952 research outputs found

    Defining Adaptive Learning Paths For Competence-Oriented Learning

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    This paper presents a way to describe educational itineraries in a competence-oriented learning system in order to solve the problem of sequencing several independent courses. The main objective is to extract adaptive learning paths composed by the subset of needed courses passed in the right order. This approach improves the courses’ re-usability allowing courses to be included in different itineraries, improving the re-usability of the courses, and making possible the definition of mechanisms to adapt the learning path to the learner’s needs in execution tim

    Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning

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    This paper describes an agent- oriented approach that aims to create learning situations by solving problems. The proposed system is designed as a multi-agent that organizes interfaces, coordinators, sources of information and mobiles. The objective of this approach is to get learners to solve a problem that leads them to get engaged in several learning activities, chosen according to their level of knowledge and preferences in order to ensure adaptive learning and reduce the rate of learner abundance in an e-learning system. The search for learning activities procedure is based on evolutionary algorithms typically: genetic algorithm, to offer learners the optimal solution adapted to their profiles and ensuring a resolution of the proposed learning problem. In terms of results, we have adopted “immigration strategies” to improve the performance of the genetic algorithm. To show the effectiveness of the proposed approach we have made a comparative study with other artificial intelligence optimization methods. We conducted a real experiment with primary school learners in order to test the effectiveness of the proposed approach and to set up its functioning. The experiment results showed a high rate of success and engagement among the learners who followed the proposed adaptive learning scenario

    Ant colony algorithm and new pheromone to adapt units sequence to learners' profiles

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    The use of new information and communication technology is increasingly common nowadays. Content adaptation of learner’s profile is an issue that concerns many researchers in education field. Several studies have been conducted to achieve high quality learning and adapt the content to learners ' profiles. Some researchers have properly applied the ant colony algorithm to the field of e-learning. In this work we are interested in the improvement of ant colony algorithm for scheduling units of courses (e.g., a Java course). We follow a pedagogical way to establish units. We define five concepts to maintain learners ' motivation and adapt the algorithm behavior to our context. So our contribution is a new pheromone that influences the algorithm to choose the right unit in a pedagogical sequence. Many changes are taken into consideration to implement the new version of ant colony algorithm. The trainers apply weights to each arc that are linking two units of the course. The profile definition is a part that was preliminary defined in previous work using fuzzy logic method. Method Roulette Weel is applied for the selection part. This method is interested in finding the final state. It is used in addition to the ant colony algorithm for the path exploration and optimal learning path

    Application of Particle Swarm Optimization to Formative E-Assessment in Project Management

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    The current paper describes the application of Particle Swarm Optimization algorithm to the formative e-assessment problem in project management. The proposed approach resolves the issue of personalization, by taking into account, when selecting the item tests in an e-assessment, the following elements: the ability level of the user, the targeted difficulty of the test and the learning objectives, represented by project management concepts which have to be checked. The e-assessment tool in which the Particle Swarm Optimization algorithm is integrated is also presented. Experimental results and comparison with other algorithms used in item tests selection prove the suitability of the proposed approach to the formative e-assessment domain. The study is presented in the framework of other evolutionary and genetic algorithms applied in e-education.Particle Swarm Optimization, Genetic Algorithms, Evolutionary Algorithms, Formative E-assessment, E-education

    The Combination between the Individual Factors and the Collective Experience for Ultimate Optimization Learning Path using Ant Colony Algorithm

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    The approach that we propose in this paper is part of the optimization of the learning path in the e-learning environment. It relates more precisely to the adaptation and the guidance of the learners according to, on one hand, their needs and cognitive abilities and, on the other hand, the collective experience of co-learners. This work is done by an optimizer agent that has the specificity to provide to each learner the best path from the beginning of the learning process to its completion. The optimization in this approach is determined automatically and dynamically, by seeking the path that is more marked by success. This determination is concluding according to the vision of the pedagogical team and the collective experience of the learners. At the same time, we search of the path that is more adapted to the specificities of the learner in terms of preferences, level of knowledge and learner history. This operation is accomplished by exploiting their profile for perfect customization and the adaptation of ant colony algorithm for guidance tends towards maximizing the acquisition of the learner. The design of our work is unitary; it is based on the integration of individual collective factors of the learner. And the results are very conclusive. They show that the proposed approach is able to efficiently select the optimal path and that it participates fully in the satisfaction and success of the learner

    An Automated Adaptive Mobile Learning System Using Optimal Shortest Path Algorithms

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    Technological innovation opens the door to create a personal learning experience for any student. In this research, we discuss adaptive learning techniques and the style of learning that integrates existing learning techniques combined with new ideas. To create an effective user friendly learning environment, adaptive learning techniques should be used in order to identify the personal needs of students and reduce their individual knowledge gaps. The result will produce learning path containing relevant content that will provide a better learning direction for each student. This dissertation explores the opportunity of using adaptive learning techniques to identify the personal needs of each student by combining different learning styles, student profiles and individualized course content. By using a directed graph, we are able to represent an accurate picture of the course descriptions for online courses through computer-based implementation of various educational systems. E-learning (electronic learning) and m-learning (mobile learning) systems are modeled as a weighted directed graph where each node represents a course unit. The Learning Path Graph represents and describes the structure of the domain knowledge, including the learning goals, and all other available learning paths. In this research, we propose a system prototype that implements optimal adaptive learning path algorithms using students’ information from their profiles and their learning style. Our goal is to improve students’ learning performances through the m-learning system in order to provide suitable course contents sequenced in a dynamic form for each student

    An ACO-based personalized learning technique in support of people with acquired brain injury

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    This is the author’s version of a work that was accepted for publication in Applied Soft Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing 47 (2016) 316–331. DOI 10.1016/j.asoc.2016.04.039The ever-increasing cases of acquired brain injury (ABI), especially among young people, have prompted a rapid progress in research involving neurological disorders. One important path is the concept of relearning, which attempts to help people regain basic motor and cognitive skills lost due to illness or accident. The goals of relearning are twofold. First, there must exist a way to properly assess the necessities of an affected person, leading to a diagnosis, followed by a recommendation regarding the exercises, tests and tasks to perform; and second, there must be a way to confirm the results obtained from these recommendations in order to fine-tune and personalize the relearning process. This presents a challenge, as there is a deeply-rooted duality between the personalized and the generalized approach. In this work we propose a personalization algorithm based on the ant colony optimization (ACO), which is a bio-inspired meta-heuristic. As we show, the stochastic nature of ants has certain similarities to the human learning process. We combine the adaptive and exploratory capabilities of ACO systems to respond to rapidly changing environments and the ubiquitous human factor. Finally, we test the proposed solution extensively in various scenarios, achieving high quality results. © 2016 Elsevier B.V. All rights reservedThis research has been funded by the Spanish Ministry of Economy and Competitiveness and by the FEDER funds of the EU under the project SUPEREMOS (TIN2014-60077-R) and insPIre (TIN2012-34003). Kamil Krynicki is supported by the FPI fellowship from Universitat Politecnica de Valencia.Krynicki, K.; Jaén Martínez, FJ.; Navarro, E. (2016). An ACO-based personalized learning technique in support of people with acquired brain injury. Applied Soft Computing. 47:316-331. doi:10.1016/j.asoc.2016.04.039S3163314

    Development and evaluation of an engaging web-based content sequencing system for learning basic programming

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    Java basic programming is one of programming languages that is offered to students as a compulsory course for Information Technology or Computer Science programs. This subject requires students to learn skills and techniques of programming rather than theoretical concepts. Usually, students have problems to capture and understand the content of the course which resulted in low performance or withdrawal from the program and even the education system. In general, web-based learning can be used as a tool to improve learning including programming courses. A specific instance of web-based learning; called content sequencing systems have a high potential to provide adaptive learning for programming languages. Adaptive content sequencing systems analyze individual difference of students and sequence the learning contents based on the students’ needs. By addressing students’ individual differences, it helps students to be actively engaged in the learning process. An engagement is a key element in learning. In this research, the level of students’ engagement is measured using "flow theory". This theory suggested three cognitive conditions when one is doing a particular activity, namely flow (engaged), boredom, and anxiety. Engagement occurs when an individual has an equal level of skill with the given level of challenge. Anxiety and boredom occur when there is unequal level of challenge and skill. The fundamental concepts of the theory are represented in a user interface design by imposing a component known as "flow buttons". The used of the buttons is described as Skill-Challenge Balancing (SCB) technique and it is adapted in a web-based learning system called "LearnJava". It incorporates SCB where its main components are a user interface design and a sequencing engine. Based on this technique, the students’ level of knowledge will be evaluated and analyzed to identify their current level of skill. The technique will sequence the learning contents based on the students’ current level of skill to keep them engage in the web-based learning. An experimental study was conducted to evaluate how effective SCB in helping students to engage in web-based learning. The results suggested that the SCB technique improved students’ engagement in web-based learning

    Sequencing of learning activities oriented towards reuse and auto-organization for intelligent tutoring systems

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    Three have been the main contributions of this thesis. First, a platform for the deployment of Intelligent Tutoring Systems (ITS) with a modular architecture has been designed. This platform, called SIT, focuses on the adaptation of the sequencing of learning content, not adaptation of the content itself. This separation permits specialization of pedagogical experts and encourages reuse of learning resources. Second, a tool for the adaptation of the sequencing of learning units has been presented: Sequencing Graphs. It is a specialization of the finite automata paradigm, adapted for the specific needs of learning. Sequencing graphs focus on reuse, both of learning units and of adaptive sequencings definitions. They are hierarchical to prevent scalability problems. Two ITS have developed using sequencing graphs for SIT. Experimental results support the hypothesis that sequencing adaptation has a good influence on learning and that Sequencing Graphs are a useful tool to achieve this objective. Finally, the thesis analyzes the current initiatives in the emerging field of swarm intelligence techniques in education. Apart of the theoretical overview, three results are presented: an experimental study performed on the Paraschool system, a system of pedagogical alarms based on learning pheromones on the same system, and a swarm paths information module for SIT. This module synthesizes the best results from swarm-based adaptation sequencing and collaborative filtering for providing an additional level of adaptation to the content sequencing in SI
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