Adaptive feedback selection for intelligent tutoring systems

Gutiérrez, Fernando; Atkinson, John

doi:10.1016/j.eswa.2010.11.058

Cited by 43 publications

(25 citation statements)

References 11 publications

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“…Ferreira and Atkinson (2009) developed a feedback generation system for foreign language tutoring using a simple decision tree to choose the type of feedback to be given. Gutierrez and Atkinson, 2011 utilized the combination of two ML methods, Support Vector Machine (SVM) and Conditional Random Field (CRF), trained from a set of the actual user–tutor interactions to generate adaptive feedback for the virtual foreign language tutor. NLP has been frequently used for ITS especially for a foreign language tutor.…”

Section: Relevant Workmentioning

confidence: 99%

Automated feedback generation for formal manufacturing rule extraction

Kang

Patil

Rangarajan

et al. 2019

AIEDAM

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Manufacturing knowledge is maintained primarily in the unstructured text in industry. To facilitate the reuse of the knowledge, previous efforts have utilized Natural Language Processing (NLP) to classify manufacturing documents or to extract structured knowledge (e.g. ontology) from manufacturing text. On the other hand, extracting more complex knowledge, such as manufacturing rule, has not been feasible in a practical scenario, as standard NLP techniques cannot address the input text that needs validation. Specifically, if the input text contains the information irrelevant to the rule-definition or semantically invalid expression, standard NLP techniques cannot selectively derive precise information for the extraction of the desired formal manufacturing rule. To address the gap, we developed the feedback generation method based on Constraint-based Modeling (CBM) coupled with NLP and domain ontology, designed to support formal manufacturing rule extraction. Specifically, the developed method identifies the necessity of input text validation based on the predefined constraints and provides the relevant feedback to help the user modify the input text, so that the desired rule can be extracted. We proved the feasibility of the method by extending the previously implemented formal rule extraction framework. The effectiveness of the method is demonstrated by enabling the extraction of correct manufacturing rules from all the cases that need input text validation, about 30% of the dataset, after modifying the input text based on the feedback. We expect the feedback generation method will contribute to the adoption of semantics-based technology in the manufacturing field, by facilitating precise knowledge acquisition from manufacturing-related documents in a practical scenario.

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Section: Relevant Workmentioning

confidence: 99%

Automated feedback generation for formal manufacturing rule extraction

Kang

Patil

Rangarajan

et al. 2019

AIEDAM

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“…4.6). Scientists Gutierrez and Atkinson and others during their research found that it is very important to guarantee the appropriate feedback between the participants of study process (Gutierrez and Atkinson 2011;Urban-Lurain 2010).…”

Section: Case Study Of Students' Learning Process By Using Intelligenmentioning

confidence: 99%

“…An important form of student-tutor interaction is feedback (Gutierrez and Atkinson 2011). Negative feedback can be provided by the tutor in response to students mistakes.…”

Section: Introduction To Intelligent Tutoring Systemsmentioning

confidence: 99%

Development of the intelligent tutoring system for built environment

Seniut¹

2012

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The dissertation examines the problem of students' training and testing. To solve the problem, new model, algorithms and system have been developed and adapted. The developed system enables organizing of individual and group training and self-control process. Integrated intelligent search subsystem helps to develop the study plan for the themes of interest. The system continuously monitors the user and can respond to his failure. The developed built environment computer learning subsystem performs practical calculations to assess the built environment projects. The main problem solved by development of the subsystem is reduction of time needed for users to create their own systems. Knowledge database created during the system development can be a good source of knowledge for new or similar projects evaluations. Calculation results can be saved for further research or shared with other users of the system. The dissertation consists of Introduction, 4 Chapters, Conclusions, References, and List of Author's Publications. The introduction reveals the investigated problem, importance of the thesis and the object of research and describes the purpose and tasks of the dissertation, research methodology, scientific novelty, the practical significance of results examined in the dissertation and defended statements. The introduction ends in presenting the author's publications on the subject of the defended dissertation, offering the material of made presentations in conferences and defining the structure of the dissertation. Chapter 1 revises scientific literature. The world experience in development of intelligent tutoring systems is analyzed. Also definitions of intelligent tutoring systems and the built environment are provided. Chapter 2 overviews the multi objective assessment methods and discusses their integration into the overall system. Chapter 3 presents the model of intelligent tutoring system for built environment. All parts off developed system are reviewed including methods, submodels, algorithms and databases. Chapter 4 presents three real life case studies that show practical application results of the created system. 12 articles focusing on the subject of the discussed dissertation are published: 7 articles-in the Thomson ISI Web of Science register and 5 articlesin the proceedings in Thomson ISI data base. Patent "Electronic information retrieval system and method" on behalf of VGTU was acquired.

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“…The three main processes in advisory systems are knowledge acquisition, cognition, and interface [12]. In an experiment it is proven that continuous feedback to a learner allows him to achieve correct answers by reducing their errors [13].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Based Intelligent Advice Generation for Self-Instructional e-Learner

Kuila¹

2011

IJEEEE

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This paper presents an agent based Intelligent Tutoring System (ITS). The agent plays the role of an advisor for an e-learner to facilitate the learner to achieve his learning objective. It provides advices to assist an e-learner while solving problems that are normally provided by human experts. A Bayesian network is employed to construct the possible solution states of a problem with the statistics of mistakes; those can be committed by a learner in their problem solving process. The agent will collect statistical data from Bayesian network on learner's mistakes and the way in which one learner may commit a mistake. On the basis of the statistical data on the learner's behaviour, especially with respect to his tendency to commit an error, the agent anticipates the point of difficulty during a problem solving session and accordingly guides the learners to solve the given problem free of errors. The Bayesian network is trained with training data, prior information (e.g., expert knowledge, casual relationships, and estimated graph topology or network structure) and the parameters of the joint probability distribution.

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Adaptive feedback selection for intelligent tutoring systems

Cited by 43 publications

References 11 publications

Automated feedback generation for formal manufacturing rule extraction

Automated feedback generation for formal manufacturing rule extraction

Development of the intelligent tutoring system for built environment

Bayesian Network Based Intelligent Advice Generation for Self-Instructional e-Learner

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