co-adaptation of Technology and Education through the Creation of an Open-Source Repository of Interoperable Code}, Author = {Pavlik, Philip and Maass, Jaclyn and Rus, Vasile and Olney, Andrew}, Booktitle = {Intelligent Tutoring Systems}, Year = {2012}, Editor = {Cerri, Stefano and Clancey, William and Papadourakis, Giorgos and Panourgia, Kitty}, Pages = {677-678}, Publisher = {Springer Berlin / Heidelberg}, Series = {Lecture Notes in Computer Science}, Volume = {7315}, Affiliation = {University of Memphis, Memphis, Tennessee, USA}, Comment = {Acceptance rate 76\% as poster}, ISBN = {978-3-642-30949-6}, Keyword = {Computer Science} } @InProceedings{Person2012, author = {Person, Natalie K. and Olney, Andrew and D'Mello, Sidney and Lehman, Blair}, title = {Interactive Concept Maps and Learning Outcomes in {Guru}}, booktitle = {Proceedings of the Twenty-Fifth International FLAIRS Conference}, year = {2012}, publisher = {AAAI Press}, pages = {456--461}, address = {Menlo Park, CA}, comment = {Acceptance rate 50\% approx}, owner = {aolney}, timestamp = {2012.05.30}, } @InCollection{Person2007, Title = {Toward Socially Intelligent Interviewing Systems}, Author = {Natalie K. Person and Sidney {D'Mello} and Andrew Olney}, Booktitle = {Envisioning the Survey Interview of the Future}, Year = {2007}, Editor = {Frederick G. Conrad and Michael F. Schober}, Pages = {195--214}, Url = {http://dx.doi.org/10.1002/9780470183373.ch10} } @InProceedings{Rasor2011, author = {Rasor, Travis and Olney, Andrew and D'Mello, Sidney}, title = {Student Speech Act Classification Using Machine Learning}, booktitle = {Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference}, year = {2011}, publisher = {AAAI Press}, month = may, pages = {275-280}, address = {Palm Beach, Florida}, comment = {Acceptance rate 50\% approx}, owner = {aolney}, timestamp = {2011.05.13}, } @InProceedings{Riordan2011, author = {Riordan, M. A. and Dale, R. and Kreuz, R. J. and Olney, A.}, title = {Evidence for alignment in a computer-mediated text-only environment.}, booktitle = {Proceedings of the 33rd {Annual Meeting of the Cognitive Science Society}}, year = {2011}, editor = {L. Carlson and C. Hoelscher and T. F. Shipley}, publisher = {Cognitive Science Society}, pages = {2411-2416}, address = {Austin, TX}, comment = {Acceptance rate 75\% as poster}, owner = {aolney}, timestamp = {2011.10.11}, } @Article{Riordan2014, author = {Riordan, Monica A. and Kreuz, Roger J. and Olney, Andrew M.}, title = {Alignment Is a Function of Conversational Dynamics}, journal = {Journal of Language and Social Psychology}, year = {2014}, volume = {33}, number = {5}, pages = {465--481}, doi = {10.1177/0261927X13512306}, eprint = {http://jls.sagepub.com/content/early/2013/11/27/0261927X13512306.full.pdf+html}, abstract = {Two prominent theories of alignment (priming and grounding) are tested in human--human text-only computer interactions. In two experiments, dyads of strangers and dyads of friends conducted conversations using Instant Messenger. These conversations were either neutral in nature or interlocutors were told to disagree on a particular topic. Conversations were assessed for paralinguistic, linguistic, semantic, affective, and typographical alignment. Results show distinct differences in alignment patterns dependent on conversational dynamics. Grounding theory is supported and discussion includes examining how nonverbal cues are translated into text-only conversation.}, } @InProceedings{Samei2015, author = {Borhan Samei and Andrew M. Olney and Sean Kelly and Nystrand, Martin and Sidney D'Mello and Nathan Blanchard and Art Graesser}, title = {Modeling Classroom Discourse: Do Models that Predict Dialogic Instruction Properties Generalize across Populations?}, booktitle = {Proceedings of the 8th International Conference on Educational Data Mining}, year = {2015}, editor = {Olga C. Santos and Jesus G. Boticario and Cristobal Romero and Mykola Pechenizkiy and Agathe Merceron and Piotr Mitros and Jos\'{e} Mar\'{i}a Luna and Cristian Mihaescu and Pablo Moreno and Arnon Hershkovitz and Sebastian Ventura and and Michel Desmarais}, publisher = {International Educational Data Mining Society}, pages = {444--447}, comment = {Acceptance rate 36\% as short paper}, owner = {aolney}, timestamp = {2015.07.04}, } @INPROCEEDINGS{Ventura2004, author = {Ventura, M. J. and Hu, X. and Graesser, A. C. and Louwerse, M. M. and Olney, A.}, title = {The Context Dependent Sentence Abstraction Model}, booktitle = {Proceedings of the 26rd Annual Meeting of the Cognitive Science Society}, year = {2004}, editor = {Forbus, K. D. and Gentner, D. and Regier, T.}, pages = {1387-1392}, address = {Mahwah, NJ}, publisher = {Erlbaum}, comment = {Acceptance rate 72\% as poster}, owner = {aolney}, timestamp = {2009.03.01} } @InProceedings{Samei2014, author = {Borhan Samei and Andrew M. Olney and Sean Kelly and Martin {Nystrand} and Sidney D'Mello and Nathan Blanchard and Xiaoyi Sun and Marci Glaus and Art Graesser}, title = {Domain Independent Assessment of Dialogic Properties of Classroom Discourse}, booktitle = {Proceedings of the 7th International Conference on Educational Data Mining}, year = {2014}, editor = {Stamper, J. and Pardos, Z. and Mavrikis, M. and McLaren, B.M.}, pages = {233--236}, comment = {acceptance rate 41\% as short paper}, owner = {aolney}, timestamp = {2014.06.05}, } @INPROCEEDINGS{Chipman2005, author = {Chipman, P. and Olney, A. and Graesser, A.}, title = {The {AutoTutor} 3 Architecture: A Software Architecture for an Expandable, High-Availability {ITS}}, booktitle = {Proceedings of {WEBIST} 2005: First International Conference on Web Information Systems and Technologies}, year = {2005}, editor = {Cordeiro, J. and Pedrosa, V. and Encarnacao, B. and Filipe, J.}, pages = {466-473}, address = {Portugal}, publisher = {INSTICC Press}, comment = {Acceptance rate 30\% approx}, owner = {aolney}, timestamp = {2009.03.01} } @Article{Shiva2005, Title = {Engineering agent-based software systems}, Author = {Shiva, Sajjan G. and Sherrell, Linda and Lee, Sarah and Olney, Andrew}, Journal = {Journal of Computing Sciences in Colleges}, Year = {2005}, Number = {6}, Pages = {28--28}, Volume = {20}, Address = {, USA}, ISSN = {1937-4771}, Publisher = {Consortium for Computing Sciences in Colleges} } @InProceedings{Snaider2011, author = {Javier Snaider and Andrew M. Olney and Natalie Person}, title = {Nonverbal Action Selection for Explanations Using an Enhanced Behavior Net}, booktitle = {Intelligent Virtual Agents}, year = {2011}, editor = {Hannes H\"{o}gni Vilhj\'{a}lmsson and Stefan Kopp and Stacy Marsella and Kristinn R. Th\'{o}risson}, volume = {6895}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, location = {Heidelberg}, isbn = {978-3-642-23973-1}, pages = {141--147}, comment = {Acceptance rate 49\% as short paper}, owner = {aolney}, timestamp = {2011.10.11}, } @Article{Song2004, Title = {A Framework of Synthesizing Tutoring Conversation Capability with Web Based Distance Education Courseware}, Author = {Song, K. and Hu, X. and Olney, A. and Graesser, A. C.}, Journal = {Computers \& Education}, Year = {2004}, Month = May, Number = {4}, Pages = {375-388}, Volume = {42} } @Article{VanLehn2007, Title = {When are Tutorial Dialogues More Effective than Reading?}, Author = {VanLehn, Kurt and Graesser, Arthur C. and Jackson, G. Tanner and Jordan, Pamela and Olney, Andrew and Rose, Carolyn}, Journal = {Cognitive Science}, Year = {2007}, Pages = {3-62}, Volume = {31}, Owner = {aolney}, Timestamp = {2007.04.13} } @InProceedings{VanLehn2005, Title = {When is Reading Just as Effective as One-on-One Interactive Tutoring?}, Author = {VanLehn, K. and Graesser, A. C. and Jackson, G. T. and Jordan, P. and Olney, A. and Rose, C. P.}, Booktitle = {Proceedings of the 27th Annual Meetings of the Cognitive Science Society}, Year = {2005}, Address = {Mahwah, NJ}, Editor = {Bara, B. and Barsalou, L. and Bucciarelli, M.}, Pages = {2259-2264}, Publisher = {Erlbaum}, Comment = {Acceptance rate 26\%} } @Article{Warlaumont2015, author = {Warlaumont, Anne S and Olney, Andrew M}, title = {Evolution of reflexive signals using a realistic vocal tract model}, journal = {Adaptive Behavior}, year = {2015}, volume = {23}, number = {4}, pages = {183-205}, doi = {10.1177/1059712315585941}, eprint = {http://adb.sagepub.com/content/23/4/183.full.pdf+html}, abstract = {We introduce a model of the evolution of reflexive primate signals that incorporates a realistic vocal tract model for generating the signals. Signaler neural networks receive signal types as inputs and produce vocal tract muscle activations as outputs. These muscle activations are input to a model of the primate vocal tract, generating real sounds. Receiver neural networks receive spectrograms of these sounds as inputs and produce signal type classifications as outputs. Incorporating a realistic vocal tract has a substantial effect on the types of signals that can evolve. Compared to a model with abstract signals, the realistic model signals are more similar and have more correlated elements. The realistic, embodied model also exhibits more variability in rate of adaptation, usually adapting more slowly. This may be explained by the more jagged fitness landscapes in the realistic model. The realistic signals also tend to be quiet. Environmental noise results in louder signals but makes the evolutionary process even slower and less robust. These results indicate that signal evolution with a more realistic genotype--phenotype mapping can differ substantially from evolution with abstract signals. Including realistic signal generation mechanisms may enable computational models to provide greater insights into natural signal evolution.}, } @Article{Westlund2015, author = {Westlund, Jacqueline Kory and D'Mello, Sidney K. and Olney, Andrew M.}, title = {{Motion Tracker}: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes}, journal = {PLoS ONE}, year = {2015}, volume = {10}, number = {6}, month = jun, pages = {e0130293}, doi = {10.1371/journal.pone.0130293}, abstract = {Researchers in the cognitive and affective sciences investigate how thoughts and feelings are reflected in the bodily response systems including peripheral physiology, facial features, and body movements. One specific question along this line of research is how cognition and affect are manifested in the dynamics of general body movements. Progress in this area can be accelerated by inexpensive, non-intrusive, portable, scalable, and easy to calibrate movement tracking systems. Towards this end, this paper presents and validates Motion Tracker, a simple yet effective software program that uses established computer vision techniques to estimate the amount a person moves from a video of the person engaged in a task (available for download from http://jakory.com/motion-tracker/). The system works with any commercially available camera and with existing videos, thereby affording inexpensive, non-intrusive, and potentially portable and scalable estimation of body movement. Strong between-subject correlations were obtained between Motion Trackers estimates of movement and body movements recorded from the seat (r =.720) and back (r = .695 for participants with higher back movement) of a chair affixed with pressure-sensors while completing a 32-minute computerized task (Study 1). Within-subject cross-correlations were also strong for both the seat (r =.606) and back (r = .507). In Study 2, between-subject correlations between Motion Trackers movement estimates and movements recorded from an accelerometer worn on the wrist were also strong (rs = .801, .679, and .681) while people performed three brief actions (e.g., waving). Finally, in Study 3 the within-subject cross-correlation was high (r = .855) when Motion Trackers estimates were correlated with the movement of a person's head as tracked with a Kinect while the person was seated at a desk (Study 3). Best-practice recommendations, limitations, and planned extensions of the system are discussed.}, owner = {aolney}, shorttitle = {Motion {Tracker}}, timestamp = {2018-01-02}, } @InProceedings{Willits2007, Title = {{Distributional statistics and thematic role relationships}}, Author = {Willits, J.A. and D'Mello, S.K. and Duran, N.D. and Olney, A.}, Booktitle = {{Proceedings of the 29th annual Conference of the Cognitive Science Society}}, Year = {2007}, Address = {Austin, TX.}, Editor = {D. S. McNamara and J. G. Trafton}, Pages = {707-712}, Publisher = {Cognitive Science Society}, Comment = {Acceptance rate 30\%} } @Book{DMello2013, title = {Proceedings of the 6th {International Conference on Educational Data Mining}}, year = {2013}, editor = {Sidney K. D'Mello and Raphael A. Calvo and Andrew M. Olney}, publisher = {International Educational Data Mining Society}, isbn = {978-0-9839525-2-7}, owner = {aolney}, timestamp = {2013.09.27}, } @Book{Sottilare2016, title = {Design Recommendations for Intelligent Tutoring Systems: Domain Modeling}, year = {2016}, editor = {Sottilare, Robert A. and Graesser, Arthur C. and Hu, Xiangen and Olney, Andrew and Nye, Benjamin and Sinatra, Anne M.}, volume = {4}, series = {Adaptive Tutoring}, note = {Available at: https://gifttutoring.org/documents/}, publisher = {U.S. Army Research Laboratory}, isbn = {978-0-9893923-9-6}, address = {Orlando, FL}, owner = {aolney}, timestamp = {2016.07.15}, } @InProceedings{Donnelly2017, author = {Donnelly, Patrick J. and Blanchard, Nathaniel and Olney, Andrew M. and Kelly, Sean and Nystrand, Martin and D'Mello, Sidney K.}, title = {Words Matter: Automatic Detection of Teacher Questions in Live Classroom Discourse Using Linguistics, Acoustics, and Context}, booktitle = {Proceedings of the Seventh International Learning Analytics \& Knowledge Conference}, year = {2017}, series = {LAK '17}, publisher = {ACM}, location = {Vancouver, British Columbia, Canada}, isbn = {978-1-4503-4870-6}, pages = {218--227}, doi = {10.1145/3027385.3027417}, acmid = {3027417}, address = {New York, NY, USA}, comment = {Acceptance rate 32\%}, keywords = {automatic speech recognition, classroom analytics, natural language processing, question detection}, numpages = {10}, } @InProceedings{DMello2015, author = {D'Mello, Sidney K. and Olney, Andrew M. and Blanchard, Nathan and Samei, Borhan and Sun, Xiaoyi and Ward, Brooke and Kelly, Sean}, title = {Multimodal Capture of Teacher-Student Interactions for Automated Dialogic Analysis in Live Classrooms}, booktitle = {Proceedings of the 2015 ACM on International Conference on Multimodal Interaction}, year = {2015}, series = {ICMI '15}, publisher = {ACM}, location = {Seattle, Washington, USA}, isbn = {978-1-4503-3912-4}, pages = {557--566}, doi = {10.1145/2818346.2830602}, acmid = {2830602}, address = {New York, NY, USA}, comment = {Acceptance rate 17\%}, keywords = {classroom discourse, dialogic instruction, multimodal}, numpages = {10}, } @InProceedings{Blanchard2016a, author = {Blanchard, Nathaniel and Donnelly, Patrick and Olney, Andrew M. and Borhan Samei and Ward, Brooke and Sun, Xiaoyi and Kelly, Sean and Nystrand, Martin and D'Mello, Sidney K.}, title = {Identifying Teacher Questions Using Automatic Speech Recognition in Classrooms}, booktitle = {Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue}, year = {2016}, publisher = {Association for Computational Linguistics}, pages = {191--201}, address = {Los Angeles}, comment = {Acceptance rate 52\%}, } @InProceedings{Donnelly2016a, author = {Donnelly, Patrick J. and Blanchard, Nathaniel and Samei, Borhan and Olney, Andrew M. and Sun, Xiaoyi and Ward, Brooke and Kelly, Sean and Nystrand, Martin and D'Mello, Sidney K.}, title = {Multi-sensor Modeling of Teacher Instructional Segments in Live Classrooms}, booktitle = {Proceedings of the 18th ACM International Conference on Multimodal Interaction}, year = {2016}, series = {ICMI 2016}, publisher = {ACM}, location = {Tokyo, Japan}, isbn = {978-1-4503-4556-9}, pages = {177--184}, doi = {10.1145/2993148.2993158}, acmid = {2993158}, address = {New York, NY, USA}, comment = {Acceptance rate 38\%}, keywords = {automatic feedback, classroom discourse, dialogic instruction, educational data mining, speech recognition}, numpages = {8}, } @InProceedings{Mills2017, author = {Mills, Caitlin and Fridman, Igor and Soussou, Walid and Waghray, Disha and Olney, Andrew M. and D'Mello, Sidney K.}, title = {Put Your Thinking Cap on: Detecting Cognitive Load Using {EEG} During Learning}, booktitle = {Proceedings of the Seventh International Learning Analytics \& Knowledge Conference}, year = {2017}, series = {LAK '17}, publisher = {ACM}, location = {Vancouver, British Columbia, Canada}, isbn = {978-1-4503-4870-6}, pages = {80--89}, doi = {10.1145/3027385.3027431}, acmid = {3027431}, address = {New York, NY, USA}, comment = {Acceptance rate 32\%}, keywords = {EEG, cognitive load, engagement, intelligent tutoring systems}, numpages = {10}, } @InProceedings{Olney2017, author = {Olney, Andrew M. and Bakhtiari, Dariush and Greenberg, Daphne and Graesser, Art}, title = {Assessing Computer Literacy of Adults with Low Literacy Skills}, booktitle = {Proceedings of the 10th International Conference on Educational Data Mining}, year = {2017}, editor = {Xiangen Hu and Tiffany Barnes and Arnon Hershkovitz and Luc Paquette}, pages = {128-134}, comment = {Acceptance rate 25\%}, } @InProceedings{Olney2017a, author = {Olney, Andrew M. and Samei, Borhan and Donnelly, Patrick J. and D'Mello, Sidney K.}, title = {Assessing the Dialogic Properties of Classroom Discourse: Proportion Models for Imbalanced Classes}, booktitle = {Proceedings of the 10th International Conference on Educational Data Mining}, year = {2017}, editor = {Xiangen Hu and Tiffany Barnes and Arnon Hershkovitz and Luc Paquette}, pages = {162--167}, comment = {Acceptance rate 32\% as short paper}, } @InProceedings{Olney2017b, author = {Olney, Andrew M. and Walker, Breya and Davis, Raven N. and Graesser, Art}, title = {The reading ability of college freshmen}, booktitle = {Proceedings of the 10th International Conference on Educational Data Mining}, year = {2017}, editor = {Xiangen Hu and Tiffany Barnes and Arnon Hershkovitz and Luc Paquette}, pages = {396--397}, comment = {Acceptance rate 57\% as poster}, } @InProceedings{Olney2017c, author = {Olney, Andrew M. and Hosman, Eric and Graesser, Art and D'Mello, Sidney K.}, title = {Tracking Online Reading of College Students}, booktitle = {Proceedings of the 10th International Conference on Educational Data Mining}, year = {2017}, editor = {Xiangen Hu and Tiffany Barnes and Arnon Hershkovitz and Luc Paquette}, pages = {406--407}, comment = {Acceptance rate 57\% as poster}, } @InProceedings{Olney2017d, author = {Olney, Andrew M. and Pavlik Jr., Philip J. and Maass, Jaclyn K.}, title = {Improving Reading Comprehension with Automatically Generated Cloze Item Practice}, booktitle = {Artificial Intelligence in Education}, year = {2017}, editor = {Andr{\'e}, Elisabeth and Baker, Ryan and Hu, Xiangen and Rodrigo, Ma Mercedes T and du Boulay, Benedict}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, pages = {262--273}, doi = {10.1007/978-3-319-61425-0}, comment = {Acceptance rate 30\%}, } @InCollection{Rus2017, author = {Vasile Rus and Olney, Andrew M. and Foltz, Peter W. Foltz and Xiangen Hu}, title = {Automated Assessment of Learner-Generated Natural Language Responses}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Assessment Methods}, year = {2017}, editor = {Sottilare, Robert. and Graesser, Arthur and Hu, Xiangen and Gregory Goodwin}, volume = {5}, series = {Adaptive Tutoring}, note = {Available at: https://gifttutoring.org/documents/}, publisher = {U.S. Army Research Laboratory}, isbn = {978-0-9977257-2-8}, chapter = {13}, pages = {155--170}, address = {Orlando, FL}, owner = {aolney}, timestamp = {2016.07.15}, } @InCollection{Olney2017e, author = {Olney, Andrew M. and Kelly, Sean and Samei, Borhan and Donnelly, Patrick and D'Mello, Sidney K.}, title = {Assessing Teacher Questions in Classrooms}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Assessment Methods}, year = {2017}, editor = {Sottilare, Robert. and Graesser, Arthur and Hu, Xiangen and Gregory Goodwin}, volume = {5}, series = {Adaptive Tutoring}, note = {Available at: https://gifttutoring.org/documents/}, publisher = {U.S. Army Research Laboratory}, isbn = {978-0-9977257-2-8}, chapter = {23}, pages = {261--274}, address = {Orlando, FL}, owner = {aolney}, timestamp = {2016.07.15}, } @Article{Nye2018, author = {Nye, Benjamin D. and Pavlik, Philip I. and Windsor, Alistair and Olney, Andrew M. and Hajeer, Mustafa and Hu, Xiangen}, title = {{SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors)}: overlaying natural language tutoring on an adaptive learning system for mathematics}, journal = {International Journal of STEM Education}, year = {2018}, volume = {5}, number = {1}, month = {4}, pages = {12}, doi = {10.1186/s40594-018-0109-4}, abstract = {This study investigated learning outcomes and user perceptions from interactions with a hybrid intelligent tutoring system created by combining the AutoTutor conversational tutoring system with the Assessment and Learning in Knowledge Spaces (ALEKS) adaptive learning system for mathematics. This hybrid intelligent tutoring system (ITS) uses a service-oriented architecture to combine these two web-based systems. Self-explanation tutoring dialogs were used to talk students through step-by-step worked examples to algebra problems. These worked examples presented an isomorphic problem to the preceding algebra problem that the student could not solve in the adaptive learning system. Due to crossover issues between conditions, experimental versus control condition assignment did not show significant differences in learning gains. However, strong dose-dependent learning gains were observed that could not be otherwise explained by either initial mastery or time-on-task. User perceptions of the dialog-based tutoring were mixed, and survey results indicate that this may be due to the pacing of dialog-based tutoring using voice, students judging the agents based on their own performance (i.e., the quality of their answers to agent questions), and the students’ expectations about mathematics pedagogy (i.e., expecting to solving problems rather than talking about concepts). Across all users, learning was most strongly influenced by time spent studying, which correlated with students’ self-reported tendencies toward effort avoidance, effective study habits, and beliefs about their ability to improve in mathematics with effort. Integrating multiple adaptive tutoring systems with complementary strengths shows some potential to improve learning. However, managing learner expectations during transitions between systems remains an open research area. Finally, while personalized adaptation can improve learning efficiency, effort and time-on-task for learning remains a dominant factor that must be considered by interventions.}, } @InCollection{Olney2018, author = {Olney, Andrew M.}, title = {The Unreasonable Effectiveness of {AutoTutor}}, booktitle = {Deep Comprehension: Multi-Disciplinary Approaches to Understanding, Enhancing, and Measuring Comprehension}, year = {2018}, editor = {Millis, K.K. and Long, D. and Magliano, J. and Wiemer, K.}, publisher = {Routledge}, location = {New York}, chapter = {12}, pages = {154-165}, } @InCollection{Dede2018, author = {Chris Dede and Tina Grotzer and Amy Kamarainen and Shari Metcalf and Andrew M. Olney and Vasile Rus and Robert Sottilare and Maggie Wang}, title = {Graphical Supports for Collaboration: Constructing Shared Mental Models}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Teams}, year = {2018}, editor = {Sottilare, Robert A. and Graesser, Arthur C. and Hu, Xiangen and Sinatra, Anne M.}, volume = {6}, series = {Adaptive Tutoring}, note = {Available at: https://gifttutoring.org/documents/}, publisher = {U.S. Army Research Laboratory}, isbn = {978-0-9977257-4-2}, chapter = {3}, pages = {33--43}, address = {Orlando, FL}, owner = {aolney}, } @Article{Graesser2018, author = {Graesser, Arthur C. and Hu, Xiangen and Nye, Benjamin D. and VanLehn, Kurt and Kumar, Rohit and Heffernan, Cristina and Heffernan, Neil and Woolf, Beverly and Olney, Andrew M. and Rus, Vasile and Andrasik, Frank and Pavlik, Philip and Cai, Zhiqiang and Wetzel, Jon and Morgan, Brent and Hampton, Andrew J. and Lippert, Anne M. and Wang, Lijia and Cheng, Qinyu and Vinson, Joseph E. and Kelly, Craig N. and McGlown, Cadarrius and Majmudar, Charvi A. and Morshed, Bashir and Baer, Whitney}, title = {{ElectronixTutor}: an intelligent tutoring system with multiple learning resources for electronics}, journal = {International Journal of STEM Education}, year = {2018}, volume = {5}, number = {1}, month = apr, pages = {15}, issn = {2196-7822}, doi = {10.1186/s40594-018-0110-y}, abstract = {The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources.}, day = {16}, } @InProceedings{Cook2018, author = {Cook, Connor and Olney, Andrew M. and Kelly, Sean and D'Mello, Sidney K.}, title = {An Open Vocabulary Approach for Detecting Authentic Questions in Classroom Discourse}, booktitle = {Proceedings of the 11th International Conference on Educational Data Mining}, year = {2018}, editor = {Boyer, Kristy Elizabeth and Michael Yudelson}, pages = {116--126}, comment = {Acceptance rate 16\%}, } @InProceedings{Olney2018a, author = {Olney, Andrew M.}, title = {Using novices to scale up intelligent tutoring systems}, booktitle = {Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2018}, year = {2018}, chapter = {Paper No. 18028}, comment = {Acceptance rate 31\%}, } @InProceedings{Graesser2018a, author = {Graesser, A. C. and Hampton, Drew and Morgan, Brent and Wang, Lijia and Majmudar, Charvi A. and Morshed, Bashir and Hu, Xiangen and Cai, Zhiqiang and Tackett, Andrew C. and Olney, Andrew M. and Rus, Vasile and Nye, Benjamin D.}, title = {{ElectronixTutor}: An adaptive learning platform with multiple resources}, booktitle = {Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2018}, year = {2018}, chapter = {Paper No. 18064}, comment = {Acceptance rate 31\%}, } @Article{Kelly2018, author = {Sean Kelly and Andrew M. Olney and Patrick Donnelly and Martin Nystrand and Sidney K. D’Mello}, title = {Automatically Measuring Question Authenticity in Real-World Classrooms}, doi = {10.3102/0013189X18785613}, eprint = {https://doi.org/10.3102/0013189X18785613}, number = {7}, pages = {451-464}, volume = {47}, abstract = {Analyzing the quality of classroom talk is central to educational research and improvement efforts. In particular, the presence of authentic teacher questions, where answers are not predetermined by the teacher, helps constitute and serves as a marker of productive classroom discourse. Further, authentic questions can be cultivated to improve teaching effectiveness and consequently student achievement. Unfortunately, current methods to measure question authenticity do not scale because they rely on human observations or coding of teacher discourse. To address this challenge, we set out to use automatic speech recognition, natural language processing, and machine learning to train computers to detect authentic questions in real-world classrooms automatically. Our methods were iteratively refined using classroom audio and human-coded observational data from two sources: (a) a large archival database of text transcripts of 451 observations from 112 classrooms; and (b) a newly collected sample of 132 high-quality audio recordings from 27 classrooms, obtained under technical constraints that anticipate large-scale automated data collection and analysis. Correlations between human-coded and computer-coded authenticity at the classroom level were sufficiently high (r = .602 for archival transcripts and .687 for audio recordings) to provide a valuable complement to human coding in research efforts.}, journal = {Educational Researcher}, year = {2018}, } @InProceedings{Hanson2005, author = {Hanson, D. and Olney, A. and Prilliman, S. and Mathews, E. and Zielke, M. and Hammons, D. and Fernandez, R. and Stephanou, H.}, title = {Upending the Uncanny Valley}, booktitle = {Proceedings of the Twentieth National Conference on Artificial Intelligence and the Seventeenth Annual Conference on Innovative Applications of Artificial Intelligence}, year = {2005}, publisher = {AAAI Press}, pages = {1728-1729}, address = {Menlo Park, CA}, comment = {Acceptance rate 18\%}, owner = {aolney}, timestamp = {2009.03.01}, } @Software{Olney2018b, author = {Andrew M. Olney and Peter Rosconi}, title = {aolney/{FsBeaker} v0.33-alpha}, date = {2018-12-29}, doi = {10.5281/zenodo.2528830}, url = {https://zenodo.org/record/2528830#.XCe0hsZMHJw}, urldate = {2018-12-29}, abstract = {F\# implementation for {BeakerNotebook}}, file = {Zenodo Snapshot:/home/aolney/Zotero/storage/8S4PAJSV/2528830.html:text/html}, publisher = {Zenodo}, } @InProceedings{Piech2013, author = {Piech, Chris and Huang, Jonathan and Chen, Zhenghao and Do, Chuong and Ng, Andrew and Koller, Daphne}, title = {Tuned Models of Peer Assessment in {MOOCs}}, booktitle = {Proceedings of the 6th International Conference on Educational Data Mining}, year = {2013}, editor = {D’Mello, S. K. and Calvo, R. A. and Olney, A. M.}, publisher = {International Educational Data Mining Society}, isbn = {978-0-9839525-2-7}, pages = {153--160}, } @InCollection{Graesser2019, author = {Graesser, Arthur C. and Greenberg, Daphne and Olney, Andrew and Lovett, Maureen W.}, title = {Educational Technologies that Support Reading Comprehension for Adults Who Have Low Literacy Skills}, booktitle = {The {Wiley} Handbook of Adult Literacy}, year = {2019}, publisher = {John Wiley \& Sons, Ltd}, isbn = {9781119261407}, chapter = {22}, pages = {471-493}, doi = {10.1002/9781119261407.ch22}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119261407.ch22}, abstract = {Summary There is an abundance of digital technologies designed to help adults improve basic reading skills (such as word decoding, letter sound correspondence, vocabulary), but there are fewer technologies to help adults improve reading comprehension (such as sentence meaning, inferences, text cohesion, discourse structure) and use digital media. This chapter focuses on the technologies that target comprehension strategies for adults with low literacy skills. We propose that it is important to ground the technologies in science and practice by aligning: (a) multilevel theoretical frameworks of comprehension, (b) interventions to improve comprehension, (c) reading curriculum standards, (d) assessments of comprehension, and (e) text characteristics. Technologies have been developed with conversational agents (talking heads, avatars) because they provide oral communication, scaffold comprehension strategies on texts, and navigate digital technologies. Some technologies address motivational variables, such as independent reading and the selection of documents that consider adult interests, vocations, and practical value.}, keywords = {AutoTutor, comprehension, cohesion, independent reading, inferences, multilevel theoretical framework, rhetorical structure, text difficulty}, } @InCollection{Hu2019, author = {Hu, Xiangen and Cai, Zhiqiang, and Olney, Andrew M.}, title = {{Semantic Representation and Analysis (SRA)} and its Application in Conversion-Based Intelligent Tutoring Systems ({CbITS})}, booktitle = {Learning Science: Theory, Research, and Practice}, year = {2019}, editor = {Feldman, R.}, publisher = {McGraw-Hill Education}, isbn = {9781260457995}, chapter = {4}, pages = {103--126}, } @InProceedings{Olney2019, author = {Olney, A. M. and Fleming, S. D.}, title = {A Cognitive Load Perspective on the Design of Blocks Languages for Data Science}, booktitle = {2019 IEEE Blocks and Beyond Workshop (B\&B)}, year = {2019}, month = {Oct}, pages = {95-97}, doi = {10.1109/BB48857.2019.8941224}, issn = {null}, keywords = {blocks language;data science;abstraction;cognitive load;R}, } @Misc{Olney2020, author = {Olney, Andrew M. and Olney, Rachel C.}, date = {2020-06-19}, title = {{US} State Social Distancing Intervention Dates (valid to 4/25/20)}, doi = {10.5281/zenodo.3901617}, urldate = {2020-06-19}, abstract = {This dataset contains dates for the implementations of the following interventions in 50 {US} states plus Shelby County {TN} in response to {COVID}-19. Each intervention date has an associated comment containing sources for that date and a rationale when the decision was not strictly objective. Interventions are valid to 4/25/20 after which some states began to reverse some interventions. schools\_universities Primary/secondary school closing; partial closing is {OK}; State university closing if it precedes primary/secondary travel\_restrictions Out of state travel quarantine restrictions {OR} state-level guidance to avoid traveling out of state public\_events Banning of {ALL} public events of more than 100 participants sport Banning/canceling of sporting events. Banning of public events of 1000 or more also qualifies. lockdown Definitions vary, but include: banning of non-essential gatherings/business operations, ordering stay at home except for exercise and essential tasks; stay at home/safer at home orders Lockdown encompasses all other intervensions by definition, so if a state skips multiple interventions and goes to lockdown, the lockdown date is used for those interventions as well. social\_distancing\_encouraged State advice on distancing including: work from home, reduce public transport, avoid non-essential contact; any guidance for maintaining a physical distance from others will also qualify. Mere words "social distancing" do not count unless they are elaborated with what that means in practice. Messaging must be to public and not selected group (e.g. state employees). self\_isolating\_if\_ill Strong recommendations/laws about self-isolating if showing {COVID} like symptoms; Statewide testing implies this, so whichever comes first. Messaging must be to public and not selected group (e.g. state employees).}, file = {Zenodo Snapshot:/home/aolney/Zotero/storage/IDLX6AF7/3901617.html:text/html}, keywords = {{COVID}, {COVID}-19, intervention, lockdown, {SARS}-{CoV}-2, social distancing, United States}, publisher = {Zenodo}, year = {2020}, } @Article{Olney2021, author = {Olney, Andrew M and Smith, Jesse and Sen, Saunak and Thomas, Fridtjof and Unwin, H Juliette T}, title = {{Estimating the Effect of Social Distancing Interventions on COVID-19 in the United States}}, doi = {10.1093/aje/kwaa293}, eprint = {https://academic.oup.com/aje/article-pdf/190/8/1504/39503422/kwaa293.pdf}, issn = {0002-9262}, number = {8}, pages = {1504-1509}, url = {https://doi.org/10.1093/aje/kwaa293}, volume = {190}, abstract = {{Since its global emergence in 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused multiple epidemics in the United States. When medical treatments for the virus were still emerging and a vaccine was not yet available, state and local governments sought to limit its spread by enacting various social-distancing interventions, such as school closures and lockdowns; however, the effectiveness of these interventions was unknown. We applied an established, semimechanistic Bayesian hierarchical model of these interventions to the spread of SARS-CoV-2 from Europe to the United States, using case fatalities from February 29, 2020, up to April 25, 2020, when some states began reversing their interventions. We estimated the effects of interventions across all states, contrasted the estimated reproduction numbers before and after lockdown for each state, and contrasted the predicted number of future fatalities with the actual number of fatalities as a check of the model’s validity. Overall, school closures and lockdowns were the only interventions modeled that had a reliable impact on the time-varying reproduction number, and lockdown appears to have played a key role in reducing that number to below 1.0. We conclude that reversal of lockdown without implementation of additional, equally effective interventions will enable continued, sustained transmission of SARS-CoV-2 in the United States.}}, journal = {American Journal of Epidemiology}, month = {01}, year = {2021}, } @InProceedings{Pavlik2020, author = {Pavlik Jr., Philip I. and Olney, Andrew M. and Banker, Amanda and Eglington, Luke and Yarbro, Jeff}, booktitle = {Proceedings of the Second International Workshop on Intelligent Textbooks 2020 co-located with 21st International Conference on Artificial Intelligence in Education (AIED 2020)}, title = {The Mobile Fact and Concept Textbook System ({MoFaCTS})}, editor = {Sergey Sosnovsky and Peter Brusilovsky and Richard Baraniuk and Andrew Lan}, pages = {35--49}, year = {2020}, } @InProceedings{Olney2021a, author = {Olney, Andrew M. and Fleming, Scott D. and Johnson, Jillian C.}, booktitle = {Proceedings of the 52nd {ACM} Technical Symposium on Computer Science Education}, title = {Learning Data Science with {Blockly} in {JupyterLab}}, doi = {10.1145/3408877.3439534}, isbn = {9781450380621}, location = {Virtual Event, USA}, pages = {1373}, publisher = {Association for Computing Machinery}, series = {SIGCSE '21}, url = {https://doi.org/10.1145/3408877.3439534}, abstract = {Blocks languages are widely used to teach children programming, and research over the past decade has generally supported their benefits in terms of motivation and actual learning. However, little work has been done on using blocks languages to teach adults, and even less work has looked at blocks languages for data science. We have integrated Blockly, a blocks-based programming environment, into JupyterLab, one of the leading computational notebook development environments for data science. Our integration, which is publicly released as a JupyterLab extension, allows users to assemble blocks-based programs in a GUI workspace and then render the blocks as textual code (e.g., Python) in a computational notebook cell. Additional features of the extension are notebook sync, which clears and restores the blocks workspace as the user navigates to different notebook cells, and intelliblocks, dynamically generated blocks that are created when users load software packages. In this demonstration, we show how learners can use blocks to solve data science problems in a JupyterLab computational notebook. We have released 20 worked example companion notebooks at https://github.com/memphis-iis/datawhys-content-notebooks and an extended tutorial on the extension at https://youtu.be/-luPzplPDI0. This material is based upon work supported by the National Science Foundation under Grant No. 1918751.}, address = {New York, NY, USA}, keywords = {jupyterlab, education, blocks programming, blockly, data science, computational notebooks}, numpages = {1}, year = {2021}, } @InProceedings{Olney2021b, author = {Olney, Andrew M.}, booktitle = {Proceedings of the 22nd International Conference on Artificial Intelligence in Education}, title = {Paraphrasing Academic Text: A Study of Back-Translating Anatomy and Physiology with Transformers}, editor = {Roll, Ido and McNamara, Danielle and Sosnovsky, Sergey and Luckin, Rose and Dimitrova, Vania}, isbn = {978-3-030-78270-2}, pages = {279--284}, publisher = {Springer International Publishing}, abstract = {This paper explores a general approach to paraphrase generation using a pre-trained seq2seq model fine-tuned using a back-translated anatomy and physiology textbook. Human ratings indicate that the paraphrase model generally preserved meaning and grammaticality/fluency: 70{\%} of meaning ratings were above 75, and 40{\%} of paraphrases were considered more grammatical/fluent than the originals. An error analysis suggests potential avenues for future work.}, address = {Cham}, comment = {Acceptance rate 63\% as short paper}, year = {2021}, } @InProceedings{Yarbro2021, author = {Yarbro, Jeffrey T. and Olney, Andrew M.}, booktitle = {Proceedings of the 22nd International Conference on Artificial Intelligence in Education}, title = {WikiMorph: Learning to Decompose Words into Morphological Structures}, editor = {Roll, Ido and McNamara, Danielle and Sosnovsky, Sergey and Luckin, Rose and Dimitrova, Vania}, isbn = {978-3-030-78270-2}, pages = {406--411}, publisher = {Springer International Publishing}, abstract = {This paper presents WikiMorph, a tool that automatically breaks down words into morphemes, etymological compounds (morphemes from root languages), and generates contextual definitions for each component. It comes in two flavors: a dataset and a deep-learning-based model. The dataset was extracted from Wiktionary and contains over 450k entries. We then used this dataset to train a GPT-2 model to generalize and decompose any word into morphemes and their definitions. We find that the model accurately generates complex breakdowns when given a high-quality initial definition.}, address = {Cham}, comment = {Acceptance rate 63\% as short paper}, year = {2021}, } @InProceedings{Olney2021c, author = {Olney, Andrew M.}, booktitle = {Proceedings of the Eighth ACM Conference on Learning @ Scale}, title = {Generating Response-Specific Elaborated Feedback Using Long-Form Neural Question Answering}, doi = {10.1145/3430895.3460131}, isbn = {9781450382151}, location = {Virtual Event, Germany}, pages = {27–36}, publisher = {Association for Computing Machinery}, series = {L@S '21}, url = {https://doi.org/10.1145/3430895.3460131}, abstract = {In contrast to simple feedback, which provides students with the correct answer, elaborated feedback provides an explanation of the correct answer with respect to the student's error. Elaborated feedback is thus a challenge for AI in education systems because it requires dynamic explanations, which traditionally require logical reasoning and knowledge engineering to generate. This study presents an alternative approach that formulates elaborated feedback in terms of long-form question answering (LFQA). An off-the-shelf LFQA system was evaluated by human raters in a 2x2x2x2 ablation design that manipulated the context documents given to the LFQA model and the post-processing of model output. Results indicate that context manipulations improve performance but that post-processing can have detrimental results.}, address = {New York, NY, USA}, comment = {Acceptance rate 30\%}, keywords = {deep learning, information retrieval, question answering, elaborated feedback, human evaluation, natural language generation}, numpages = {10}, year = {2021}, } @InProceedings{Yarbro2021a, author = {Jeffrey T. Yarbro and Andrew McGregor Olney}, booktitle = {Proceedings of the Third International Workshop on Intelligent Textbooks}, title = {Contextual Definition Generation}, editor = {Sergey A. Sosnovsky and Peter Brusilovsky and Richard G. Baraniuk and Andrew S. Lan}, pages = {74--83}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, volume = {2895}, year = {2021}, } @Article{Olney2021d, author = {Olney, Andrew M. and Gilbert, Stephen B. and Rivers, Kelly}, title = {Preface to the Special Issue on Creating and Improving Adaptive Learning: Smart Authoring Tools and Processes}, doi = {10.1007/s40593-021-00277-9}, issn = {1560-4306}, pages = {1--3}, url = {https://doi.org/10.1007/s40593-021-00277-9}, volume = {32}, journal = {International Journal of Artificial Intelligence in Education}, refid = {Olney2021}, timestamp = {2021-08-20}, year = {2021}, } @Article{Payne2021, author = {Payne, Linda Ann and Tawfik, Andrew and Olney, Andrew}, title = {Datawhys Phase 1: Problem Solving to Facilitate Data Science \& STEM Learning Among Summer Interns}, doi = {10.14434/ijdl.v12i3.31555}, number = {3}, pages = {102–117}, url = {https://scholarworks.iu.edu/journals/index.php/ijdl/article/view/31555}, volume = {12}, abstractnote = {This design case details a data science summer learning experience designed by University of Memphis faculty for HBCU students (NSF #: 1918751) with recruiting assistance provided by LeMoyne-Owen College. The summer learning experience included elements of didactic and collaborative problem-solving during the first five weeks of the internship, followed by a three-week, team-based, problem-solving project using real-world data. While the course was originally designed as a face-to-face learning experience, the impact of COVID-19 necessitated a shift toward online digital spaces. The design case details the opportunities and challenges of STEM online learning and especially underscores the limitations of (a) existing data science technologies for instruction, (b) the shift toward instructional design of materials that supported more self-directed learning, and (c) collaborative problem-solving. Implications for design and practice are also considered.}, journal = {International Journal of Designs for Learning}, month = {Nov.}, year = {2021}, } @InProceedings{Olney2021e, author = {Andrew McGregor Olney and Scott D. Fleming}, booktitle = {Joint Proceedings of the Workshops at the 14th International Conference on Educational Data Mining}, title = {JupyterLab Extensions for Blocks Programming, Self-Explanations, and HTML Injection}, editor = {Thomas W. Price and San Pedro, Sweet}, pages = {1-6}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, volume = {3051}, articleno = {CSEDM-8}, comment = {Acceptance rate 33\%}, eid = {CSEDM-8}, paper = {CSEDM-8}, year = {2021}, } @InProceedings{Olney2021f, author = {Andrew McGregor Olney}, booktitle = {Joint Proceedings of the Workshops at the 14th International Conference on Educational Data Mining}, title = {Sentence Selection for Cloze Item Creation: A Standardized Task and Preliminary Results}, editor = {Thomas W. Price and San Pedro, Sweet}, pages = {1-5}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, volume = {3051}, articleno = {LDI-6}, comment = {Acceptance rate 75\%}, eid = {LDI-6}, paper = {LDI-6}, year = {2021}, } @InProceedings{Olney2022, author = {Olney, Andrew M.}, booktitle = {Proceedings of the 22nd International Conference on Artificial Intelligence in Education}, title = {Assessing Readability by Filling Cloze Items with Transformers}, doi = {10.1007/978-3-031-11644-5_25}, editor = {Rodrigo, Maria Mercedes and Matsuda, Noburu and Cristea, Alexandra I. and Dimitrova, Vania}, isbn = {978-3-031-11644-5}, location = {Cham}, pages = {307--318}, publisher = {Springer International Publishing}, abstract = {Cloze items are a foundational approach to assessing readability. However, they require human data collection, thus making them impractical in automated metrics. The present study revisits the idea of assessing readability with cloze items and compares human cloze scores and readability judgments with predictions made by T5, a popular deep learning architecture, on three corpora. Across all corpora, T5 predictions significantly correlated with human cloze scores and readability judgments, and in predictive models, they could be used interchangeably with average word length, a common readability predictor. For two corpora, combining T5 and Flesch reading ease predictors improved model fit for human cloze scores and readability judgments.}, comment = {Acceptance rate 20\%}, timestamp = {2022-08-02}, year = {2022}, } @InProceedings{Johnson2022, author = {Jillian C. Johnson and Andrew M. Olney}, booktitle = {Proceedings of the 15th International Conference on Educational Data Mining}, title = {Using community-based problems to increase motivation in a data science virtual internship}, doi = {10.5281/zenodo.6853167}, editor = {Antonija Mitrovic and Nigel Bosch}, isbn = {978-1-7336736-3-1}, location = {Durham, United Kingdom}, pages = {500--507}, publisher = {International Educational Data Mining Society}, abstract = {Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. This study contrasted learning with generic datasets and artificial problems (phase 1) with a community sourced dataset and authentic problems (phase 2) in the context of an 8 week virtual internship. Retrospective survey questions indicated interns experienced increased motivation in phase 2. Additionally, analysis of intern discourse using Linguistic Inquiry and Word Count (LIWC) indicated a significant difference in linguistic measures between the two phases. Phase 2 had significantly greater measures of words per sentence, analytic thinking, clout (i.e. confidence), affect, positive emotion, certainty, drives, and affiliation, and significantly smaller measures of word count, cognitive processes, negative emotion, and emotional tone. These results suggest that community sourced data and problems may increase motivation for learning data science.}, comment = {Acceptance rate 30\%}, timestamp = {2022-08-02}, year = {2022}, } @InProceedings{Olney2022a, author = {Olney, Andrew M.}, booktitle = {Proceedings of the 3rd Workshop of the Learner Data Institute}, title = {Generating Multiple Choice Questions with a Multi-Angle Question Answering Model}, doi = {10.5281/zenodo.7761561}, editor = {Fancsali, S. E. and Rus, V.}, pages = {18--23}, timestamp = {2022-08-02}, year = {2022}, } @Article{Payne2022, author = {Payne, Linda and Tawfik, Andrew and Olney, Andrew M.}, title = {Computational {Thinking} in {Education}: {Past} and {Present}}, doi = {10.1007/s11528-022-00766-1}, issn = {1559-7075}, language = {en}, pages = {745–747}, url = {https://doi.org/10.1007/s11528-022-00766-1}, volume = {66}, abstract = {As computers have become commonplace in everyday life, educators have begun to shift focus from working with computers (computer literacy) to thinking with computers (computational thinking). This article describes the progression of computational thinking (CT) from a historical perspective. This paper will first review the early stages of CT in the mid-1900s, along with its evolution over the succeeding decades. Finally, the article concludes with a discussion of proposed educational benefits, along with implications for future learning.}, journal = {TechTrends}, keywords = {Computational thinking in education, Computer science, Constructivism}, shorttitle = {Computational {Thinking} in {Education}}, year = {2022}, } @Article{Banker2022, author = {Banker, Amanda M. and Pavlik, Philip I. and Olney, Andrew M. and Eglington, Luke G.}, title = {Online Tutoring System ({MoFaCTS}) for Anatomy and Physiology: Implementation and Initial Impressions}, doi = {https://doi.org/10.21692/haps.2022.012}, number = {2}, pages = {44--54}, volume = {26}, journal = {HAPS Educator}, year = {2022}, } @Book{Olney2023, author = {Andrew M. Olney}, title = {{Computational Thinking through Modular Sound Synthesis}}, doi = {10.5281/zenodo.7502148}, url = {https://olney.ai/ct-modular-book/}, month = jan, year = {2023}, } @Article{Tawfik2024, author = {Tawfik, Andrew A. and Payne, Linda and Olney, Andrew M.}, title = {Scaffolding Computational Thinking Through Block Coding: {A} Learner Experience Design Study}, doi = {10.1007/s10758-022-09636-4}, issn = {2211-1670}, language = {en}, number = {1}, pages = {21--43}, url = {https://doi.org/10.1007/s10758-022-09636-4}, urldate = {2023-02-04}, volume = {29}, abstract = {Theorists and educators increasingly highlight the importance of computational thinking in STEM education. While various scaffolding strategies describe how to best support this skillset (i.e., paired programming, worked examples), less research has focused on the design and development of these digital tools. One way to support computational thinking and data science is through block coding and other ways that visualize the coding process. However, less is known about the learning experience design of these tools. Based on this gap, this work-in-progress study compared the learning experience design of novices and those with more advanced understanding of computational thinking. Results found differences emerge in the perceived dynamic interaction and scaffolding constructs of learning experience design. Implications for theory and practice are discussed.}, file = {Full Text PDF:/home/aolney/Zotero/storage/H6CXMRJ5/Tawfik et al. - 2022 - Scaffolding Computational Thinking Through Block C.pdf:application/pdf}, journal = {Technology, Knowledge and Learning}, keywords = {Computational thinking, Data science, Scaffolding, STEM}, shorttitle = {Scaffolding {Computational} {Thinking} {Through} {Block} {Coding}}, timestamp = {2023-02-03}, year = {2024}, } @InBook{Rus2023, author = {Vasile Rus and Andrew M. Olney and Arthur C. Graesser}, booktitle = {Handbook of Artificial Intelligence in Education}, title = {Deeper learning through interactions with students in natural language}, doi = {10.4337/9781800375413.00021}, editor = {Benedict du Boulay and Antonija Mitrovic and Kalina Yacef}, isbn = {9781800375413}, pages = {250 - 272}, publisher = {Edward Elgar Publishing}, address = {Cheltenham, UK}, timestamp = {2023-05-02}, year = {2023}, } @InProceedings{Olney2023a, author = {Andrew M. Olney}, booktitle = {Proceedings of Empowering Education with LLMs - the Next-Gen Interface and Content Generation}, title = {Generating multiple choice questions from a textbook: {LLMs} match human performance on most metrics}, editor = {Steven Moore and John Stamper and Richard Tong and Chen Cao and Zitao Liu and Xiangen Hu and Yu Lu and Joleen Liang and Hassan Khosravi and Paul Denny and Anjali Singh and Chris Brooks}, eventdate = {2023-07-07}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-3487/paper7.pdf}, venue = {Tokyo, Japan}, comment = {Acceptance rate 45\%}, year = {2023}, } @Article{Tawfik2023, author = {Tawfik, A. A. and Payne, L. A. and Olney, A. M. and Ketter, H.}, journal = {Journal of Applied Instructional Design}, title = {Exploring the Relationship Between Usability and Cognitive Load in Data Science Education}, doi = {https://dx.doi.org/10.59668/515.13078}, number = {3}, volume = {12}, year = {2023}, } @Article{Hollander2024, author = {Hollander, John and Olney, Andrew}, journal = {Cognitive Science}, title = {Raising the Roof: Situating Verbs in Symbolic and Embodied Language Processing}, doi = {https://doi.org/10.1111/cogs.13442}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cogs.13442}, number = {4}, pages = {e13442}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.13442}, volume = {48}, abstract = {Abstract Recent investigations on how people derive meaning from language have focused on task-dependent shifts between two cognitive systems. The symbolic (amodal) system represents meaning as the statistical relationships between words. The embodied (modal) system represents meaning through neurocognitive simulation of perceptual or sensorimotor systems associated with a word's referent. A primary finding of literature in this field is that the embodied system is only dominant when a task necessitates it, but in certain paradigms, this has only been demonstrated using nouns and adjectives. The purpose of this paper is to study whether similar effects hold with verbs. Experiment 1 evaluated a novel task in which participants rated a selection of verbs on their implied vertical movement. Ratings correlated well with distributional semantic models, establishing convergent validity, though some variance was unexplained by language statistics alone. Experiment 2 replicated previous noun-based location-cue congruency experimental paradigms with verbs and showed that the ratings obtained in Experiment 1 predicted reaction times more strongly than language statistics. Experiment 3 modified the location-cue paradigm by adding movement to create an animated, temporally decoupled, movement-verb judgment task designed to examine the relative influence of symbolic and embodied processing for verbs. Results were generally consistent with linguistic shortcut hypotheses of symbolic-embodied integrated language processing; location-cue congruence elicited processing facilitation in some conditions, and perceptual information accounted for reaction times and accuracy better than language statistics alone. These studies demonstrate novel ways in which embodied and linguistic information can be examined while using verbs as stimuli.}, keywords = {Embodied cognition, Verbs, Semantic judgment, Modal, Amodal, Language processing, Distributional semantic models}, year = {2024}, } @Book{Olney2024, title = {{Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 25th International Conference, AIED 2024, Recife, Brazil, July 8-12, 2024, Proceedings, Part I}}, doi = {10.1007/978-3-031-64315-6}, editor = {Andrew M. Olney and Irene{-}Angelica Chounta and Zitao Liu and Olga C. Santos and Ig Ibert Bittencourt}, isbn = {978-3-031-64314-9}, publisher = {Springer}, series = {Communications in Computer and Information Science}, url = {https://doi.org/10.1007/978-3-031-64315-6}, volume = {2150}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/aied/2024c1.bib}, timestamp = {Wed, 10 Jul 2024 17:24:57 +0200}, year = {2024}, } @Book{Olney2024a, title = {{Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 25th International Conference, AIED 2024, Recife, Brazil, July 8-12, 2024, Proceedings, Part II}}, doi = {10.1007/978-3-031-64312-5}, editor = {Andrew M. Olney and Irene{-}Angelica Chounta and Zitao Liu and Olga C. Santos and Ig Ibert Bittencourt}, isbn = {978-3-031-64311-8}, publisher = {Springer}, series = {Communications in Computer and Information Science}, url = {https://doi.org/10.1007/978-3-031-64312-5}, volume = {2151}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/aied/2024c2.bib}, timestamp = {Wed, 10 Jul 2024 17:24:43 +0200}, year = {2024}, } @Book{Olney2024b, title = {{Artificial Intelligence in Education. 25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part I}}, doi = {10.1007/978-3-031-64302-6}, editor = {Andrew M. Olney and Irene{-}Angelica Chounta and Zitao Liu and Olga C. Santos and Ig Ibert Bittencourt}, isbn = {978-3-031-64301-9}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, url = {https://doi.org/10.1007/978-3-031-64302-6}, volume = {14829}, year = {2024}, } @Book{Olney2024c, title = {{Artificial Intelligence in Education. 25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part II}}, doi = {10.1007/978-3-031-64299-9}, editor = {Andrew M. Olney and Irene{-}Angelica Chounta and Zitao Liu and Olga C. Santos and Ig Ibert Bittencourt}, isbn = {978-3-031-64298-2}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, url = {https://doi.org/10.1007/978-3-031-64299-9}, volume = {14830}, year = {2024}, } @InProceedings{Barboza2024, author = {Barboza, Luiz and Ferreira Mello, Rafael and Souza Teixeira, Erico and Olney, Andrew M.}, booktitle = {Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2}, title = {Visual Data Science with Blockly-DS}, doi = {10.1145/3626253.3633408}, isbn = {9798400704246}, location = {Portland, OR, USA}, pages = {1892}, publisher = {Association for Computing Machinery}, series = {SIGCSE 2024}, url = {https://doi.org/10.1145/3626253.3633408}, abstract = {The workshop will give educators an introduction to graphical data analysis techniques for exploring, summarizing, and effectively communicating data using Visual Blocks (Blockly) and Jupiter Notebooks. Participants will gain skills to create and interpret various graphs and plots, fostering insights into data patterns and relationships. The emphasis will be on adeptly selecting visualizations. Upon completion of this course, educators should be proficient in: Understanding the principles of graphical data analysis and their practical applications; and creating and interpreting diverse graph types and plots. The workshop will further delve into statistical methods essential for data analysis, guiding educators on employing descriptive statistics to explore data and derive meaningful business insights. The focus will be on fostering an understanding of statistical concepts and their practical application, as well as the effective interpretation and communication of findings. Additionally, the program will introduce educators to machine learning regressors, with a primary focus on linear regression. Participants will learn the skills to train, evaluate, and apply regressors to predict continuous target variables from input features. The emphasis will be on grasping and applying machine learning principles, as well as effectively interpreting and communicating model predictions. Furthermore, participants will engage in hands-on experience with machine learning classifiers, encompassing logistic regression, decision trees, naive Bayes, and neural networks. Educators will acquire the expertise to train, evaluate, and apply classifiers for predicting categorical target variables from input features. The emphasis here is on understanding and applying machine learning principles, as well as adeptly interpreting model predictions.}, address = {New York, NY, USA}, keywords = {block-based programming, data science, ia, machine learning}, numpages = {1}, year = {2024}, } @Misc{Olney2025a, author = {Andrew M. Olney and Sidney K. D'Mello and Natalie Person and Whitney Cade and Patrick Hays and Claire W. Dempsey and Blair Lehman and Betsy Williams and Art Graesser}, title = {Efficacy of a Computer Tutor that Models Expert Human Tutors}, eprint = {2504.16132}, url = {https://arxiv.org/abs/2504.16132}, archiveprefix = {arXiv}, primaryclass = {cs.CY}, year = {2025}, } @InProceedings{Olney2025b, author = {Olney, Andrew M. and D'Mello, Sidney K. and Person, Natalie and Cade, Whitney and Hays, Patrick and Dempsey, Claire W. and Lehman, Blair and Williams, Betsy and Graesser, Art}, booktitle = {Artificial Intelligence in Education}, title = {Efficacy of a Computer Tutor that Models Expert Human Tutors}, editor = {Cristea, Alexandra I. and Walker, Erin and Lu, Yu and Santos, Olga C. and Isotani, Seiji}, isbn = {978-3-031-98462-4}, pages = {301--308}, publisher = {Springer Nature Switzerland}, abstract = {The contribution of expertise to tutoring effectiveness is unclear and continues to be debated. We conducted a 9-week learning efficacy study of an intelligent tutoring system (ITS) for biology modeled on expert human tutors with two control conditions: human tutors who were experts in the domain but not in tutoring and a no-tutoring condition. All conditions were supplemental to classroom instruction, and students took learning tests immediately before and after tutoring sessions as well as delayed tests 1--2 weeks later. Analysis using logistic mixed-effects modeling indicates significant positive effects on the immediate post-test for the ITS ({\$}{\$}d = .71{\$}{\$}d=.71) and human tutors ({\$}{\$}d = .66{\$}{\$}d=.66) which are in the 99th percentile of meta-analytic effects, as well as significant positive effects on the delayed post-test for the ITS ({\$}{\$}d = .36{\$}{\$}d=.36) and human tutors ({\$}{\$}d = .39{\$}{\$}d=.39). We discuss implications for the role of expertise in tutoring and the design of future studies.}, address = {Cham}, comment = {Acceptance rate 22\%}, year = {2025}, } @InProceedings{Olney2025c, author = {Olney, Andrew M. and Cade, Whitney L.}, booktitle = {Artificial Intelligence in Education}, title = {Learning by Correcting AI Errors: Effort is Essential}, editor = {Cristea, Alexandra I. and Walker, Erin and Lu, Yu and Santos, Olga C. and Isotani, Seiji}, isbn = {978-3-031-98459-4}, pages = {148--161}, publisher = {Springer Nature Switzerland}, abstract = {Concerns about declining student comprehension and critical thinking skills have led to new pedagogies based on fact-checking generative AI. However, it is unclear whether these pedagogies foster learning for all students. This study investigated a simplified scenario for humans fact-checking AI where participants corrected a virtual student. In two experiments, participants took a pre-test and then participated in three conditions: read-only (Read), read with an erroneous virtual student (Err), and read with a correct virtual student (Cor), followed by a post-test. Experiment 1 found that participants from an undergraduate subject pool ({\$}{\$}N=92{\$}{\$}N=92) learned more in Cor than Err, {\$}{\$}d = .38{\$}{\$}d=.38, and more in Corr than Read, {\$}{\$}d = .37{\$}{\$}d=.37, but that no learning occurred in Err and Read conditions. Experiment 2 found that participants from Amazon Mechanical Turk ({\$}{\$}N=85{\$}{\$}N=85) learned more from the Err than Read, {\$}{\$}d = .72{\$}{\$}d=.72, but that Corr was not significantly different from Err or Read. Follow-up analyses suggest that participants in the two experiments exhibited drastically different correcting behaviors: only 52{\%} of undergraduates corrected the virtual student on selected tasks, whereas 98{\%} of crowdworkers corrected the virtual student on the same tasks. Mediation analysis indicates that for crowd workers, learning was entirely mediated by their correcting behavior. Altogether, these results suggest that learning by correcting errors can be effective but only if students put in the effort.}, address = {Cham}, comment = {Acceptance rate 20\%}, year = {2025}, } @InProceedings{Barboza2025, author = {Barboza, Luiz and Farzan, Farshid and Olney, Andrew M.}, booktitle = {Proceedings of the Twelfth ACM Conference on Learning @ Scale}, title = {Dataset Personalization Methods based on LLMs for Data Science Education: A Comparative Study of Rescaling and Sampling Approaches}, doi = {10.1145/3698205.3733936}, isbn = {9798400712913}, location = {Palermo, Italy}, pages = {261–265}, publisher = {Association for Computing Machinery}, series = {L@S '25}, url = {https://doi.org/10.1145/3698205.3733936}, abstract = {This work-in-progress study explores the use of Large Language Models (LLMs) to dynamically personalize datasets used in data science education according to learner interests. The study outlines two dataset personalization methods that transform each variable in the original dataset to a new variable: a scaling method that transforms while preserving the original distributions and correlation structure, and a sampling method that transforms while preserving the original correlation structure but changes distributions to match the new variables. An evaluation with subject matter experts revealed that datasets personalized using the scaling method were not significantly different from the original datasets in terms of the appropriateness of variable names and ranges. Further evaluation using the personalized datasets in the context of instructional materials designed for the original datasets indicated that more inconsistencies were found with the sampling method than the scaling method. These results suggest that dataset personalization can create datasets that serve as drop-in replacements for the original datasets in existing instructional materials.}, address = {New York, NY, USA}, comment = {Acceptance rate 27\%}, keywords = {data science education, llms, personalization}, numpages = {5}, year = {2025}, } @InProceedings{Farzan2025, author = {Farshid Farzan and Hasan Mashrique and Olney, Andrew M.}, booktitle = {Proceedings of 9th Educational Data Mining in Computer Science Education Workshop ({CSEDM} Workshop)}, title = {Exploring the Link between Cognitive Abilities and Data Science Skills using Alternative Raven's Progressive Matrices}, editor = {Bita Akram and Yang Shi and Peter Brusilovsky and Thomas Price and Ken Koedinger and Paulo Carvalho and Shan Zhang and Andrew Lan and Juho Leinonen}, location = {Palermo, Sicily, Italy}, part = {7}, publisher = {CEUR}, url = {https://ceur-ws.org/Vol-4019/paper_07.pdf}, year = {2025}, } @InCollection{Olney2025d, author = {Olney, Andrew M.}, booktitle = {Design Recommendations for Intelligent Tutoring Systems: Assessing Early Successes, Challenges, and the Potential of Generative AI in Intelligent Tutoring Systems}, title = {Generative AI for Concept Learning: Evolution of the Mobile Fact and Concept Training System}, editor = {Sinatra, A.}, note = {Available at: https://gifttutoring.org/documents/}, publisher = {U.S. Army Research Laboratory}, series = {Adaptive Tutoring}, address = {Orlando, FL}, owner = {aolney}, year = {2025}, }