Abstract
In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.
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Aleven V., McLaren B.M., Roll I., Koedinger K.R. (2006) Toward meta-cognitive tutoring: a model of help seeking with a cognitive tutor. I. J. Artif. Intell. Educ. 16(2): 101–128
Almond R.G., Mislevy R.J. (1999) Graphical models and computerized adaptive testing. Appl. Psychol. Meas. 23(3): 223–237
Almond, R.G., Dibello, L., Jenkins F., Senturk, D., Mislevy, R., Steinberg, L., Yan, D.: Models for conditional probability tables in educational assessment. In: Proceedings of the 2001 Conference on AI and Statistics, Key West, FL, 3–6 January 2001
Almond R.G., DiBello L.V., Moulder B., Zapata-Rivera J.-D. (2007) Modeling diagnostic assessments with Bayesian Networks. J. Educ. Meas. 44: 341–359. doi:10.1111/j.1745-3984.2007.00043.x
Alrifai, M., Dolog, P., Nejdl, W.: Learner Profile Management for Collaborative Adaptive eLearning Application. In: APS’2006: Joint International Workshop on Adaptivity, Personalisation and the Semantic Web at the 17th ACM Hypertext’06 Conference, Odense, Denmark, 22–25 August 2006
Aroyo L., Dolog P., Houben G.-J., Kravcik M., Naeve A., Nilsson M., Wild F. (2006) Interoperability in personalized adaptive learning. Educ. Technol. Soc. 9(2): 4–18
Arroyo, I., Ferguson, K., Johns, J., Dragon, T., Meheranian, H., Fisher, D., Barto, A.G., Mahadevan, S., Woolf, B.P.: Repairing disengagement with non-invasive interventions. In: Luckin, R., Koedinger, K.R., Greer, J.E. (eds.) Proceedings of the 13th International Conference on Artificial Intelligence in Education, AIED 2007, Los Angeles, CA, USA, pp. 195–202, 9–13 July 2007
Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion sensors go to school. In: Dimitrova, V., Mizoguchi, R., du Boulay, B., Graesser, A.C. (eds.) Proceedings of the 14th International Conference on Artificial Intelligence in Education, AIED 2009, Brighton, UK, pp. 17–24, 6–10 July 2009
Augustin T., Hockemeyer C., Kickmeier-Rust M., Albert D. (2011) Individualized skill assessment in educational games: basic definitions and mathematical formalism. IEEE Trans. Learn. Technol. 4: 138–148
Baker, R.S.J.: Modeling and understanding students’ off-task behavior in intelligent tutoring systems. In: Rosson, M.B., Gilmore, D.J. (eds.) Proceedings of the 2007 Conference on Human Factors in Computing Systems, CHI 2007, San Jose, CA, USA, pp. 1059–1068, 28 April–3 May 2007
Baker, R., Corbett, A.: More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian Knowledge Tracing. In: Proceedings of Ninth Intelligent Tutoring System Conference (ITS2008), pp. 406–415, Montreal, Canada, 24–27 June 2008. doi:10.1007/978-3-540-69132-7_44
Baker, R.S.J., de Carvalho, A.M.J.B.: Labeling student behavior faster and more precisely with text replays. In: Proceedings of the 1st International Conference on Educational Data Mining, EDM 2008, pp. 38–47, Montreal, Canada, 20–21 June 2008
Baker F.B., Kim S.-H. (2004) Item Response Theory, Parameter Estimation Techniques, 2nd edn. Marcel Dekker Inc, New York, NY
Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z.: ‘Off-Task Behavior in the Cognitive Tutor Classroom: When Students “Game The System”’. In: Proceedings of ACM CHI 2004: Computer–Human Interaction, pp. 383–390, Vienna, Austria, 24–29 April 2004
Baker, R.S.J., Corbett, A.T., Koedinger, K.R., Evenson, S., Roll, I., Wagner, A.Z., Naim, M., Raspat, J., Baker, D.J., Beck, J.E.: Adapting to when students game an intelligent tutoring system. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) Intelligent Tutoring Systems, 8th International Conference, ITS 2006, Jhongli, Taiwan, 26–30 June 2006, Proceedings, Vol. 4053 of Lecture Notes in Computer Science, pp. 392–401. Springer, Berlin (2006)
Baker, R.S.J., Corbett, A.T., Aleven, V.: Improving contextual models of guessing and slipping with a truncated training set. In: Baker, R.S.J., Barnes, T., Beck, J.E. (eds.) Proceedings of EDM 2008, The 1st International Conference on Educational Data Mining, pp. 67–76 (2008a)
Baker R.S.J., Corbett A.T., Roll I., Koedinger K.R. (2008b) Developing a generalizable detector of when students game the system. User Model. User-Adapt. Interact. 18(3): 287–314
Baker, R.S.J., Mitrovic, A., Mathews, M.: Detecting gaming the system in constraint-based tutors. In: User Modeling, Adaptation, and Personalization, 18th International Conference, UMAP 2010, Big Island, HI, USA, 20–24 June 2010, Proceedings, pp. 267–278 (2010)
Beal, C.R., Qu, L., Lee, H.: Classifying learner engagement through integration of multiple data sources. In: Proceedings of AAAI06, Boston, MA, 16–20 July 2006
Beck, J.E.: Engagement tracing: using response times to model student disengagement. In: Looi, C.-K., McCalla, G.I., Bredeweg, B., Breuker, J. (eds.) Proceedings of the 12th International Conference on Artificial Intelligence in Education, AIED 2005, 18–22 July 2005, Amsterdam, The Netherlands, pp. 88–95 (2005)
Beck, J.E., Chang, K.-m., Mostow, J., Corbett, A.T.: Does help help? Introducing the Bayesian evaluation and assessment methodology. In: Proceedings of Intelligent Tutoring Systems, ITS 2008, Montreal, Canada, pp. 383–394, 23–27 June 2008
Biswas G., Jeong H., Kinnebrew J., Sulcer B., Roscoe R. (2010) Measuring self-regulated learning skills through social interactions in a teachable agent environment. Res. Pract. Technology-Enhanced Learn. 5(2): 123–152
Bloom B.S. (1984) The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ. Res. 13(4): 4–16
Briggs D.C., Wilson M. (2003) An introduction to multidimensional measurement using Rasch models. J. Appl. Meas. 4: 87–100
Brusilovsky P. (2001) Adaptive hypermedia. User Model. User-Adapt. Interact. 11(1–2): 87–110
Brusilovsky P. (2003) Adaptive and intelligent web-based educational systems. Int. J. Artif. Intell. Educ. 13: 156–169
Brusilovsky P., Vassileva J. (2003) Course sequencing techniques for large-scale webbased education. Int. J. Cont. Eng. Lifelong Learn. 13: 75–94
Brusilovsky, P., Sosnovsky, S., Yudelson, M.: Ontology-based framework for user model interoperability in distributed learning environments. In: World Conference on E-Learning, E-Learn 2005, pp. 2851–2855, Vancouver, Canada (2005)
Bull, S., Broady, E.: Spontaneous peer tutoring from sharing student models. In: Proceedings of the 8th World Conference on Artificial Intelligence in Education (AIED97), pp. 143–150, Kobe, Japan, 19–23 August 1997
Bull S., Kay J. (2007) Student models that invite the learner. In: The SMILI open learner modelling framework. Int. J. Artif. Intell. Educ. 17(2): 89–120
Bull S., Kay J. (2010) Open learner models. In: Nkambou R., Bourdeau R., Mizoguchi J. (eds) Advances in Intelligent Tutoring Systems. Springer, Berlin, pp 301–322
Bull S., Pain H. (1995) Did I say what I think I said, and do you agree with me?: inspecting and questioning the student model. In: Greer J. (eds) Proceedings of World Conference on Artificial Intelligence and Education. Association for the Advancement of Computing in Education, Charlottesville, pp 501–508
Carmona, C., Millán, E., de-la Cruz, J.-L.P., Trella, M., Conejo, R.: Introducing prerequisite relations in a multi-layered Bayesian student model. In: Ardissono, L., Brna, P., Mitrovic, A. (eds.) User Modeling 2005, 10th International Conference, UM 2005, pp. 347–356. Edinburgh, Scotland, UK, 24–29 July 2005
Cen, H., Koedinger, K.R., Junker, B.: Learning factors analysis—a general method for cognitive model evaluation and improvement. In: Intelligent Tutoring Systems, 8th International Conference, ITS 2006, Jhongli, Taiwan, Proceedings, pp. 164–175, 26–30 June 2006
Cetintas, S., Si, L., Xin, Y.P., Hord, C., Zhang, D.: Learning to identify students’ off-task behavior in intelligent tutoring systems. In: Dimitrova, V., Mizoguchi, R., du Boulay, B., Graesser, A.C. (eds.) Proceedings of the 14th International Conference on Artificial Intelligence in Education, AIED 2009, Brighton, UK, pp. 701–703, 6–10 July 2009
Chaouachi, M., Frasson, C.: Exploring the relationship between learner EEG mental engagement and affect. In: Aleven, V., Kay, J., Mostow, J. (eds.) Intelligent Tutoring Systems, 10th International Conference, ITS 2010, Pittsburgh, PA, USA, Proceedings, Part II. pp. 291–293, 14–18 June 2010
Cohen J.A. (1960) A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1): 37–46
Cohen P.A., Kulik J.A., Kulik C.L.C. (1982) Educational outcomes of tutoring: a meta-analysis of findings. Am. Educ. Res. J. 19: 237–248
Cole D.A. (1987) Utility of confirmatory factor analysis in test validation research. J. Consult. Clin. Psychol. 55(4): 584–594
Conati C., Maclaren H. (2009) Empirically building and evaluating a probabilistic model of user affect. User Model. User-Adapt. Interact. 19(3): 267–303
Conati C., Gertner A., VanLehn K. (2002) Using Bayesian Networks to manage uncertainty in student modeling. User Model. User-Adapt. Interact. 12(4): 371–417
Conati, C., Chabbal, R., Maclaren, H.: A study on using biometric sensors for detecting user emotions in educational games. In: Proceedings of the Workshop “Assessing and Adapting to UserAttitude and Affects: Why, When and How? In Conjunction with User Modeling (UM-03), Pittsburg, PA”, 22–26 June 2003
Conejo R., Guzman E., Millán E., Trella M., Pérez-de-la Cruz J.L., Rios A. (2004) SIETTE: a web-based tool for adaptive teaching. Int. J. Artif. Intell. Educ. 14: 29–61
Corbett A.T., Anderson J.R. (1995) Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adapt. Interact. 4(4): 253–278
Cumming G., Mcdougall A. (2000) Mainstreaming AIED into education?. Int. J. Artif. Intell. Educ. (IJAIED) 11: 197–207
de Vicente A., Pain H. (2002) Informing the detection of the students’ motivational state: an empirical study. Lect. Notes Comput. Sci. 2363: 933–943
Desmarais, M.C.: Performance comparison of item-to-item skills models with the IRT single latent trait model. In: User Modeling, Adaptation and Personalization, UMAP 2011, pp. 75–86, Gerona, Spain, 11–15 July 2011
Desmarais M.C., Maluf A., Liu J. (1996) User-expertise modeling with empirically derived probabilistic implication networks. User Model. User-Adapt. Interact. 5(3–4): 283–315
Desmarais M.C., Meshkinfam P., Gagnon M. (2006) Learned student models with item to item knowledge structures. User Model. User-Adapt. Interact. 16(5): 403–434
DiEugenio B., Glass M. (2004) The Kappa statistic: a second look. Comput. Linguist. 30(1): 95–101
Dimitrova V. (2003) StyLE-OLM: interactive open learner modelling. Int. J. Artif. Intell. Educ. 13(1): 35–78
D’Mello, S.K., Taylor, R.S., Graesser, A.: Affective Trajectories during Complex Learning. In: Proceedings of the 29th Annual Meeting of the Cognitive Science Society, Austin, TX, pp. 203–208 (2007)
D’Mello S.K., Craig S.D., Witherspoon A.M., McDaniel B., Graesser A.C. (2008) Automatic detection of learner’s affect from conversational cues. User Model. User-Adapt. Interact. 18(1–2): 45–80
Doignon J.-P., Falmagne J.-C. (1985) Spaces for the assessment of knowledge. International Journal of Man-Machine Studies 23: 175–196
Doignon J.-P., Falmagne J.-C. (1999) Knowledge Spaces. Springer, Berlin
Dolog, P., Schäfer, M.: A framework for browsing, manipulating and maintaining interoperable learner profiles. In: Proceedings of UM’2005, pp. 397–401, Edinburgh, Scotland, 2005
Dütsch I., Gediga G. (1995) Skills and knowledge structures. Br. J. Math. Stat. Psychol. 48: 9–27
Efrong B., Gong G. (1983) A leisurely look at the bootstrap, the jackknife, and cross-validation. Am. Stat. 37: 36–48
Ekman P., Friesen W.V., O’Sullivan M., Chan A., Diacoyanni-Tarlatzi I., Heider K., Krause R., Lecompte W.A., Pitcairn T., Picci-Bitti P.E., Sherer K., Tomita M., Tzavaras A. (1987) Universal and cultural differences in the judgment of facial expression and emotion. J. Pers. Soc. Psychol. 53: 712–717
Falmagne J.-C., Cosyn E., Doignon J.-P., Thiéry N. (2006) The assessment of knowledge, in theory and in practice. In: Missaoui R., Schmid J. (eds) ICFCA, Vol. 3874 of Lecture Notes in Computer Science. Springer, Berlin, pp 61–79
Feng, M., Heffernan, N.T., Koedinger, K.R.: Addressing the testing challenge with a web-based e-assessment system that tutors as it assesses. In: Carr, L., Roure, D.D., Iyengar, A., Goble, C.A., Dahlin, M. (eds.) Proceedings of the 15th international conference on World Wide Web, WWW 2006, Edinburgh, Scotland, UK, pp. 307–316, 23–26 May 2006
Fogarty, J., Baker, R.S., Hudson, S.E.: Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction. In: Inkpen, K., van de Panne, M. (eds.) Proceedings of the Graphics Interface 2005 Conference, Victoria, BC, Canada, pp. 129–136, 9–11 May 2005
Forbes-Riley, K., Litman, D.J.: Predicting emotion in spoken dialogue from multiple knowledge sources. In: HLT-NAACL, pp. 201–208, Boston, MA, 2–7 May 2004
Friesen N. (2005) Interoperability and learning objects: an overview of e-learning standardization. Interdiscip. J. Knowl. Learn. Objects 1: 23–31
Gong, Y., Beck, J.E., Heffernan, N.T.: Using multiple Dirichlet distributions to improve parameter plausibility. In: Baker, R.S.J.d., Merceron, A.M., Pavlik, P.I., Jr. (eds.) Educational Data Mining 2010 (EDM2010), pp. 61–70, Pittsburgh, PA, 11–13 June 2010
Greer, J.E., McCalla, G.I.: A computational framework for granularity and its application to educational diagnosis. In: IJCAI, pp. 477–482, Detroit, MI (1989)
Guzmán E., Conejo R., Pérez-de-la Cruz J.-L. (2007) Adaptive testing for hierarchical student models. User Model. User-Adapt. Interact. 17: 119–157
Hacker D.J. (1999) Definitions and empirical foundations. In: Hacker D.J., Dunlosky J., Graesser A.C. (eds) Metacognition in Educational Theory and Practice. Lawrence Erlbaum Associates, New Jersey, pp 1–24
Haertel E.H. (1989) Using restricted latent class models to map the skill structure of achievement items. J. Educ. Meas. 26: 301–321. doi:10.1111/j.1745-3984.1989.tb00336.x
Hanley J.A., McNeil B.J. (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29–36
Hastie T., Tibshirani R., Friedman J.H. (2001) The elements of statistical learning. Springer, New York
Hatala, M., Richards, G., Eap, T., Willms, J.: Interoperability of learning object repositories and services: standards, implementations and lessons learned. In: The Proceedings of the 13th World Wide Web Conference, New York, NY (2004)
Heller J., Steiner C., Hockemeyer C., Albert D. (2006) Competence-based knowledge structures for personalised learning. Int. J. E-Learn. 5(1): 75–88
Hockemeyer, C., Held, T., Albert, D.: RATH—a relational adaptive tutoring hypertext WWW–environment based on knowledge space theory. In: Alvegård (ed.) Proceedings of 4th International Conference on Computer Aided Learning and Instruction in Science and Engineering, Proceedings of the CALISCE’98, pp. 417–423, Göteborg, Sweden (1997)
Jameson A. (1995) Numerical uncertainty management in user and student modeling: an overview of systems and issues. User Model. User-Adapt. Interact. 5(3–4): 193–251
Johns, J., Woolf, B.P.: A dynamic mixture model to detect student motivation and proficiency. In: Proceedings of AAAI2006, Boston, MA (2006)
Junker B., Sijtsma K. (2001) Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Appl. Psychol. Meas. 25(3): 258–272
Kay J., Maisonneuve N., Yacef K., Reimann P. (2006) The big five and visualisations of team work activity. In: Ikeda M., Ashley K., Chan T.-W. (eds) Proceedings of Intelligent Tutoring Systems (ITS06). Springer, Berlin, pp 197–206
Khalid, M.N.: IRT model fit from different perspectives. Ph.D. thesis, University of Twente (2009)
Koedinger K.R., Anderson J.R., Hadley W.H., Mark M.A. (1997) Intelligent tutoring goes to school in the big city. Int. J. Artif. Intell. Educ. 8: 30–43
Koedinger, K.R., Corbett, A.T., Perfetti, C.: The Knowledge–Learning–Instruction (KLI) framework: toward bridging the science-practice chasm to enhance robust student learning. Technical report, Carnegie-Mellon University, Human Computer Interaction Institute, Pittsburgh (2011)
Koper E.J.R., Giesbers B., Van Rosmalen P., Sloep P., Van Bruggen J., Tattersall C., Vogten H., Brouns F. (2005) A design model for lifelong learning networks. Interact. Learn. Environ. 1–2: 71–92
Kumar, R., Rosé, C.P., Wang, Y.C., Joshi, M., Robinson, A.: Tutorial dialogue as adaptive collaborative learning support. In: Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED 2007), Marina Del Rey, CA, 9–13 July 2007
Lajoie S. (2005) Extending the scaffolding metaphor. Instr. Sci. 33: 541–557
Lepper, M.R., Woolverton, M., Mumme, D.L., Gurtner, J.L.: Motivational techniques of expert human tutors: lessons for the design of computer-based tutors. In: Lajoie, S.P., Derry, S.J. (eds). Computers as Cognitive tools. Lawrence Erlbaum Associates, Hillsdale (1991)
Ley T., Kump B., Albert D. (2010) A methodology for eliciting, modelling, and evaluating expert knowledge for an adaptive work-integrated learning system. Int. J. Hum.-Comput. Stud. 68(4): 185–208
Liu C.-L. (2009) . Behaviormetrikasimulation-based experience in learning structures of Bayesian networks to represent how students learn composite concepts 36(1): 1–25
Mabbott, A., Bull, S.: Student preferences for editing, persuading and negotiating the open learner model. In: Proceedings of the International Conference on Intelligent Tutoring Systems (ITS 2006), pp. 481–490, Jhongli, Taiwan, 26–30 June 2006
Mayo M., Mitrovic A. (2001) Optimising ITS behaviour with Bayesian Networks and decision theory. Int. J. Artif. Intell. Educ. 12: 124–153
McCalla G. (2004) The ecological approach to the design of e-learning environments: purpose-based capture and use of information about learners. J. Interact. Media Educ. 7: 1–23
McCalla, G.I., Peachey, D.R., Ward, B.: An architecture for the design of large-scale intelligent teaching systems. In: Cercone, N., McCalla, G. (eds.) Proceedings of the 4th National Conference of the CSCSI, pp. 85–91 (1982)
McCalla G., Greer J., Barrie B., Pospisil P. (1992) Granularity hierarchies. Comput. Math. Appl. 23(2–5): 363–375
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pp. 935–940, Philadelphia, PA, 20–23 August 2006
Millán E., Pérez-de-la-Cruz J.L. (2002) A Bayesian diagnostic algorithm for student modeling and its evaluation. User Model. User-Adapt. Interact. 12(2–3): 281–330
Mislevy R.J., Gitomer D. (1995) The role of probability-based inference in an intelligent tutoring system. User Model. User-Adapt. Interact. 42(5): 253–282
Mislevy, R.J., Almond, R.G., Yan, D., Steinberg L.S.: Bayes nets in educational assessment: where the numbers come from. In: Laskey, K.B., Prade, H. (eds.) Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI-99). San Francisco, CA, pp. 437–446, 30 July– 1 Aug 1999
Mitrovic, A.: Fifteen years of constraint-based tutors: what we have achived and where we are going. User Model. User-Adapt. Interact. 22 (this issue, 2012)
Mitrovic, A., Koedinger, K.R., Martin, B.: A comparative analysis of cognitive tutoring and constraint-based modeling. In: User Modeling 2003, 9th International Conference, UM 2003, Johnstown, PA, USA, pp. 313–322, 22–26 June 2003
Montalvo, O., Baker, R.S.J., Sao Pedro, M.A., Nakama, A., Gobert, J.D.: Identifying student’ inquiry planning using machine learning. In: Proceedings of the 3rd International Conference on Educational Data Mining, pp. 141–150, Pittsburgh, PA, 11–13 June 2010
Mota, S., Picard, R.: Automated posture analysis for detecting learner’s interest level. In: Workshop Computer Vision and Pattern Recognition for Human Computer Interaction, in Conjunction with CVPR 2003, Madison, WI, June 2003
Muldner, K., Burleson, W., van de Sande, B., VanLehn, K.: An analysis of gaming behaviors in an intelligent tutoring system. In: Aleven, V., Kay, J., Mostow, J. (eds.) Intelligent Tutoring Systems, 10th International Conference, ITS 2010, Pittsburgh, PA, USA, 14–18 June 2010, Proceedings, Part I, Vol. 6094 of Lecture Notes in Computer Science, pp. 184–193. Springer, Berlin (2010)
Neapolitan R.E. (2004) Learning Bayesian Networks. Prentice Hall, New Jersey
Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds) (2010) Advances in Intelligent Tutoring Systems. Springer, Berlin
Ortony A., Clore G.L., Collins A. (1988) The Cognitive Structure of Emotions. Cambridge University Press, Cambridge
Pardos, Z.A., Heffernan, N.: Navigation the parameter space of Bayesian Knowledge Tracing models: visualizations of the convergence of the expectation maximization algorithm. In: Proceedings of the 3rd Educational Data Mining Conference 2010, pp. 161–170, Pittsburgh, PA, 11–13 June 2010a
Pardos Z.A., Heffernan N.T.: Modeling individualization in a Bayesian networks implementation of knowledge tracing. In: Proceedings of the 18th International Conference on User Modeling Adaptation and Personalization, Big Island of Hawai, USA, 20–24 June 2010b
Pardos, Z., Heffernan, N.: KT-IDEM: Introducing item difficulty to the knowledge tracing model. In: User Modeling, Adaptation, and Personalization, 19th International Conference, UMAP 2011, Gerona, Spain, pp. 243–254, 11–15 July 2011
Pavlik, P.I., Cen, H., Koedinger, K.R.: Learning factors transfer analysis: using learning curve analysis to automatically generate domain models. In: Barnes, T., Desmarais, M.C., Romero, C., Ventura, S. (eds.) 2nd International Conference on Educational Data Mining—EDM2009, pp. 121–130, Cordoba, Spain, 1–3 July 2009a
Pavlik, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis—a new alternative to knowledge tracing. In: Proceeding of the 2009 conference on Artificial Intelligence in Education, Amsterdam, The Netherlands, pp. 531–538 (2009b)
Peachey D.R., McCalla G.I. (1986) Using planning techniques in intelligent tutoring systems. Int. J. Man-Mach. Stud. 24(1): 77–98
Perera D., Kay J., Koprinska I., Yacef K., Zaïane O.R. (2009) Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6): 759–772
Prata, D.N., Baker, R.S.J.d., Costa, E., Rosé, C.P., Cui, Y., de Carvalho, A.M.J.B.: Detecting and understanding the impact of cognitive and interpersonal conflict in computer supported collaborative learning environments. In: Proceedings of 2nd International Conference on Educational Data Mining—EDM2009, pp. 131–140, Cordoba, Spain, 1–3 July 2009
Raftery A.E. (1995) Bayesian model selection in social research (with discussion). Sociol. Methodol. 25: 111–193
Rebolledo-Mendez, G., du Boulay, B., Luckin, R.: Motivating the learner: an empirical evaluation. In: 8th International Conference on Intelligent Tutoring Systems (ITS2006), pp. 545–554, Jhongli, Taiwan, 26–30 June 2006
Reckase M.D., McKinley R.L. (1991) The discriminating power of items that measure more than one dimension. Appl. Psychol. Meas. 15(4): 361–373
Reye J. (2004) Student modelling based on belief networks. Int. J. Artif. Intell. Educ. 14: 63–96
Roll, I., Aleven, V., Mclaren, B.M., Koedinger, K.R.: Can help seeking be tutored? Searching for the secret sauce of metacognitive tutoring. In: Artificial Intelligence in Education (AIED 2007), pp. 203–210 (2009)
Roussos L.A., Templin J.L., Henson R.A. (2007) Skills diagnosis using IRT-based latent class models. J. Educ. Meas. 44: 293–311
Self, J.: Bypassing the intractable problem of student modelling. In: Proceedings of Intelligent Tutoring Systems, ITS’88, pp. 18–24, Montreal, Canada (1988)
Shih, B., Koedinger, K.R., Scheines, R.: A response time model for bottom-out hints as worked examples. In: Proceedings of the First International Conference on Educational Data Mining, EDM2008, pp. 117–126, Montreal, Canada, 20–21 June 2008
Stout W. (2007) Skills diagnosis using IRT-based continuous latent trait models. J. Educ. Meas. 44: 313–324. doi:10.1111/j.1745-3984.2007.00041.x
Tatsuoka K.K. (1983) Rule space: an approach for dealing with misconceptions based on item response theory. J. Educ. Meas. 20: 345–354
Thai-Nghe, N., Horváth, T., Schmidt-Thieme, L.: Factorization Models for forecasting student performance. In: Conati, C., Ventura, S., Pechenizkiy, M., Calders, T. (eds.) Proceedings of EDM 2011, The 4th International Conference on Educational Data Mining, pp. 11–20, Eindhoven, Netherlands, 6–8 July 2011
VanLehn, K., Niu, Z., Siler, S., Gertner, A.S.: Student modeling from conventional test data: a Bayesian approach without priors. In: ITS’98: Proceedings of the 4th International Conference on Intelligent Tutoring Systems, London, UK, pp. 434–443 (1998)
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. Int. J. Artif. Intell. Educ. 15(3): 147–204
Vassileva, J.: Dynamic courseware generation: at the cross point of CAL, ITS and authoring. In: Proceedings International Conference on Computers in Education (ICCE’95), pp. 290–297, Singapore (1995)
Vassileva J., McCalla G., Greer J. (2003) Multi-agent multi-user modeling in I-help. User Model. User-Adapt. Interact. 13: 179–210
Villano, M.: Probabilistic student models: Bayesian belief networks and knowledge space theory. In: Frasson, C. e. a. (ed.) Proceedings of the Second International Conference on Intelligent Tutoring Systems, pp. 492–498, Montréal, Canada, 10–12 June 1992
Vomlel J. (2004) Bayesian networks in educational testing. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 12: 83–100
Vygotsky L.S. (1978) Mind in Society: Development of Higher Psychological Processes. Harvard University Press, Cambridge
Walker, E., Walker, S., Rummel, N., Koedinger, K.: Using problem-solving context to assess help quality in computer-mediated peer tutoring. In: 10th International Conference on Intelligent Tutoring Systems (ITS2010), Pittsburgh, PA, 14–18 June 2010
Walonoski, J.A., Heffernan, N.T.: Detection and analysis of off-task gaming behavior in intelligent tutoring systems. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) Intelligent Tutoring Systems, 8th International Conference, ITS 2006, Jhongli, Taiwan, Proceedings, , 26–30 June 2006, Vol. 4053 of Lecture Notes in Computer Science, pp. 382–391. Springer, Berlin (2006)
Woolf B.P. (2009) Building Intelligent Interactive Tutors Student-Centered Strategies for Revolutionizing e-Learning. Elsevier, Amsterdam
Xu, Y., Mostow J.: Using logistic regression to trace multiple subskills in a dynamic Bayes Net. In: Conati, C., Ventura, S., Pechenizkiy, M., Calders, T. (eds.) Proceedings of EDM 2011, The 4th International Conference on Educational Data Mining, pp. 241–246, Eindhoven, Netherlands, 6–8 July 2011
Yudelson, M., Pavlik, P.I., Koedinger, K.R.: User modeling—a notoriously black art. In: User Modeling, Adaptation, and Personalization, 19th International Conference, UMAP 2011, Gerona, Spain, pp. 317–328, 11–15 July 2011
Zapata-Rivera J.-D., Greer J. (2004) Inspectable Bayesian student modeling servers in multi-agent tutoring systems. Int. J. Hum.-Comput. Stud. 61(4): 535–563
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Desmarais, M.C., Baker, R.S.J.d. A review of recent advances in learner and skill modeling in intelligent learning environments. User Model User-Adap Inter 22, 9–38 (2012). https://doi.org/10.1007/s11257-011-9106-8
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DOI: https://doi.org/10.1007/s11257-011-9106-8