An adaptive hypermedia model based on student's lexicon

Modeling the learner in adaptive systems involves different information. There are several methods to manage the learner model. They do not handle the uncertainty in the dynamic modeling of the learner. The main hypothesis of this chapter is the management of the learner model based on multi-entity Bayesian networks. This chapter focuses on modeling the learner model in a dynamic and probabilistic way. The authors propose in this work the use of the notion of fragments and m-theory to lead to a Bayesian multi-entity network. The use of this Bayesian method can handle the whole course of a learner as well as all of its shares in an adaptive educational hypermedia.

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A Learner Model Based on Multi-Entity Bayesian Networks in Adaptive Hypermedia Educational Systems

Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-7413-2.ch007 ◽

2018 ◽

pp. 149-174

Keyword(s):

Artificial Intelligence ◽

Bayesian Networks ◽

Dynamic Modelling ◽

Educational Systems ◽

Probabilistic Modelling ◽

Adaptive Hypermedia ◽

Modelling Language ◽

Learner Model ◽

Model Based ◽

Learning Path

This chapter presents a probabilistic and dynamic learner model based on multi-entity Bayesian networks and artificial intelligence. There are several methods for modelling the learner in AHES, but they're based on the initial profile of the learner created in his entry into the learning situation. They do not handle the uncertainty in the dynamic modelling of the learner based on the actions of the learner. The main purpose of this chapter is the management of the learner model based on MEBN and artificial intelligence, taking into account the different actions that the learner could take during his/her whole learning path. The approach that the authors followed in this chapter is marked initially by modelling the learner model in three levels: they started with the conceptual level of modelling with the unified modelling language, followed by the model based on Bayesian networks to be able to achieve probabilistic modelling in the three phases of learner modelling.

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Information Imperfection as an Inherent Characteristic of Adaptive Hypermedia

Adaptable and Adaptive Hypermedia Systems ◽

10.4018/978-1-59140-567-2.ch008 ◽

2011 ◽

pp. 150-168 ◽

Cited By ~ 1

Author(s):

Miguel-Ángel Sicila ◽

Elena García Barriocanal

Keyword(s):

Fuzzy Set ◽

User Model ◽

Adaptive Hypermedia ◽

User Models ◽

Adaptive Hypermedia Systems ◽

User Data ◽

Approximate Nature ◽

Hypermedia Model ◽

Hypermedia Applications

daptive hypermedia applications are aimed at tailoring hypermedia structures according to some form of user model, in an attempt to increase the usability and utility of the application for each individual or group. Existing research in the field has resulted in many systems, techniques, and paradigms, both for modelling user data and for the subsequent exploitation of such model for the sake of personalisation. As a matter of fact, the majority of adaptive hypermedia systems work with user models that are imperfect in some way, and the theories or hypotheses that guide adaptation are also often of a heuristic or approximate nature. Although some existing systems provide explicit means for dealing with imperfection in one or several of its multiple facets, there exists a lack of support for information imperfection in adaptive hypermedia models and architectures. In an attempt to provide such conceptual support, the MAZE model was proposed as a generalisation of an existing abstract hypermedia model, providing built-in support for fuzzy set-theoretic notions. This chapter provides an overall account of the MAZE model, along with its rationale, and an overview of a possible instance of a MAZE-based architecture. In addition, the use of MAZE to model common adaptive hypermedia technologies is illustrated through a concrete case study.

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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Model-Based Clustering and Classification for Data Science

10.1017/9781108644181 ◽

2019 ◽

Cited By ~ 17

Author(s):

Charles Bouveyron ◽

Gilles Celeux ◽

T. Brendan Murphy ◽

Adrian E. Raftery

Keyword(s):

Data Science ◽

Model Based Clustering ◽

Model Based ◽

Clustering And Classification

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