Mathematical Approaches to User Modeling (original) (raw)
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Zebra: A New User Modeling System for Triangular Model of Learners' Characteristics
AIED 2009: 14th conference on Artificial Intelligence in Education, Proceedings of the Workshop on “Enabling creative learning design: how HCI, User Modeling and Human Factors Help”, 2009
The core of adaptive system is the user model that is representation of information about an individual. User model is necessary for an adaptive system to provide the adaptation effect, i.e., to behave differently for different users. The system that collects user information to build up user model and reasons out new assumptions about user is called user modeling system (UMS). There are two main tendencies towards implementing UMS: domain-independent UMS and domain-dependent UMS. The latter is called generic UMS known widely but our approach focuses on the domain-dependent UMS applied into adaptive e-learning especially. The reason is that domain-independent UMS is too generic to “cover” all learners’ characteristics in e-learning, which may cause unpredictable bad consequences in adaptation process. Note that user is considered as learner in e-learning context. Many users’ characteristics can be modeled but each characteristic is in accordance with respective modeling method. It is impossible to model all learners’ characteristics because of such reason “there is no modeling method fit all characteristics”. To overcome these obstacles and difficulties, we propose the new model of learner “Triangular Learner Model (TLM)” composed by three main learners’ characteristics: knowledge, learning style and learning history. TLM with such three underlying characteristics will cover the whole of learner’s information required by learning adaptation process. The UMS which builds up and manipulates TLM is also described in detail and named Zebra. We also propose the new architecture of an adaptive application and the interaction between such application and Zebra.
User modelling for adaptive computer systems: a survey of recent developments
Artificial Intelligence Review, 1993
User modelling is becoming an important sub-area of Artificial Intelligence with both theoretical and practical consequences. The theoretical foundations of user modelling are to be found in key areas of AI, such as knowledge representation and plan recognition, while its practical applications impinge on the construction of intelligent user interfaces and adaptive systems. This paper provides a survey of current work in user modelling. The paper begins by distinguishing between AI approaches, which are the subject of this survey, and those of HCI (Human-Computer Interaction) and then considers the major issues in user modelling such as: types of user modelling system, the sorts of information modelled, how the information is acquired, represented and used. The paper concludes by examining some of the more problematic aspects of user modelling as well as indicating areas for future research.
User Modeling in Adaptive Hypermedia Educational Systems
Educational Technology & Society, 2008
This document is a survey in the research area of User Modeling (UM) for the specific field of Adaptive Learning. The aims of this document are: To define what it is a User Model; To present existing and well known User Models; To analyze the existent standards related with UM; To compare existing systems. In the scientific area of User Modeling (UM), numerous research and developed systems already seem to promise good results, but some experimentation and implementation are still necessary to conclude about the utility of the UM. That is, the experimentation and implementation of these systems are still very scarce to determine the utility of some of the referred applications. At present, the Student Modeling research goes in the direction to make possible reuse a student model in different systems. The standards are more and more relevant for this effect, allowing systems communicate and to share data, components and structures, at syntax and semantic level, even if most of them still only allow syntax integration.
Learner Modeling in Adaptive Educational Systems: A Comparative Study
International Journal of Modern Education and Computer Science, 2016
It's worth noting that the present paper lies within the range of modeling the learner in adaptive educational system as a conceptual modeling of the learner. Thought they are several methods that deal with the learner model; like stereotypes methods or learner profile…, but they are likely unable to handle the uncertainty embedded in the dynamic modeling of the learner. The present paper aims at studding different models and approaches to model the learner in an adaptive educational systems, and coming up with the most appropriate method based on the dynamic aspect of this model. The aim of this study is the argue that the learner model cannot be completely modeled based on one single method through the entire development process, but it needs a combination between several methods that will help for a complete modeling.
2 Adaptation and Learning Objects 2 . 1 User Modeling
2014
The aim of this paper is presenting the modules of the Adaptive Educational Hypermedia System PCMAT, responsible for the recommendation of learning objects. PCMAT is an online collaborative learning platform with a constructivist approach, which assesses the user’s knowledge and presents contents and activities adapted to the characteristics and learning style of students of mathematics in basic schools. The recommendation module and search and retrieval module choose the most adequate learning object, based on the user's characteristics and performance, and in this way contribute to the system’s adaptability. Key-Words: Adaptive Educational Hypermedia, User Model, Adaptation Model, Learning Objects, Recommendation module, Search and Retrieval module
Design and development of a computer system adaptative for the management of the learner profile
World Journal of Advanced Engineering Technology and Sciences
In the educational context, an adaptive hypermedia will make it possible to better guide the learner in his learning, to offer him courses and educational content that are adapted and personalized, and to take into account his learning profile. The idea developed in this article lies in the design and management of a valid learner model for all adaptive hypermedia systems (adaptive, macro-adaptive e-learning, ITS, AHES). Through this work, we first offer an overview of the learner profile by citing the difference between the model and the profile of a learner. Next, we deal with the learner model in adaptive systems by identifying the different systems. Then, we process learner modeling based on learner model content, specific information domain, and independent information domain and learner model components. Finally, we propose the process of developing a learner model: Steps and techniques especially data collection, learner model initialization and learner model update.
Modelling The Learner Model Based Ontology In Adaptive Learning Environment
Journal of Disruptive Learning Innovation (JODLI), 2019
Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research. Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits.K...
OSF Preprints, 2022
User model is description of users' information and characteristics in abstract level. User model is very important to adaptive software which aims to support user as much as possible. The process to construct user model is called user modeling. Within learning context where users are learners, the research proposes a so-called Triangular Learner Model (TLM) which is composed of three essential learners' properties such as knowledge, learning style, and learning history. TLM is the user model that supports built-in inference mechanism. So the strong point of TLM is to reason out new information from users, based on mathematical tools. This paper focuses on fundamental algorithms and mathematical tools to construct three basic components of TLM such as knowledge sub-model, learning style sub-model, and learning history submodel. In general, the paper is a summary of results from research on TLM. Algorithms and formulas are described by the succinct way.