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Se dan unas recomendaciones en la enseñanza asistida por ordenador (e-learning) basada en la inteligencia colectiva. This paper analyses aspects about the recommendation process in distributed information systems. It extracts similarities and differences between recommendations in estores and the recommendations applied to an e-learning environment...
Citations
... (Babad & Tayeb, 2003) From Gil A. B. and Garciapenalvo F. J., there are many methods and algorithms for choosing a course to solve the problem of recommending a course. (Gil & Garciapenalvo, 2008) However, none of them has been specifically designed for the requirements of smart education. With their development, electronic education is becoming a promising direction, meeting the needs of modern society as much as possible, the hallmarks of which are working with a large amount of information on a mobile / electronic medium, and analyzing it in a short period of time. ...
The article presents a study on the problem of using SMART-technologies in higher education. The purpose of the article is methodological recommendations on the use of SMART-technologies and the Internet of Things (IoT-Internet of Things) in a pedagogical higher educational institution teaching the discipline "Network Technologies", the basic principles of their functioning and basic characteristics are formulated. As research methods, the author used interpretation, comparative analysis and generalization of the scientific literature on the problem; an example of tools of SMART-technologies and the Internet of Things (IoT-Internet of Things) analyzes the relevance and validity of their use from a didactic point of view, focuses on the need to constantly improve the learning process with their help.
... As learning could be undertaken anytime, anywhere in the smart education context by using the intelligent devices and the number of courses in the framework of smart education has greatly increased, the corresponding course selection issue is playing a significant role in the process of modern education and has transferred into the determination of the curriculum that are suitable for the students accurately and efficiently. In the past, a plethora of methods and algorithms [2,3] for course selection have been proposed to deal with course recommendation problem. However, none of them was specifically designed for the requirements of smart education. ...
Being an essential component of smart education, we propose a novel recommendationsystem for course selection in the specialty of information management inChinese Universities.To implement this system, we firstly collect the course enrollment data-set for specific group of students. The sparse linear method (SLIM) is introduced in our framework to generate the top-N recommendations of courses appropriate to the students. Meanwhile, aL0 regularization term isexploited as the optimization strategywhich is established on the observation of the course items in the current recommendation system. The comparison experiments betweenstate-of-the-art methods and our approachare conducted to evaluate the performance of our method. Experimental results of different topics and number of courses both show that our proposed method outperforms state-of-the-art methods both in accuracy and efficiency.
... Pero no siempre el resultado es el esperado por el usuario si la búsqueda se realiza sólo considerando el tema, porque un material recuperado no es el adecuado para todos los usuarios. En los últimos años, los sistemas recomendadores surgen para ayudar a resolver este tipo de problema ya que son capaces de seleccionar, de forma automática y personalizada, el material que mejor se adapte a las preferencias y necesidades de un usuario [10]. ...
... Social learning, which is done with the presence of many individuals, has allowed humans to build up extensive cultural repertories, enabling them to adapt to a wide variety of environmental and social conditions [14]. The Distributed Knowledge Management (DKM) approach or swarm intelligence is also a kind of social or organizational approach [15,16]. The present study includes the learning within the boundaries of organizations like firms in an open regional innovation system [17]. ...
What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.
... Gil created an e-learning recommendation system in 2008 [9], which used the multi-agent approach. After experiment, they found that the system can retrieve and recommend documents faster when testing with ACM CR categories. ...
... Second, the learning content (i.e. LOs) is usually presented as different services (simulations, discussion boards, assessments, etc.) rather than a simple text or document (Gil & Garcia-Penalvo, 2008). Finding the right match in this context is a major problem (Yang & Wu, 2009). ...
The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners' preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors - by helping them to monitor, refine, and improve e-learning modules and courses according to the learners' behaviour.
... Recommendation systems are particularly relevant to a virtual classroom environment, where learners benefit from the experience of the community. It is often argued that recommendation systems offer many advantages in a learning environment [8]. Relevant material can be found easily and learners are better engaged with the learning material. ...
This paper is concerned with the presentation of a collaborative recommendation system that implements a cascade of strategies in order to support the learning process. Similarities between learners are determined by taking advantage of the underlying implicit or explicit personalisation and of the non-personalised modes of interaction. In the personalised approach implicit profiles are based on the patterns of behaviour of learners, while explicit profiles are generated from the results of a questionnaire on learning style. The non-personalisation approach relies on the cumulative intervention of a community of learners implied by the recorded frequency of the usage of objects by learners, and by the expert rating of objects by teachers. Content-based and collaborative approaches are combined into a hybrid model that widens the range of objects to which a learner may be exposed. The quality of service of the recommendation system is evaluated by considering the accuracy of its predictive capability on a publicly available data set.
... The increasing number of Learning Management Systems (LMS) for online teaching, quiz, assignment delivery, discussion forum, email, chat, et cetera, means that dynamic educational online services will be needed for efficient management of all educational resources on the Web. Selecting and organizing learning resources based on learner's interest is cumbersome (Gil and García-Penalvo, 2008). The process of selection may be easier with the normal users, but for certain category of learners with a visual impairment, navigating a Voice User Interface (VUI) for the desired learning content is a strenuous task. ...
With the proliferation of learning resources on the Web, finding suitable content (using telephone) has become a rigorous task for voice-based online learners to achieve better performance. The problem with Finding Content Suitability (FCS) with voice E-Learning applications is more complex when the sight-impaired learner is involved. Existing voice-enabled applications in the domain of E-Learning lack the attributes of adaptive and reusable learning objects to be able to address the FCS problem. This study provides a Voice-enabled Framework for Recommender and Adaptation (VeFRA) Systems in E-learning and an implementation of a system based on the framework with dual user interfaces - voice and Web. A usability study was carried out in a visually impaired and non-visually impaired school using the International Standard Organization's (ISO) 9241-11 specification to determine the level of effectiveness, efficiency and user satisfaction. The result of the usability evaluation reveals that the prototype application developed for the school has "Good Usability" rating of 4.13 out of 5 scale. This shows that the application will not only complement existing mobile and Web-based learning systems, but will be of immense benefit to users, based on the system's capacity for taking autonomous decisions that are capable of adapting to the needs of both visually impaired and non-visually impaired learners.
... Then, it should be recommended that he/she read some review papers. For example, an editorial article by two of the leading researchers in this area [5], although there are many high quality technical papers related to his/her interest. On the other hand, for the learner coming from industry with some prior knowledge who wants to know how web mining can be utilized to solve e-commerce problems, should be recommended, because the paper is the KDD-Cup 20001 organizers' report on how web mining can support business decision making for a real-life e-commerce vendor, and points out challenges, as well as lessons learned from the competition, which can benefit both researchers and industry practitioners. ...
... Finally, the present recommendation is applied to e-learning by proposing recommendation by emergence in Multi-Agent System architecture. [5] Recommended learning resources are computed based on the current learner's recent navigation history, as well as exploiting similarities and dissimilarities among learners' preferences and educational content. The proposed framework for building automatic recommendations in e-learning platforms is composed of two modules: an off-line module which pre-processes data to build learner and content models, and an online module which uses these models on-the-fly to recognize the students' needs and goals, and predict a recommendation list. ...
... These techniques abstract away the individual properties of learners drawing efficient learning paths from the emergent and collective behavior of a ''swarm'' of learners (Tattersall et al. 2005). The essence of these social systems stems from the well-established e-commerce recommendation systems, where services are marketed according to user interests based on a large amount of other similar customers' profiles (Gil and Garcia-Penalvo 2008). These systems do not only rely on the customer's profile, but also incorporate and benefit from the history and performance of similar customers in the process of recommendation (Gil and Garcia-Penalvo 2008). ...
... The essence of these social systems stems from the well-established e-commerce recommendation systems, where services are marketed according to user interests based on a large amount of other similar customers' profiles (Gil and Garcia-Penalvo 2008). These systems do not only rely on the customer's profile, but also incorporate and benefit from the history and performance of similar customers in the process of recommendation (Gil and Garcia-Penalvo 2008). e-Learning is among the most challenging e-environments to implement. ...
... Second, learning content is usually presented as different services (simulations, discussion boards, assessments …etc.) rather than a simple text or document (Gil and Garcia-Penalvo 2008). Finding the right match in this context is a major problem (Yang and Wu 2009). ...
Within the field of e-Learning, a learning path represents a match between a learner profile and his preferences from one side, and the learning content presentation and the pedagogical requirements from the other side. The Curriculum Sequencing problem (CS) concerns the dynamic generation of a personal optimal learning path for a learner. This problem has gained an increased research interest in the last decade, as it is not possible to have a single learning path that suits every learner in the widely heterogeneous e-Learning environment. Since this problem is NP-hard, heuristics and meta-heuristics are usually used to approximate its solutions, in particular Evolutionary Computation approaches (EC). In this paper, a review of recent developments in the application of EC approaches to the CS problem is presented. A classification of these approaches is provided with emphasis on the tools necessary for facilitating learning content reusability and automated sequencing.
