Intelligent decision, E-learning, novel algorithm
More and more digital learning resources are available online through various learning management systems; this has made it a challenging task for learning institutions to choose the appropriate learning management system that will enable its achieve its goals as well the learning goals of its learner. This project therefore, proposes to implement an intelligent decision support system for recommending learning management systems or e-learning platforms based on a novel algorithm. The recommendations are on the basis of weighting criteria based on a mathematical model that analyses a user's performance during the training process on one hand and the search queries made by the user on the other hand.
This tool uses and applies k-means clustering algorithm and fuzzy logic to determine document arrangement based on their performance level. Further, the project proposes a new ontology domain alignment technique that uses contextual data of the knowledge sources for decision making from e-learning domain. This domain has been tested empirically, and the results show that it performs better than other existing methods. This paper makes salient contributions such as the use of k-means clustering algorithm for decision-making and fuzzy approach for ontology alignment.
[...] In this regard, researchers such as Miller et al. are also working on developing suitable techniques that are able to capture and epitomize appropriate and suitable knowledge. Miller et al. proposed a data extraction approach that would help discover Meta data relations that define different learning resources. Terms from meta-data files were, in this study, parsed, while stop words were removed. The author applied tools such as Word Net to extract word roots; on the part of tagging speech, an algorithm, the Brill tagger, was used. [...]
[...] This is important in estimating membership values with regards to a concept. They are computed and determined for attribute, relation, and the definition of concept in fuzzy domain ontology. Using Jaccard's coefficient, the similarity measure is given by: In this similarity measure formula, the Jaccard's coefficient is used to determine the measure of similarity just as it is used in many other systems which utilize this formula to determine representation in ontology model for machine learning methods. The proposed model in this paper maps the similarity between two ontologies onto one new ontology Using this coefficient, similarity probabilities measure of the structure of ontology of each document can be computed. [...]
[...] This is a system used to retrieve relevant information with regards to the learners as well as support instructional design. It analyses and processes the data results which thus enables significant recommendations for e-learning. Today, most e-learning platforms or learning management systems only put into consideration one feature. Thus, it is important for academic institutions to select learning systems that support all the features that would otherwise be considered appropriate and efficient. Learners Knowledge Pre-processing Basically, this is made up of a process of optimizing the list of terms which identify a taxonomy or collection. [...]
[...] Therefore, if an learning platform is deemed deficient, ineffective or inappropriate, the model enablesthe determination, based on AHP weights and on impact-digraph- map, of the problem and deficiencies of the e-learning platform. Bibliography Azzeh, M., Neagu, D. & Cowling, P Software Project Similarity effective Measurement Based on Fuzzy C-Means, Berlin: Springer. Kay, A.J. & Holden, S Automatic Extraction of Ontologies derived from Teaching Document Metadata. In Ongoings of the 2002 International Conference on personal Computers in Education. pp. 1555–1556. Lau, R. et al Towards a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning. IEEE Transactions on Knowledge and Data, pp.800–813. [...]
[...] Ontology plays a crucial role in seizing and distributing the idea for effective and efficient HCI-human computer interaction. It should however, be noted that domain ontology engineering is just as cumbersome and time consuming as the manual process. Various researchers have in past examined various machine learning methods for semi and automatic domain ontology discoveries. E-learning technologies or learning management systems technologies can automatically support an automatic method for constructing concept maps and for the analysis of learners in order to determine and characterize learners understanding of the topics that they learn via an e-learning platform. [...]
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