Seminar: Intelligent Tutoting Systems

• Analysis
• Generation
• Dialog
• Specific aspects such as tutorial strategies and system architecture

• A. Graesser et al., Intelligent Tutoring Systems with Conversational Dialogue. AI Magazine 22, S39-59

Analysis

• P. Wiemer-Hastings et al., Improving an Intelligent Tutor's Comprehension of Students with Latent Semantic Analysis, Int. Journal of AI in Education 1999, S535-542
• A. Graesser et al., Using Latent Semantic Analysis to Evaluate the Contributions of Students in AutoTutor, Interactive Learning Environments 8, 2000, S129-148
• Graesser, A.C., Penumatsa, P., Ventura, M., Cai, Z., & Hu, X. (in press). Using LSA in AutoTutor: Learning through mixed initiative dialogue in natural language. In T. Landauer, D. McNamara, S. Dennis, and W. Kintsch (Eds.), LSA: A Road to meaning. Mahwah, NJ: Erlbaum.
• C. Rose et al. A Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals. 6th Int. Conf. Intelligent Tutoring Systems, S552-561, 2002
• P. Jordan, M. Makatchev, K. VanLehn, Combining Competing Language Understanding Approaches in an Intelligent Tutoring System. Proceedings of Intelligent Tutoring Systems Conference, S346-357, 2004.
• M. Makatchev, et al., Mixed Language Processing in the Why2-Atlas Tutoring System. AIED 05 Workshop 8 on Mixed Language Explanations in Learning Environments, S35-42, 2005.
• P. Jordan, M. Makatchev, U. Pappuswamy, Relating Student Text to Ideal Proofs: Issues of Efficiency of Expression. AIED 05 Workshop 8 on Mixed Language Explanations in Learning Environments, S43-50, 2005.
• Makatchev, M. & VanLehn, K. (2005). Analyzing completeness and correctness of utterances using an ATMS. In G. McCalla, C. K. Looi, B. Bredeweg & J. Breuker (Eds.), Artificial Intelligence in Education (pp. 403-410). Amsterdam, Netherlands: IOS Press.
• Makatchev, M., VanLehn, K., Jordan, P. W., & Pappuswamy, U. (2006). Representation and reasoning for deeper natural language understanding in a physics tutoring system. In G. Sutcliffe & R. Goebel (Eds.), Proceedings of the 19th International FLAIRS conference. Menlo Park, CA: AAAI Press.
• M. Makatchev, K. VanLehn, Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances. Artificial Intelligence in Education, S307-314, 2007.
• Claire Williams and Sidney Mello, Predicting Student Knowledge Level from Domain-Independent Function and Content Words, Intelligent Tutoring Systems 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part II, Pages 62-71
• Yaakov Gal, Elif Yamangil, Stuart M. Shieber, Andee Rubin and Barbara J. Grosz, Towards Collaborative Intelligent Tutors: Automated Recognition of Users Strategies, Lecture Notes in Computer Science, 2008, Volume 5091, Intelligent Tutoring Systems, Pages 162-172
• Rodney D. Nielsen, Wayne Ward and James H. Martin, Automatic Generation of Fine-Grained Representations of Learner Response Semantics, Lecture Notes in Computer Science, 2008, Volume 5091, Intelligent Tutoring Systems, Pages 173-183
• Anagha Kulkarni, Michael Heilman, Maxine Eskenazi and Jamie Callan, Word Sense Disambiguation for Vocabulary Learning, Lecture Notes in Computer Science, 2008, Volume 5091, Intelligent Tutoring Systems, Pages 500-509
• Myroslava O. Dzikovska, Elaine Farrow, and Johanna D. Moore. Combining Semantic Interpretation and Statistical Classification for Improved Explanation Processing in a Tutorial Dialogue System. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 279-288, 2013.
• Automated Summarization Evaluation (ASE) Using Natural Language Processing Tools Scott A. Crossley, Minkyung Kim, Laura Allen, and Danielle McNamara S. Isotani et al. (Eds.): AIED 2019, LNAI 11625, pp. 84–95, 2019
• A Concept Map Based Assessment of Free Student Answers in Tutorial Dialogues Nabin Maharjan and Vasile Rus S. Isotani et al. (Eds.): AIED 2019, LNAI 11625, pp. 244–257, 2019
• Generalizability of Methods for Imputing Mathematical Skills Needed to Solve Problems from Texts Thanaporn Patikorn , David Deisadze, Leo Grande, Ziyang Yu, and Neil Heffernan S. Isotani et al. (Eds.): AIED 2019, LNAI 11625, pp. 396–405, 2019

Presentation

• James Lester and Bruce Porter, Developing and Empirically Evaluating Robust Explanation Generators: The KNIGHT Experiments Computational Linguistics, 23(1): 65-101, 1997.
• B. Di Eugenio, M. Glass, M. Trolio, The DIAG experiments: Natural Language Generation for Intelligent Tutoring Systems, 2002 Int. Conference on Natural Language Generation, S120-127.
• B. Di Eugenio, S. Haller, M. Glass, Development and Evaluation of NL Interfaces in a Small Shop, 2003 AAAI Spring Symposium on Natural Language Generation in Written and Spoken Dialogue, S15-22.
• B. Di Eugenio et al. Natural Langauge Generation for Intelligent Tutoring Systems: a Case Study. Artificial Intelligence in Education, 2005.
• Nancy Milik, Antonija Mitrovic and Michael Grimley, Investigating the Relationship between Spatial Ability and Feedback Style in ITSs, Lecture Notes in Computer Science, 2008, Volume 5091, Intelligent Tutoring Systems, Pages 281-290
• Tiffany Barnes and John Stamper, Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data, Lecture Notes in Computer Science, 2008, Volume 5091, Intelligent Tutoring Systems, Pages 373-382
• Ming Liu, Rafael A. Calvo and Vasile Rus, Automatic Question Generation for Literature Review Writing Support, Intelligent Tutoring Systems 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part I, Pages 45-54
• Ilya M. Goldin and Ryan Carlson. Learner Differences and Hint Content K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 522-531, 2013.
• Karen Mazidi and Paul Tarau, Automatic Question Generation: From NLU to NLG, A. Micarelli et al. (Eds.): ITS 2016, LNCS 9684, pp. 23-33, 2016.
• A Comparative Study
on Question-Worthy Sentence Selection Strategies for Educational Question Generation Guanliang Chen, Jie Yang, and Dragan Gasevic S. Isotani et al. (Eds.): AIED 2019, LNAI 11625, pp. 59–70, 2019
• Autonomy and Types of Informational Text Presentations in Game-Based Learning Environments Daryn A. Dever and Roger Azevedo S. Isotani et al. (Eds.): AIED 2019, LNAI 11625, pp. 110–120, 2019
• Automatic Generation of Problems and Explanations for an Intelligent Algebra Tutor Eleanor O’Rourke, Eric Butler, Armando D ́ıaz Tolentino, and Zoran Popovi ́c S. Isotani et al. (Eds.): AIED 2019, LNAI 11625, pp. 383–395, 2019

Dialog

• Zinn, Moore, Core, A 3-tier Planning Architecture for Managing Tutorial Dialogue, 6th Int. Conf. Intelligent Tutoring Systems, S 574-584.
• R. Murray, K. VanLehn, J. Mostow. Looking Ahead to Select tutorial Actions: A decision-theoretic Approach. Journal of Artificial Intelligence in Education 14:235-278, 2004.
• H. Lane, K. VanLehn. Teaching the Tacit Knowledge of Programming to Novices with Natural Language Tutoring, Computer Science Education, vol 15, S183-201, 2005.
• S. Ohlsson, et al. Beyond the code-and-count analysis of tutorial dialogues. Artificial Intelligence in Education, S349-356, 2007.
• K. Boyer et al. The Influence of Learner Characteristics on Task-Oriented Tutorial Dialogue, Artificial Intelligence in Education, S365-372, 2007.
• Kim, Jung Hee, Reva Freedman, Michael Glass, and Martha W. Evens. Annotation of Tutorial Dialogue Goals for Natural Language Generation Discourse Processes vol. 42 no. 1 (2006), pp. 37--74.
• Toby Dragon, Mark Floryan, Beverly Woolf and Tom Murray, Recognizing Dialogue Content in Student Collaborative Conversation, 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part II, Pages 113-122
• Whitney L. Cade, Jessica L. Copeland, Natalie K. Person and Sidney K. D Mello, Dialogue Modes in Expert Tutoring, Lecture Notes in Computer Science, 2008, Volume 5091, Intelligent Tutoring Systems, Pages 470-479
• Kate Forbes-Riley and Diane Litman, Metacognition and Learning in Spoken Dialogue Computer Tutoring, Intelligent Tutoring Systems 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part I, Pages 379-388
• Latham, A.M., Crockett, K.A., McLean, D.A. and Edmonds, B.(2012), 'A conversational intelligent tutoring system to automatically predict learning styles', Computers & Education Journal, vol. 59 (1), pp. 95-109 http://dx.doi.org/10.1016/j.compedu.2011.11.001
• Dzikovska, M., Steinhauser, N., Farrow, E. et al., BEETLE II: Deep Natural Language Understanding and Automatic Feedback Generation for Intelligent Tutoring in Basic Electricity and Electronics, Int J Artif Intell Educ (2014) 24: 284-332.
• Impact of Pedagogical Agents’ Conversational Formality on Learning and Engagement Haiying Li and Art Graesser E. André et al. (Eds.): AIED 2017, LNAI 10331, pp. 188–200, 2017

Teaching Strategies

• Leena Razzaq and Neil T. Heffernan, Hints: Is It Better to Give or Wait to Be Asked? Intelligent Tutoring Systems 0th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part I, Pages 349-358
• Jaclyn K. Maass and Philip I. Pavlik Jr. Using Learner Modeling to Determine Effective Conditions of Learning for Optimal Transfer. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 189-198, 2013.
• Yanjin Long and Vincent Aleven. Supporting Students Self-Regulated Learning with an Open Learner Model in a Linear Equation Tutor. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 219-228, 2013.
• Yanjin Long and Vincent Aleven. Skill Diaries: Improve Student Learning in an Intelligent Tutoring System with Periodic Self-Assessment. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 249-258, 2013.
• Rod D. Roscoe, Erica L. Snow, and Danielle S. McNamara. Feedback and Revising in an Intelligent Tutoring System for Writing Strategies. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 259-268, 2013.
• Butler, J., Britt, M.: Investigating instruction for improving revision of argumentative essays. Written Communication 28, 70-96 (2011)
• McNamara, D., Raine, R., Roscoe, R., Crossley, S., Jackson, G., Dai, J., Cai, Z., Renner, A., Brandon, R., Weston, J., Dempsey, K., Carney, D., Sullivan, S., Kim, L., Rus, V., Floyd, R., McCarthy, P., Graesser, A.: The Writing Pal: natural language algorithms to support intelligent tutoring of writing strategies. In: McCarthy, P.M., Boonthum-Denecke, C. (eds.) Applied Natural Language Processing and Content Analysis: Identification, Investigation, and Resolution, pp. 298-311. IGI Global, Hershey (2012)
• McNamara, D., Crossley, S., Roscoe, R.: Natural language processing in an intelligent writing strategy tutoring system. Behavior Research Methods (2012), doi:10.3758/s13428-012-0258-1
• Amir Shareghi Najar and Antonija Mitrovic. Examples and Tutored Problems: How Can Self-Explanation Make a Difference to Learning? K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 339-348, 2013.
• Miguel Arevalillo-Herraez and David Arnau. A Hypergraph Based Framework for Intelligent Tutoring of Algebraic Reasoning. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 512-521, 2013.
• Azevedo, R., et al.: The effectiveness of pedagogical agents prompting and feedback in facilitating co-adapted learning with metatutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 212-221. Springer, Heidelberg (2012)
• James R. Segedy, Gautam Biswas, Emily Feitl Blackstock, and Akailah Jenkins. Guided Skill Practice as an Adaptive Scaffolding Strategy in Open-Ended Learning Environments. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 532-541, 2013.
• Blair Lehman and Arthur Graesser, Impact of Agent Role on Confusion Induction and Learning, S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 45-54, 2014.
• Noboru Matsuda et al., Investigating the Effect of Meta-cognitive Scaffolding for Learning by Teaching, S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 104-113, 2014.
• Kurt VanLehn et al., The Affective Meta-Tutoring Project: Lessons Learned, S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 84-93, 2014.
• Min Chi, Pamela Jordan, and Kurt VanLehn, When Is Tutorial Dialogue More Effective Than Step-Based Tutoring? S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 210-219, 2014.
• Amruth N. Kumar, An Evaluation of Self-explanation in a Programming Tutor, S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 248-253, 2014.
• Frank Paiva et al., Comprehension SEEDING: Comprehension through Self Explanation, Enhanced Discussion, and INquiry Generation, S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 283-293, 2014.
• Wei Jin et al., Evaluation of Guided-Planning and Assisted-Coding with Task Relevant Dynamic Hinting, S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 318-328, 2014.
• Kelly Rivers and Kenneth R. Koedinger, Automating Hint Generation with Solution Space Path Construction, S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 329-339, 2014.
• Xingliang Chen, Antonija Mitrovic, and Moffat Mathews, Do Erroneous Examples Improve Learning in Addition to Problem Solving and Worked Examples? A. Micarelli et al. (Eds.): ITS 2016, LNCS 9684, pp. 13-22, 2016.
• Nguyen-Thinh Le and Nico Huse, Evaluation of the Formal Models for the Socratic Method A. Micarelli et al. (Eds.): ITS 2016, LNCS 9684, pp. 69-78, 2016.
• Yanjin Long and Vincent Aleven Mastery-Oriented Shared Student/System Control Over Problem Selection in a Linear Equation Tutor, A. Micarelli et al. (Eds.): ITS 2016, LNCS 9684, pp. 90-100, 2016.
• Noboru Matsuda et al., Tell Me How to Teach, I ll Learn How to Solve Problems A. Micarelli et al. (Eds.): ITS 2016, LNCS 9684, pp. 111-121, 2016.
• Giuseppe Fenza and Francesco Orciuoli, Building Pedagogical Models by Formal Concept Analysis, A. Micarelli et al. (Eds.): ITS 2016, LNCS 9684, pp. 144-153, 2016.
• Vincent Aleven, Ido Roll, Bruce M. McLaren, Kenneth R. Koedinger, Help Helps, But Only So Much: Research on Help Seeking with Intelligent Tutoring Systems, Int J Artif Intell Educ, March 2016, Volume 26, Issue 1, pp 205-223.
• Benedict du Boulay Rosemary Luckin, Modelling Human Teaching Tactics and Strategies for Tutoring Systems: 14 years on, Int J Artif Intell Educ (2016) 26:393-404.
• Vincent Aleven et al., Example-Tracing Tutors: Intelligent Tutor Development for Non-programmers, Int J Artif Intell Educ (2016) 26:224-269.
• Adapting Step Granularity in Tutorial Dialogue Based on Pretest Scores Pamela Jordan, Patricia Albacete, and Sandra Katz E. Andr ́e et al. (Eds.): AIED 2017, LNAI 10331, pp. 137–148, 2017
• Automatic Extraction of AST Patterns for Debugging Student Programs Timotej Lazar, Martin Moˇzina, and Ivan Bratko E. Andr ́e et al. (Eds.): AIED 2017, LNAI 10331, pp. 162–174, 2017
Hint Generation Under Uncertainty: The Effect of Hint Quality on Help-Seeking Behavior Thomas W. Price, Rui Zhi, and Tiffany Barnes E. Andr ́e et al. (Eds.): AIED 2017, LNAI 10331, pp. 311–322, 2017
• The Impact of Student Model Updates on Contingent Scaffolding
in a Natural-Language Tutoring System Patricia Albacete, Pamela Jordan, Sandra Katz, Irene-Angelica Chounta, and Bruce M. McLaren S. Isotani et al. (Eds.): AIED 2019, LNAI 11625, pp. 37–47, 2019
• An Adaptive Approach to Provide Feedback for Students in Programming Problem Solving Priscylla Silva1,3(&), Evandro Costa2, and Joseana Régis de Araújo A. Coy et al. (Eds.): ITS 2019, LNCS 11528, pp. 14–23, 2019
• Analyzing the Usage of the Classical ITS Software Architecture and Refining It Nikolaj Troels Graf von Malotky and Alke Martens A. Coy et al. (Eds.): ITS 2019, LNCS 11528, pp. 40–46, 2019

Emotions

• D Mello, S.K., Lehman, B., Person, N.: Monitoring affect states during effortful problem solving activities. International Journal of Artificial Intelligence in Education 20(4), 361-389 (2010), doi:10.3233/JAI-2010-012
• Sylvie Girard, Maria Elena Chavez-Echeagaray, Javier Gonzalez-Sanchez, Yoalli Hidalgo-Pontet, Lishan Zhang, Winslow Burleson, and Kurt VanLehn. Defining the Behavior of an Affective Learning Companion in the Affective Meta-tutor Project. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 21-30, 2013.
• Hayashi, Y.: On pedagogical effects of learner-support agents in collaborative interaction. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 22-32. Springer, Heidelberg (2012)
• Kim, Y., Baylor, A., Shen, E.: Pedagogical agents as learning companions: the impact of agent emotion and gender. Journal of Computer Assisted Learning 23(3), 220-234 (2007)
• Lehman, B., D Mello, S., Graesser, A.: Interventions to regulate confusion during Learning. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 576-578. Springer, Heidelberg (2012)
• Muldner, K., Burleson, W., Van de Sande, B., VanLehn, K.: An analysis of students’ gaming behaviors in an intelligent tutoring system: predictors and impacts. User Modeling and User-Adapted Interaction 21(1-2), 99-135 (2011), doi:10.1007/s11257-010-9086-0
• Rodrigo, M.M.T., Baker, R.S.J.d., Agapito, J., Nabo, J., Repalam, M.C., Reyes, S.S., San Pedro, M.O.C.Z.: The Effects of an Interactive Software Agent on Student Affective Dynamics while Using an Intelligent Tutoring System. IEEE Transactions on Affective Computing 3, 224-236 (2012), doi:http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2011.41
• Maria Ofelia Z. San Pedro, Ryan S.J.d. Baker, Sujith M. Gowda, and Neil T. Heffernan. Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 41-50, 2013.
• Acee, T.W., Kim, H., Kim, H.J., Kim, J., Hsiang-Ning, R.C., Kim, M.: Academic Boredom in Under- and Overchallenging Situations. Contemporary Ed. Psy. 35, 17-27 (2010)
• Baker, R.S.J.d., Corbett, A.T., Gowda, S.M., Wagner, A.Z., MacLaren, B.A., Kauffman, L.R., Mitchell, A.P., Giguere, S.: Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 52-63. Springer, Heidelberg (2010)
• Baker, R.S.J.d., D Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: Better to Be Frustrated than Bored: The Incidence, Persistence, and Impact of Learners Cognitive-Affective States during Interactions with Three Different Computer-Based Learning Environments. Int l. J. Human-Computer Studies 68(4), 223-241 (2010)
• Baker, R.S.J.d., Gowda, S.M., Wixon, M., Kalka, J., Wagner, A.Z., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., Rossi, L.: Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra. In: EDM 2012, pp. 126-133 (2012)
• Caitlin Mills, Sidney D Mello, , Blair Lehman, Nigel Bosch, Amber Strain, and Art Graesser. What Makes Learning Fun? Exploring the Influence of Choice and Difficulty on Mind Wandering and Engagement during Learning. K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 71-80, 2013.
• Nigel Bosch, Yuxuan Chen, Sidney D Mello, It s Written on Your Face: Detecting Affective States from Facial Expressions while Learning Computer Programming, S. Trausan-Matu et al. (Eds.): ITS 2014, LNCS 8474, pp. 39-44, 2014.
• The Role of Negative Emotions
and Emotion Regulation on Self-Regulated Learning with MetaTutor Megan J. Price, Nicholas V. Mudrick, Michelle Taub, and Roger Azevedo R. Nkambou et al. (Eds.): ITS 2018, LNCS 10858, pp. 170–179, 2018
• Changes in Emotion and Their Relationship with Learning Gains in the Context of MetaTutor Jeanne Sinclair, Eunice Eunhee Jang, Roger Azevedo, Clarissa Lau, Michelle Taub, and Nicholas V. Mudrick R. Nkambou et al. (Eds.): ITS 2018, LNCS 10858, pp. 202–211, 2018
• Adaptive Feedback Based on Student Emotion in a System for Programming Practice Thomas James Tiam-Lee and Kaoru Sumi R. Nkambou et al. (Eds.): ITS 2018, LNCS 10858, pp. 243–255, 2018
• Analysis and Prediction of Student Emotions While Doing Programming Exercises Thomas James Tiam-Lee and Kaoru Sumi A. Coy et al. (Eds.): ITS 2019, LNCS 11528, pp. 24–33, 2019

Ms. Lindquist (http://www.algebratutor.org/)

• N. Heffernan et al. An Intelligent Tutoring System Incorporating a Model of an Experienced Human Tutor. 6th Int. Conf. Intelligent Tutoring Systems, S596-608, 2002

Geometry tutor

• Aleven, V. & Koedinger, K. R. (2000). The Need for Tutorial Dialog to Support Self-Explanation. In C. P. Rose & R. Freedman (Eds.), Building Dialogue Systems for Tutorial Applications, Papers of the 2000 AAAI Fall Symposium (pp. 65-73). Technical Report FS-00-01. Menlo Park, CA: AAAI Press.
• Aleven V., Popescu, O. & Koedinger, K. R. (2001). Towards tutorial dialog to support self-explanation: Adding natural language understanding to a cognitive tutor. In J. D. Moore, C. L. Redfield, & W. L. Johnson (Eds.), Artificial Intelligence in Education: AI-ED in the Wired and Wireless Future, Proceedings of AI-ED 2001 (pp. 246-255). Amsterdam: IOS Press.
• Aleven, V., & Koedinger, K. R. (2002). An Effective Meta-cognitive Strategy: Learning by Doing and Explaining with a Computer-Based Cognitive Tutor. Cognitive Science, 26(2), 147-179.

AutoTutor (http://www.autotutor.org/)

• A. Graesser, et al. Teaching tactics and Dialog in AutoTutor. Int. Journal of AI in Education 12, S257-279, 2001
• Person et al. Simulating Human tutor Dialog Moves in AutoTutor, Int. Journal of AI in Education 2001, S23-39
• Graesser, A.C., Olney, A., Haynes, B.C., & Chipman, P. (2005). AutoTutor: A cognitive system that simulates a tutor that facilitates learning through mixed-initiative dialogue. In C. Forsythe, M.L. Bernard, and T.E. Goldsmith (Eds.), Cognitive systems: Human cognitive models in systems design. Mahwah, NJ: Erlbaum.
• D Mello, S.K., Craig, S.D., McDaniel, B., Witherspoon, A., Sullins, J., Gholson, B., Graesser, A.C. (2005). The relationship between affective states and dialog patterns during interactions with AutoTutor. Proceedings for the e-Learning 2005: World Conference on E-learning in Corporate, Government, Healthcare, and Business. Vancouver, CA: AACE.
• D Mello, S., & Graesser, A.C. (2006). Affect detection from human-computer dialogue with an intelligent tutoring system. In J. Gratch, M. Young, R. Aylett, D. Ballin, and P. Oliver (Eds.), Lecture Notes in Computer Science: Intelligent Virtual Agents: 6th International Conference (pp. 54-67). Berlin, Heidelberg, Germany: Springer.
• Benjamin D. Nye & Arthur C. Graesser & Xiangen Hu, AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring Int J Artif Intell Educ (2014) 24:427-469.

Atlas Why2

• K. VanLehn et al. The Architecture of Why2-Atlas: A Coach for Qualitative Physics. 6th Int. Conf. Intelligent Tutoring Systems, S158-167, 2002.
• P. Jordan, K. VanLehn Discourse Processing for Explanatory Essays in Tutorial Applications, 3rd SIGDIAL Workshop on Discourse and Dialogue, 2002.
• P. Jordan, M. Makatchev, U. Pappuswamy, Extended Explanations as Student Models for Guiding Tutorial Dialogue, 2003 AAAI Spring Symposium on Natural Language Generation in Written and Spoken Dialogue, S65-70.
• Jordan, P., Makatchev, M., Pappuswamy, U., VanLehn, K., & Albacete, P. (2006). A natural language tutorial dialogue system for physics. In G. Sutcliffe & R. Goebel (Eds.), Proceedings of the 19th International FLAIRS Conference. Menlo Park, CA: AAAI Press.
• VanLehn, K., Lynch, C., Schulze, K. Shapiro, J. A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., & Wintersgill, M. (2005). The Andes physics tutoring system: Lessons Learned. In International Journal of Artificial Intelligence and Education, 15 (3), 1-47.

Circsim (http://www.cs.iit.edu/~circsim/)

• Evens, Martha W., Stefan Brandle, Ru-Charn Chang, Reva Freedman, Michael Glass, Yoon Hee Lee, Leem Seop Shim, Chong Woo Woo, Yuemei Zhang, Yujian Zhou, Joel A. Michael, and Allen A. Rovick CIRCSIM-Tutor: An Intelligent Tutoring System Using Natural Language Dialogue Twelfth Midwest AI and Cognitive Science Conference, MAICS 2001, Oxford, OH, pp. 16-23.
• Glass, Michael Processing Language Input in the CIRCSIM-Tutor Intelligent Tutoring System Johanna Moore, Carol Luckhardt Redfield, and W. Lewis Johnson, eds., Artificial Intelligence in Education, IOS Press, 2001, pp. 210-221.
• Lulis, Evelyn, Reva Freedman, and Martha W. Evens. Implementing analogies using APE rules in an electronic tutoring system. In C.K. Looi, G. McCalla, and H. Pain (Eds.), Proceedings of the International Conference on AI in Education AIED-2005, Amsterdam, pp. 866-888. Amsterdam: IOS Press.

LARGO

• Niels Pinkwart, Collin Lynch, Kevin Ashley and Vincent Aleven, Re-evaluating LARGO in the Classroom: Are Diagrams Better Than Text for Teaching Argumentation Skills? Lecture Notes in Computer Science, 2008, Volume 5091, Intelligent Tutoring Systems, Pages 90-100

KERMIT (http://www.cosc.canterbury.ac.nz/tanja.mitrovic/kermit.html)

• Suraweera, P., Mitrovic, A. KERMIT: a Constraint-based Tutor for Database Modeling. In: S. Cerri, G. Gouarderes and F. Paraguacu (eds.) Proc. 6th Int. Conf on Intelligent Tutoring Systems ITS 2002, Biarritz, France, LCNS 2363, 377-387, 2002.
• Suraweera, P., Mitrovic, A. An Intelligent Tutoring System for Entity Relationship Modeling Int. J. Artificial Intelligence in Education, vol. 14, no. 3-4, 2004, 375-417.
• Baghaei, N., Mitrovic, A. COLLECT-UML: Supporting individual and collaborative learning of UML class diagrams in a constraint-based tutor . In: Rajiv Khosla, Robert J. Howlett, Lakhmi C. Jain (eds) Proc. KES 2005, Springer-Verlag, LCNS 3684, pp. 458-464, 2005
• Antonija Mitrovic and Stellan Ohlsson, Implementing CBM: SQL-Tutor After Fifteen Years, Int J Artif Intell Educ (2016) 26:150-159.
• Antonija Mitrovic1 & Pramuditha Suraweera, Teaching Database Design with Constraint-Based Tutors, Int J Artif Intell Educ (2016) 26:448-456.
• Stellan Ohlsson, Constraint-Based Modeling: From Cognitive Theory to Computer Tutoring – and Back Again, Int J Artif Intell Educ (2016) 26:457-473.

Prerequisites/Remarks

Joint lecture computer science/computational linguistisc/educational technology

Certificates

Presentation, contribution to discussions, written summary

Credit points

E-mail Helmut Horacek