Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios

2014 ◽  
Vol 4 (1) ◽  
pp. 1-14 ◽  
Author(s):  
M. Waseem Chughtai ◽  
Imran Ghani ◽  
Ali Selamat ◽  
Seung Ryul Jeong
Keyword(s):  
Collaborative Filtering ◽  
Cold Start ◽  
Traditional Education ◽  
Experimental Result ◽  
Personalized Recommendation ◽  
Web Based ◽  
Web Based Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Content Based Filtering

Web-based learning or e-Learning in contrast to traditional education systems offer a lot of benefits. This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering. Because only traditional content-based filtering is not sufficient to generate the recommendations for new-users, therefore, the proposed work hybridized multi user's collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental has been done by using the famous ‘MovieLens' dataset with 15.86% density of the user-item matrix with respect to ratings, while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of Goal-based personalized recommendation framework. The Experimental result Precision: 81.90% and Recall: 86.56% show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals Adaptive Learning Recommendation Strategy Based on Deep Q-learning

2019 ◽  
Vol 44 (4) ◽  
pp. 251-266 ◽  
Author(s):  
Chunxi Tan ◽  
Ruijian Han ◽  
Rougang Ye ◽  
Kani Chen
Keyword(s):  
Adaptive Learning ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Early Stopping ◽  
Q Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Efficient Learning ◽  
Recommendation Strategy ◽  
Full Utilization

Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learner’s own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learner’s proficiency on knowledge points, and then, the well-designed recommendation strategy selects a sequence of actions to meet the objective of maximizing learner’s learning efficiency. This article proposes a novel adaptive recommendation strategy under the framework of reinforcement learning. The proposed strategy is realized by the deep Q-learning algorithms, which are the techniques that contributed to the success of AlphaGo Zero to achieve the super-human level in playing the game of go. The proposed algorithm incorporates an early stopping to account for the possibility that learners may choose to stop learning. It can properly deal with missing data and can handle more individual-specific features for better recommendations. The recommendation strategy guides individual learners with efficient learning paths that vary from person to person. The authors showcase concrete examples with numeric analysis of substantive learning scenarios to further demonstrate the power of the proposed method.

Enhanced Speech-Enabled Tools for Intelligent and Mobile E-Learning Applications

2010 ◽  
pp. 147-165
Author(s):  
S. A. Selouani ◽  
T. H. Lê ◽  
Y. Benahmed ◽  
D. O’Shaughnessy
Keyword(s):  
New Media ◽  
Recognition Performance ◽  
User Profile ◽  
Learning Material ◽  
Speech Technology ◽  
Web Based ◽  
C Language ◽  
Web Based Learning ◽  
E Learning ◽  
The One

Web-based learning is rapidly becoming the preferred way to quickly, efficiently, and economically create and deliver training or educational content through various communication media. This chapter presents systems that use speech technology to emulate the one-on-one interaction a student can get from a virtual instructor. A Web-based learning tool, the Learn IN Context (LINC+) system, designed and used in a real mixed-mode learning context for a computer (C++ language) programming course taught at the Université de Moncton (Canada) is described here. It integrates an Internet Voice Searching and Navigating (IVSN) system that helps learners to search and navigate both the web and their desktop environment through voice commands and dictation. LINC+ also incorporates an Automatic User Profile Building and Training (AUPB&T) module that allows users to increase speech recognition performance without having to go through the long and fastidious manual training process. New Automated Service Agents based on the Artificial Intelligence Markup Language (AIML) are used to provide naturalness to the dialogs between users and machines. The portability of the e-learning system across a mobile platform is also investigated. The findings show that when the learning material is delivered in the form of a collaborative and voice-enabled presentation, the majority of learners seem to be satisfied with this new media, and confirm that it does not negatively affect their cognitive load.

Towards a Personalized E-Learning System

2011 ◽  
pp. 2124-2134
Author(s):  
Elvis Wai Chung Leung ◽  
Qing Li
Keyword(s):  
Online Education ◽  
Economic Benefit ◽  
Learning System ◽  
Development Cost ◽  
Web Based ◽  
Course Materials ◽  
Web Based Learning ◽  
Interactive Environment ◽  
E Learning ◽  
The Web

To cope with the increasing trend of learning demand and limited resources, most universities are taking advantage of Web-based technology for their distance education or e-learning (Montelpare & Williams, 2000). One of the reasons is due to the significant price drop of personal computers in recent decades; the Internet and multimedia have penetrated into most households. Moreover, most students prefer to learn from an interactive environment through a self-paced style. Under the Web-based learning model, students can learn anytime, anywhere because they are not required to go to school on schedule (Appelt, 1997). Meanwhile, universities also enjoy the economic benefit due to the large student base that can share the development cost of course materials and other operational expenses. Gradually, more and more universities follow this similar way to provide online education.

Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development

2020 ◽  
Vol 34 (10) ◽  
pp. 2059033
Author(s):  
Gang Huang ◽  
Man Yuan ◽  
Chun-Sheng Li ◽  
Yong-he Wei
Keyword(s):  
Collaborative Filtering ◽  
Domain Knowledge ◽  
Cold Start ◽  
Petroleum Exploration ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Exploration And Development

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

An Ontology-Based Collaborative Filtering Personalized Recommendation

2012 ◽  
Vol 267 ◽  
pp. 79-82
Author(s):  
Pu Wang
Keyword(s):  
Collaborative Filtering ◽  
Web Sites ◽  
Information Overload ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Collaborative Approach ◽  
The Matrix ◽  
Special Domain ◽  
Sparsity Problem ◽  
The Given

Recommender systems have been successfully used to tackle the problem of information overload, where users of products have too many choices and overwhelming amount of information about each choice. Personalization is widely used in various fields to provide users with more suitable and personalized service. Many e-commerce web sites such as online shop retailers make use of recommendation systems. In order to make recommendations to a user, collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. The collaborative approach faces the hard issue of cold start problem and the matrix sparsity problem. The paper presents a collaborative filtering personalized recommendation approach based on ontology in the special domain. The method combines ontology technology and item-based collaborative filtering. The given recommendation approach can tackle the traditional recommenders problems, such as matrix sparsity and cold start problems.

e-Learning competency for practice nurses: an evaluation report

2013 ◽  
Vol 19 (4) ◽  
pp. 287 ◽  
Author(s):  
Marie Heartfield ◽  
Andrea Morello ◽  
Melanie Harris ◽  
Sharon Lawn ◽  
Vincenza Pols ◽  
...  
Keyword(s):  
Health Care Professionals ◽  
Chronic Disease Management ◽  
Learning Experience ◽  
Self Management ◽  
Policy Outcomes ◽  
Management Support ◽  
Web Based ◽  
Practice Nurses ◽  
Web Based Learning ◽  
E Learning

Practice nurses in Australia are now funded to facilitate chronic condition management, including self-management support. Chronic disease management requires an established rapport, support and proactivity between general practitioners, patients and the practice nurses. To achieve this, training in shared decision making is needed. e-Learning supports delivery and achievement of such policy outcomes, service improvements and skill development. However, e-learning effectiveness for health care professionals’ is determined by several organisational, economic, pedagogical and individual factors, with positive e-learning experience linked closely to various supports. This paper reinforces previous studies showing nurses’ expanding role across general practice teams and reports on some of the challenges of e-learning. Merely providing practice nurses with necessary information via web-based learning systems does not ensure successful learning or progress toward improving health outcomes for patients.

scholarly journals PEMANFAATAN TEKNOLOGI INFORMASI DAN KOMUNIKASI PADA SEKOLAH MENENGAH PERTAMA NEGERI AKREDITASI A DI PROVINSI JAWA TIMUR

2017 ◽  
Vol 5 (1) ◽  
pp. 21
Author(s):  
Herry Widyastono
Keyword(s):  
High School ◽  
Data Base ◽  
Junior High School ◽  
Digital Libraries ◽  
School Management ◽  
Web Based ◽  
Web Based Learning ◽  
E Learning ◽  
Application Data ◽  
Multi Media

AbstractThe aim of this study is to obtain information on the use of Information and Communication Technology (ICT) in teaching and school management in SMP Negeri Accreditation of A in East Java province. The study was conducted in August-September 2016 on SMP Negeri 4 Accreditation of A in East Java province. The study concluded that the ICT has been used in teaching and school management in Junior High School Accreditation in East Java province. The use of ICT in the form of ICT-based learning, blended e-learning, Web-based learning/Blog, assessment based on ICT, ICT labs, class multi-media, digital libraries, and application data base school. The study concluded that the ICT has been used in teaching and school management in Junior High School Accreditation in East Java province. The use of ICT in the form of ICT-based learning, blended e-learning, Web-based learning/Blog, assessment based on ICT, ICT labs, class multi-media, digital libraries, and application data base school. The study recommends that the use of ICT optimized, particularly in terms of: learning in the form of mailing lists/discussion groups, distance learning, virtual world class, assessment of learning outcomes that can be accessed by students and parents, as well as documents and digital library services. Abstrak Penelitian ini bertujuan untuk mendapatkan informasi mengenai pemanfaatan teknologi informasi dan komunikasi (TIK) dalam pembelajaran dan manajemen sekolah di SMP negeri akreditasi A di Provinsi Jawa Timur. Penelitian dilakukan pada bulan Agustus-September 2016, pada empat SMP negeri akreditasi A di Provinsi Jawa Timur. Hasil penelitian menyimpulkan bahwa TIK telah dimanfaatkan dalam pembelajaran dan manajemen sekolah pada sekolah menengah pertama negeri akreditasi A di  Provinsi Jawa Timur. Pemanfaatan TIK berupa pembelajaran berbasis TIK, blended e-learning, pembelajaran berbasis Web/ Blog, penilaian berbasis TIK, laboratorium TIK, kelas multi media, perpustakaan digital, dan aplikasi data base sekolah. Penelitian merekomendasikan agar pemanfaatan TIK dioptimalkan, terutama dalam hal: pembelajaran dalam bentuk mailing list/grup diskusi, pembelajaran langsung jarak jauh, kelas dunia maya, penilaian hasil belajar yang dapat diakses oleh peserta didik dan orang tua, serta dokumen dan layanan perpustakaan digital.

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