Relevant bibliographies by topics / Effects of Intelligent Tutoring System

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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 'Effects of Intelligent Tutoring System'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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

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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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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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Dissertations / Theses on the topic "Effects of Intelligent Tutoring System"

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Al-Aqbi, Ali Talib Qasim. "Intelligent Tutoring System Effects on the Learning Process." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1502139817641618.

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Hrubik-Vulanovic, Tatjana. "Effects of intelligent tutoring systems in basic algebra courses on subsequent mathematics lecture courses." Thesis, Kent State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3618883.

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The purpose of this study was to investigate how intelligent tutoring system ALEKS, which was implemented in remedial Basic Algebra courses, affected students' success in subsequent lecture courses and how former ALEKS students and instructors in lecture courses perceived ALEKS learning environment. ALEKS courses were delivered in emporium style: instructors were available to answer students' questions, while ALEKS guided students through online exercises individually based on their skills and knowledge.

The participants were students from four mathematics lecture courses and their instructors. Some students took remedial courses in ALEKS prior to the lecture courses while some students did not. The quantitative part of the study compared ALEKS and non-ALEKS students on the final examination and students' self-reported-preparedness. The qualitative part of the study discussed students' and instructors' perceptions of ALEKS based on student surveys and instructor interviews.

No difference between ALEKS and non-ALEKS students was found in final examination scores and self-reported-preparedness. Students rated learning experience in ALEKS emporium on average at 2.74 on the scale of one to five, with five being the highest. One third of students liked studying at their own pace and ALEKS content (they rated ALEKS emporium at 3.29), while one fourth claimed that “nothing was good” in emporium courses (they rated ALEKS emporium at 1.55). Although ALEKS emporium was very different from lecture courses, only one fifth of students reported changes in their study habits. The instructors did not observe any difference between ALEKS and non-ALEKS students and mentioned benefits of ALEKS-like tool for drill-and-practice. One instructor observed positive shifts in student attitude towards mathematics but advised longer study to be conducted to confirm this observation.

Providing a choice to students between online and lecture courses, while increasing the role of instructors in online courses, may result in better student satisfaction. Students could also be gradually trained to effectively use online resources. The design changes in ALEKS could include the replacement of the “pie” with the bar chart, different types of feedback, explanation of how assessments are done, and ability to revisit problems on assessments.

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Hrubik-Vulanovic, Tatjana. "EFFECTS OF INTELLIGENT TUTORING SYSTEMS IN BASIC ALGEBRA COURSES ON SUBSEQUENT MATHEMATICS LECTURE COURSES." Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1373337020.

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Strayer, Jeremy F. "The effects of the classroom flip on the learning environment a comparison of learning activity in a traditional classroom and a flip classroom that used an intelligent tutoring system /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1189523914.

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Strayer, Jeremy. "The effects of the classroom flip on the learning environment: a comparison of learning activity in a traditional classroom and a flip classroom that used an intelligent tutoring system." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1189523914.

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Hanson, Ruth Mary. "Feedback in intelligent computer-assisted language learning and second language acquisition : a study of its effect on the acquisition of French past tense aspect using an Intelligent Language Tutoring System." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/2605.

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Questions surrounding the impact of feedback in response to learner error are of interest in the fields of both Second Language Acquisition (SLA) and Intelligent Computer-Assisted Language Learning (ICALL). Current empirical SLA research seeks to ascertain what feedback types have a statistically significant positive impact on the process of acquiring a second language. Similarly, research in ICALL focuses on testing Intelligent Language Tutoring Systems (ILTSs) generally as well as the effectiveness of the feedback that they deliver. Despite this common interest in feedback, to date there has been no significant interdisciplinary research involving the two fields. The experiment reported here seeks to bridge this gap. Using a purpose-built ILTS, we tested the effect of two types of feedback on the acquisition of French past tense aspect among anglophone learners. Inspired by previous work in SLA, Explicit Inductive (EI) and Input Processing (IP) feedback were tested against a control group using a pre test/post test design. The learners completed a transformation and a grammaticality judgment task. For the transformation, they were presented with texts in the present tense and asked to re-write them in the past tense. For the grammaticality judgment, they had to rank the grammaticality of each sentence in a set of texts. In response to errors, EI feedback interpreted the aspectual meaning of the learners' answer and explicitly told them that it was not the most natural according to the context. In order to encourage formmeaning mapping, IP feedback asked the learners to match their erroneous answer to its interpretation. Two interpretations were presented: one was the target interpretation and the other matched the learner's answer. Having made their choice, they were then told whether it was correct as well as which interpretation was in fact target-like. The quantitative evaluation of the effectiveness of the EI and IP feedback was not statistically significant. We argued that this was due to a combined effect of learner level, target structure and feedback.
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Nwaogu, Eze. "The Effect of Aleks on Students' Mathematics Achievement in an Online Learning Environment and the Cognitive Complexity of the Initial and Final Assessments." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/msit_diss/94.

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For many courses, mathematics included, there is an associated interactive e-learning system that provides assessment and tutoring. Some of these systems are classified as Intelligent Tutoring Systems. MyMathLab, Mathzone, and Assessment of LEarning in Knowledge Space (ALEKS) are just a few of the interactive e-learning systems in mathematics. In ALEKS, assessment and tutoring are based on the Knowledge Space Theory. Previous studies in a traditional learning environment have shown ALEKS users to perform equally or better in mathematics achievement than the group who did not use ALEKS. The purpose of this research was to investigate the effect of ALEKS on students’ achievement in mathematics in an online learning environment and to determine the cognitive complexity of mathematical tasks enacted by ALEKS’s initial (pretest) and final (posttest) assessments. The targeted population for this study was undergraduate students in College Mathematics I, in an online course at a private university in the southwestern United States. The study used a quasi-experimental One-Group non-randomized pretest and posttest design. Five methods of analysis and one model were used in analyzing data: t-test, correctional analysis, simple and multiple regression analysis, Cronbach’s Alpha reliability test and Webb’s depth of knowledge model. A t-test showed a difference between the pretest and posttest reports, meaning ALEKS had a significant effect on students’ mathematics achievement. The correlation analysis showed a significant positive linear relationship between the concept mastery reports and the formative and summative assessments reports meaning there is a direct relationship between the ALEKS concept mastery and the assessments. The regression equation showed a better model for predicting mathematics achievement with ALEKS when the time spent learning in ALEKS and the concept mastery scores are used as part of the model. According to Webb’s depth of knowledge model, the cognitive complexity of the pretest and posttest question items used by ALEKS were as follows: 50.5% required application of skills and concepts, 37.1% required recall of information, and 12.4% required strategic thinking: None of the questions items required extended thinking or complex reasoning, implying ALEKS is appropriate for skills and concepts building at this level of mathematics.
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Goldberg, Benjamin. "Explicit Feedback Within Game-Based Training: Examining the Influence of Source Modality Effects on Interaction." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5746.

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This research aims to enhance Simulation-Based Training (SBT) applications to support training events in the absence of live instruction. The overarching purpose is to explore available tools for integrating intelligent tutoring communications in game-based learning platforms and to examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research was the Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools and methods to author, deliver, and evaluate intelligent tutoring technologies within any training platform. Influenced by research surrounding Social Cognitive Theory and Cognitive Load Theory, the resulting experiment tested varying approaches for utilizing an Embodied Pedagogical Agent (EPA) to function as a tutor during interaction in a game-based environment. Conditions were authored to assess the tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT's browser-based Tutor-User Interface (TUI), or using audio prompts alone with no social grounding. The resulting data supports the application of using an EPA embedded in GIFT's TUI to provide explicit feedback during a game-based learning event. Analyses revealed conditions with an EPA situated in the TUI to be as effective as embedding the agent directly in the game environment. This inference is based on evidence showing reliable differences across conditions on the metrics of performance and self-reported mental demand and feedback usefulness items. This research provides source modality tradeoffs linked to tactics for relaying training relevant explicit information to a user based on real-time performance in a game.
Ph.D.
Doctorate
Psychology
Sciences
Modeling & Simulation
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Xiong, Xiaolu. "Theory and Practice: Improving Retention Performance through Student Modeling and System Building." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/139.

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The goal of Intelligent Tutoring systems (ITSs) is to engage the students in sustained reasoning activity and to interact with students based on a deep understanding of student behavior. In order to understand student behavior, ITSs rely on student modeling methods to observes student actions in the tutor and creates a quantitative representation of student knowledge, interests, affective states. Good student models are going to effectively help ITSs customize instructions, engage student's interest and then promote learning. Thus, the work of building ITSs and advancing student modeling should be considered as two interconnected components of one system rather than two separate topics. In this work, we utilized the theoretical support of a well-known learning science theory, the spacing effect, to guide the development of an ITS, called Automatic Reassessment and Relearning System (ARRS). ARRS not only validated the effectiveness of spacing effect, but it also served as a testing field which allowed us to find out new approaches to improve student learning by conducting large-scale randomized controlled trials (RCTs). The rich data set we gathered from ARRS has advanced our understanding of robust learning and helped us build student models with advanced data mining methods. At the end, we designed a set of API that supports the development of ARRS in next generation ASSISTments platform and adopted deep learning algorithms to further improve retention performance prediction. We believe our work is a successful example of combining theory and practice to advance science and address real- world problems.
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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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Books on the topic "Effects of Intelligent Tutoring System"

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Clibbon, Kelvin. Intelligent tutoring system architectures. Leicester: De Montfort University, Computing & Mathematical Sciences Research, 1992.

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Canavan, Bernadette. An intelligent tutoring system for database design. [s.l: The Author], 1996.

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Mottram-Brown, Denise. An intelligent tutoring system for teaching fact finding. Leicester: De Montfort University, 1992.

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Lane, Patrick. Tutoring Mendelian genetics: The simulation of genetic inheritance in an intelligent tutoring system. [s.l: TheAuthor], 1996.

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Stankov, Slavomir. Intelligent tutoring systems in e-learning environments: Design, implementation, and evaluation. Hershey PA: Information Science Reference, 2010.

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McGinn, Elizabeth Marie. A hypermedia approach to the design of an intelligent tutoring system. Monterey, Calif: Naval Postgraduate School, 1991.

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DeLooze, Lori L. ITS Ada: An intelligent tutoring system for the Ada programming language. Monterey, Calif: Naval Postgraduate School, 1991.

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Domeshek, Eric A. Intelligent tutoring system for teaching battlefield command reasoning skills: Phase I final report. Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences, 2002.

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Varlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.

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Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.
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Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Amsterdam: Elsevier, 2009.

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Book chapters on the topic "Effects of Intelligent Tutoring System"

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Morales, Rafael, Helen Pain, and Michael Ramscar. "Modeling the Cognitive Effects of Participative Learner Modeling." In Intelligent Tutoring Systems, 613. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-68716-5_85.

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Hayashi, Yugo. "On Pedagogical Effects of Learner-Support Agents in Collaborative Interaction." In Intelligent Tutoring Systems, 22–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30950-2_3.

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Dickison, Daniel, Steven Ritter, Tristan Nixon, Thomas K. Harris, Brendon Towle, R. Charles Murray, and Robert G. M. Hausmann. "Predicting the Effects of Skill Model Changes on Student Progress." In Intelligent Tutoring Systems, 300–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13437-1_51.

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Lomas, Derek, John Stamper, Ryan Muller, Kishan Patel, and Kenneth R. Koedinger. "The Effects of Adaptive Sequencing Algorithms on Player Engagement within an Online Game." In Intelligent Tutoring Systems, 588–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30950-2_79.

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Wang, Ning, W. Lewis Johnson, and Jonathan Gratch. "Facial Expressions and Politeness Effect in Foreign Language Training System." In Intelligent Tutoring Systems, 165–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13388-6_21.

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Derbali, Lotfi, and Claude Frasson. "Exploring the Effects of Prior Video-Game Experience on Learner’s Motivation during Interactions with HeapMotiv." In Intelligent Tutoring Systems, 129–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30950-2_17.

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Tajariol, Federico, Jean-Michel Adam, and Michel Dubois. "Seeing the Face and Observing the Actions: The Effects of Nonverbal Cues on Mediated Tutoring Dialogue." In Intelligent Tutoring Systems, 480–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69132-7_51.

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Dolan, Robert P., and Sonya Powers. "Effects of Text and Visual Element Integration Schemes on Online Reading Behaviors of Typical and Struggling Readers." In Intelligent Tutoring Systems, 660–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30950-2_109.

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Muñoz-Merino, Pedro J., Carlos Delgado Kloos, and Mario Muñoz-Organero. "Behavior Effect of Hint Selection Penalties and Availability in an Intelligent Tutoring System." In Intelligent Tutoring Systems, 384–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13437-1_78.

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McLaren, Bruce M., Sung-Joo Lim, France Gagnon, David Yaron, and Kenneth R. Koedinger. "Studying the Effects of Personalized Language and Worked Examples in the Context of a Web-Based Intelligent Tutor." In Intelligent Tutoring Systems, 318–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11774303_32.

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Conference papers on the topic "Effects of Intelligent Tutoring System"

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Shih, Shu Chuan, Hao Yu Tsai, and Mei Ling Chen. "THE EFFECT OF A ONE-ON-ONE DIALOGUE-BASED MATHEMATICAL INTELLIGENT TUTORING SYSTEM FOR LEARNING EQUIVALENT FRACTION." In International Conference on Education and New Developments. inScience Press, 2021. http://dx.doi.org/10.36315/2021end077.

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The purposes of this study are to develop a one-on-one dialogue-based mathematical intelligent tutoring system (ITS) for learning equivalent fraction in the 4th grade math, and evaluate its learning effect. The system used the course content and dialogue script designed by the math teaching experts in advance, and a computer agent teacher asked questions based on the course script. After the student answered, the system was able to identify the error pattern and misconception according to the student's response, then provided each student with adaptive teaching guidance or feedback. Students could construct correct equivalent fraction concepts through a series of interactive dialogues between students and the computer agent teacher step by step. In order to evaluate the effectiveness of this ITS, a quasi-experiment design was conducted. The pretest and post-test were parallel tests involving equivalent fraction. The participants of the study were 76 students in the fourth grade of two elementary schools chosen from midland of Taiwan. They were divided into the experimental group of 39 and the control group of 37. The experimental group used the "one-on-one dialogue-based mathematical intelligent tutoring system" for teaching. The control group used traditional classroom instruction by a human teacher. The learning content and time were controlled to be the same. Finally, the learning effectiveness and learning interest were assessed by comparing the pre-test and post-test performance of students. The results of the study showed that both teaching methods can significantly improve the students’ learning achievements of equivalent fraction, and the learning effectiveness of "one-on-one dialogue-based mathematical intelligent tutoring system" was significantly better than that of traditional classroom instruction. In the "one-on-one dialogue-based mathematical intelligent tutoring system" group, the learning improvement of students with different genders and different ability levels were also reaching a significant level. It indicated that this system benefited the learning achievements of students with different genders and different abilities. Furthermore, from the response data of the learning interest questionnaire, both teaching methods could significantly improve the learning interest of students. But there was no significant difference between the two teaching methods. By interviewing students, the probable causes included that low learning interest students of the experimental group also lack interest to familiar system operation, and some students think this ITS is not interesting enough because of lacking learning games.
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Kozierkiewicz-Hetmanska, Adrianna, and Jaroslaw Bernacki. "A conception for use of user profile to prediction learning effects in Intelligent Tutoring Systems." In 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF). IEEE, 2015. http://dx.doi.org/10.1109/cybconf.2015.7175913.

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Mitrovic, Antonija, and Jay Holland. "INVESTIGATING THE EFFECT OF VOLUNTARY USE OF AN INTELLIGENT TUTORING SYSTEM ON STUDENTS' LEARNING." In 11th International Conference on Education and New Learning Technologies. IATED, 2019. http://dx.doi.org/10.21125/edulearn.2019.2224.

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Srivastava, R. P., and S. L. Sin. "An intelligent tutoring system." In Ninth Annual International Phoenix Conference on Computers and Communications. 1990 Conference Proceedings. IEEE, 1990. http://dx.doi.org/10.1109/pccc.1990.101690.

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Mohanan, Ramya, Catherine Stringfellow, and Dittika Gupta. "An emotionally intelligent tutoring system." In 2017 Computing Conference. IEEE, 2017. http://dx.doi.org/10.1109/sai.2017.8252228.

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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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Hernández, Yasmín, Gustavo Arroyo-Figueroa, and L. Enrique Sucar. "Intelligent Tutoring System with Affective Behavior." In 2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session, MICAI. IEEE, 2007. http://dx.doi.org/10.1109/micai.2007.15.

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Mendjoge, Neha, Abhijit R. Joshi, and Meera Narvekar. "Intelligent tutoring system for Database Normalization." In 2016 International Conference on Computing Communication Control and automation (ICCUBEA). IEEE, 2016. http://dx.doi.org/10.1109/iccubea.2016.7860013.

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Sun, Yu, and Zhiping Li. "A multi-agent intelligent tutoring system." In 2009 4th International Conference on Computer Science & Education (ICCSE 2009). IEEE, 2009. http://dx.doi.org/10.1109/iccse.2009.5228267.

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Yan, Jie, and Ruth Agada. "Intelligent Agent Embodied Online Tutoring System." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00123.

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Reports on the topic "Effects of Intelligent Tutoring System"

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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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Domeshek, Eric A. Intelligent Tutoring System for Teaching Battlefield Command Reasoning Skills. Fort Belvoir, VA: Defense Technical Information Center, March 2002. http://dx.doi.org/10.21236/ada400494.

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Stottler, Richard H., Randy Jensen, Bill Pike, and Rick Bingham. Adding an Intelligent Tutoring System to an Existing Training Simulation. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada454493.

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Dillon, Ronna F., and William F. Kieckhaefer. Information Processing and Coaching Treatments in an Intelligent Tutoring System,. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada362134.

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Gitomer, Drew H., Linda S. Steinberg, and Robert J. Mislevy. Diagnostic Assessment of Troubleshooting Skill in an Intelligent Tutoring System. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada280554.

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Stottler, Richard H., and Bill Pike. An Embedded Training Solution: FBCB2/Tactical Decision Making Intelligent Tutoring System. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada438493.

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Mislevy, Robert J., and Drew H. Gitomer. The Role of Probability-Based Inference in an Intelligent Tutoring System. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada303316.

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Mislevy, Robert J., and Drew H. Gitomer. The Role of Probability-Based Inference in an Intelligent Tutoring System. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada303584.

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Ryder, Joan M., Arthur C. Graesser, Jean-Christophe Le Mentec, Max M. Louwerse, and Ashish Karnavat. A Dialog-Based Intelligent Tutoring System for Practicing Battle Command Reasoning. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada425430.

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Miller, Mark L. A Prototype Intelligent Maintenance Tutoring System for Troubleshooting the M16A1 Automatic Rifle. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada210702.

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