Relevant bibliographies by topics / Intelligent tutoring systems

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intelligent tutoring systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Intelligent tutoring systems"

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Bradáč, Vladimír, and Kateřina Kostolányová. "Intelligent Tutoring Systems." Journal of Intelligent Systems 26, no. 4 (September 26, 2017): 717–27. http://dx.doi.org/10.1515/jisys-2015-0144.

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AbstractThe importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both as a support of presence lessons and, primarily, as the main educational environment for students in the distance form of studies – e-learning. The systems are described from the point of view of their functionalities and typical features that show their differences. The authors conclude with an attempt to choose the best features of each system, which would lead to creation of an even more sophisticated intelligent tutoring system for e-learning.
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Anderson, J. R., C. F. Boyle, and B. J. Reiser. "Intelligent Tutoring Systems." Science 228, no. 4698 (April 26, 1985): 456–62. http://dx.doi.org/10.1126/science.228.4698.456.

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ROSS, P. "Intelligent tutoring systems." Journal of Computer Assisted Learning 3, no. 4 (December 1987): 194–203. http://dx.doi.org/10.1111/j.1365-2729.1987.tb00331.x.

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Maher, Mary Lou. "Intelligent tutoring systems." Artificial Intelligence in Engineering 2, no. 1 (January 1987): 50. http://dx.doi.org/10.1016/0954-1810(87)90075-6.

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Auguste, Donna. "Intelligent tutoring systems." Artificial Intelligence 26, no. 2 (May 1985): 233–38. http://dx.doi.org/10.1016/0004-3702(85)90033-5.

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Stefik, Mark. "Intelligent tutoring systems." Artificial Intelligence 26, no. 2 (May 1985): 238–45. http://dx.doi.org/10.1016/0004-3702(85)90034-7.

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Del Soldato, Teresa. "Intelligent Tutoring Systems 92." AI Communications 5, no. 4 (1992): 215–16. http://dx.doi.org/10.3233/aic-1992-5411.

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Woolf, Beverly Park. "Intelligent multimedia tutoring systems." Communications of the ACM 39, no. 4 (April 1996): 30–31. http://dx.doi.org/10.1145/227210.227217.

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Nkambou, Roger, and Froduald Kabanza. "Designing intelligent tutoring systems." ACM SIGCUE Outlook 27, no. 2 (March 2001): 46–60. http://dx.doi.org/10.1145/381234.381246.

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Yazdani, M. "Intelligent tutoring systems survey." Artificial Intelligence Review 1, no. 1 (March 1986): 43–52. http://dx.doi.org/10.1007/bf01988527.

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Dissertations / Theses on the topic "Intelligent tutoring systems"

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Brown, Quincy Lee Frank Salvucci Dario. "Mobile intelligent tutoring system : moving intelligent tutoring systems off the desktop /." Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3114.

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Razzaq, Leena M. "Tutorial dialog in an equation solving intelligent tutoring system." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0107104-155853.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: cognitive model; model-tracing; intelligent tutoring system; tutoring; artificial intelligence. Includes bibliographical references (p. 55-57).
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Thompson, Allan. "Adaptive intelligent tutoring systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22783.pdf.

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MATOS, Diego Dermeval Medeiros da Cunha. "Authoring gamified intelligent tutoring systems." Universidade Federal de Campina Grande, 2017. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/867.

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Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-06-04T13:17:59Z No. of bitstreams: 1 DIEGO DERMEVAL MEDEIROS DA CUNHA MATOS - TESE (PPGCC) 2017.pdf: 5848671 bytes, checksum: b890812e50eefda440fc048fd77b0f93 (MD5)
Made available in DSpace on 2018-06-04T13:17:59Z (GMT). No. of bitstreams: 1 DIEGO DERMEVAL MEDEIROS DA CUNHA MATOS - TESE (PPGCC) 2017.pdf: 5848671 bytes, checksum: b890812e50eefda440fc048fd77b0f93 (MD5) Previous issue date: 2017-03-17
Sistemas Tutores Inteligentes (STIs) têm recibo a atenção de acadêmicos e profissionais desde da década de 70. Tem havido um grande número de estudos recentes em apoio da efetividade de STIs. Entretanto, é muito comum que estudantes fiquem desengajados ou entediados durante o processo de aprendizagem usando STIs. Para considerar explicitamente os aspectos motivacionais de estudantes, pesquisadores estão cada vez mais interessados em usar gamificação em conjunto com STIs. Contudo, apesar de prover tutoria individualizada para estudantes e algum tipo de suporte para professores, estes usuários não têm recebido alta prioridade no desenvolvimento destes tipos de sistemas. De forma a contribuir para o uso ativo e personalizado de STIs gamificados por professores, três problemas técnicos devem ser considerados. Primeiro, projetar STI é muito complexo (deve-se considerar diferentes teorias, componentes e partes interessadas) e incluir gamificação pode aumentar significativamente tal complexidade e variabilidade. Segundo, as funcionalidades de STIs gamificados podem ser usadas de acordo com vários elementos (ex.: nível educacional, domínio de conhecimento, teorias de gamificaçãoe STI, etc). Desta forma, é imprescindível tirar proveito das teorias e práticas de ambos os tópicos para reduzir o espaço de design destes sistemas. Terceiro, para efetivamente auxiliar professores a usarem ativamente estes sistemas, faz-se necessário prover uma solução simples e usável para eles. Para lidar com estes problemas, o principal objetivo desta tese é projetar uma solução computacional de autoria para fornecer aos professores uma forma de personalizar as funcionalidades de STIs gamificados gerenciando a alta variabilidade destes sistemas e considerando as teorias/práticas de gamificação e STI. Visando alcançar este objetivo, nós identificamos o espaço de variabilidade e o representamos por meio do uso de uma abordagem de modelagem de features baseada em ontologias (OntoSPL). Desenvolvemos um modelo ontológico integrado (Ontologia de tutoria gamificada ou Gamified tutoring ontology) que conecta elementos de design de jogos apoiados por evidências no domínio de e-learning, além de teorias e frameworks de gamificação aos conceitos de STI. Finalmente, desenvolvemos uma solução de autoria (chamada AGITS) que leva em consideração tais ontologias para auxiliar professores na personalização de funcionalidades de STIs gamificados. As contribuições deste trabalho são avaliadas por meio da condução de quatro estudos empíricos: (1) conduzimos um experimento controlado para comparar a OntoSPL com uma abordagem de modelagem de features bem conhecida na literatura. Os resultados sugerem que esta abordagem é mais flexível e requer menos tempo para mudar; (2) avaliamos o modelo ontológico integrado usando um método de avaliação de ontologias (FOCA) com especialistas tanto de contexto acadêmico quanto industrial. Os resultados sugerem que as ontologias estão atendendo adequadamente os papeis de representação do conhecimento; (3) avaliamos versões não-interativas da solução de autoria desenvolvida com 59 participantes. Os resultados indicam uma atitude favorável ao uso da solução de autoria projetada,nos quais os participantes concordaram que a solução é fácil de usar, usável, simples, esteticamente atraente,tem um suporte bem percebido e alta credibilidade; e (4) avaliamos, por fim,versões interativas (do zero e usando um modelo) da solução de autoria com 41 professores. Os resultados sugerem que professores podem usar e reusar, com um alto nível de aceitação, uma solução de autoria que inclui toda a complexidade de projetar STI gamificado.
Intelligent Tutoring Systems (ITSs) have been drawing the attention of academics and practitioners since early 70’s. There have been a number of recent studies in support of the effectiveness of ITSs. However, it is very common that students become disengaged or bored during the learning process by using ITSs. To explicitly consider students’ motivational aspects, researchers are increasingly interested in using gamification along with ITS.However, despite providing individualized tutoring to students and some kind of support for teachers, teachers have been not considered as first-class citizens in the development of these kinds of systems. In order to contribute to the active and customized use of gamified ITS by teachers, three technical problems should be considered. First, designing ITS is very complex (i.e., take into account different theories, components, and stahekolders) and including gamification may significantly increase such complexity and variability. Second, gamified ITS features can be used depending on several elements (e.g., educational level, knowledge domain, gamification and ITS theories, etc). Thus, it is imperative to take advantage of theories and practices from both topics to reduce the design space of these systems. Third, in order to effectively aid teachers to actively use such systems, it is needed to provide a simple and usable solution for them. To deal with these problems, the main objective of this thesis is to design an authoring computational solution to provide for teachers a way to customize gamified ITS features managing the high variability of these systems and considering gamification and ITS theories/practices. To achieve this objective, we identify the variability space and represent it using an ontology-based feature modeling approach (OntoSPL). We develop an integrated ontological model (Gamified tutoring ontology) that connects evidence-supported game design elements in the e-learning domain as well as gamification theories and frameworks to existing ITS concepts. Finally, we develop an authoring solution (named AGITS) that takes into account these ontologies to aid teachers in the customization of gamified ITS features. We evaluate our contributions by conducting four empirical studies: (1) we perform a controlled experiment to compare OntoSPL against a well-known ontology-based feature modeling approach. The results suggest that our approach is more flexible and requires less time to change; (2) we evaluate the ontological integrated model by using an ontology evaluation method (FOCA) with experts from academic and industrial settings. The results suggest that our ontologies are properly targeting the knowledge representation roles; (3) we evaluate non-interactive versions of the designed authoring solution with 59 participants. The results indicate a positive attitude towards the use of the designed authoring solutions, in which participants agreed that they are ease to use, usable, simple, aesthetically appealing, have a well-perceived system support and high credibility; and (4) we also evaluate interactive versions (scratch and template) of our authoring solution with 41 teachers. The results suggest that teachers can use and reuse, with a high acceptance level, an authoring solution that includes all the complexity to design gamified ITS.
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Gong, Yue. "Student Modeling in Intelligent Tutoring Systems." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/403.

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"After decades of development, Intelligent Tutoring Systems (ITSs) have become a common learning environment for learners of various domains and academic levels. ITSs are computer systems designed to provide instruction and immediate feedback, which is customized to individual students, but without requiring the intervention of human instructors. All ITSs share the same goal: to provide tutorial services that support learning. Since learning is a very complex process, it is not surprising that a range of technologies and methodologies from different fields is employed. Student modeling is a pivotal technique used in ITSs. The model observes student behaviors in the tutor and creates a quantitative representation of student properties of interest necessary to customize instruction, to respond effectively, to engage students¡¯ interest and to promote learning. In this dissertation work, I focus on the following aspects of student modeling. Part I: Student Knowledge: Parameter Interpretation. Student modeling is widely used to obtain scientific insights about how people learn. Student models typically produce semantically meaningful parameter estimates, such as how quickly students learn a skill on average. Therefore, parameter estimates being interpretable and plausible is fundamental. My work includes automatically generating data-suggested Dirichlet priors for the Bayesian Knowledge Tracing model, in order to obtain more plausible parameter estimates. I also proposed, implemented, and evaluated an approach to generate multiple Dirichlet priors to improve parameter plausibility, accommodating the assumption that there are subsets of skills which students learn similarly. Part II: Student Performance: Student Performance Prediction. Accurately predicting student performance is one of the most desired features common evaluations for student modeling. for an ITS. The task, however, is very challenging, particularly in predicting a student¡¯s response on an individual problem in the tutor. I analyzed the components of two common student models to determine which aspects provide predictive power in classifying student performance. I found that modeling the student¡¯s overall knowledge led to improved predictive accuracy. I also presented an approach, which, rather than assuming students are drawn from a single distribution, modeled multiple distributions of student performances to improve the model¡¯s accuracy. Part III: Wheel-spinning: Student Future Failure in Mastery Learning. One drawback of the mastery learning framework is its possibility to leave a student stuck attempting to learn a skill he is unable to master. We refer to this phenomenon of students being given practice with no improvement as wheel-spinning. I analyzed student wheel-spinning across different tutoring systems and estimated the scope of the problem. To investigate the negative consequences of see what wheel-spinning could have done to students, I investigated the relationships between wheel-spinning and two other constructs of interest about students: efficiency of learning and ¡°gaming the system¡±. In addition, I designed a generic model of wheel-spinning, which uses features easily obtained by most ITSs. The model can be well generalized to unknown students with high accuracy classifying mastery and wheel-spinning problems. When used as a detector, the model can detect wheel-spinning in its early stage with satisfying satisfactory precision and recall. "
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Buckenmeyer, Michelle. "User characteristics in intelligent tutoring systems /." Online version of thesis, 1992. http://hdl.handle.net/1850/10998.

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Green, Derek Tannell. "INTELLIGENT TUTORING SYSTEMS FOR SKILL ACQUISITION." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/203476.

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Throughout history education has been restricted to a relatively small percentage of the world's population. The cause can be attributed to a number of factors; how- ever, it has been chiefly due to excessive cost. As we enter the information age it becomes conceivable to make education freely available to anyone, anywhere, any- time. The Intelligent Tutoring System is an automated teaching system designed to improve through experience, eventually learning to tailor its teaching to perfectly match each individual student's needs and preferences. In this dissertation we describe a template which we use for building problem-oriented skill teaching intelligent tutoring systems based on a Dynamic Bayes network framework. We present two case studies in which the template is adapted to very different teaching domains, documenting in each case the process of building, training, and testing the resulting ITS. In both case studies, the performance of the ITS is validated through human subject experiments. The results of these studies show that our template is a viable technique for designing ITSs that teach in skill based domains. We also show that, while conducting artificial intelligence research on the design of an ITS and collecting data for use in that regard, we can concurrently run educational research experiments. We find that the two are quite inextricably tied and that showing good general results regarding the performance of the ITS is not sufficient; a strong understanding of the experience of the students is also required. We report some interesting results covering the effect of choice in learning and a gender bias that shows up in our tutoring system.
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Riccucci, Simone <1978&gt. "Knowledge management in intelligent tutoring systems." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/916/1/Tesi_Riccucci_Simone.pdf.

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In the last years, Intelligent Tutoring Systems have been a very successful way for improving learning experience. Many issues must be addressed until this technology can be defined mature. One of the main problems within the Intelligent Tutoring Systems is the process of contents authoring: knowledge acquisition and manipulation processes are difficult tasks because they require a specialised skills on computer programming and knowledge engineering. In this thesis we discuss a general framework for knowledge management in an Intelligent Tutoring System and propose a mechanism based on first order data mining to partially automate the process of knowledge acquisition that have to be used in the ITS during the tutoring process. Such a mechanism can be applied in Constraint Based Tutor and in the Pseudo-Cognitive Tutor. We design and implement a part of the proposed architecture, mainly the module of knowledge acquisition from examples based on first order data mining. We then show that the algorithm can be applied at least two different domains: first order algebra equation and some topics of C programming language. Finally we discuss the limitation of current approach and the possible improvements of the whole framework.
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Riccucci, Simone <1978&gt. "Knowledge management in intelligent tutoring systems." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/916/.

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In the last years, Intelligent Tutoring Systems have been a very successful way for improving learning experience. Many issues must be addressed until this technology can be defined mature. One of the main problems within the Intelligent Tutoring Systems is the process of contents authoring: knowledge acquisition and manipulation processes are difficult tasks because they require a specialised skills on computer programming and knowledge engineering. In this thesis we discuss a general framework for knowledge management in an Intelligent Tutoring System and propose a mechanism based on first order data mining to partially automate the process of knowledge acquisition that have to be used in the ITS during the tutoring process. Such a mechanism can be applied in Constraint Based Tutor and in the Pseudo-Cognitive Tutor. We design and implement a part of the proposed architecture, mainly the module of knowledge acquisition from examples based on first order data mining. We then show that the algorithm can be applied at least two different domains: first order algebra equation and some topics of C programming language. Finally we discuss the limitation of current approach and the possible improvements of the whole framework.
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Baker, Michael J. "Negotiated tutoring : an approach to interaction in intelligent tutoring systems." Thesis, Open University, 1990. http://oro.open.ac.uk/54150/.

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This thesis describes a general approach to tutorial interaction in Intelligent Tutoring Systems, called "Negotiated Tutoring". Some aspects of the approach have been implemented as a computer program in the 'KANT' (Kritical Argument Negotiated Tutoring) system. Negotiated Tutoring synthesises some recent trends in Intelligent Tutoring Systems research, including interaction symmetry, use of explicit negotiation in dialogue, multiple interaction styles, and an emphasis on cognitive and metacognitive skill acquisition in domains characterised by justified belief. This combination of features has not been previously incorporated into models for intelligent tutoring dialogues. Our approach depends on modelling the high-level decision-making processes and memory representations used by a participant in dialogue. Dialogue generation is controlled by reasoning mechanisms which operate on a 'dialogue state', consisting of conversants' beliefs, a set of possible dialogue moves, and a restricted representation of the recent utterances generated by both conversants. The representation for conversants' beliefs is based on Anderson's (1983) model for semantic memory, and includes a model for dialogue focus based on spreading activation. Decisions in dialogue are based on preconditions with respect to the dialogue state, higher level educational preferences which choose between relevant alternative dialogue moves, and negotiation mechanisms designed to ensure cooperativity. The domain model for KANT was based on a cognitive model for perception of musical structures in tonal melodies, which extends the theory of Lerdahl and Jackendoff (1983). Our model ('GRAF' - GRouping Analysis with Frames) addresses a number of problems with Lerdahl and Jackendoff's theory, notably in describing how a number of unconscious processes in music cognition interact, including elements of top-down and bottom-up processing. GRAF includes a parser for musical chord functions, a mechanism for performing musical reductions, low-level feature detectors and a frame-system (Minsky 1977) for musical phrase structures.
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Books on the topic "Intelligent tutoring systems"

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Cristea, Alexandra I., and Christos Troussas, eds. Intelligent Tutoring Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80421-3.

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Crossley, Scott, and Elvira Popescu, eds. Intelligent Tutoring Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09680-8.

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Cerri, Stefano A., Guy Gouardères, and Fàbio Paraguaçu, eds. Intelligent Tutoring Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47987-2.

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Coy, Andre, Yugo Hayashi, and Maiga Chang, eds. Intelligent Tutoring Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22244-4.

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Nkambou, Roger, Roger Azevedo, and Julita Vassileva, eds. Intelligent Tutoring Systems. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91464-0.

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Woolf, Beverley P., Esma Aïmeur, Roger Nkambou, and Susanne Lajoie, eds. Intelligent Tutoring Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69132-7.

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Cerri, Stefano A., William J. Clancey, Giorgos Papadourakis, and Kitty Panourgia, eds. Intelligent Tutoring Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30950-2.

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Frasson, Claude, Gilles Gauthier, and Gordon I. McCalla, eds. Intelligent Tutoring Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55606-0.

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Micarelli, Alessandro, John Stamper, and Kitty Panourgia, eds. Intelligent Tutoring Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39583-8.

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Aleven, Vincent, Judy Kay, and Jack Mostow, eds. Intelligent Tutoring Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13388-6.

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Book chapters on the topic "Intelligent tutoring systems"

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Bradáč, Vladimír, and Kateřina Kostolányová. "Intelligent Tutoring Systems." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 71–78. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49625-2_9.

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Graesser, Arthur C., Mark W. Conley, and Andrew Olney. "Intelligent tutoring systems." In APA educational psychology handbook, Vol 3: Application to learning and teaching., 451–73. Washington: American Psychological Association, 2012. http://dx.doi.org/10.1037/13275-018.

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Graesser, Arthur C., Xiangen Hu, and Robert Sottilare. "Intelligent Tutoring Systems." In International Handbook of the Learning Sciences, 246–55. New York, NY : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315617572-24.

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D'Mello, Sidney K., and Art Graesser. "Intelligent Tutoring Systems." In Handbook of Educational Psychology, 603–29. 4th ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9780429433726-31.

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Kurni, Muralidhar, Mujeeb Shaik Mohammed, and K. G. Srinivasa. "Intelligent Tutoring Systems." In A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning, 29–44. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32653-0_2.

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Razzaq, Leena M., and Neil T. Heffernan. "Tutorial Dialog in an Equation Solving Intelligent Tutoring System." In Intelligent Tutoring Systems, 851–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30139-4_97.

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Tseytlin, Eugene, Melissa Castine, and Rebecca Crowley. "DomainBuilder – An Authoring System for Visual Classification Tutoring Systems." In Intelligent Tutoring Systems, 441–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13437-1_99.

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Gonzalez-Sanchez, Javier, Maria Elena Chavez-Echeagaray, Kurt VanLehn, Winslow Burleson, Sylvie Girard, Yoalli Hidalgo-Pontet, and Lishan Zhang. "A System Architecture for Affective Meta Intelligent Tutoring Systems." In Intelligent Tutoring Systems, 529–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07221-0_67.

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Allen, Richard, Cyrille Desmoulins, and Laurent Trilling. "Tuteurs Intelligents et Intelligence Artificielle: problèmes posés en construction de figures géométriques." In Intelligent Tutoring Systems, 325–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55606-0_40.

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Chi, Min, Pamela Jordan, and Kurt VanLehn. "When Is Tutorial Dialogue More Effective Than Step-Based Tutoring?" In Intelligent Tutoring Systems, 210–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07221-0_25.

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Conference papers on the topic "Intelligent tutoring systems"

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Meshref, Hossam, and Isbudeen Noor Mohamed. "Intelligent tutoring systems." In the International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2345396.2345585.

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Ramesh, Vyshnavi Malathi, and N. J. Rao. "Tutoring and Expert Modules of Intelligent Tutoring Systems." In 2012 IEEE Fourth International Conference on Technology for Education (T4E). IEEE, 2012. http://dx.doi.org/10.1109/t4e.2012.52.

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Ebale Nnemete, Alphonse, and Laurence Capus. "ARCHITECTURAL SCALABILITY OF TUTORING WITHIN INTELLIGENT TUTORING SYSTEMS." In 15th International Conference on Education and New Learning Technologies. IATED, 2023. http://dx.doi.org/10.21125/edulearn.2023.2040.

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González, Carina, Alberto Mora, and Pedro Toledo. "Gamification in intelligent tutoring systems." In the Second International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2669711.2669903.

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Dermeval, Diego, and Ig Ibert Bittencourt. "Authoring Gamified Intelligent Tutoring Systems." In VI Congresso Brasileiro de Informática na Educação. Brazilian Computer Society (Sociedade Brasileira de Computação - SBC), 2017. http://dx.doi.org/10.5753/cbie.wcbie.2017.14.

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O'Lander, Richard. "Intelligent tutoring systems (abstract only)." In the 19th annual conference. New York, New York, USA: ACM Press, 1991. http://dx.doi.org/10.1145/327164.328846.

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Ning Ning, Hong-wen Xia, and Hui Zan. "Task-driven Intelligent Tutoring Systems." In 2010 International Conference on Artificial Intelligence and Education (ICAIE). IEEE, 2010. http://dx.doi.org/10.1109/icaie.2010.5641451.

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Yu, Sun, and Li Zhiping. "Intelligent Pedagogical Agents for Intelligent Tutoring Systems." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.414.

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Niculescu, Cristina. "INTELLIGENT TUTORING SYSTEMS - TRENDS ON DESIGN, DEVELOPMENT AND DEPLOYMENT." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-218.

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Abstract:
Intelligent tutoring systems (ITS) are computerized learning environments incorporating computational models in cognitive sciences, educational sciences, computational linguistics, artificial intelligence, mathematics and other areas. There is a close relationship between training smart, cognitive learning theories and design. ITS development is the marked progress of the relationship between educational research and research in educational technologies. ITS research consists of seven areas of investigation: aspects of teaching aspects of learning, assessment, cognitive science, structuring knowledge, intelligent instruments and intelligent interfaces. Design and development of an intelligent tutoring system can be summarized in four iterative stages: needs assessment, cognitive task analysis, implementing the initial instructional stage and evaluation. The principles of design and development of an intelligent tutoring system are: student competence, target structure, training, promoting a better understanding of abstract knowledge of problem solving, minimize working memory load, providing immediate feedback errors, adjusting the size of the training packages, facilitating successive approximation desired experience. ITS offers students more individualized approaches such as learning, real-time feedback, and flexibility in time and space. Internet of Things (IoT) is the next phase of educational applications of technology and innovation in the next decade. The next generation of e-Learning, e-Learning 3.0, foresees at least four key factors: distributed computerization, expansion of smart mobile technology, intelligent collaborative filtering, 3D visualization and interaction. E-Learning 3.0 will promote collaborative learning, will be closer to learning "anytime, anywhere", will provide smart solutions to search the web, document management and content organization. ITS use areas such as natural language processing, machine learning, planning, multi-agent systems, ontologies, semantic web and social computing and emotionally. ITS have expanded in many areas critical and complex, and the results were far-reaching. ITS have cemented a place in formal education and these systems have found their place within corporate training and organizational learning.
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"INTELLIGENT TUTORING SYSTEM: AN ASSESSMENT STRATEGY FOR TUTORING ON-LINE." In 8th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0002457001570160.

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Reports on the topic "Intelligent tutoring systems"

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Shute, Valerie J., and Joseph Psotka. Intelligent Tutoring Systems: Past, Present, and Future. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada280011.

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Di Eugenio, Barbara. Natural Language Dialogue for Intelligent Tutoring Systems. Fort Belvoir, VA: Defense Technical Information Center, August 2007. http://dx.doi.org/10.21236/ada470806.

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Ong, James, and Sowmya Ramachandran. An Intelligent Tutoring System Approach to Adaptive Instructional Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada437533.

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Porter, Bruce. An Interactive, Intelligent Tutoring Systems for Prediction Tasks. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada300063.

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Steuck, Kurt, and J. L. Fleming. Intelligent Tutoring Systems: A Taxonomy of Evaluation Issues. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada222090.

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Kim, Jung Hee. Language Analysis and Generation in Algebra Tutorial Dialogues for Language-Based Intelligent Tutoring Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2004. http://dx.doi.org/10.21236/ada422821.

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Richardson, J. J., and Martha C. Polson. Proceedings of the Air Force Forum for Intelligent Tutoring Systems. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada207096.

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Sottilare, Robert A. Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada614161.

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Martinak, R., Anthony E. Kelly, D. Sleeman, J. Moore, and R. D. Ward. Diagnosis and Remediation in the Context of Intelligent Tutoring Systems. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada199024.

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Murray, William R. Control for Intelligent Tutoring Systems: A Comparison of Blackboard Architectures and Discourse Management Networks. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada201534.

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