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Brown, Quincy Lee Frank Salvucci Dario. "Mobile intelligent tutoring system : moving intelligent tutoring systems off the desktop /". Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3114.
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Razzaq, Leena M. "Tutorial dialog in an equation solving intelligent tutoring system". Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0107104-155853.
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Thesis (M.S.)--Worcester Polytechnic Institute.Keywords: cognitive model; model-tracing; intelligent tutoring system; tutoring; artificial intelligence. Includes bibliographical references (p. 55-57).
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Thompson, Allan. "Adaptive intelligent tutoring systems". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22783.pdf.
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MATOS, Diego Dermeval Medeiros da Cunha. "Authoring gamified intelligent tutoring systems". Universidade Federal de Campina Grande, 2017. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/867.
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Sistemas Tutores Inteligentes (STIs) têm recibo a atenção de acadêmicos e profissionais desde da década de 70. Tem havido um grande número de estudos recentes em apoio da efetividade de STIs. Entretanto, é muito comum que estudantes fiquem desengajados ou entediados durante o processo de aprendizagem usando STIs. Para considerar explicitamente os aspectos motivacionais de estudantes, pesquisadores estão cada vez mais interessados em usar gamificação em conjunto com STIs. Contudo, apesar de prover tutoria individualizada para estudantes e algum tipo de suporte para professores, estes usuários não têm recebido alta prioridade no desenvolvimento destes tipos de sistemas. De forma a contribuir para o uso ativo e personalizado de STIs gamificados por professores, três problemas técnicos devem ser considerados. Primeiro, projetar STI é muito complexo (deve-se considerar diferentes teorias, componentes e partes interessadas) e incluir gamificação pode aumentar significativamente tal complexidade e variabilidade. Segundo, as funcionalidades de STIs gamificados podem ser usadas de acordo com vários elementos (ex.: nível educacional, domínio de conhecimento, teorias de gamificaçãoe STI, etc). Desta forma, é imprescindível tirar proveito das teorias e práticas de ambos os tópicos para reduzir o espaço de design destes sistemas. Terceiro, para efetivamente auxiliar professores a usarem ativamente estes sistemas, faz-se necessário prover uma solução simples e usável para eles. Para lidar com estes problemas, o principal objetivo desta tese é projetar uma solução computacional de autoria para fornecer aos professores uma forma de personalizar as funcionalidades de STIs gamificados gerenciando a alta variabilidade destes sistemas e considerando as teorias/práticas de gamificação e STI. Visando alcançar este objetivo, nós identificamos o espaço de variabilidade e o representamos por meio do uso de uma abordagem de modelagem de features baseada em ontologias (OntoSPL). Desenvolvemos um modelo ontológico integrado (Ontologia de tutoria gamificada ou Gamified tutoring ontology) que conecta elementos de design de jogos apoiados por evidências no domínio de e-learning, além de teorias e frameworks de gamificação aos conceitos de STI. Finalmente, desenvolvemos uma solução de autoria (chamada AGITS) que leva em consideração tais ontologias para auxiliar professores na personalização de funcionalidades de STIs gamificados. As contribuições deste trabalho são avaliadas por meio da condução de quatro estudos empíricos: (1) conduzimos um experimento controlado para comparar a OntoSPL com uma abordagem de modelagem de features bem conhecida na literatura. Os resultados sugerem que esta abordagem é mais flexível e requer menos tempo para mudar; (2) avaliamos o modelo ontológico integrado usando um método de avaliação de ontologias (FOCA) com especialistas tanto de contexto acadêmico quanto industrial. Os resultados sugerem que as ontologias estão atendendo adequadamente os papeis de representação do conhecimento; (3) avaliamos versões não-interativas da solução de autoria desenvolvida com 59 participantes. Os resultados indicam uma atitude favorável ao uso da solução de autoria projetada,nos quais os participantes concordaram que a solução é fácil de usar, usável, simples, esteticamente atraente,tem um suporte bem percebido e alta credibilidade; e (4) avaliamos, por fim,versões interativas (do zero e usando um modelo) da solução de autoria com 41 professores. Os resultados sugerem que professores podem usar e reusar, com um alto nível de aceitação, uma solução de autoria que inclui toda a complexidade de projetar STI gamificado.
Intelligent Tutoring Systems (ITSs) have been drawing the attention of academics and practitioners since early 70’s. There have been a number of recent studies in support of the effectiveness of ITSs. However, it is very common that students become disengaged or bored during the learning process by using ITSs. To explicitly consider students’ motivational aspects, researchers are increasingly interested in using gamification along with ITS.However, despite providing individualized tutoring to students and some kind of support for teachers, teachers have been not considered as first-class citizens in the development of these kinds of systems. In order to contribute to the active and customized use of gamified ITS by teachers, three technical problems should be considered. First, designing ITS is very complex (i.e., take into account different theories, components, and stahekolders) and including gamification may significantly increase such complexity and variability. Second, gamified ITS features can be used depending on several elements (e.g., educational level, knowledge domain, gamification and ITS theories, etc). Thus, it is imperative to take advantage of theories and practices from both topics to reduce the design space of these systems. Third, in order to effectively aid teachers to actively use such systems, it is needed to provide a simple and usable solution for them. To deal with these problems, the main objective of this thesis is to design an authoring computational solution to provide for teachers a way to customize gamified ITS features managing the high variability of these systems and considering gamification and ITS theories/practices. To achieve this objective, we identify the variability space and represent it using an ontology-based feature modeling approach (OntoSPL). We develop an integrated ontological model (Gamified tutoring ontology) that connects evidence-supported game design elements in the e-learning domain as well as gamification theories and frameworks to existing ITS concepts. Finally, we develop an authoring solution (named AGITS) that takes into account these ontologies to aid teachers in the customization of gamified ITS features. We evaluate our contributions by conducting four empirical studies: (1) we perform a controlled experiment to compare OntoSPL against a well-known ontology-based feature modeling approach. The results suggest that our approach is more flexible and requires less time to change; (2) we evaluate the ontological integrated model by using an ontology evaluation method (FOCA) with experts from academic and industrial settings. The results suggest that our ontologies are properly targeting the knowledge representation roles; (3) we evaluate non-interactive versions of the designed authoring solution with 59 participants. The results indicate a positive attitude towards the use of the designed authoring solutions, in which participants agreed that they are ease to use, usable, simple, aesthetically appealing, have a well-perceived system support and high credibility; and (4) we also evaluate interactive versions (scratch and template) of our authoring solution with 41 teachers. The results suggest that teachers can use and reuse, with a high acceptance level, an authoring solution that includes all the complexity to design gamified ITS.
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Gong, Yue. "Student Modeling in Intelligent Tutoring Systems". Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/403.
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"After decades of development, Intelligent Tutoring Systems (ITSs) have become a common learning environment for learners of various domains and academic levels. ITSs are computer systems designed to provide instruction and immediate feedback, which is customized to individual students, but without requiring the intervention of human instructors. All ITSs share the same goal: to provide tutorial services that support learning. Since learning is a very complex process, it is not surprising that a range of technologies and methodologies from different fields is employed. Student modeling is a pivotal technique used in ITSs. The model observes student behaviors in the tutor and creates a quantitative representation of student properties of interest necessary to customize instruction, to respond effectively, to engage students¡¯ interest and to promote learning. In this dissertation work, I focus on the following aspects of student modeling. Part I: Student Knowledge: Parameter Interpretation. Student modeling is widely used to obtain scientific insights about how people learn. Student models typically produce semantically meaningful parameter estimates, such as how quickly students learn a skill on average. Therefore, parameter estimates being interpretable and plausible is fundamental. My work includes automatically generating data-suggested Dirichlet priors for the Bayesian Knowledge Tracing model, in order to obtain more plausible parameter estimates. I also proposed, implemented, and evaluated an approach to generate multiple Dirichlet priors to improve parameter plausibility, accommodating the assumption that there are subsets of skills which students learn similarly. Part II: Student Performance: Student Performance Prediction. Accurately predicting student performance is one of the most desired features common evaluations for student modeling. for an ITS. The task, however, is very challenging, particularly in predicting a student¡¯s response on an individual problem in the tutor. I analyzed the components of two common student models to determine which aspects provide predictive power in classifying student performance. I found that modeling the student¡¯s overall knowledge led to improved predictive accuracy. I also presented an approach, which, rather than assuming students are drawn from a single distribution, modeled multiple distributions of student performances to improve the model¡¯s accuracy. Part III: Wheel-spinning: Student Future Failure in Mastery Learning. One drawback of the mastery learning framework is its possibility to leave a student stuck attempting to learn a skill he is unable to master. We refer to this phenomenon of students being given practice with no improvement as wheel-spinning. I analyzed student wheel-spinning across different tutoring systems and estimated the scope of the problem. To investigate the negative consequences of see what wheel-spinning could have done to students, I investigated the relationships between wheel-spinning and two other constructs of interest about students: efficiency of learning and ¡°gaming the system¡±. In addition, I designed a generic model of wheel-spinning, which uses features easily obtained by most ITSs. The model can be well generalized to unknown students with high accuracy classifying mastery and wheel-spinning problems. When used as a detector, the model can detect wheel-spinning in its early stage with satisfying satisfactory precision and recall. "
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Buckenmeyer, Michelle. "User characteristics in intelligent tutoring systems /". Online version of thesis, 1992. http://hdl.handle.net/1850/10998.
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Green, Derek Tannell. "INTELLIGENT TUTORING SYSTEMS FOR SKILL ACQUISITION". Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/203476.
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Throughout history education has been restricted to a relatively small percentage of the world's population. The cause can be attributed to a number of factors; how- ever, it has been chiefly due to excessive cost. As we enter the information age it becomes conceivable to make education freely available to anyone, anywhere, any- time. The Intelligent Tutoring System is an automated teaching system designed to improve through experience, eventually learning to tailor its teaching to perfectly match each individual student's needs and preferences. In this dissertation we describe a template which we use for building problem-oriented skill teaching intelligent tutoring systems based on a Dynamic Bayes network framework. We present two case studies in which the template is adapted to very different teaching domains, documenting in each case the process of building, training, and testing the resulting ITS. In both case studies, the performance of the ITS is validated through human subject experiments. The results of these studies show that our template is a viable technique for designing ITSs that teach in skill based domains. We also show that, while conducting artificial intelligence research on the design of an ITS and collecting data for use in that regard, we can concurrently run educational research experiments. We find that the two are quite inextricably tied and that showing good general results regarding the performance of the ITS is not sufficient; a strong understanding of the experience of the students is also required. We report some interesting results covering the effect of choice in learning and a gender bias that shows up in our tutoring system.
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Riccucci, Simone <1978>. "Knowledge management in intelligent tutoring systems". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/916/1/Tesi_Riccucci_Simone.pdf.
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In the last years, Intelligent Tutoring Systems have been a very successful way for improving
learning experience. Many issues must be addressed until this technology can be defined mature.
One of the main problems within the Intelligent Tutoring Systems is the process of contents
authoring: knowledge acquisition and manipulation processes are difficult tasks because they
require a specialised skills on computer programming and knowledge engineering. In this thesis we
discuss a general framework for knowledge management in an Intelligent Tutoring System and
propose a mechanism based on first order data mining to partially automate the process of
knowledge acquisition that have to be used in the ITS during the tutoring process. Such a
mechanism can be applied in Constraint Based Tutor and in the Pseudo-Cognitive Tutor.
We design and implement a part of the proposed architecture, mainly the module of knowledge
acquisition from examples based on first order data mining. We then show that the algorithm can be
applied at least two different domains: first order algebra equation and some topics of C
programming language. Finally we discuss the limitation of current approach and the possible
improvements of the whole framework.
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Riccucci, Simone <1978>. "Knowledge management in intelligent tutoring systems". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/916/.
Texto completo da fonteResumo:
In the last years, Intelligent Tutoring Systems have been a very successful way for improving
learning experience. Many issues must be addressed until this technology can be defined mature.
One of the main problems within the Intelligent Tutoring Systems is the process of contents
authoring: knowledge acquisition and manipulation processes are difficult tasks because they
require a specialised skills on computer programming and knowledge engineering. In this thesis we
discuss a general framework for knowledge management in an Intelligent Tutoring System and
propose a mechanism based on first order data mining to partially automate the process of
knowledge acquisition that have to be used in the ITS during the tutoring process. Such a
mechanism can be applied in Constraint Based Tutor and in the Pseudo-Cognitive Tutor.
We design and implement a part of the proposed architecture, mainly the module of knowledge
acquisition from examples based on first order data mining. We then show that the algorithm can be
applied at least two different domains: first order algebra equation and some topics of C
programming language. Finally we discuss the limitation of current approach and the possible
improvements of the whole framework.
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Baker, Michael J. "Negotiated tutoring : an approach to interaction in intelligent tutoring systems". Thesis, Open University, 1990. http://oro.open.ac.uk/54150/.
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This thesis describes a general approach to tutorial interaction in Intelligent Tutoring Systems, called "Negotiated Tutoring". Some aspects of the approach have been implemented as a computer program in the 'KANT' (Kritical Argument Negotiated Tutoring) system. Negotiated Tutoring synthesises some recent trends in Intelligent Tutoring Systems research, including interaction symmetry, use of explicit negotiation in dialogue, multiple interaction styles, and an emphasis on cognitive and metacognitive skill acquisition in domains characterised by justified belief. This combination of features has not been previously incorporated into models for intelligent tutoring dialogues. Our approach depends on modelling the high-level decision-making processes and memory representations used by a participant in dialogue. Dialogue generation is controlled by reasoning mechanisms which operate on a 'dialogue state', consisting of conversants' beliefs, a set of possible dialogue moves, and a restricted representation of the recent utterances generated by both conversants. The representation for conversants' beliefs is based on Anderson's (1983) model for semantic memory, and includes a model for dialogue focus based on spreading activation. Decisions in dialogue are based on preconditions with respect to the dialogue state, higher level educational preferences which choose between relevant alternative dialogue moves, and negotiation mechanisms designed to ensure cooperativity. The domain model for KANT was based on a cognitive model for perception of musical structures in tonal melodies, which extends the theory of Lerdahl and Jackendoff (1983). Our model ('GRAF' - GRouping Analysis with Frames) addresses a number of problems with Lerdahl and Jackendoff's theory, notably in describing how a number of unconscious processes in music cognition interact, including elements of top-down and bottom-up processing. GRAF includes a parser for musical chord functions, a mechanism for performing musical reductions, low-level feature detectors and a frame-system (Minsky 1977) for musical phrase structures.
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Macasek, Michael A. "Towards teachers quickly creating tutoring systems". Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-122005-162550/.
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Thesis (M.S.)--Worcester Polytechnic Institute.Keywords: collaboration; Intelligent Tutoring System; portal; teacher tools; Assistment; Assistment Project Includes bibliographical references. (p.37-38)
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Weragama, Dinesha Samanthi. "Intelligent tutoring system for learning PHP". Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63202/1/Dinesha%20Samanthi_Weragama_Thesis.pdf.
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This thesis investigates the possibility of using an adaptive tutoring system for beginning programming students. The work involved, designing, developing and evaluating such a system and showing that it was effective in increasing the students’ test scores. In doing so, Artificial Intelligence techniques were used to analyse PHP programs written by students and to provide feedback based on any specific errors made by them. Methods were also included to provide students with the next best exercise to suit their particular level of knowledge.
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Weerasinghe, A. "A General Model of Adaptive Tutorial Dialogues for Intelligent Tutoring Systems". Thesis, University of Canterbury. Computer Science and Software Engineering, 2013. http://hdl.handle.net/10092/8732.
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Adaptive tutorial dialogues have been successfully employed by ITSs to facilitate deep learning of conceptual domain knowledge. But none of the approaches used for generating dialogues have been used across instructional domains and tasks. The objective of this project was twofold: (i) to propose a general model that provides adaptive dialogue support in both well- and ill-defined instructional tasks (ii) to explore whether adaptive tutorial dialogues are better than non-adaptive dialogues in acquiring domain knowledge.
Our model provides adaptive dialogue support by identifying the concepts that the student has most difficulty with, and then selecting the tutorial dialogues corresponding to those concepts. The dialogues are customised based on the student’s knowledge and explanation skills, in terms of the length and the exact content of the dialogue. The model consists of three parts: an error hierarchy, tutorial dialogues and rules for adapting them.
We incorporated our model into EER-Tutor, a constraint-based tutor that teaches database design. The effectiveness of adaptive dialogues compared to non-adaptive dialogues in learning this ill-defined task was evaluated in an authentic classroom environment. The results revealed that the acquisition of the domain knowledge (represented as constraints) of the experimental group who received adaptive dialogues was significantly higher than their peers in the control group with non-adaptive dialogues. We also incorporated our model into NORMIT, a constraint-based tutor that teaches data normalization. We repeated the experiment using NORMIT in a real-world class room environment with a much smaller group of students (18 in NORMIT study vs 65 in EER-Tutor study) but did not find significant differences. We also investigated whether our model could support dialogues in logical database design and fraction addition using paper-based methods.
Our evaluation studies and investigations on paper indicated that our model can provide adaptive support for both ill-and well-defined tasks associated with a well-defined domain theory. The results also indicated that adaptive dialogues are more effective than non-adaptive dialogues in teaching the ill-defined task of database design.
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Tong, Amelia Ka Yan. "Developing a model for tutoring strategy selection in intelligent tutoring systems". Thesis, London School of Economics and Political Science (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267977.
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Variation in tutoring strategy plays an important part in Intelligent Tutoring Systems (ITSs). The potential for providing an adaptive ITS depends initially on having a range of tutoring strategies to select from. However, in order to react effectively to the student's needs, an ITS not only has to be able to simply offer different tutoring strategies but to choose intelligently among them and determine which one is best for an individual student at a particular moment. This thesis first examines, through literature review and interactions with existing systems, the current practices of ITSs regarding the provision of multiple tutoring strategies and tutoring strategy selection. What stems from this examination are the principles that underlie tutoring strategys election. These principles of tutoring strategy selection serve as a foundation for the construction of the model for tutoring strategy selection. To demonstrate the benefits of having such a model for formalising selection, the model is then implemented in ARISTOTLE, an existing ITS for tutoring zoology that includes several tutoring strategies but uses ad hoc mechanisms for choosing among them. This research is therefore contributing, through the principles of, and the model for tutoring strategy selection, a formal basis for selecting among tutoring strategies in ITSs that incorporate multiple tutoring strategies.
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Landau, Harry Edward. "Intelligent tutoring systems : a design support tool /". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA288489.
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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, September 1994.Thesis advisor(s): Kishore Sengupta, B. Ramesh. "September 1994." Bibliography: p. 41-42. Also available online.
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Moore, David John. "Dialogue game theory for intelligent tutoring systems". Thesis, Leeds Beckett University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333697.
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Nielsen, Rodney D. "Learner answer assessment in Intelligent Tutoring Systems". Connect to online resource, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3303833.
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Moyse, Roderick. "Multiple viewpoint the tutoring systems". Thesis, Open University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.290206.
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Hall, Douglas Lee. "A Comparative Analysis of Guided vs. Query-Based Intelligent Tutoring Systems (ITS) Using a Class-Entity-Relationship-Attribute (CERA) Knowledge Base". Thesis, North Texas State University, 1987. https://digital.library.unt.edu/ark:/67531/metadc331475/.
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One of the greatest problems facing researchers in the sub field of Artificial Intelligence known as Intelligent Tutoring Systems (ITS) is the selection of a knowledge base designs that will facilitate the modification of the knowledge base. The Class-Entity-Relationship-Attribute (CERA), proposed by R. P. Brazile, holds certain promise as a more generic knowledge base design framework upon which can be built robust and efficient ITS. This study has a twofold purpose. The first is to demonstrate that a CERA knowledge base can be constructed for an ITS on a subset of the domain of Cretaceous paleontology and function as the "expert module" of the ITS. The second is to test the validity of the ideas that students guided through a lesson learn more factual knowledge, while those who explore the knowledge base that underlies the lesson through query at their own pace will be able to formulate their own integrative knowledge from the knowledge gained in their explorations and spend more time on the system. This study concludes that a CERA-based system can be constructed as an effective teaching tool. However, while an ITS - treatment provides for statistically significant gains in achievement test scores, the type of treatment seems not to matter as much as time spent on task. This would seem to indicate that a query-based system which allows the user to progress at their own pace would be a better type of system for the presentation of material due to the greater amount of on-line computer time exhibited by the users.
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Choksey, Sanket Dinesh. "Developing an affordable authoring tool for intelligent tutoring systems". Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0825104-161218/.
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Thesis (M.S.)--Worcester Polytechnic Institute.Keywords: Model Tracing; Intelligent Tutoring Systems; JESS production system; Debugging Tool; Cognitive Tutor Authoring Tools. Includes bibliographical references (p. 58-60).
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Lloyd, Nicholas M. "Measuring student engagement in an intelligent tutoring system". Link to electronic thesis, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-050307-134149/.
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Lindsey, Levi Scott. "Pen-Based Interfaces for Intelligent Statics Tutoring Systems". Thesis, University of California, Riverside, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1547829.
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Here we present two intelligent tutoring systems for statics, the sub-discipline of engineering mechanics concerned with the analysis of mechanical systems in equilibrium under the action of forces. These systems are pen-based: one runs on Windows tablet PCs and the other on LivescribeTM smartpens with specially-designed paper worksheets. It is common for novice students to attempt to solve problems without understanding the fundamental concepts involved. For example, they may attempt to solve a new problem by adapting the solution to an example problem. This approach can lead to errors as novices often categorize problems on the basis of surface similarity rather than the structural—i.e., conceptual—similarity. Our new instructional model guides students in explicitly examining the structural elements that govern the solution. For example, before the student draws forces on a free-body diagram, the system requires the student to explicitly identify all interaction points, points at which other objects apply forces to the body. The student must then identify what kind of interaction occurs at each interaction point before representing them by force arrows. The system critiques the student's work for each of these steps and provides appropriate tutorial feedback. This instructional design has a number of benefits. It helps students to identify the structural elements that guide the solution process, which is important for problem-solving transfer. It also enables the system to accurately diagnose student errors. Because each step in the reasoning is explicitly recorded, the system can unambiguously determine the cause of an error and provide focused tutorial feedback. Also, the use of natural pen-based interfaces unburdens the student from extraneous cognitive load inherent in more traditional interfaces. We conducted two studies to evaluate these systems. The first included 43 students enrolled in Statics (ME 10) at UCR, while the second included 10 students enrolled in Introduction to Mechanical Engineering (ME 2). The results suggest that students find the systems to be useful for learning statics. However, the tablet-based system is more effective than the smartpen-based one, with the former leading to large and statistically significant learning gains in the second study.
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Zakharov, Konstantin. "Affect Recognition and Support in Intelligent Tutoring Systems". Thesis, University of Canterbury. Computer Science and Software Engineering, 2007. http://hdl.handle.net/10092/1216.
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Empirical research provides evidence of strong interaction between cognitive and affective processes in the human mind. Education research proposes a model of constructive learning that relates cognitive and affective processes in an evolving cycle of affective states. Intelligent Tutoring Systems (ITSs) are capable of providing comprehensive cognitive support. Affective support in ITSs, however, is lagging behind; the in-depth exploration of cognitive and affective processes in ITSs is yet to be seen. Our research focuses on the integration of affective support in an ITS enhanced with an affective pedagogical agent. In our work we adopt the dimensional (versus categorical) view of emotions for modelling affective states of the agent and the ITSs users. In two stages we develop and evaluate an affective pedagogical agent. The affective response of the first agent version is based on the appraisal of the interaction state; this agent's affective response is displayed as affective facial expressions. The pilot study at the end of the first stage of the project confirms the viability of our approach which combines the dimensional view of emotions with the appraisal of interaction state. In the second stage of the project we develop a facial feature tracking application for real-time emotion recognition in a video-stream. Affective awareness of the second version of the agent is based on the output from the facial feature tracking application and the appraisal of the interaction state. This agent's response takes the form of affectoriented messages designed to interrupt the state of negative flow. The evaluation of the affect-aware agent against an unemotional affect-unaware agent provides positive results, thus confirming the superiority of the affect-aware agent. Although the uptake of the agent was not unanimous, the agent established and maintained good rapport with the users in a role of a caring tutor. The results of the pilot study and the final evaluation validate our choices in the design of affective interaction. In both experiments, the participants appreciated the addition of audible feedback messages, describing it as an enhancement which helped them save time and maintain their focus. Finally, we offer directions for future research on affective support which can be conducted within the framework developed in the course of this project.
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Atolagbe, Tajudeen Abayomi. "A generic architecture for interactive intelligent tutoring systems". Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/5013.
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This research is focused on developing a generic intelligent architecture for an interactive tutoring system. A review of the literature in the areas of instructional theories, cognitive and social views of learning, intelligent tutoring systems development methodologies, and knowledge representation methods was conducted. As a result, a generic ITS development architecture (GeNisa) has been proposed, which combines the features of knowledge base systems (KBS) with object-oriented methodology. The GeNisa architecture consists of the following components: a tutorial events communication module, which encapsulates the interactive processes and other independent computations between different components; a software design toolkit; and an autonomous knowledge acquisition from a probabilistic knowledge base. A graphical application development environment includes tools to support application development, and learning environments and which use a case scenario as a basis for instruction. The generic architecture is designed to support client-side execution in a Web browser environment, and further testing will show that it can disseminate applications over the World Wide Web. Such an architecture can be adapted to different teaching styles and domains, and reusing instructional materials automatically can reduce the effort of the courseware developer (hence cost and time) in authoring new materials. GeNisa was implemented using Java scripts, and subsequently evaluated at various commercial and academic organisations. Parameters chosen for the evaluation include quality of courseware, relevancy of case scenarios, portability to other platforms, ease of use, content, user-friendliness, screen display, clarity, topic interest, and overall satisfaction with GeNisa. In general, the evaluation focused on the novel characteristics and performances of the GeNisa architecture in comparison with other ITS and the results obtained are discussed and analysed. On the basis of the experience gained during the literature research and GeNisa development and evaluation. a generic methodology for ITS development is proposed as well as the requirements for the further development of ITS tools. Finally, conclusions are drawn and areas for further research are identified.
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Hawkins, William J. "Boredom and student modeling in intelligent tutoring systems". Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/307.
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Over the past couple decades, intelligent tutoring systems (ITSs) have become popular in education. ITSs are effective at helping students learn (VanLehn, 2011; Razzaq, Mendicino & Heffernan, 2008; Koedinger et al, 1997) and help researchers understand how students learn. Such research has included modeling how students learn (Corbett & Anderson, 1995), the effectiveness of help given within an ITS (Beck et al, 2008), the difficulty of different problems (Pardos & Heffernan, 2011), and predicting long-term outcomes like college attendance (San Pedro et al, 2013a), among many other studies.
While most studies have focused on ITSs from a cognitive perspective, a growing number of researchers are paying attention to the motivational and affective aspects of tutoring, which have been recognized as important components of human tutoring (Lepper et al, 1993). Recent work has shown that student affect within an ITS can be detected, even without physical sensors or cameras (D’Mello et al, 2008; Conati & Maclaren, 2009; Sabourin et al, 2011; San Pedro et al, 2013b).
Initial studies with these sensor-less affect detectors have shown that certain problematic affective states, such as boredom, confusion and frustration, are prevalent within ITSs (Baker et al, 2010b). Boredom in particular has been linked to negative learning outcomes (Pekrun et al, 2010; Farmer & Sundberg, 1986) and long-term disengagement (Farrell, 1988). Therefore, reducing or responding effectively to these affective states within ITSs may improve both short- and long-term learning outcomes.
This work is an initial attempt to determine what causes boredom in ITSs. First, we determine which is more responsible for boredom in ITSs: the content in the system, or the students themselves. Based on the findings of that analysis, we conduct a randomized controlled trial to determine the effects of monotony on student boredom. In addition to the work on boredom, we also perform analyses that concern student modeling, specifically how to improve Knowledge Tracing (Corbett & Anderson, 1995), a popular student model used extensively in real systems like the Cognitive Tutors (Koedinger et al, 1997) and in educational research.
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Wheeldon, Alan. "Improving human computer interaction in intelligent tutoring systems". Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16587/1/Alan_Wheeldon_Thesis.pdf.
Texto completo da fonteResumo:
ITSs (Intelligent Tutoring Systems) provide a way of addressing some of the issues that the more traditional CAI (Computer Aided Instruction) systems do not address - the individual learning needs and individual learning abilities and levels of users - so that the user is in control of their learning experience. An ITS needs to be able to provide an explanation, for a real world situation, that successfully meets the needs of the user. To ensure relevant explanation content requires the ITS be based on sound planning principles and tutoring knowledge as well as knowledge of the domain and the user. To ensure a coherent explanation structure requires that the tutoring knowledge be applied with full recognition of the knowledge of the domain and the user. For a model of the user's knowledge to be effective, the system should be able to use it to enhance the flexibility and responsiveness of explanations generated. A user model should guide the generation of explanations so they are pitched at the correct level of the user's existing knowledge; models should be able to actively support the needs of the user so that the user's efforts in seeking out information are minimised. The aim of this research is to generate effective, flexible and responsive explanations, in educational software systems, through developing better explanation facilities than exist in currently available ITS software. In achieving this aim, I am advancing research into dialogue planning and user modelling. The explanation facilities described meet the requirements of an explanation that is tailored to the user's needs, a sound theory from which particular explanations are constructed, and a user model that can accurately represent the behaviour and beliefs of the user. My research contributions include explicitly and formally representing discourse planning / reasoning, from both the user's view and the tutor's view so that they can be clearly understood and represented in the ITS. More recent planners have adopted approaches that can be characterised as using adaptations of the classical planning approach, with informally specified planning algorithms and planning languages. Without clear, explicit and full descriptions of actions and the planning algorithm we can not be certain of the plans that such planners produce. I adopt a theoretically rigorous approach based on classical planning theory - the actions available to the planner, the planning language and algorithm should be explicitly represented to ensure that plans are complete and consistent. Classical regression planning uses dynamic planning thus enabling the system to be flexible in a variety of situations and providing the responsiveness required for an ITS. I take a theoretically rigorous approach in constructing a well specified model of discourse, building upon existing research in the area. I present a tutoring module that is able to find a way to motivate the user to take a recommended action, by relating the action to the user's goals, and that is able to reason about the text structure to generate an effective explanation - putting together several clauses of text whilst maintaining coherency. As part of developing such constructs for motivating, enabling and recommending, as well as constructs for structuring text, I use a pedagogic model based on the principled approach of (i) advising the user to take an action (ii) motivating the user to want to take the action and (iii) ensuring the user knows how to do the action. I take a clear and realistic approach to user modelling, making explicit models of the user's behaviour and beliefs. I adopt a theoretically rigorous approach, formally distinguishing between the user's reasoning and their actions, so they can be focused on separately. Formally making this distinction, more easily enables models of the user's reasoning to be tailored to the individual user. To enable the tutor to consider the full impact on the user, of the information to be delivered to the user, I use different plan spaces. I explicitly identify the different perspectives of the user and the tutor so that they can be focused on separately to generate an explanation that is tailored to the user. In my approach, reasoning about the user's skills, rules and knowledge is independent from reasoning about those of the tutor.
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Wheeldon, Alan. "Improving human computer interaction in intelligent tutoring systems". Queensland University of Technology, 2007. http://eprints.qut.edu.au/16587/.
Texto completo da fonteResumo:
ITSs (Intelligent Tutoring Systems) provide a way of addressing some of the issues that the more traditional CAI (Computer Aided Instruction) systems do not address - the individual learning needs and individual learning abilities and levels of users - so that the user is in control of their learning experience. An ITS needs to be able to provide an explanation, for a real world situation, that successfully meets the needs of the user. To ensure relevant explanation content requires the ITS be based on sound planning principles and tutoring knowledge as well as knowledge of the domain and the user. To ensure a coherent explanation structure requires that the tutoring knowledge be applied with full recognition of the knowledge of the domain and the user. For a model of the user's knowledge to be effective, the system should be able to use it to enhance the flexibility and responsiveness of explanations generated. A user model should guide the generation of explanations so they are pitched at the correct level of the user's existing knowledge; models should be able to actively support the needs of the user so that the user's efforts in seeking out information are minimised. The aim of this research is to generate effective, flexible and responsive explanations, in educational software systems, through developing better explanation facilities than exist in currently available ITS software. In achieving this aim, I am advancing research into dialogue planning and user modelling. The explanation facilities described meet the requirements of an explanation that is tailored to the user's needs, a sound theory from which particular explanations are constructed, and a user model that can accurately represent the behaviour and beliefs of the user. My research contributions include explicitly and formally representing discourse planning / reasoning, from both the user's view and the tutor's view so that they can be clearly understood and represented in the ITS. More recent planners have adopted approaches that can be characterised as using adaptations of the classical planning approach, with informally specified planning algorithms and planning languages. Without clear, explicit and full descriptions of actions and the planning algorithm we can not be certain of the plans that such planners produce. I adopt a theoretically rigorous approach based on classical planning theory - the actions available to the planner, the planning language and algorithm should be explicitly represented to ensure that plans are complete and consistent. Classical regression planning uses dynamic planning thus enabling the system to be flexible in a variety of situations and providing the responsiveness required for an ITS. I take a theoretically rigorous approach in constructing a well specified model of discourse, building upon existing research in the area. I present a tutoring module that is able to find a way to motivate the user to take a recommended action, by relating the action to the user's goals, and that is able to reason about the text structure to generate an effective explanation - putting together several clauses of text whilst maintaining coherency. As part of developing such constructs for motivating, enabling and recommending, as well as constructs for structuring text, I use a pedagogic model based on the principled approach of (i) advising the user to take an action (ii) motivating the user to want to take the action and (iii) ensuring the user knows how to do the action. I take a clear and realistic approach to user modelling, making explicit models of the user's behaviour and beliefs. I adopt a theoretically rigorous approach, formally distinguishing between the user's reasoning and their actions, so they can be focused on separately. Formally making this distinction, more easily enables models of the user's reasoning to be tailored to the individual user. To enable the tutor to consider the full impact on the user, of the information to be delivered to the user, I use different plan spaces. I explicitly identify the different perspectives of the user and the tutor so that they can be focused on separately to generate an explanation that is tailored to the user. In my approach, reasoning about the user's skills, rules and knowledge is independent from reasoning about those of the tutor.
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Suraweera, Pramuditha. "Widening the Knowledge Acquisition Bottleneck for Intelligent Tutoring Systems". Thesis, University of Canterbury. Computer Science and Software Engineering, 2007. http://hdl.handle.net/10092/1150.
Texto completo da fonteResumo:
Empirical studies have shown that Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing an ITS is a labour-intensive and time-consuming process. A major share of the development effort is devoted to acquiring the domain knowledge that accounts for the intelligence of the system. The goal of this research is to reduce the knowledge acquisition bottleneck and enable domain experts to build the domain model required for an ITS. In pursuit of this goal an authoring system capable of producing a domain model with the assistance of a domain expert was developed. Unlike previous authoring systems, this system (named CAS) has the ability to acquire knowledge for non-procedural as well as procedural tasks. CAS was developed to generate the knowledge required for constraint-based tutoring systems, reducing the effort as well as the amount of expertise in knowledge engineering and programming required. Constraint-based modelling is a student modelling technique that assists in somewhat easing the knowledge acquisition bottleneck due to the abstract representation. CAS expects the domain expert to provide an ontology of the domain, example problems and their solutions. It uses machine learning techniques to reason with the information provided by the domain expert for generating a domain model. A series of evaluation studies of this research produced promising results. The initial evaluation revealed that the task of composing an ontology of the domain assisted with the manual composition of a domain model. The second study showed that CAS was effective in generating constraints for the three vastly different domains of database modelling, data normalisation and fraction addition. The final study demonstrated that CAS was also effective in generating constraints when assisted by novice ITS authors, producing constraint sets that were over 90% complete.
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Neubauer, Paul Richard. "An intelligent tutoring system for phonetic transcription". Virtual Press, 1992. http://liblink.bsu.edu/uhtbin/catkey/845952.
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This thesis presents an intelligent system for tutoring phonetic transcription in introductory linguistics courses. It compares and contrasts this system with previous intelligent tutoring systems and presents an implementation of the present system. The problems and solutions encountered in implementing the system are described.Among the contributions and innovations are the fact that this system guides the student through several attempts at transcribing a word with increasingly specific feedback, and the fact that the system is organized in such a way that an instructor can add, modify or delete data at any time with no assistance required from a programmer.A significant contribution of this system lies in the fact that although there is only one correct answer for any given item to be transcribed, the possibilities for the student's responses and hence for incorrect answers must be open-ended. The student's answer will be a string that may not have the same length as the correct answer, may contain few or none of the same symbols as the correct answer, and those that it does contain may be in a different order. The student's answer is intended to correspond to the correct answer, but is known not to be an exact match. Arbitrary strings representing the student's answers must thus be matched up with the pattern of the correct answer in such a way that the system can give the student meaningful comments that will aid the student in identifying errors. The usual pattern recognition program is designed to identify instances where a match succeeds. This tutor must identify instances where the match fails as well as how it fails.Department of Computer Science
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Csizmadia, Vilmos. "Constructing an authoring tool for intelligent tutoring systems with hierarchical domain models". Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-1222103-161814.
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Walonoski, Jason A. "Visual Feedback for Gaming Prevention in Intelligent Tutoring Systems". Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/23.
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A major issue in Intelligent Tutoring Systems is off-task student behavior, especially performance-based gaming, where students systematically exploit tutor behavior in order to advance through a curriculum quickly and easily, with as little active thought directed at the educational content as possible. The goal of this research was to explore the phenomena of off-task gaming behavior within the Assistments system, as well as to develop a passive visual indicator to deter and prevent off-task gaming behavior without active intervention via graphical feedback to the student and teachers. Traditional active intervention approaches were also constructed for comparison purposes, and machine-learned gaming-detection models were developed as a potential invocation and evaluation mechanism. Passive graphical interventions have been well received by teachers, and results are suggestive that they are effective at reducing off-task gaming behavior.
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Vasandani, Vijay. "Intelligent tutoring for diagnostic problem solving in complex dynamic systems". Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/24934.
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Walonoski, Jason A. "Visual feedback for gaming prevention in intelligent tutoring systems". Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-010806-205001/.
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Long, Yanjin. "Supporting Learner-Controlled Problem Selection in Intelligent Tutoring Systems". Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/653.
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Many online learning technologies grant students great autonomy and control, which imposes high demands for self-regulated learning (SRL) skills. With the fast development of online learning technologies, helping students acquire SRL skills becomes critical to student learning. Theories of SRL emphasize that making problem selection decisions is a critical SRL skill. Research has shown that appropriate problem selection that fit with students’ knowledge level will lead to effective and efficient learning. However, it has also been found that students are not good at making problem selection decisions, especially young learners. It is critical to help students become skilled in selecting appropriate problems in different learning technologies that offer learner control. I studied this question using, as platform, a technology called Intelligent Tutoring Systems (ITSs), a type of advanced learning technology that has proven to be effective in supporting students’ domain level learning. It has also been used to help students learn SRL skills such as help-seeking and self-assessment. However, it is an open question whether ITS can be designed to support students’ learning of problem selection skills that will have lasting effects on their problem selection decisions and future learning when the tutor support is not in effect. ITSs are good at adaptively selecting problems for students based on algorithms like Cognitive Mastery. It is likely, but unproven, that ITS problem selection algorithms could be used to provide tutoring on students’ problem selection skills through features like explicit instructions and instant feedback. Furthermore, theories of SRL emphasize the important role of motivations in facilitating effective SRL processes, but not much prior work in ITS has integrated designs that could foster the motivations (i.e., motivational design) to stimulate and sustain effective problem selection behaviors. Lastly, although students generally appreciate having learner control, prior research has found mixed results concerning the effects of learner control on students’ domain level learning outcomes and motivation. There is need to investigate how learner control over problem selection can be designed in learning technologies to enhance students’ learning and motivation. My dissertation work consists of two parts. The first part focuses on creating and scaffolding shared student/system control over problem selection in ITSs by redesigning an Open Learner Model (OLM, visualizations of learning analytics that show students’ learning progress) and integrating gamification features to enhance students’ domain level learning and enjoyment. I conducted three classroom experiments with a total of 566 7th and 8th grade students to investigate the effectiveness of these new designs. The results of the experiments show that an OLM can be designed to support students’ self-assessment and problem selection, resulting in greater learning gains in an ITS when shared control over problem selection is enabled. The experiments also showed that a combination of gamification features (rewards plus allowing re-practice of completed problems, a common game design pattern) integrated with shared control was detrimental to student learning. In the second part of my dissertation, I apply motivational design and user-centered design techniques to extend an ITS with shared control over problem selection so that it helps students learn problem selection skills, with a lasting effect on their problem selection decisions and future learning. I designed a set iv of tutor features that aim at fostering a mastery-approach orientation and learning of a specific problem selection rule, the Mastery Rule. (I will refer to these features as the mastery-oriented features.) I conducted a fourth classroom experiment with 200 6th – 8th grade students to investigate the effectiveness of shared control with mastery-oriented features on students’ domain level learning outcomes, problem selection skills and enjoyment. This experiment also measured whether there were lasting effects of the mastery-oriented shared control on students’ problem selection decisions and learning in new tutor units. The results of the experiment show that shared control over problem selection accompanied by the mastery-oriented features leads to significantly better learning outcomes, as compared to full system-controlled problem selection in the ITS. Furthermore, the mastery-oriented shared control has lasting effects on students’ declarative knowledge of problem selection skills. Nevertheless, there was no effect on future problem selection and future learning, possibly because the tutor greatly facilitated problem selection (through its OLM and badges). My dissertation contributes to the literatures on the effects of learner control on students’ domain level learning outcomes in learning technologies. Specifically, I have shown that a form of learner control (i.e., shared control over problem selection, with mastery-oriented features) can lead to superior learning outcomes than system-controlled problem selection, whereas most prior work has found results in favor of system control. I have also demonstrated that Open Learner Models can be designed to enhance student learning when shared control over problem selection is provided. Further, I have identified a specific combination of gamification features integrated with shared control that may be detrimental to student learning. A second line of contributions of my dissertation concerns research on supporting SRL in ITSs. My work demonstrates that supporting SRL processes in ITSs can lead to improved domain level learning outcomes. It also shows that the shared control with mastery-oriented features have lasting effects on improving students’ declarative knowledge of problem selection skills. Regarding using ITSs to help students learn problem selection skill, the user-centered motivational design identifies mastery-approach orientation as important design focus plus tutor features that can support problem selection in a mastery-oriented way. Lastly, the dissertation contributes to human-computer interaction by generating design recommendations for how to design learner control over problem selection in learning technologies that can support students’ domain level learning, motivation and SRL.
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Srisethanil, Chaisak. "Pedagogical framework for an engineering intelligent tutoring system". Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/20240.
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Okpo, Juliet Airenvbiegbe. "Adaptive exercise selection for an intelligent tutoring system". Thesis, University of Aberdeen, 2018. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=238127.
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Adapting to learner characteristics is essential when selecting exercises for learners in an intelligent tutoring system. This thesis investigates how humans adapt next exercise selection (in particular difficulty level) to learner personality (self-esteem), invested mental effort, and performance to inspire an adaptive exercise selection algorithm. First, we describe the investigations to produce validated materials for the main studies, namely the creation and validation of self-esteem personality stories, mental effort statements, and mathematical exercises with varying levels of difficulty. Next, through empirical studies, we investigate the impact on exercise selection of learner's selfesteem (low versus high self-esteem) and effort (minimal, little, moderate, much, and all possible effort). Three studies investigate this for learners who had different performances on a previous exercise: just passing, just failing, and performed well. Participants considered a fictional learner with a certain performance, self-esteem and effort, and selected the difficulty level of the next mathematical exercise. We found that self-esteem, mental effort, and performance all impacted the difficulty level of the exercises selected for learners. Using the results from the studies, we generated an algorithm that selects exercises with varying difficulty levels adapted to learner characteristics. Finally, through a survey with professional teachers, we evaluated our algorithm and found that the algorithm's adaptations were appropriate in general.
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Mayo, Michael John. "Bayesian Student Modelling and Decision-Theoretic Selection of Tutorial Actions in Intelligent Tutoring Systems". Thesis, University of Canterbury. Computer Science, 2001. http://hdl.handle.net/10092/2565.
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This thesis proposes, demonstrates, and evaluates, the concept of the normative
Intelligent Tutoring System (ITS). Normative theories are ideal, optimal
theories of rational behaviour. Two normative theories suitable for reasoning
under conditions of uncertainty are Bayesian probability theory, which allows
one to update one’s beliefs about the world given previous beliefs and new
observations, and decision theory, which shows how to fuse one’s preferences
with one’s beliefs in order to rationally decide how to behave. A normative ITS
is a tutoring system in which beliefs about the student (the student model) are
represented with a Bayesian network, and teaching actions are selected using
decision-theoretic principles. The main advantage of a normative ITS is that the
normative theories provide an optimal framework for implementing learning
theories. In other words, the particular learning theory underlying the ITS is
guaranteed to be optimally applied to the student if it is defined as a set of
normative representations (probability distributions and utility functions). In
contrast, the more traditional type of ITS with an ad-hoc implementation of a
learning theory is not guaranteed to be optimal.
A general methodology for building normative ITSs is proposed and
demonstrated. The methodology advocates building an adaptive, generalised
Bayesian network student model using machine learning techniques from
student performance data collected in the classroom. The Bayesian network is
then used as the basis for the decision-theoretic selection of tutorial actions.
The methodology is demonstrated with two implementations. Both
implementations were evaluated in a classroom, rather than a lab, setting. The
first implementation is an extension to an existing ITS called SQL-Tutor. A
Bayesian network-based student model was added to SQL-Tutor, and this was applied to select the next problem for students. Although this system only partly
implemented the normative methodology, the evaluation results were promising
enough to continue in this direction.
The second evaluation was more comprehensive. An entirely new ITS
called CAPIT was implemented by application of the methodology. CAPIT
teaches the basics of English capitalisation and punctuation to 8-10 year old school children, and it uses constraint-based modelling to represent domain knowledge. The system models the child’s long-term mastery of the domain
constraints using an adaptive Bayesian network, and it selects the next problem
and best error message (when a student makes more than one error following a
solution attempt) using the decision-theoretic principle of expected utility
maximisation. Learning theories define both the semantics of the Bayesian
network and the form of the utility functions.
The evaluation of CAPIT was a success. Three groups of children, A, B,
and C, were enlisted and given a pre-test. Group B then used a randomised
(non-normative) version of CAPIT for a four week period, while Group C used
the full normative version of the tutor. All groups were then administered a post-test. The results show that while both Groups B and C gradually mastered the domain constraints, Group C mastered the constraints at a faster rate than
group B. Group A, who did not have access to an ITS in the domain, actually
regressed on the post-test.
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Zhang, Jie. "An intelligent tutor : Smart Tutor /". Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23735879.
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Kseibat, Dawod. "Adaptive intelligent tutoring for teaching modern standard Arabic". Thesis, University of Bedfordshire, 2010. http://hdl.handle.net/10547/134371.
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The aim of this PhD thesis is to develop a framework for adaptive intelligent tutoring systems (ITS) in the domain of Modern Standard Arabic language. This framework will comprise of a new approach to using a fuzzy inference mechanism and generic rules in guiding the learning process. In addition, the framework will demonstrate another contribution in which the system can be adapted to be used in the teaching of different languages. A prototype system will be developed to demonstrate these features. This system is targeted at adult English-speaking casual learners with no pre-knowledge of the Arabic language. It will consist of two parts: an ITS for learners to use and a teachers‘ tool for configuring and customising the teaching rules and artificial intelligence components among other configuration operations. The system also provides a diverse teaching-strategies‘ environment based on multiple instructional strategies. This approach is based on general rules that provide means to a reconfigurable prediction. The ITS determines the learner‘s learning characteristics using multiple fuzzy inferences. It has a reconfigurable design that can be altered by the teacher at runtime via a teacher-interface. A framework for an independent domain (i.e. pluggable-domain) for foreign language tutoring systems is introduced in this research. This approach allows the system to adapt to the teaching of a different language with little changes required. Such a feature has the advantages of reducing the time and cost required for building intelligent language tutoring systems. To evaluate the proposed system, two experiments are conducted with two versions of the software: the ITS and a cut down version with no artificial intelligence components. The learners used the ITS had shown an increase in scores between the post-test and the pre-test with learning gain of 35% compared to 25% of the learners from the cut down version.
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Staffan, Kenneth E. "An intelligent tutoring system for the German language /". Online version of thesis, 1993. http://hdl.handle.net/1850/11732.
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Rasmussen, Kai. "Developing a Cognitive Rule-Based Tutor for the ASSISTment System". Digital WPI, 2007. https://digitalcommons.wpi.edu/etd-theses/39.
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The ASSISTment system is a web-based tutor that is currently being used as an eighth and tenth-grade mathematics in both Massachusetts and Pennsylvania. This system represents its tutors as state-based "pseudo-tutors" which mimic a more complex cognitive tutor based on a set of production rules. It has been shown that building pseudo-tutors significantly decreases the time spent authoring content. This is an advantage for authoring systems such as the ASSITment builder, though it sacrifices greater expressive power and flexibility. A cognitive tutor models a student's behavior with general logical rules. Through model-tracing of a cognitive tutor's rule space, a system can find the reasons behind a student action and give better tutoring. In addition, these cognitive rules are general and can be used for many different tutors. It is the goal of this thesis to provide the architecture for using cognitive rule-based tutors in the ASSITment system. A final requirement is that running these computationally intensive model-tracing tutors do not slow down students using the pseudo-tutors, which represents the majority of ASSISTment usage. This can be achieved with remote computation, realized with SOAP web services. The system was further extended to allow the creation and implementation of user-level experiments within the system. These experiments allow the testing of pedagogical choices. We implemented a hint dissuasion experiment to test this experimental framework and provide those results.
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Mathews, Moffat Mannunkal. "A Framework for Multiple Adaptable Pedagogical Strategies in Intelligent Tutoring Systems". Thesis, University of Canterbury. Computer Science and Software Engineering, 2012. http://hdl.handle.net/10092/7334.
Texto completo da fonteResumo:
The need to give educators the ability to enter a particular teaching strategy of their choice into an Intelligent Tutoring System (ITS) and have the ITS respond appropriately to each student has been stated by many researchers. For example, an educator could tell the ITS to keep students within a certain help level ratio (how much help they request), or to introduce a new topic in a particular manner and the ITS simply carries this out at each learning point of interest. Educators could then try new strategies, ones that unaided are impossible to try out in class (such as keeping a student within a help-seeking range) or difficult within an ITS (as the ITS would have to be specially programmed in that way). Current ITSs provide adaptivity to the student at the domain level but not necessarily at the pedagogical level. While a variety of pedagogical strategies have been implemented (e.g. apprenticeship, socratic, practice), there is no system that offers parts or all of these strategies with the ability to choose between them dynamically.
In this project, we designed a new framework for an ITS to include multiple, potentially adaptable pedagogical strategies. This was done by breaking up the pedagogical module into separate components. The Pedagogical Strategy Set (PSS) contains all the strategies, written as constraints. The Pedagogical Student Model (PSM) keeps track of which pedagogical strategies were used by each student. Within the ITS, there is still a smaller, separate pedagogical module to deal with domain-specific strategies. The Pedagogical Control Centre (PCC) contains the logic of when and how to use the pedagogical strategies. It gathers its information from the other modules and uses decision logic to trigger strategies.
We implemented and evaluated this framework within the context of SQL-Tutor and found that the framework could be used to enter pedagogical strategies, which in turn compared favourably to the original SQL-Tutor. This proof of concept opens up the possibility of the logic and algorithms that could be implemented (e.g. in the PCC) in future ITSs. The PSS is a separate module, written in a different language, independent of ITSs. This could lead to sharing of pedagogical strategies between tutors. Furthermore, students learn differently to each other; this framework allows them to do so.
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Li, Vincent. "Knowledge representation and problem solving for an intelligent tutoring system". Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29657.
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As part of an effort to develop an intelligent tutoring system, a set of knowledge representation
frameworks was proposed to represent expert domain knowledge. A general representation of time points and temporal relations was developed to facilitate temporal concept deductions as well as facilitating explanation capabilities vital in an intelligent advisor system. Conventional representations of time use a single-referenced timeline and assigns a single unique value to the time of occurrence of an event. They fail to capture the notion of events, such as changes in signal states in microcomputer systems, which do not occur at precise points in time, but rather over a range of time with some probability distribution. Time is, fundamentally, a relative quantity. In conventional representations,
this relative relation is implicitly defined with a fixed reference, "time-zero", on the timeline. This definition is insufficient if an explanation of the temporal relations is to be constructed. The proposed representation of time solves these two problems by representing a time point as a time-range and making the reference point explicit.
An architecture of the system was also proposed to provide a means of integrating various modules as the system evolves, as well as a modular development approach. A production rule EXPERT based on the rule framework used in the Graphic Interactive LISP tutor (GIL) [44, 45], an intelligent tutor for LISP programming, was implemented to demonstrate the inference process using this time point representation. The EXPERT is goal-driven and is intended to be an integral part of a complete intelligent tutoring system.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
44
Williams, David C. (David Charles). "Observations of medical professionals' interactions with an intelligent tutoring system". Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=59588.
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Intelligent tutoring systems (ITS) are expert systems united with computer-aided instruction. The psychological issue of human-computer interfacing combines aspects of education, cognitive science, human performance and psycho-sociolinguistics. This study presented a situation in which physicians used their reasoning to solve a computer-simulated medical case, embedded in the NEOMYCIN ITS. Experiments were designed to assess how their anthropomorphisation of the systems affected their medical reasoning in a complex ill-defined problem-solving domain. The study examines the subjects' interpretation of textual case materials, specifically their ascription of meaning and intelligibility to the form and usage of natural language. The results indicate that these factors affect their interpretation, not only of case materials, but also of their evaluation of the program's medical reasoning. This has implications for the interactive man-machine interface and its relationship to interpersonal communication is discussed.
45
Twidale, Michael Bernard. "The use of explicit intermediate representations in intelligent tutoring systems". Thesis, Lancaster University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305950.
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Martin, Brent I. "Intelligent tutoring systems: The practical implementation of constraint-based modelling". Thesis, University of Canterbury. Computer Science, 2002. http://hdl.handle.net/10092/4834.
Texto completo da fonteResumo:
An Intelligent Tutoring System (ITS) differs from other educational systems because it uses knowledge to guide the pedagogical process. It attempts to optimise the student's mastery of domain knowledge by controlling the introduction of new problems, concepts and instruction/feedback. Central to this process is the student model, which provides information about what the student knows. The state of the art in student modelling is model tracing, which compares student actions against an "ideal" procedure.Constraint-based modelling is a new domain and student modelling method that
describes only pedagogically informative states, rather than following the procedure
the student used to arrive at their answer. Ohlsson introduced the idea, which is based
on learning from performance errors, but did not provide details of how it should be
implemented. Even his definition of constraints is very broad. SQL-Tutor is an
existing ITS that uses a constraint-based model. The representation of constraints
within this system is as loose as Ohlsson's description. The constraints in SQL-Tutor
are LISP code fragments, where domain structural knowledge is incorporated into the
constraints via ad hoc functions.
In this thesis we present a more specific representation for constraints that
obviates the need for complex user-defined functions. Constraints (and their
associated taxonomies and domain-specific functions) are specified as pattern
matches. This new approach has two advantages: the constraints are simpler to author,
and they can be used to generate solutions on demand. We have used the new
representation to create algorithms for solving problems and correcting student
mistakes, and for generating novel problems to present to the student. We present the
details of these algorithms and the results of both laboratory and classroom
evaluations. The solution generation algorithm is demonstrated in laboratory testing to
be practical, and the problem generation algorithm, together with a new problem
selection method, exhibits improved learning performance in the classroom.
We also present the design and implementation of an authoring system for
constraint-based tutors and demonstrate its efficacy in authoring tutors for two
domains. One of these, a tutor for English language skills, was evaluated in an
elementary school classroom. This evaluation was a success. The students enjoyed
using the tutor, found the interface easy to use, and felt that they had learned a lot. An
analysis of their mastery of the constraints suggested that they did indeed learn the
underlying principles in the course of the session. The authoring tool enabled us to
develop this system quickly using a spelling resource book as the source of both the
domain taxonomy from which to produce the problems (i.e. a vocabulary of words to
use) and the principles for the constraints. The authoring tool provided all other
functions. This evaluation therefore showed that our authoring tool allows the rapid
creation of an effective ITS.
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Siemer, Julika. "Developing a model for remedial operations in intelligent tutoring systems". Thesis, London School of Economics and Political Science (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294784.
Texto completo da fonteResumo:
Intelligent Tutoring Systems attempt to create a relationship between a computer and the student which resembles a human-to-human tutorial situation. For successful teaching to take place an Intelligent Tutoring System has to be able to cope with any student errors that may occur during a consultation. Remedial intervention implemented in current Intelligent Tutoring Systems lacks a formal basis. The objective of this research is to formalise the process of remediation with Intelligent Tutoring Systems and to provide a framework for the implementation of remedial tutoring in Intelligent Tutoring Systems. This research first presents a state-of-the-art account of Intelligent Tutoring Systems. It then proceeds with an investigation of both current practices with existing Intelligent Tutoring Systems and requirements for providing remedial tutoring. What stems from this investigation is a set of principles that governs remedial tutoring intervention. These principles of remediation serve as the foundation for the construction of the model for remedial operations, which can be employed in developing Intelligent Tutoring Systems capable of offering remedial tutoring. To demonstrate this, INTUITION, an Intelligent Tutoring System implementation of an existing business simulation game, is developed. The thesis then proposes an evaluation method which can be used to assess remedial intervention with Intelligent Tutoring Systems against the principles of remediation. This evaluation method is applied to INTUITION. The result of the evaluation shows that INTUITION follows the principles of remediation and that, therefore, the model for remedial operations is a useful method for providing remedial tutoring with Intelligent Tutoring Systems according to the principles of remediation.
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Cox, Benita Mary. "An explanation-driven understanding-directed model for intelligent tutoring systems". Thesis, Imperial College London, 1989. http://hdl.handle.net/10044/1/47388.
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Tao, Xiaomei. "Enhancing electronic intelligent tutoring systems by responding to affective states". Thesis, Birmingham City University, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.720002.
Texto completo da fonteResumo:
The overall aim of this research is the exploration mechanisms which allow an understanding of the emotional state of students and the selection of an appropriate cognitive and affective feedback for students on the basis of students' emotional state and cognitive state in an affective learning environment. The learning environment in which this research is based is one in which students learn by watching an instructional video.
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Kildare, RA. "The Computational and Educational Viability of Deploying Intelligent Tutoring Systems". Thesis, Honours thesis, University of Tasmania, 2003. https://eprints.utas.edu.au/21/1/Deployment_of_Intelligent_Tutoring_Systems.pdf.
Texto completo da fonteResumo:
This thesis first presents a review of the literature that has been published on adaptive educational software. After briefly describing the evolution of these programs, the review covers the main educational and computational features of current systems.
The next section of the thesis discusses the survey conducted in order to ascertain information about the performance of Intelligent Tutoring Systems. The results of this survey are used to inform the following section.
After the survey, the thesis outlines a proposed architecture for the decentralised distribution of adaptive educational software and discusses the issues surrounding the architecture. The results from implementing this architecture follow in the next section.
Conclusions and possible further research are presented last to complete the work. Key results and have been appended as well as a glossary of acronyms.