A Learner Model in a Distributed Environment

Carmona, Cristina; Conejo, Ricardo

doi:10.1007/978-3-540-27780-4_50

Cited by 18 publications

(7 citation statements)

References 4 publications

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“…If the student has a different behavior from that of the domain, it is considered a gap in the student's knowledge. As a result, the goal is to eliminate the gap between them as much as possible [48] [51]. Consequently, the domain contains a set of elements and the overlay model indicates a set of masteries over these elements.…”

Section: Overlaymentioning

confidence: 99%

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Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan¹,

Kalita²

2019

IJCA

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This paper provides interested beginners with an updated and detailed introduction to the field of Intelligent Tutoring Systems (ITS). ITSs are computer programs that use artificial intelligence techniques to enhance and personalize automation in teaching. This paper is a literature review that provides the following: First, a review of the history of ITS along with a discussion on the interface between human learning and computer tutors and how effective ITSs are in contemporary education. Second, the traditional architectural components of an ITS and their functions are discussed along with approaches taken by various ITSs. Finally, recent innovative ideas in ITS systems are presented. This paper concludes with some of the author's views regarding future work in the field of intelligent tutoring systems. General Terms

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Section: Overlaymentioning

confidence: 99%

“…These features were updated during the learning processes. For each domain concept, the learner model stores an estimation of the knowledge level of the student on this concept [51] [52].…”

Section: Overlaymentioning

confidence: 99%

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan¹,

Kalita²

2019

IJCA

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“…A second experiment with simulated students was then performed to see how the use of prerequisite relationships could improve the accuracy and efficiency of the diagnosis process, yielding satisfactory results. Future lines of research include: a) improvements in the model, like for example the development of models that allow for defining different degrees of strength for prerequisite relationships (some ideas have already been presented in [2]), or the combination of this new model with adaptive item selection criteria and b) uses of the model, which opens up a broad research field within the MEDEA project, in which different ways of using the information contained in the layers of the knowledge student model will be explored.…”

Section: Discussionmentioning

confidence: 99%

“…As described in [2], MEDEA's student model is divided in two main sub-models: the attitude model and the knowledge model. The attitude model contains information such as preferred learning styles, motivation, learning goals, preferences and technical experience.…”

Section: Building a Generic Student Model For Medeamentioning

confidence: 99%

Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model

Carmona

Millán

Pérez-de-la-Cruz

et al. 2005

Lecture Notes in Computer Science

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Abstract. In this paper we present an extension of a previously developed generic student model based on Bayesian Networks. A new layer has been added to the model to include prerequisite relationships. The need of this new layer is motivated from different points of view: in practice, this kind of relationships are very common in any educational setting, but also their use allows for improving efficiency of both adaptation mechanisms and the inference process. The new prerequisite layer has been evaluated using two different experiments: the first experiment uses a small toy example to show how the BN can emulate human reasoning in this context, while the second experiment with simulated students suggests that prerequisite relationships can improve the efficiency of the diagnosis process by allowing increased accuracy or reductions in the test length.

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“…The student model represents information about the user's current knowledge of the domain, profile, cognitive style, learning style, emotional state, learning style, errors made, misconceptions, response to feedback, etc. (Carmona & Conejo, 2004;Kazanidis & Satratzemi, 2007;Luckin & Holmes, 2016). This information determines the characteristics of the student and the interaction with the domain and pedagogical models.…”

Section: Cognitive Knowledge-based M-learning Frameworkmentioning

confidence: 99%

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Al-Hunaiyyan

Bimba

Idris

et al. 2017

IJIKM

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Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

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A Learner Model in a Distributed Environment

Cited by 18 publications

References 4 publications

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

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