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Zeitschriftenartikel zum Thema „Systèmes de tutoring intelligent“

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Veröffentlicht am 25. Mai 2024

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1

Amastini, Fitria. „Intelligent Tutoring System“. Jurnal ULTIMA InfoSys 5, Nr. 1 (01.06.2014): 1–7. http://dx.doi.org/10.31937/si.v5i1.212.

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Intelligent Tutoring System is a tutor behaviour system which can be used as an alternative goal for interactive e-learning and distant learning. This system can provide an adaptive system to support student’s learning and retention process based on their characteristic and needed. There are development method such as bayesian network, and neural network that can build fundamental component of Intelligent Tutoring System. This paper will give some concepts and examples for implementing those method from other papers. Index Terms—intelligent tutoring system, artificial intelligent, neural network, bayesian network, ontology

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2

Bradáč, Vladimír, und Kateřina Kostolányová. „Intelligent Tutoring Systems“. Journal of Intelligent Systems 26, Nr. 4 (26.09.2017): 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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3

Anderson, J. R., C. F. Boyle und B. J. Reiser. „Intelligent Tutoring Systems“. Science 228, Nr. 4698 (26.04.1985): 456–62. http://dx.doi.org/10.1126/science.228.4698.456.

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4

ROSS, P. „Intelligent tutoring systems“. Journal of Computer Assisted Learning 3, Nr. 4 (Dezember 1987): 194–203. http://dx.doi.org/10.1111/j.1365-2729.1987.tb00331.x.

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5

Maher, Mary Lou. „Intelligent tutoring systems“. Artificial Intelligence in Engineering 2, Nr. 1 (Januar 1987): 50. http://dx.doi.org/10.1016/0954-1810(87)90075-6.

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6

Auguste, Donna. „Intelligent tutoring systems“. Artificial Intelligence 26, Nr. 2 (Mai 1985): 233–38. http://dx.doi.org/10.1016/0004-3702(85)90033-5.

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7

Stefik, Mark. „Intelligent tutoring systems“. Artificial Intelligence 26, Nr. 2 (Mai 1985): 238–45. http://dx.doi.org/10.1016/0004-3702(85)90034-7.

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8

Petrovica, Sintija. „Tutoring Process in Emotionally Intelligent Tutoring Systems“. International Journal of Technology and Educational Marketing 4, Nr. 1 (Januar 2014): 72–85. http://dx.doi.org/10.4018/ijtem.2014010106.

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Research has shown that emotions can influence learning in situations when students have to analyze, reason, make conclusions, apply acquired knowledge, answer questions, solve tasks, and provide explanations. A number of research groups inspired by the close relationship between emotions and learning have been working to develop emotionally intelligent tutoring systems. Despite the research carried out so far, a problem how to adapt tutoring not only to a student's knowledge state but also to his/her emotional state has been disregarded. The paper aims to examine to what extent the tutoring process and tutoring strategies are adapted to students' emotional and knowledge states in these systems. It also presents a study on how to influence student's emotions looking from the pedagogical point of view and provides general guidelines for selection of tutoring strategies to influence and regulate student's emotions.

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9

Servan-Schreiber, David, und Yitzchak M. Binik. „Extending the intelligent tutoring system paradigm: Sex therapy as intelligent tutoring“. Computers in Human Behavior 5, Nr. 4 (Januar 1989): 241–59. http://dx.doi.org/10.1016/0747-5632(89)90003-4.

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10

Suppes, Patrick. „Intelligent Tutoring, but Not Intelligent Enough“. Contemporary Psychology: A Journal of Reviews 35, Nr. 7 (Juli 1990): 648–50. http://dx.doi.org/10.1037/028791.

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11

Angelides, Marios C., und Amelia K. Y. Tong. „Implementing Multiple Tutoring Strategies in an Intelligent Tutoring System for Music Learning“. Journal of Information Technology 10, Nr. 1 (März 1995): 52–62. http://dx.doi.org/10.1177/026839629501000107.

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Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II. 1.

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12

Tafazoli, Dara, Elena Gómez María und Cristina A. Huertas Abril. „Intelligent Language Tutoring System“. International Journal of Information and Communication Technology Education 15, Nr. 3 (Juli 2019): 60–74. http://dx.doi.org/10.4018/ijicte.2019070105.

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Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This article reviews and discusses the issues surrounding the development and use of ITSs for language learning and teaching. First, the authors look at ICALL history: its evolution from CALL. Second, issues in ICALL research and integration will be discussed. Third, they will explain how artificial intelligence (AI) techniques are being implemented in language education as ITS and intelligent language tutoring systems (ITLS). Finally, the successful integration and development of ITLS will be explained in detail.

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13

Del Soldato, Teresa. „Intelligent Tutoring Systems 92“. AI Communications 5, Nr. 4 (1992): 215–16. http://dx.doi.org/10.3233/aic-1992-5411.

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14

Woolf, Beverly Park. „Intelligent multimedia tutoring systems“. Communications of the ACM 39, Nr. 4 (April 1996): 30–31. http://dx.doi.org/10.1145/227210.227217.

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15

Nkambou, Roger, und Froduald Kabanza. „Designing intelligent tutoring systems“. ACM SIGCUE Outlook 27, Nr. 2 (März 2001): 46–60. http://dx.doi.org/10.1145/381234.381246.

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16

Yazdani, M. „Intelligent tutoring systems survey“. Artificial Intelligence Review 1, Nr. 1 (März 1986): 43–52. http://dx.doi.org/10.1007/bf01988527.

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17

Toussaint, Ben-Manson, Vanda Luengo und Francis Jambon. „Analyse de connaissances perceptivo-gestuelles dans un Système Tutoriel Intelligent“. Sciences et Technologies de l'Information et de la Communication pour l'Éducation et la Formation 24, Nr. 1 (2017): 231–64. http://dx.doi.org/10.3406/stice.2017.1732.

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18

Ford, Lindsey. „A new intelligent tutoring system“. British Journal of Educational Technology 39, Nr. 2 (März 2008): 311–18. http://dx.doi.org/10.1111/j.1467-8535.2008.00819.x.

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19

Kulik, James A., und J. D. Fletcher. „Effectiveness of Intelligent Tutoring Systems“. Review of Educational Research 86, Nr. 1 (März 2016): 42–78. http://dx.doi.org/10.3102/0034654315581420.

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20

Jerinic, Ljubomir, und Vladan Devedzic. „The friendly intelligent tutoring environment“. ACM SIGCHI Bulletin 32, Nr. 1 (Januar 2000): 83–94. http://dx.doi.org/10.1145/333329.333353.

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21

Wu, Albert K. W., und M. C. Lee. „Intelligent tutoring systems as design“. Computers in Human Behavior 14, Nr. 2 (Mai 1998): 209–20. http://dx.doi.org/10.1016/s0747-5632(98)00002-8.

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22

Schwind, Camilla B. „An intelligent language tutoring system“. International Journal of Man-Machine Studies 33, Nr. 5 (November 1990): 557–79. http://dx.doi.org/10.1016/s0020-7373(05)80053-7.

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23

Blandford, A. E. „Intelligent tutoring systems: Lessons learned“. Computers & Education 14, Nr. 6 (Januar 1990): 544–45. http://dx.doi.org/10.1016/0360-1315(90)90114-m.

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24

Leitch, Roy. „Teaching knowledge and intelligent tutoring“. Computers & Education 20, Nr. 3 (April 1993): 281–82. http://dx.doi.org/10.1016/0360-1315(93)90030-m.

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25

Sussex, Roland. „TEACHING KNOWLEDGE AND INTELLIGENT TUTORING“. Computer Assisted Language Learning 5, Nr. 3 (Januar 1992): 195–99. http://dx.doi.org/10.1080/0958822920050309.

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26

YAZDANI, MASOUD. „Intelligent tutoring systems: An overview“. Expert Systems 3, Nr. 3 (Juli 1986): 154–63. http://dx.doi.org/10.1111/j.1468-0394.1986.tb00488.x.

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27

Anderson, John R., C. Franklin Boyle, Albert T. Corbett und Matthew W. Lewis. „Cognitive modeling and intelligent tutoring“. Artificial Intelligence 42, Nr. 1 (Februar 1990): 7–49. http://dx.doi.org/10.1016/0004-3702(90)90093-f.

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28

Johnson, W. Lewis. „Intelligent tutoring systems: Lessons learned“. Artificial Intelligence 48, Nr. 1 (Februar 1991): 125–34. http://dx.doi.org/10.1016/0004-3702(91)90085-x.

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29

Baker, Ryan S. „Stupid Tutoring Systems, Intelligent Humans“. International Journal of Artificial Intelligence in Education 26, Nr. 2 (22.02.2016): 600–614. http://dx.doi.org/10.1007/s40593-016-0105-0.

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30

Ohlsson, Stellan. „Some principles of intelligent tutoring“. Instructional Science 14, Nr. 3-4 (Mai 1986): 293–326. http://dx.doi.org/10.1007/bf00051825.

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31

VanLEHN, KURT. „The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems“. Educational Psychologist 46, Nr. 4 (Oktober 2011): 197–221. http://dx.doi.org/10.1080/00461520.2011.611369.

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32

Lee, C. H., J. E. Biegel und C. M. Dixon. „Student Performance Evaluation for a Simulation Based Intelligent Expert Tutoring System“. Proceedings of the Human Factors Society Annual Meeting 32, Nr. 18 (Oktober 1988): 1212–16. http://dx.doi.org/10.1177/154193128803201805.

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Intelligent tutoring systems offer an exciting new way to train people in areas of complex domains. A simulation-based training system provides the student with the opportunity to manipulate a system without the consequences of real life mistakes. The intelligence required in the tutoring system is focused on the tutor's ability to teach the student efficient, strategic responses. This tutoring demands that the tutor is aware of the student's current ability, specific fault areas, and preferred method of tutoring. Instructional decisions are made by assessing the student's performance. The utility of an intelligent tutoring system depends on its capacity to evaluate the student's performance. Performance assessment then has significant impact on the employment of such a system. The parameters used for performance assessment of a complex task depend on the objective of the tutoring system. We present a description of a generic intelligent tutoring system which will remove the human instructor from the training loop.

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33

Anohina, Alla. „Advances in Intelligent Tutoring Systems: Problem-solving Modes and Model of Hints“. International Journal of Computers Communications & Control 2, Nr. 1 (01.01.2007): 48. http://dx.doi.org/10.15837/ijccc.2007.1.2336.

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The paper focuses on the issues of providing an adaptive support for learners in intelligent tutoring systems when learners solve practical problems. The results of the analysis of support policies of learners in the existing intelligent tutoring systems are given and the revealed problems are emphasized. The concept and the architectural parts of an intelligent tutoring system are defined. The approach which provides greater adaptive abilities of systems of such kind offering two modes of problem-solving and using a two-layer model of hints is described. It is being implemented in the intelligent tutoring system for the Minimax algorithm at present. In accordance with the proposed approach the learner solves problems in the mode which is the most appropriate for him/her and receives the most suitable hint.

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34

Petrovica, Sintija. „DESIGN OF THE PEDAGOGICAL MODULE FOR AN EMOTIONALLY INTELLIGENT TUTORING SYSTEM / PEDAGOGINIO MODULIO EMOCIJAS SUPRANTANČIAI MOKYMO SISTEMAI KŪRIMAS“. Mokslas – Lietuvos ateitis 6, Nr. 2 (24.04.2014): 138–46. http://dx.doi.org/10.3846/mla.2014.18.

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Since 1970-ties the research is being carried out for the development of intelligent tutoring systems (ITS) that aretrying to imitate human-teachers and their teaching methods. However, over the last decade researchers inspired by the closerelationship between emotions and learning have been working on the addition of an emotional component to human-computerinteraction. This has led to creation of a new generation of intelligent tutoring systems – emotionally intelligent tutoring systems(EITS). Despite the research carried out so far, a problem how to adapt tutoring not only to a student’s knowledge state butalso to his/her emotional state has been disregarded. The paper presents study on how to use the determined student’s emotionalstate further in order to change behaviour of the intelligent tutoring system looking from the pedagogical point of view and toimplement this as a part of the pedagogical module. The architecture of the planned tutoring system that adapts the tutoring bothto student’s emotions and knowledge is also described in the paper. Straipsnyje nagrinėjami klausimai, susiję su informacijos apienustatytą studento emocinę būklę taikymu sumaniosios mokymosistemos elgsenai keisti, taip pat emocinės būklės poveikis mokymoprocesui pedagoginiu požiūriu. Siūlomas pedagoginiamsaspektams įgyvendinti specializuotas informacinės sistemosmodulis. Parodoma pedagoginio modulio vieta sumaniosiosmokymo sistemos, pritaikančios mokymo procesą konkretausstudento žinių ir emociniam lygmenims, architektūroje.

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Ygnace, J. „Les systèmes de transport intelligent“. Recherche - Transports - Sécurité 68 (September 2000): 87. http://dx.doi.org/10.1016/s0761-8980(00)90026-8.

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36

Buche, Cédric, Ronan Querrec, Pierre De Loor, Pierre Chevaillier und Jacques Tisseau. „PEGASE, un système tutoriel intelligent générique et adaptatif en environnement virtuel“. Techniques et sciences informatiques 28, Nr. 8 (30.10.2009): 1051–76. http://dx.doi.org/10.3166/tsi.28.1051-1076.

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37

Phillips, Fred, und Benny G. Johnson. „Online Homework versus Intelligent Tutoring Systems: Pedagogical Support for Transaction Analysis and Recording“. Issues in Accounting Education 26, Nr. 1 (01.02.2011): 87–97. http://dx.doi.org/10.2308/iace.2011.26.1.87.

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ABSTRACT: Prior research demonstrates that students learn more from homework practice when using online homework or intelligent tutoring systems than a paper-and-pencil format. However, no accounting education research directly compares the learning effects of online homework systems with the learning effects of intelligent tutoring systems. This paper presents a quasi-experiment that compares the two systems and finds that students’ transaction analysis performance increased at a significantly faster rate when they used an intelligent tutoring system rather than an online homework system. Implications for accounting instructors and researchers are discussed.

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38

PEK, PENG-KIAT, und KIM-LENG POH. „MAKING DECISIONS IN AN INTELLIGENT TUTORING SYSTEM“. International Journal of Information Technology & Decision Making 04, Nr. 02 (Juni 2005): 207–33. http://dx.doi.org/10.1142/s0219622005001489.

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In computerized tutoring, the pace of instruction is related to the student's mastery levels of the learning objectives. The observable student's behavior that can be used to measure his knowledge is usually his responses to test items. Unobservable variables that are related to learner's motivation can affect learning but are difficult to quantify. In comparison with other decision-theoretic tutoring systems, the novelties of this research are: (1) the efficiency-centric approach to develop the Bayesian networks; (2) the formulation of utility values for different tutoring outcomes that are independent of past actions and to satisfy the separability condition; (3) the development of a common measure for student's mastery levels and item difficulties; and (4) the generation of optimal policies in polynomial time. A prototype web-based tutoring system, known as iTutor, incorporating the novelties has been developed for engineering mechanics. Formative evaluations of iTutor have shown encouraging results.

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39

Uriarte-Portillo, Aldo, Ramón Zatarain-Cabada, María Lucía Barrón-Estrada, María Blanca Ibáñez und Lucía-Margarita González-Barrón. „Intelligent Augmented Reality for Learning Geometry“. Information 14, Nr. 4 (17.04.2023): 245. http://dx.doi.org/10.3390/info14040245.

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This work describes a learning tool named ARGeoITS that combines augmented reality with an intelligent tutoring system to support geometry learning. The work depicts a study developed in Mexico to measure the impact on the learning and motivation of students using two different learning systems: an intelligent tutoring system with augmented reality (ARGeoITS) and a system with only augmented reality (ARGeo). To study the effect of this type of technology (ARGeoITS, ARGeo) and time of assessment (pre-, post-) on learning gains and motivation, we applied a 2 × 2 factorial design to 106 middle school students. Both pretest and post-test questionnaires were applied to each group to determine the students’ learning gains, as was an IMMS motivational survey to evaluate the students’ motivation. The results show that: (1) students who used the intelligent tutoring system ARGeoITS scored higher in learning gain (7.47) compared with those who used ARGeo (6.83); and (2) both the ARGeoITS and ARGeo learning tools have a positive impact on students’ motivation. The research findings imply that intelligent tutoring systems that integrate augmented reality can be exploited as an effective learning environment to help middle–high school students learn difficult topics such as geometry.

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Siemer, Julika, und Marios C. Angelides. „Integrating an Intelligent Tutoring Facility into a Gaming Simulation Environment“. Journal of Information Technology 12, Nr. 3 (September 1997): 207–22. http://dx.doi.org/10.1177/026839629701200305.

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Gaming simulations and intelligent tutoring systems are both substantive research and development areas within the field of computer-based education and training which have the potential for mutual enhancement. This paper argues that the pedagogical effectiveness of gaming simulations can be increased through the integration of an intelligent tutoring facility and examines possible roles for such support within a gaming simulation environment. It then commences to present INTUITION, the implementation of the Metal Box Business Simulation game, that illustrates how an intelligent tutoring facility may be integrated within a gaming simulation environment in order to increase its educational value.

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Petrovica, Sintija, und Alla Anohina-Naumeca. „Design and implementation of agent interaction mechanisms for emotionally intelligent tutoring systems“. Applied Computer Systems 13, Nr. 1 (08.11.2012): 44–53. http://dx.doi.org/10.2478/v10312-012-0006-2.

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Abstract - Modern intelligent systems are built from intelligent agents, but usually agent interaction mechanisms are described at the general level omitting design and implementation details. The paper presents aspects of practical implementation of agent interaction mechanisms in JADE platform on the basis of the set of agents for modelling student’s emotions in intelligent tutoring systems. The concepts of an intelligent and affective tutoring system, agent, and agent interaction are explained, as well.

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42

FUKUHARA, Yoshimi, und Kiyoshi NAKABAYASHI. „Intelligent Tutoring System using WWW(CALAT)“. Journal of JSEE 44, Nr. 3 (1996): 26–31. http://dx.doi.org/10.4307/jsee.44.3_26.

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43

Psotka, Joseph, Heinz Mandl und Alan Lesgold. „Learning Issues for Intelligent Tutoring Systems“. American Journal of Psychology 102, Nr. 4 (1989): 581. http://dx.doi.org/10.2307/1423312.

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44

Hasic, Amor, und Samed Jukic. „Intelligent Tutoring System of Linear Programming“. Advances in Linear Algebra & Matrix Theory 12, Nr. 02 (2022): 39–66. http://dx.doi.org/10.4236/alamt.2022.122003.

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45

Oliveira Neto, Jose Dutra de, und Elby Vaz Nascimento. „Intelligent Tutoring System for Distance Education“. Journal of Information Systems and Technology Management 9, Nr. 1 (30.04.2012): 109–22. http://dx.doi.org/10.4301/s1807-17752012000100006.

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46

Whitaker, Elizabeth T., und Ronald D. Bonnell. „Plan recognition in intelligent tutoring systems“. Intelligent Tutoring Media 1, Nr. 2 (Januar 1990): 73–82. http://dx.doi.org/10.1080/14626269009409091.

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47

Long, Yanjin, und Vincent Aleven. „Educational Game and Intelligent Tutoring System“. ACM Transactions on Computer-Human Interaction 24, Nr. 3 (22.07.2017): 1–27. http://dx.doi.org/10.1145/3057889.

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48

Yang, Feng-Jen. „The ideology of intelligent tutoring systems“. ACM Inroads 1, Nr. 4 (Dezember 2010): 63–65. http://dx.doi.org/10.1145/1869746.1869765.

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49

Chughtai, Rehman, Shasha Zhang und Scotty D. Craig. „Usability evaluation of intelligent tutoring system“. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 59, Nr. 1 (September 2015): 367–71. http://dx.doi.org/10.1177/1541931215591076.

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50

NIELSEN, RODNEY D., WAYNE WARD und JAMES H. MARTIN. „Recognizing entailment in intelligent tutoring systems“. Natural Language Engineering 15, Nr. 4 (16.09.2009): 479–501. http://dx.doi.org/10.1017/s135132490999012x.

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AbstractThis paper describes a new method for recognizing whether a student's response to an automated tutor's question entails that they understand the concepts being taught. We demonstrate the need for a finer-grained analysis of answers than is supported by current tutoring systems or entailment databases and describe a new representation for reference answers that addresses these issues, breaking them into detailed facets and annotating their entailment relationships to the student's answer more precisely. Human annotation at this detailed level still results in substantial interannotator agreement (86.2%), with a kappa statistic of 0.728. We also present our current efforts to automatically assess student answers, which involves training machine learning classifiers on features extracted from dependency parses of the reference answer and student's response and features derived from domain-independent lexical statistics. Our system's performance, as high as 75.5% accuracy within domain and 68.8% out of domain, is very encouraging and confirms the approach is feasible. Another significant contribution of this work is that it represents a significant step in the direction of providing domain-independent semantic assessment of answers. No prior work in the area of tutoring or educational assessment has attempted to build such domain-independent systems. They have virtually all required hundreds of examples of learner answers for each new question in order to train aspects of their systems or to hand-craft information extraction templates.

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