Improving User Profiling for a Richer Personalization (original) (raw)
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Personalized e-learning environments: considering students' contexts
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Personalization in e-learning systems is vital since they are used by a wide variety of students with different characteristics. There are several approaches that aim at personalizing e-learning environments. However, they focus mainly on technological and/or networking aspects without caring of contextual aspects. They consider only a limited version of context while providing personalization. In our work, the objective is to improve e-learning environment personalization making use of a better understanding and modeling of the user's educational and technological context using ontologies. We show an example of the use of our proposal in the AdaptWeb system, in which content and navigation recommendations are provided depending on the student's context.
An Adaptable E-Learning Architecture Based on Learners' Profiling
International Journal of Modern Education and Computer Science, 2015
These days modifications and supplements to e-learning are not enough to make them successful since flexibility and adaptability is missing. Different e-Learning architecture provide different kinds of learning experience to the learners. The lack of adaptive system is due to-One size fits for all‖ concept. Currently the research is towards to learner oriented platforms putting student motivation, habits, expectations and learning styles (additional Koper, R.; Burgos, D (2005)). The concept of adaptation has become a prominent issue in elearning. Systems allow the users to change some system parameters and adapt their behavior accordingly is known as adaption process. Systems that adapt based on the systems assumption are called as adaptive systems (additional santally mohammed issack). Since the system adapts to a person and personalizes the learning process it can be also known as personalized system (additional Paramythis, A.; Loidl-Reisinger). So to serve the learning community better there was a necessity to profile them and identify their requirements and provide an adaptive elearning architecture which suits the individuals needs better. So the main objective of this research was to provide an adaptive e-learning system architecture which satisfies Felder Silverman learning style. Felder-Silverman's learning model [FSLM] was chosen since FSLM synthesis of these theories can be designed to easily translate them into strategies into a university setup.
Improving Personalization In E-Learning Systems
International Journal of Technology and Educational Marketing, 2014
Individual learners have different requirements and characteristics, and as a result learning content should be able to be personalized and adaptable to the e-learner' profile. Little research work undertaken to tackle this issue, and it has been limited to ad-hoc work on personalizing, and adapting learning content in e-Learning. This paper presents two methods for modeling user profile and for personalizing and adapting a given content to match that profile: inductive (without user intervention) and deductive (with user intervention). These methods will be used as a base to review and classify research work undertaken on personalizing content in the domain of knowledge management and e-learning systems. Based on these reviews, especially those undertaken in personalizing knowledge content in knowledge management systems, the paper proposes a comprehensive approach for personalizing learning content.
2014
Personalization approaches in learning environments can be ad- dressed from different perspectives and also in various educational settings, in- cluding formal, informal, workplace, lifelong, mobile, contextualized, and self- regulated learning. PALE workshop offers an opportunity to present and discuss a wide spectrum of issues and solutions. In particular, this fourth edition in- cludes 8 papers dealing with student's performance, modeling the user profile in a standardize way, computing attributes for learner modeling, detecting af- fective states to improve the personalized support, and applying user modeling approaches in new contexts, such as MOOCs and gamified environments.
Models, techniques and applications of e-learning personalization
2007
In recent years Web has become mainstream medium for communication and information dissemination. This paper presents approaches and methods for adaptive learning implementation, which are used in some contemporary web-interfaced Learning Management Systems (LMSs). The problem is not how to create electronic learning materials, but how to locate and utilize the available information in personalized way. Different attitudes to personalization are briefly described in section 1. The real personalization requires a user profile containing information about preferences, aims, and educational history to be stored and used by the system. These issues are considered in section 2. A method for development and design of adaptive learning content in terms of learning strategy system support is represented in section 3. Section 4 includes a set of innovative personalization services that are suggested by several very important research projects (SeLeNe project, ELENA project, etc.) dated from the last few years. This section also describes a model for role-and competency-based learning customization that uses Web Services approach. The last part presents how personalization techniques are implemented in Learning Grid-driven applications.
International Journal of Artificial Intelligence in Education, 2016
Personalization approaches in learning environments aim to foster effective, active, efficient, and satisfactory learning. Suitable user modelling techniques are crucial to support these approaches in dealing with learners' needs within realistic learning environments, which are currently cropping up in a varied range of situations. Bearing this in mind, this paper provides an overview of relevant research over the last five years in both user modelling and education, which shows an increasing interest among researchers and practitioners who are concerned with modelling users' needs in the new and evolving educational settings that are widening the diversity of learning contexts and issues to be considered. In particular, we have identified three main areas of research: i) modelling of learners and their performance to provide engaging learning experiences, ii) designing adaptive support, and iii) building standards-based models to cope with interoperability and portability.
Adaptivity and Personalization in Learning Systems based on Students’ Characteristics and Context
2012
Providing learners with personalized recommendations and/or adaptive courses that fit their characteristics and situation has high potential to make online and mobile learning easier and more effective for learners. However, most of the learning systems that are currently used by educational institutions do not provide adaptivity based on learners' characteristics, needs or situation. In this paper, we introduce our research on considering different learner characteristics and their context in learning systems and therefore provide learners with personalized learning experiences.
Taking Rich Context and Situation in Account for Improving an Adaptive e-Learning System
2011
Although there are several approaches for adaptive e-learning systems, they focus mainly on technological and/or networking aspects without taking into account other contextual aspects, such as cultural and pedagogical context. This paper presents a context-aware situation-dependent personalization approach designed for an adaptive e-learning system called AdaptWeb ® , based on a rich context model as an extension to student modeling.
E-Learning Environment Based Intelligent Profiling System for Enhancing User Adaptation
Electronics
Online learning systems have expanded significantly over the last couple of years. Massive Open Online Courses (MOOCs) have become a major trend on the internet. During the COVID-19 pandemic, the count of learner enrolment has increased in various MOOC platforms like Coursera, Udemy, Swayam, Udacity, FutureLearn, NPTEL, Khan Academy, EdX, SWAYAM, etc. These platforms offer multiple courses, and it is difficult for online learners to choose a suitable course as per their requirements. In order to improve this e-learning education environment and to reduce the drop-out ratio, online learners will need a system in which all the platform’s offered courses are compared and recommended, according to the needs of the learner. So, there is a need to create a learner’s profile to analyze so many platforms in order to fulfill the educational needs of the learners. To develop a profile of a learner or user, three input parameters are considered: personal details, educational details, and knowl...
User Profile Modeling in the context of web-based learning management systems
Over the past two decades, great research efforts have been conducted towards the personalization of e-learning platforms. This feature increases remarkably the quality of the provided learning services, since the users' special needs and capabilities are respected. The idea of predicting the users' preferences and adapting the e-learning platform accordingly is the focal point of this paper. In particular, this paper introduces the main requirements of an advanced e-learning system, explains the way a user navigates in such a system, presents the architecture of a novel e-learning system, and describes its main components. Research is focused on the User Model component, its role in the e-learning system and the parameters that comprise it. In this context, Bayesian Networks are used as a tool for the encoding, learning and reasoning of probabilistic relationships, with the aim to effectively predict user preferences. In support of this vision, four different scenarios are presented, in order to test the way Bayesian Networks apply in the e-learning field. Therefore, the goal is to develop a novel e-learning platform, which will accelerate the learning process and render it more efficient, by diminishing the emerged problems, thanks to its enhanced functionality to dynamically plan lessons and personalize both the communication and the learning strategy .