Intelligent Market Based Learner Modeling

2006 ◽  
pp. 101-111 ◽  
Author(s):  
Maryam Ashoori ◽  
Chun Yan Miao ◽  
Angela Eck Soong Goh ◽  
Wang Qiong
Keyword(s):  
Learner Modeling

scholarly journals Using AI Techniques in a Serious Game for Socio-Moral Reasoning Development

2020 ◽  
Vol 34 (09) ◽  
pp. 13420-13427
Author(s):  
Ange Tato ◽  
Roger Nkambou ◽  
Aude Dufresne
Keyword(s):  
Machine Learning ◽  
Moral Reasoning ◽  
Serious Game ◽  
Machine Learning Techniques ◽  
Learning Gain ◽  
Learner Modeling ◽  
Learner Model ◽  
Learning Techniques ◽  
Adaptation Rules ◽  
Computerized Task

We present a serious game designed to help players/learners develop socio-moral reasoning (SMR) maturity. It is based on an existing computerized task that was converted into a game to improve the motivation of learners. The learner model is computed using a hybrid deep learning architecture, and adaptation rules are provided by both human experts and machine learning techniques. We conducted some experiments with two versions of the game (the initial version and the adaptive version with AI-Based learner modeling). The results show that the adaptive version provides significant better results in terms of learning gain.

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

2018 ◽  
pp. 65-94
Keyword(s):  
Computational Modeling ◽  
Bayesian Networks ◽  
Educational System ◽  
Probabilistic Framework ◽  
Adaptive Hypermedia ◽  
Learner Modeling ◽  
Learner Model ◽  
Learning Path ◽  
Hypermedia System ◽  
Probabilistic Context

The work presented in this chapter lies within learner modeling in an adaptive educational system construed as a computational modeling of the learner. All actions of the learner in a learning situation on an adaptive hypermedia system are not limited to valid or invalid actions (true and false), but they are a set of actions that characterize the learning path of formation. Thus, one cannot represent the information from the system of each learner using relative data. It requires putting the work in a probabilistic context due to the changes in the learner model information during formation. In this chapter, the authors propose to use Bayesian networks as a probabilistic framework to resolve the issue of dynamic management and update of the learner model. The experiments and results presented in this work are arguments in favor of the hypothesis and can also promote reusing the modeling obtained through different systems and similar modeling situations.

Intelligent Market Based Learner Modeling

2006 ◽  
pp. 101-111
Author(s):  
Maryam Ashoori ◽  
Chun Yan Miao ◽  
Angela Eck Soong Goh ◽  
Wang Qiong
Keyword(s):  
Learner Modeling

From Non-Adaptive to Adaptive Educational Hypermedia

2006 ◽  
pp. 302-330 ◽  
Author(s):  
Michael J. Jacobson
Keyword(s):  
Learner Modeling ◽  
Design Elements ◽  
Learning Agent ◽  
Rigorous Research ◽  
Adaptive Hypermedia Systems ◽  
Hypermedia Design ◽  
Learning Efficacy ◽  
Research Findings ◽  
Theory Research ◽  
Educational Hypermedia

In this chapter, it is argued that research involving adaptive educational hypermedia will be advanced by attention to two main areas: (a) the articulation of principled design features for adaptive hypermedia systems and (b) rigorous research documenting the learning efficacy of particular design approaches for different domains and learner groups. As an example of design and research in these two areas, a case study of a program of hypermedia research related to the knowledge mediator framework (KMF) is provided. First, a discussion of non-adaptive KMF hypermedia design elements and learning tasks is provided, followed by a short overview of the research findings from studies involving the use of different KMF systems. Next, current efforts are discussed to create adaptive KMF hypermedia using a learning agent module that employs semantic assessment and learner modeling in order to provide adaptive content and adaptive learner scaffolding. A general consideration of theory, research, and methodological issues related to current work in the field of adaptive educational hypermedia is also provided.

scholarly journals Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

2021 ◽  
Vol 69 (3) ◽  
pp. 3981-4001
Author(s):  
Saurabh Pal ◽  
Pijush Kanti Dutta Pramanik ◽  
Musleh Alsulami ◽  
Anand Nayyar ◽  
Mohammad Zarour ◽  
...  
Keyword(s):  
Ubiquitous Learning ◽  
Learner Modeling

Multilevel Learner Modeling in Training Environments for Complex Decision Making

2020 ◽  
Vol 13 (1) ◽  
pp. 172-185 ◽  
Author(s):  
Gautam Biswas ◽  
Ramkumar Rajendran ◽  
Naveeduddin Mohammed ◽  
Benjamin S. Goldberg ◽  
Robert A. Sottilare ◽  
...  
Keyword(s):  
Decision Making ◽  
Learner Modeling ◽  
Complex Decision Making ◽  
Complex Decision

Development of Bayesian Networks From Use Case Diagrams for Managing the Learner Model

2018 ◽  
pp. 48-63
Keyword(s):  
Problem Solving ◽  
Computational Modeling ◽  
Bayesian Networks ◽  
Unified Modeling Language ◽  
Modeling Language ◽  
Use Case ◽  
Learner Modeling ◽  
Learner Model ◽  
Unified Modeling ◽  
Use Case Diagram

First of all, and to clarify the purpose, it seems important to say that the work presented in this chapter lies within the framework of learner modeling in an adaptive system understood as computational modeling of the learner. One must also state that Bayesian networks are effective tools for learner modeling under uncertainty. They have been successfully used in many systems, with different objectives, from the assessment of knowledge of the learner to the recognition of the plan followed in problem solving. The main objective of this chapter is to develop a Bayesian networks for modeling the learner from the use case diagram of the unified modeling language. The prototypes and diagrams presented in this chapter are arguments in favor of the objective. The network obtained also promotes reusing learner modeling through similar systems.

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