A Web-Based Recommendation System for Engineering Education E-Learning Solutions

Koolinera is a web-based e-learning application about learning to cook Indonesian culinary dishes. Users are free to choose cooking classes. Culinary in Indonesia is very diverse, so many users feel confused in choosing a cooking class. No specific guidance is given to users on tips for choosing a cooking class. Therefore, it is important to develop a feature that can help users to guide the selection of cooking classes, namely by building a cooking class selection recommendation system. Class recommendations are obtained based on the last class taken by the user. The criteria used to determine the recommendations are the similarity of class names, dominant taste of cuisine, category of cuisine, area of origin, and tutor. The algorithm used is Content-Based Filtering with TF-IDF calculations. The recommendations given to users are a list of six cooking classes. Testing is carried out based on black box testing, expert validation, and user testing. The blackbox test carried out states that all functions are running well. The validity test of the media by the validator got a percentage of 96.52%. User testing in the Usability Tetsing Experience section got a percentage of 85.73%, User Acceptance Testing got a percentage of 83.89% and testing the relevance of the recommendation system got a percentage of 88.69%

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Design and Implementation of Adaptive Recommendation System

International Journal of Management, Technology, and Social Sciences ◽

10.47992/ijmts.2581.6012.0039 ◽

2018 ◽

pp. 101-117

Author(s):

MagedEla zony ◽

Ahmed Khalifa ◽

Sayed Nouh ◽

Mohamed Hussein

Keyword(s):

Semantic Web ◽

Semantic Similarity ◽

Recommendation System ◽

Learning Objects ◽

Just In Time ◽

Web Based ◽

Design And Implementation ◽

Knowledge Based ◽

E Learning ◽

Learner Profile

E-learning offers advantages for E-learners by making access to learning objects at any time or place, very fast, just-in-time and relevance. However, with the rapid increase of learning objects and it is syntactically structured it will be time-consuming to find contents they really need to study.In this paper, we design and implementation of knowledge-based industrial reusable, interactive web-based training and use semantic web based e-learning to deliver learning contents to the learner in flexible, interactive, and adaptive way. The semantic and recommendation and personalized search of Learning objects is based on the comparison of the learner profile and learning objects to determine a more suitable relationship between learning objects and learner profiles. Therefore, it will advise the e-learner with most suitable learning objects using the semantic similarity.

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Web-Based Approaches in Engineering Education

Cases on Digital Technologies in Higher Education ◽

10.4018/978-1-61520-869-2.ch017 ◽

2010 ◽

pp. 241-256

Author(s):

Masoumeh Valizadeh ◽

Giancarlo Anzelotti ◽

Arezou S. Salehi

Keyword(s):

Engineering Education ◽

Virtual Environment ◽

Educational Software ◽

Daily Life ◽

Educational Material ◽

Human Knowledge ◽

Web Based ◽

Training Technique ◽

E Learning

Among different branches of human knowledge and sciences, engineering, like medicine, is more involved in practical and daily-life aspects where the virtual utilities and educational software can be utilized to consummate the practical features of engineering education. Furthermore the virtual environment of e-learning courses can provide cheaper, safer, more comprehensive and more inclusive approaches to engineering educational material. The aim of this case is to count the requirements of engineering education and to accord the facilities and inadequacies of e-learning as training technique in engineering instruction.

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A Personalized Web Based E-Learning Recommendation System To Enhance and User Learning Experience

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9991.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1186-1195

Keyword(s):

Data Mining ◽

Genetic Algorithm ◽

Clustering Algorithm ◽

Recommendation System ◽

User Behavior ◽

Search Algorithm ◽

Learning Experience ◽

Research Work ◽

Web Based ◽

E Learning

The key aim of the data mining techniques is to help the user by reducing the effort for exploring the data, recovering the patterns, and implementing applications that help to find the knowledge specific contents, decision making, and predictions. This research work develops a recommendation system by using the merits of data mining algorithms. They are used for designing web-based e-learning recommendation systems. This model aims to understand the user behavior and contents requirements of the learner. This purpose is solved by obtaining the information from the data source and producing the suggestions of suitable content to the learner. The concept of web content mining and web usage mining has been combined together for performing the required work. This technique involves the genetic algorithm and k-means clustering algorithm for designing the presented model. In this work the k-means clustering algorithm has been used to track user behavior and the genetic algorithm has been used as a search algorithm to find the necessary resources in the database. Finally, the presented system is implemented and its performance is measured. The estimated results demonstrate that the presented model enhances the accuracy of recommendations and also speeds up the computations. A related performance calculation has also provided to justify this conclusion. The obtained results demonstrate that this technique is acceptable for new generation application designs

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