Suitable Site Selection of Water ATMs (Basis of Interior/Exterior Conditions) Using Graph Theory

E-learning is an innovative strategy for enhancing teaching and learning in digital environments with the goal of enhancing education. In the same context, recommendation models have been developed for predicting the user’s learning preferences. A task that has become urgently necessary is enhancing the learning process by designing recommendation models for e-learning software that then helps users choose the most pertinent learning materials (contents) from a wide number of sources. The general consensus is that designing a recommendation model for e-learning is influenced by parameters that are related to e-learning, and much effort has been exerted to determine those parameters. However, no agreement has been reached as to what constitutes such parameters. Keeping this issue in mind, this study aims to identify the parameters that should be considered when generating e-learning recommendations in developing countries. On the basis of the relevant literature, with the use of the Delphi method and with aid from e-learning experts, this paper identifies ten critical parameters related to e-learning. The results show that perceived ease of use is the most critical parameter out of the ten e-learning-related parameters, while user preference is the parameter that contributes least to e-learning.

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Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches

Shahbazi

Byun

2022

Mathematics

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E-learning is a popular area in terms of learning from social media websites in various terms and contents for every group of people in this world with different knowledge backgrounds and jobs. E-learning sites help users such as students, business workers, instructors, and those searching for different educational institutions. Excluding the benefits of this system, there are various challenges that the users face in online platforms. One of the important challenges is the true information and right content based on these resources, search results and quality. This research proposes virtual and intelligent agent-based recommendation, which requires users’ profile information and preferences to recommend the proper content and search results based on their search history. We applied Natural Language Processing (NLP) techniques and semantic analysis approaches for the recommendation of course selection to e-learners and tutors. Moreover, machine learning performance analysis applied to improve the user rating results in the e-learning environment. The system automatically learns and analyzes the learner characteristics and processes the learning style through the clustering strategy. Compared with the recent state-of-the-art in this field, the proposed system and the simulation results show the minimizing number of metric errors compared to other works. The achievements of the presented approach are providing a comfortable platform to the user for course selection and recommendations. Similarly, we avoid recommending the same contents and courses. We analyze the user preferences and improving the recommendation system performance to provide highly related content based on the user profile situation. The prediction accuracy of the proposed system is 98% compared to hybrid filtering, self organization systems and ensemble modeling.

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Suitable Site Selection of Water ATMs (Basis of Interior/Exterior Conditions) Using Graph Theory

Cited by 3 publications

References 14 publications

Applying Machine Learning & Knowledge Discovery to Intelligent Agent-Based Recommendation for Online Learning Systems

Applying Machine Learning & Knowledge Discovery to Intelligent Agent-Based Recommendation for Online Learning Systems

Identification of Critical Parameters Affecting an E-Learning Recommendation Model Using Delphi Method Based on Expert Validation

Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches

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