Applying Classification Techniques in E-Learning System: An Overview

The aim of this paper is to provide an overview of application of data mining methods in e-learning process. E-learning is concerned with web-based learning which is totally depending upon internet. Use of data mining algorithms can help to discover the relevant information from database obtained from web based education system. This paper focused on e-learning problems to which data mining techniques have been applied, including: student’s classification based on their learning performance, detection of irregular learning behavior of students. This paper shows types of various modeling techniques used which includes: neural network, fuzzy logic, graph and trees, association rules and multi agent systems

Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Švietimo duomenų tyryba: apžvalga ir tyrimų kryptys

The article presents a systematic literature review about the most commonly used data sources and data mining methods in education. International database Web of Science was selected. Excluding short conference proceedings and articles without empirical data, 14 papers were analyzed in detail. It was obtained that the most explored databases of learners consisted of subjects evaluation results together with contextual information. Classification methods were the most commonly used; to a lesser extent regression analysis and clustering. An educational data mining research overview in Lithuania ends the article.Straipsnyje pateikiama sisteminė literatūros apžvalga, kurios tikslas dažniausiai naudojami duomenų šaltiniai ir taikomi duomenų analizės metodai švietimo duomenų tyryboje. Apžvalgai pasirinkta tarptautinė duomenų bazė „Web of science“. Atmetus trumpus konferencijų pranešimus bei straipsnius, kuriuose nepateikti empiriniai duomenys, išsamiai nagrinėjami 14 straipsnių. Nustatyta, kad dažniausiai tiriamos besimokančiųjų duomenų bazės, kuriuose su mokomųjų dalykų įvertinimais pateikiama ir kontekstinė informacija. Švietimo duomenų tyryboje dažniausiai taikomi klasifikavimo metodai ir kiek rečiau regresijos analizė bei klasterizavimas. Straipsnis baigiamas švietimo duomenų tyrybos apžvalga Lietuvoje

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

M.A. rural development methodology paper

Master's Project (M.A.) University of Alaska Fairbanks, 201