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Journal articles on the topic 'Effects of Intelligent Tutoring System'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Phillips, Fred, and Benny G. Johnson. "Online Homework versus Intelligent Tutoring Systems: Pedagogical Support for Transaction Analysis and Recording." Issues in Accounting Education 26, no. 1 (February 1, 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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Mohamed, Hafidi, and Bensebaa Taher. "Combining Expert Systems and Multiple Intelligences in an Adaptive and Intelligent Tutoring System." International Journal of Information Technology and Web Engineering 8, no. 3 (July 2013): 22–33. http://dx.doi.org/10.4018/ijitwe.2013070102.

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This paper describes an adaptive and intelligent tutoring system (AITS) based on multiple intelligences and expert system. Most of adaptive and intelligent tutoring systems based their adaptation to user’s skill level. Other learner features taken into account are background, hyperspace experience, preferences and interests. However, less attention was paid to multiple intelligences and their effects on learning. Moreover, to design AITS which can manage both different disciplinary domains and a guide for the learner is difficult. The specialization of the analysis treatments is responsible for the loss of reusability for the other disciplinary domains. To overcome these limitations, the authors will try to combine the benefits of paradigms (adaptive hypermedia, intelligent tutoring system, multiple intelligences) in order to adapt the course to the needs and intellectual abilities of each learner.

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Livergood, Norman D. "A Study of the Effectiveness of a Multimedia Intelligent Tutoring System." Journal of Educational Technology Systems 22, no. 4 (June 1994): 337–44. http://dx.doi.org/10.2190/dthe-9hmv-qgr9-d38m.

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In this study, college undergraduate students were presented identical material on the domain of intelligent tutoring systems. The author designed his research to isolate the key variables, disallow for the effect of some of those variables, and determine in a precise way what effects are produced by a multimedia delivery system, repetition of instructional and testing material, and an intelligent tutoring system delivery system. The first phase of this study, comparing test scores of students who were presented material in two delivery systems: 1) hard-copy (printed material), and 2) a computer-based multimedia program, indicated no statistically significant difference in scores. A second part of that phase, comparing test scores and re-test scores of students studying the computer-based multimedia program, indicated no statistically significant difference in scores. The second phase of this study, comparing scores of the two initial delivery systems and scores on a multimedia modified intelligent tutoring system, resulted in a statistically significant difference in scores.

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Sun, Yu, Zhi Ping Li, and Yao Wen Xia. "Emotional Interaction Agents in Intelligent Tutoring Systems." Applied Mechanics and Materials 347-350 (August 2013): 2682–87. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2682.

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A model of emotional interaction agents in intelligent tutoring systems is presented in this paper, and the functionalities of the key components of the agents are described. To improve the emotional interaction between learners and the system, a kind of emotional interaction agents which can deduce users emotional statues, provide helps needed, and mark emotional difficulty of the learned pedagogical units, is introduced and discussed in detail in order to improve pedagogical effects.

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Akyuz, Yasar. "Effects of Intelligent Tutoring Systems (ITS) on Personalized Learning (PL)." Creative Education 11, no. 06 (2020): 953–78. http://dx.doi.org/10.4236/ce.2020.116069.

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McCarthy, Kathryn S., Aaron D. Likens, Amy M. Johnson, Tricia A. Guerrero, and Danielle S. McNamara. "Metacognitive Overload!: Positive and Negative Effects of Metacognitive Prompts in an Intelligent Tutoring System." International Journal of Artificial Intelligence in Education 28, no. 3 (February 21, 2018): 420–38. http://dx.doi.org/10.1007/s40593-018-0164-5.

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7

Serrano, M. Á., E. Vidal-Abarca, and A. Ferrer. "Teaching self-regulation strategies via an intelligent tutoring system (TuinLECweb): Effects for low-skilled comprehenders." Journal of Computer Assisted Learning 34, no. 5 (April 2, 2018): 515–25. http://dx.doi.org/10.1111/jcal.12256.

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Karaci, Abdulkadir, Halil Ibrahim, Goksal Bilgici, and Nursal Arici. "Effects of Web-based Intelligent Tutoring Systems on Academic Achievement and Retention." International Journal of Computer Applications 181, no. 16 (September 17, 2018): 35–41. http://dx.doi.org/10.5120/ijca2018917806.

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Ritter, Markus, Christiane Kallenbach, and James Pankhurst. "The ‘all-inclusive’ tutor – excluding learner autonomy?" ReCALL 11, no. 1 (May 1999): 111–16. http://dx.doi.org/10.1017/s0958344000002147.

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A multimedia learning environment would appear to benefit from an intelligent tutoring system that draws on didactic expertise, knowledge of the program structure, and knowledge of the learner's previous activities. On the other hand, one may argue against a tutor because of the damaging effects on learner autonomy: the tutor may hamper genuine learning by taking the learner by the hand, whereas what the learner needs is to have sufficient space to move freely through material in an explorative rather than an executive mode, generating her own queries and finding her own solutions. It is argued that tutoring may be a necessary stage on the road to autonomy.

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Mingyu Feng, N. T. Heffernan, C. Heffernan, and M. Mani. "Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models in an Intelligent Tutoring System." IEEE Transactions on Learning Technologies 2, no. 2 (April 2009): 79–92. http://dx.doi.org/10.1109/tlt.2009.17.

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Rodrigo, M. M. T., R. S. J. D. Baker, J. Agapito, J. Nabos, M. C. Repalam, S. S. Reyes, and M. O. C. Z. San Pedro. "The Effects of an Interactive Software Agent on Student Affective Dynamics while Using ;an Intelligent Tutoring System." IEEE Transactions on Affective Computing 3, no. 2 (April 2012): 224–36. http://dx.doi.org/10.1109/t-affc.2011.41.

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Crowley, R. S., E. Legowski, O. Medvedeva, E. Tseytlin, E. Roh, and D. Jukic. "Evaluation of an Intelligent Tutoring System in Pathology: Effects of External Representation on Performance Gains, Metacognition, and Acceptance." Journal of the American Medical Informatics Association 14, no. 2 (March 1, 2007): 182–90. http://dx.doi.org/10.1197/jamia.m2241.

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Bringula, Rex P., Roselle S. Basa, Cecilio Dela Cruz, and Ma Mercedes T. Rodrigo. "Effects of Prior Knowledge in Mathematics on Learner-Interface Interactions in a Learning-by-Teaching Intelligent Tutoring System." Journal of Educational Computing Research 54, no. 4 (December 24, 2015): 462–82. http://dx.doi.org/10.1177/0735633115622213.

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Su, Sheng-Hsiung, Hao-Chiang Koong Lin, Cheng-Hung Wang, and Zu-Ching Huang. "Multi-Modal Affective Computing Technology Design the Interaction between Computers and Human of Intelligent Tutoring Systems." International Journal of Online Pedagogy and Course Design 6, no. 1 (January 2016): 13–28. http://dx.doi.org/10.4018/ijopcd.2016010102.

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In this paper, the authors are using emotion recognition in two ways: facial expression recognition and emotion recognition from text. Through this dual-mode operation, not only can strength the effects of recognition, but also increase the types of emotion recognition to handle the learning situation smoothly. Through the training of image processing to identify facial expression, the emotion from text is identifying by emotional keywords, syntax, semantics and calculus with logic. The system identify learns' emotions and learning situations by analyzing, choosing the appropriate instructional strategies and curriculum content, and through agents to communicate between user and system, so that learners can get a well learning. This study uses triangular system evaluation methods, observation, questionnaires and interviews. Experimental design to the subjects by the level of awareness on art and non-art to explore the traditional teaching, affective tutoring system and no emotional factors learning course website these three kinds of ways to get results, analysis and evaluate the data.

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Ji, Xuejun Ryan, Andrea Beerwinkle, Kausalai Wijekumar, Puiwa Lei, R. Malatesha Joshi, and Shuai Zhang. "Using latent transition analysis to identify effects of an intelligent tutoring system on reading comprehension of seventh-grade students." Reading and Writing 31, no. 9 (July 25, 2018): 2095–113. http://dx.doi.org/10.1007/s11145-018-9888-8.

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Bahçeci, Ferhat, and Mehmet Gürol. "The Effect of Individualized Instruction System on the Academic Achievement Scores of Students." Education Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/7392125.

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A web-based learning portal offering individualized learning was developed by utilizing rule-based knowledge representation and artificial intelligence techniques of expert systems in order to reduce the uncertainties of learning to minimum and to construct an intelligent tutoring system. This portal offers individualized learning content based on the individual’s level of cognitive knowledge. In order to determine the effects of the developed system on the student achievement, the system was tested in an 8-week-long study on the students of Software Engineering Department of Technology Faculty. The pretest-posttest control group experimental design was used in the study. The experimental group received education with Individualized Instruction Portal while the control group received education in traditional learning environment. Academic achievement test was used as the data collection tool. In order to test the research hypotheses, data obtained from the data collection tools were analysed in terms of frequency, percentages, and dependent-independentt-test with statistical software program. Based on the results, no significant differences were found between the groups in terms of the pretest. On the other hand, significant differences were found between experimental and control group in terms of the posttest. It was concluded that individualized learning portal had positive effect on the students’ learning when used in combination with traditional learning environment.

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Găbureanu, Simona, and Olimpius Istrate. "The Effects of using Intelligent Tutoring Systems for Language Learning – Findings of a Research Evaluation Report." Procedia - Social and Behavioral Sciences 76 (April 2013): 351–55. http://dx.doi.org/10.1016/j.sbspro.2013.04.126.

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De Benedictis, Riccardo, Carlo De Medio, Augusto Palombini, Gabriella Cortellessa, Carla Limongelli, and Amedeo Cesta. "Fostering the Creation of Personalized Content for Cultural Visits." Applied Sciences 11, no. 16 (August 12, 2021): 7401. http://dx.doi.org/10.3390/app11167401.

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Among more dramatic effects, the COVID-19 scenario also raised the need for new online information and communication services, promoting the spread of software solutions whose usefulness will last well beyond the pandemic situation. Particularly in the cultural heritage domain, it has been unveiled the relevance of new AI-based approaches, able to dynamically aggregate information and making them available for a customized fruition aimed to the individual cultural growth. Here, we integrate machine learning techniques for the automatic generation of contents for an intelligent tutoring system grounded on automated planning techniques. We present a solution for semantic, intelligent creation of personalized cultural contents, born as a lesson-making assistant, but developed as to become a multi-function “cultural crossover”, useful in the frame of a wide range of planning, dissemination, and managing activities for cultural heritage contents.

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Mohammadzadeh, Ahmad, and Mehdi Sarkhosh. "The Effects of Self-Regulatory Learning through Computer-Assisted Intelligent Tutoring System on the Improvement of EFL Learner' Speaking Ability." International Journal of Instruction 11, no. 2 (April 3, 2018): 167–84. http://dx.doi.org/10.12973/iji.2018.11212a.

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Yang, Euijung, and Michael C. Dorneich. "Affect-Aware Adaptive Tutoring Based on Human–Automation Etiquette Strategies." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 4 (March 28, 2018): 510–26. http://dx.doi.org/10.1177/0018720818765266.

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Objective: We investigated adapting the interaction style of intelligent tutoring system (ITS) feedback based on human–automation etiquette strategies. Background: Most ITSs adapt the content difficulty level, adapt the feedback timing, or provide extra content when they detect cognitive or affective decrements. Our previous work demonstrated that changing the interaction style via different feedback etiquette strategies has differential effects on students’ motivation, confidence, satisfaction, and performance. The best etiquette strategy was also determined by user frustration. Method: Based on these findings, a rule set was developed that systemically selected the proper etiquette strategy to address one of four learning factors (motivation, confidence, satisfaction, and performance) under two different levels of user frustration. We explored whether etiquette strategy selection based on this rule set (systematic) or random changes in etiquette strategy for a given level of frustration affected the four learning factors. Participants solved mathematics problems under different frustration conditions with feedback that adapted dynamic changes in etiquette strategies either systematically or randomly. Results: The results demonstrated that feedback with etiquette strategies chosen systematically via the rule set could selectively target and improve motivation, confidence, satisfaction, and performance more than changing etiquette strategies randomly. The systematic adaptation was effective no matter the level of frustration for the participant. Conclusion: If computer tutors can vary the interaction style to effectively mitigate negative emotions, then ITS designers would have one more mechanism in which to design affect-aware adaptations that provide the proper responses in situations where human emotions affect the ability to learn.

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ÇAKIR, Recep. "Effect of Web-Based Intelligence Tutoring System on Students’ Achievement and Motivation." Malaysian Online Journal of Educational Technology 7, no. 4 (October 1, 2019): 45–59. http://dx.doi.org/10.17220/mojet.2019.04.004.

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Amastini, Fitria. "Intelligent Tutoring System." Jurnal ULTIMA InfoSys 5, no. 1 (June 1, 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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Verschaffel, Lieven, Fien Depaepe, and Zemira Mevarech. "Learning Mathematics in Metacognitively Oriented ICT-Based Learning Environments: A Systematic Review of the Literature." Education Research International 2019 (September 16, 2019): 1–19. http://dx.doi.org/10.1155/2019/3402035.

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This article encompasses a systematic review of the research on ICT-based learning environments for metacognitively oriented K-12 mathematics education. This review begins with a brief overview of the research on metacognition and mathematics education and on ICT and mathematics education. Based on a systematic screening of the databases Web of Science and ERIC wherein three elements—ICT-based learning environments, metacognitive pedagogies, and mathematics—are combined, 22 articles/studies were retrieved, situated at various educational levels (kindergarten, elementary school, and secondary school). This review revealed a variety of studies, particularly intervention studies, situated in elementary and secondary schools. Most studies involved drill-and-practice software, intelligent tutoring systems, serious games, multimedia environments, and computer-supported collaborative learning environments, with metacognitive pedagogies either integrated into the ICT software itself or provided externally by the teacher, mainly for arithmetic or algebraic word problem-solving but also related to other mathematical topics. All studies reported positive effects on mathematical and/or metacognitive learning outcomes. This review ends with a discussion of issues for further theoretical reflection and empirical research.

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Tafazoli, Dara, Elena Gómez María, and Cristina A. Huertas Abril. "Intelligent Language Tutoring System." International Journal of Information and Communication Technology Education 15, no. 3 (July 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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Servan-Schreiber, David, and Yitzchak M. Binik. "Extending the intelligent tutoring system paradigm: Sex therapy as intelligent tutoring." Computers in Human Behavior 5, no. 4 (January 1989): 241–59. http://dx.doi.org/10.1016/0747-5632(89)90003-4.

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Schwind, Camilla B. "An intelligent language tutoring system." International Journal of Man-Machine Studies 33, no. 5 (November 1990): 557–79. http://dx.doi.org/10.1016/s0020-7373(05)80053-7.

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Ford, Lindsey. "A new intelligent tutoring system." British Journal of Educational Technology 39, no. 2 (March 2008): 311–18. http://dx.doi.org/10.1111/j.1467-8535.2008.00819.x.

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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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Zhan, Zehui, Jun Wu, Hu Mei, Qianyi Wu, and Patrick S. W. Fong. "Individual difference on reading ability tested by eye-tracking: from perspective of gender." Interactive Technology and Smart Education 17, no. 3 (March 4, 2020): 267–83. http://dx.doi.org/10.1108/itse-12-2019-0082.

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Purpose This paper aims to investigate the individual difference on digital reading, by examining the eye-tracking records of male and female readers with different reading ability (including their pupil size, blink rate, fixation rate, fixation duration, saccade rate, saccade duration, saccade amplitude and regression rate). Design/methodology/approach A total of 74 participants were selected according to 6,520 undergraduate students’ university entrance exam scores and the follow-up reading assessments. Half of them are men and half are women, with the top 3% good readers and the bottom 3% poor readers, from different disciplines. Findings Results indicated that the major gender differences on reading abilities were indicated by saccade duration, regression rate and blink rate. The major effects on reading ability have a larger effect size than the major effect on gender. Among all the indicators that have been examined, blink rate and regression rates are the most sensitive to the gender attribute, while the fixation rate and saccade amplitude showed the least sensitiveness. Originality/value This finding could be helpful for user modeling with eye-tracking data in intelligent tutoring systems, where necessary adjustments might be needed according to users’ individual differences. In this way, instructors could be able to provide purposeful guidance according to what the learners had seen and personalized the experience of digital reading.

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Oliveira Neto, Jose Dutra de, and Elby Vaz Nascimento. "Intelligent Tutoring System for Distance Education." Journal of Information Systems and Technology Management 9, no. 1 (April 30, 2012): 109–22. http://dx.doi.org/10.4301/s1807-17752012000100006.

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Chughtai, Rehman, Shasha Zhang, and Scotty D. Craig. "Usability evaluation of intelligent tutoring system." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 59, no. 1 (September 2015): 367–71. http://dx.doi.org/10.1177/1541931215591076.

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Long, Yanjin, and Vincent Aleven. "Educational Game and Intelligent Tutoring System." ACM Transactions on Computer-Human Interaction 24, no. 3 (July 22, 2017): 1–27. http://dx.doi.org/10.1145/3057889.

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FUKUHARA, Yoshimi, and Kiyoshi NAKABAYASHI. "Intelligent Tutoring System using WWW(CALAT)." Journal of JSEE 44, no. 3 (1996): 26–31. http://dx.doi.org/10.4307/jsee.44.3_26.

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Atilola, Olufunmilola, Stephanie Valentine, Hong-Hoe Kim, David Turner, Erin McTigue, Tracy Hammond, and Julie Linsey. "Mechanix: A natural sketch interface tool for teaching truss analysis and free-body diagrams." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, no. 2 (May 2014): 169–92. http://dx.doi.org/10.1017/s0890060414000079.

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AbstractMassive open online courses, online tutoring systems, and other computer homework systems are rapidly changing engineering education by providing increased student feedback and capitalizing upon online systems' scalability. While online homework systems provide great benefits, a growing concern among engineering educators is that students are losing both the critical art of sketching and the ability to take a real system and reduce it to an accurate but simplified free-body diagram (FBD). For example, some online systems allow the drag and drop of forces onto FBDs, but they do not allow the user to sketch the FBDs, which is a vital part of the learning process. In this paper, we discuss Mechanix, a sketch recognition tool that provides an efficient means for engineering students to learn how to draw truss FBDs and solve truss problems. The system allows students to sketch FBDs into a tablet computer or by using a mouse and a standard computer monitor. Using artificial intelligence, Mechanix can determine not only the component shapes and features of the diagram but also the relationships between those shapes and features. Because Mechanix is domain specific, it can use those relationships to determine not only whether a student's work is correct but also why it is incorrect. Mechanix is then able to provide immediate, constructive feedback to students without providing final answers. Within this manuscript, we document the inner workings of Mechanix, including the artificial intelligence behind the scenes, and present studies of the effects on student learning. The evaluations have shown that Mechanix is as effective as paper-and-pencil-based homework for teaching method of joints truss analysis; focus groups with students who used the program have revealed that they believe Mechanix enhances their learning and that they are highly engaged while using it.

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Angelides, Marios C., and Amelia K. Y. Tong. "Implementing Multiple Tutoring Strategies in an Intelligent Tutoring System for Music Learning." Journal of Information Technology 10, no. 1 (March 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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PEK, PENG-KIAT, and KIM-LENG POH. "MAKING DECISIONS IN AN INTELLIGENT TUTORING SYSTEM." International Journal of Information Technology & Decision Making 04, no. 02 (June 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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Kaklauskas, Artûras, Ruslanas Ditkevičius, and Leonarda Gargasaite. "INTELLIGENT TUTORING SYSTEM FOR REAL ESTATE MANAGEMENT." International Journal of Strategic Property Management 10, no. 2 (June 30, 2006): 113–30. http://dx.doi.org/10.3846/1648715x.2006.9637548.

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The review on the worldwide intelligent tutoring systems and their application possibilities is presented in the paper. The intelligent tutoring system for real estate management developed by the authors is described. This system is applied in Vilnius Gediminas Technical University, Department of Construction Economics and Property Management. Besides the common components ‐ student model, domain model, pedagogical model and graphical interface, the new developed system has testing model, decision support subsystem and database of computer learning systems. Domain model includes knowledge with the supplemental audio and video material for 63 modules being taught in Vilnius Gediminas Technical University. Student model enables to adapt to a learner needs and knowledge level. Decision support subsystem is used for all components of intelligent tutoring system giving them different level of intelligence. Database of computer learning systems enables using the following web‐based learning systems: construction, real estate, facilities management, international trade, ethics, innovation, sustainable development, building refurbishment, etc. Tutor and testing model provide a model of the teaching process and support transition to a new knowledge state. Graphic interface is used to create an effective system‐user dialogue.

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Kanjilal, Drishti. "Study Buddy: An Emotionally Intelligent Tutoring System." International Journal of Computer Applications 99, no. 9 (August 20, 2014): 20–23. http://dx.doi.org/10.5120/17402-7966.

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Lecroq, Florence, Jean Grieu, Patrick Person, Thierry Galinho, and Hadhoum Boukachour. "Intelligent Tutoring System in GE3D Virtual Campus." International Journal of Computer Science and Artificial Intelligence 2, no. 1 (March 30, 2012): 1–6. http://dx.doi.org/10.5963/ijcsai0201001.

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Aleshchenko, A. S., and V. M. Trembach. "Intelligent tutoring system of the university department." Open Education, no. 5 (January 1, 2016): 47–52. http://dx.doi.org/10.21686/1818-4243-2016-5-47-52.

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Durães, Dalila, Rámon Toala, Filipe Gonçalves, and Paulo Novais. "Intelligent tutoring system to improve learning outcomes." AI Communications 32, no. 3 (July 15, 2019): 161–74. http://dx.doi.org/10.3233/aic-190624.

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Yi, Zhen Zhen, Ke Zhao, Ya Tao Li, and Wei Xu. "Research on Knowledge-Based Intelligent Tutoring System." Applied Mechanics and Materials 55-57 (May 2011): 1424–29. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1424.

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Aiming at students’ learning process, and based on the analysis of tutorship rules of students’ learning after class, a Knowledge-Based Intelligent Tutoring System is given. The system comprehensively uses agent technology, the knowledge-based automatic reasoning, resource modeling for knowledge classification, field natural language understanding, data mining, computer networks, databases and other technologies. It creates a student-oriented self-motivated learning environment in which students can learn abundant knowledge of one or many subjects, send the problems encountered in their own learning to the system server by network, and get real-time multiple tutorship information as an excellent teacher do.

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Ifeanyi Isaiah, Achi, and Agwu Chukwuemeka Odi. "Multi Software Agent Based Intelligent Tutoring System." International Journal of Engineering Trends and Technology 20, no. 5 (February 25, 2015): 218–22. http://dx.doi.org/10.14445/22315381/ijett-v20p242.

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Isaiah, Achi Ifeanyi, Prof Inyiama Hyacinth Chibueze, and Agwu Chukwuemeka Odi. "Multi User Student Model Intelligent Tutoring System." International Journal of Engineering Trends and Technology 24, no. 2 (June 25, 2015): 73–76. http://dx.doi.org/10.14445/22315381/ijett-v24p214.

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Nwana, Hyacinth S. "The evaluation of an intelligent tutoring system." Intelligent Tutoring Media 1, no. 3 (January 1990): 117–32. http://dx.doi.org/10.1080/14626269009408269.

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Soh, Leen-Kiat. "Incorporating an intelligent tutoring system into CS1." ACM SIGCSE Bulletin 38, no. 1 (March 31, 2006): 486–90. http://dx.doi.org/10.1145/1124706.1121494.

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Nakamura, Manabu, Noriyuki Iwane, and Setsuko Otsuki. "Intelligent tutoring system for reading mathematical expressions." Systems and Computers in Japan 31, no. 10 (2000): 56–64. http://dx.doi.org/10.1002/1520-684x(200009)31:10<56::aid-scj6>3.0.co;2-s.

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Yoshikawa, A., M. Shintani, and Y. Ohba. "Intelligent tutoring system for electric circuit exercising." IEEE Transactions on Education 35, no. 3 (1992): 222–25. http://dx.doi.org/10.1109/13.144648.

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Butz, B. P., M. Duarte, and S. M. Miller. "An Intelligent Tutoring System for Circuit Analysis." IEEE Transactions on Education 49, no. 2 (May 2006): 216–23. http://dx.doi.org/10.1109/te.2006.872407.

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Sehrawat, Anjali, Robert Keelan, Kenji Shimada, Dona M. Wilfong, James T. McCormick, and Yoed Rabin. "Simulation-Based Cryosurgery Intelligent Tutoring System Prototype." Technology in Cancer Research & Treatment 15, no. 2 (May 3, 2015): 396–407. http://dx.doi.org/10.1177/1533034615583187.

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