Conference Paper

Personalized Learning Path Delivery: Models and Example of Application

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Abstract

In this article we present the Lausanne Model: a learning object based reference model that: (i) considers learning issues such as granularity level, description formalism,(ii) organizes learning objects in a network where links are explicated, (iii) enhances user mobility from one environment to another and (iv) considers both individual and social adaptation.

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... Diferentemente dos trabalhos anteriores, (Hwang, Kuo, Yin, & Chuang, 2010) considerara a descoberta de caminhos de aprendizagem como sendo um problema de otimização, na qual a solução ideal seria a maximização da aprendizagem dos alunos. Já Madhour et al. (2008) utilizaram um algoritmo inspirado em colônia de formigas (ACO) baseado no modelo do usuário e na experiência de outros alunos, proporcionando caminhos de aprendizagem personalizados. O trabalho desenvolvido por Durand et al. (2013) propôs um modelo baseado em grafos dinâmicos e uma heurística para encontrar o caminho de aprendizagem em um grafo contendo objetos de aprendizagem. ...
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A definição de caminhos de aprendizagem é um ponto fundamental para a aprendizagem eficiente. Este artigo apresenta um novo método inteligente para otimizar o processo de seleção de caminhos de aprendizagem para um grupo de indivíduos, levando em conta múltiplos critérios: satisfação dos membros da equipe e tempo gastos na realização da atividade. O método foi avaliado quanto ao desempenho, sendo comparado às abordagens exaustiva e aleatória; e quanto ao aspecto pedagógico, sendo comparado com os métodos aleatório e auto-selecionado. Os resultados alcançados revelaram o potencial do método proposto tanto do ponto de vista computacional quanto do pedagógico.
... Diferentemente dos trabalhos anteriores, [Hwang et al. 2010] considerara a descoberta de caminhos de aprendizagem como sendo um problema de otimização, na qual a solução ideal seria a maximização da aprendizagem dos alunos. Já [Madhour and Wentland Forte 2008] utilizaram um algoritmo inspirado em colônia de formigas (ACO) baseado no modelo do usuário e na experiência de outros alunos, proporcionando caminhos de aprendizagem personaliza-dos. Também vale mencionar o trabalho de [Belacel et al. 2014], que aplicou um modelo de programação inteira binária para a otimização de caminhos de aprendizagem. ...
... The main challenge faced in this area is combining the wide diversity among the individuals with the specific characteristic of the learning objects. These include the consideration of learning style, cognitive style, learning goal [16,17], as well as the domain model, knowledge structure, and curriculum [17][18][19]. The challenging continues as the nature of online learning environment that constantly changing as learning resources are being introduced/removed and the learning experience is constantly evolving. ...
... An approach [2] on the use of concept maps for deriving prerequisite relations and structures based on CbKST has been generated by Steiner and Albert, with the purpose to achieve personalization in web-based learning. Madhour and Forte [3] presented the Lausanne Model and introduced the ACO algorithm which is based on the User model and other user's experience so as to provide the best-possible personalized learning path to users. Another recent work done by Zhao and Wan [4], in which an algorithm for selecting the shortest learning path to learn the target knowledge was proposed to save the time and efforts. ...
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With the explosion of knowledge nowadays, it is urgent for people to learn new things quickly and effectively. To meet such a requirement , how we can find a suitable path for learning has become a crucial issue. Meanwhile, in our daily life, it is important and necessary for people from various backgrounds to achieve a certain task (eg. survey, report, business plan, etc.) collaboratively in the form of the group. For these group-based task, it often requires members to learn new knowledge by using e-learning system. In this paper, we focus on addressing the problem on discovering an appropriate study path to facilitate a group of people rather than a single person for effective learning under e-learning environment. Furthermore, we propose a group model to capture the expertise of each member. Based on this model, a groupized 1 learning path discovering (GLPD) algorithm is proposed in order to help a group of learners to grasp new knowledge effectively and efficiently. Finally, we conduct a practical experiment whose result verifies the soundness of our approach.
... An approach [2] on the use of concept maps for deriving prerequisite relations and structures based on CbKST has been generated by Steiner and Albert, with the purpose to achieve personalization in web-based learning. Madhour and Forte [3] presented the Lausanne Model and introduced the ACO algorithm which is based on the User model and other user's experience so as to provide the best-possible person- alized learning path to users. Another recent work done by Zhao and Wan [4], in which an algorithm for selecting the shortest learning path to learn the target knowledge was proposed to save the time and efforts. ...
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With the explosion of knowledge nowadays, it is urgent for people to learn new things quickly and effectively. To meet such a requirement, how we can find a suitable path for learning has become a crucial issue. Meanwhile, in our daily life, it is important and necessary for people from various backgrounds to achieve a certain task (eg. survey, report, business plan, etc.) collaboratively in the form of the group. For these group-based task, it often requires members to learn new knowledge by using e-learning system. In this paper, we focus on addressing the problem on discovering an appropriate study path to facilitate a group of people rather than a single person for effective learning under e-learning environment. Furthermore, we propose a group model to capture the expertise of each member. Based on this model, a groupized learning path discovering (GLPD) algorithm is proposed in order to help a group of learners to grasp new knowledge effectively and efficiently. Finally, we conduct a practical experiment whose result verifies the soundness of our approach.
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Chapter
This chapter discusses a challenging hot topic in the area of Web 2.0 technologies for Lifelong Learning: how to merge such technologies with research on personalization and adaptive e-learning, in order to provide the best learning experience, customized for a specific learner or group of learners, in the context of communities of learning and authoring. The authors of this chapter discuss the most well-known frameworks and then show how an existing framework for personalized e-learning can be extended, in order to allow the specification of the complex new relationships that social aspects bring to e-learning platforms. This is not just about creating learning content, but also about developing new ways of learning. For instance, adaptation does not refer to an individual only, but also to groups, which can be groups of learners, designers or course authors. Their interests, objectives, capabilities, and backgrounds need to be catered to, as well as their group interaction. Furthermore, the boundaries between authors and learners become less distinct in the Web 2.0 context. This chapter presents the theoretical basis for this framework extension, as well as its implementation and evaluation, and concludes by discussing the results and drawing conclusions and interesting pointers for further research.
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Recognising the fact that learning objects are still a new concept, D. Rehak and R. Mason have tackled the significant issue of how to make learning objects work in practice in a very interesting and thorough manner. They clearly state that the topic of learning objects is rather new and in an experimental phase. There is an agreement about the attributes of a learning object (reusability, accessibility, interoperability-portability, duration) within the learning technology community. However, stakeholders see the usefulness of learning objects from different optical angles: on the one hand, the training sector tends to be engaged in reuse and just-in-time/on-demand content aggregation in order to augment their market share; on the other hand universities and the learning sector in general, consider the reuse and repurposing of learning material as a big opportunity to save resources as well as to offer new (more enhanced) learning experiences. The authors analyse the concept of learning objects in five stages which are common for new concepts in any domain. Along the presentation of these stages (confusions, stakeholders, precedents, investigations of how to apply and exploit, acceptance), they pinpoint several crucial issues that need to be tackled in order that the learning process will take benefit of the notion of learning objects.
Modelisation d’un domaine de connaissance et orientation conceptuelle dans un hypertexte pedagogique
  • M Wentland
Ant algorithms for discrete optimization
  • M Dorigo
  • G Di Caro
  • M. Dorigo