Practical applications of Intelligent Systems: An E-learning platforms recommender

López, Victoria; Santos, Matilde; Esteve, Pablo Navarro; Roldán, David; deMendoza, Juan C.

doi:10.1109/iske.2010.5680766

Cited by 4 publications

(4 citation statements)

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“…In [5], the authors presented a recommendation system to recommend to users the adequate e-Learning platforms for their cases, which is based on decision trees using ID3algorithm. The system takes into consideration several attributes namely, the type of questions of the test required for a course or content, the images as a Boolean variable that gives the information to the system that the user will use the images or not.…”

Section: Related Workmentioning

confidence: 99%

Towards an Intelligent Hybrid Recommendation System for E-Learning Platforms Using Data Mining

Mabrouk

Gaou

Rtili

2017

Int. J. Emerg. Technol. Learn.

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Nowadays, Information and communications technology (ICT) becomes a very important thing in human life in different fields. They are used in many fields as information systems (software, middleware) using various telecommunication media to give users the ability to manipulate digital data. In addition, with new technology development, a new concept appeared in the late 90s and early millennium, which is distance learning through e-Learning platform. Recommendation systems become increasingly used in information systems and especially in e-learning platform. These systems are used to propose and recommend content of these platforms to users according to needs of the latter in order to allow them to have the maximum information for learning. In this paper, we present an intelligent hybrid recommendation system based on data mining. This system has four parts, the first for data collection and for center of interest construction by two modes: explicit data collection, which based on users and what they filled in their profiles, and implicit and automatic data collection by proposing a survey to users in order to gather information about their interest. A second part for processing information already collected in the previous part and for creating the learning model, classifying users who posted the content and classifying content also in order to send the results to the recommendation module. The third part is for making the similarity between learners and content and doing the recommendation for learners and the final part is for creating a log file of recommendation by learner, which will be used in the upcoming recommendation. According to results already done, we noticed that our proposition is satisfactory and the system is well optimized in terms of accuracy, response and processing time compared to the standard recommendation.

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Section: Related Workmentioning

confidence: 99%

Towards an Intelligent Hybrid Recommendation System for E-Learning Platforms Using Data Mining

Mabrouk

Gaou

Rtili

2017

Int. J. Emerg. Technol. Learn.

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“…In the existing literature, various approaches have been explored, with classification techniques being prominently used, particularly those based on decision trees. However, these techniques often result in the generation of numerous rules that the system can utilize to identify suitable learning objects (LOs) [27][28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

Azzi,

Radouane,

Laaouina

et al. 2024

Informatics

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In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.

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“…Data Tamer [27] in MIT, which deals even with data consolidation, is a good example. And not only tools for monitoring, integrate, store or analyze data but also for organizing e-learning [12,25]. All of these tools were working over a server or a huge system that need a very important first investment.…”

Section: Big Data Systems In Health and Social Sciencesmentioning

confidence: 99%

Big+Open Data: Some applications for a Smartcity

López

Miñana

Sánchez

et al. 2015

2015 IEEE International Conference on Progress in Informatics and Computing (PIC)

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The way of thinking on Big Data, Open Data and their use by organizations or individuals has been a trending topic over the last few years. Big Data deals with collecting, storing, analyzing and putting data in value. Big, medium and small enterprises want to include information technologies in their management and decision processes. At the same time, movements about rights on public data have increased their presence and force. Data from governments must be open. Every day, more and more cities and countries are opening their data. Open Data has emerged as a new paradigm for the public service provision model with a special role in Smart City. The main goal of Big and Open Data in a Smart City is to develop systems which can be useful for citizens. In this work we analyze how both private enterprises and governments manage to improve their data value by combining private and public datasets and we give some examples of our work in this area.

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Practical applications of Intelligent Systems: An E-learning platforms recommender

Cited by 4 publications

References 0 publications

Towards an Intelligent Hybrid Recommendation System for E-Learning Platforms Using Data Mining

Towards an Intelligent Hybrid Recommendation System for E-Learning Platforms Using Data Mining

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

Big+Open Data: Some applications for a Smartcity

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