Recommend Strategies for E-Learning 2.0 Communities

2014 ◽  
Vol 568-570 ◽  
pp. 1547-1550
Author(s):  
Bing Wu ◽  
Chen Yan Zhang
Keyword(s):  
Learning Communities ◽  
Knowledge Sharing ◽  
Recommendation System ◽  
Current Knowledge ◽  
Research Direction ◽  
Personalized Recommendation ◽  
Future Research ◽  
Rapid Progress ◽  
General Recommendation ◽  
E Learning

With the rapid progress of Web 2.0, E-Learning has evolved into E-Learning 2.0, which has been highlighted as an effective method for interactive learning. To improve the efficiency of learning, many researches focused on the personalized recommendation for knowledge sharing. However, these researches proposed the general recommendation system without considering the current knowledge sharing status in E-learning 2.0 communities. Therefore, the purpose of this study is to proposed recommend strategies according to characteristics of E-Learning communities based on social network analysis. Firstly, knowledge activity nodes in E-Learning communities are identified into four types. Secondly, based on four node types, E-Learning communities are classified into four corresponding types. Then, different recommend strategies are provided according to the types of E-Learning communities. Finally, conclusion and future research direction are discussed in the end.

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.

Learning Path Recommendation System for Programming Education Based on Neural Networks

2020 ◽  
Vol 18 (1) ◽  
pp. 36-64 ◽  
Author(s):  
Tomohiro Saito ◽  
Yutaka Watanobe
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recommendation System ◽  
Current Knowledge ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Programming Education ◽  
Learning Path ◽  
E Learning ◽  
Trained Teachers

Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. This article proposes a learning path recommendation system that applies a recurrent neural network to a learner's ability chart, which displays the learner's scores. In brief, a learning path is constructed from a learner's submission history using a trial-and-error process, and the learner's ability chart is used as an indicator of their current knowledge. An approach for constructing a learning path recommendation system using ability charts and its implementation based on a sequential prediction model and a recurrent neural network, are presented. Experimental evaluation is conducted with data from an e-learning system.

The Study of User Model of Personalized Recommendation System Based on Linked Course Data

2014 ◽  
Vol 519-520 ◽  
pp. 1609-1612
Author(s):  
Ji Yan Wu ◽  
Chan Le Wu
Keyword(s):  
Linked Data ◽  
Recommendation System ◽  
User Model ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learning Path ◽  
E Learning ◽  
Linked Courses ◽  
Efficient Learning ◽  
Massive Information

The massive information on Internet makes users obtain information become in efficient.Similarly,the users in the field of e-learning face with the problems that learning resources are inefficient and learning paths unreasonable,etc .Linked Courses data is a standard of W3C proposed will be organized the courses in the national excellent courses into a linked data database. On the basis the author design a user model to meet the user's learning requirements, which will be the most suitable learning Resources recommended to the user. Finally, the author validate the model with experimental ,the application shows that the model can indicate the user's learning requirements,and rational and efficient learning path is recommended.

scholarly journals Salivary Metabolomics: From Diagnostic Biomarker Discovery to Investigating Biological Function

Metabolites ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 47 ◽  
Author(s):  
Alexander Gardner ◽  
Guy Carpenter ◽  
Po-Wah So
Keyword(s):  
Biomarker Discovery ◽  
Current Knowledge ◽  
Metabolite Profile ◽  
Research Direction ◽  
Diagnostic Value ◽  
Taste Perception ◽  
Future Research ◽  
Diagnostic Biomarker ◽  
Literature Base

Metabolomic profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, metabolomic analysis of saliva, the most readily-available human biofluid, has lagged. This review explores the history of saliva-based metabolomics and summarizes current knowledge of salivary metabolomics. Current applications of salivary metabolomics have largely focused on diagnostic biomarker discovery and the diagnostic value of the current literature base is explored. There is also a small, albeit promising, literature base concerning the use of salivary metabolomics in monitoring athletic performance. Functional roles of salivary metabolites remain largely unexplored. Areas of emerging knowledge include the role of oral host–microbiome interactions in shaping the salivary metabolite profile and the potential roles of salivary metabolites in oral physiology, e.g., in taste perception. Discussion of future research directions describes the need to begin acquiring a greater knowledge of the function of salivary metabolites, a current research direction in the field of the gut metabolome. The role of saliva as an easily obtainable, information-rich fluid that could complement other gastrointestinal fluids in the exploration of the gut metabolome is emphasized.

Research on the Key Technology of Personalized Learning for Peasant Based on Wireless Network

2014 ◽  
Vol 568-570 ◽  
pp. 1577-1580 ◽  
Author(s):  
Ji Chun Zhao ◽  
Shi Hong Liu ◽  
Jian Xin Guo ◽  
Zhu Feng Qiao
Keyword(s):  
Wireless Network ◽  
Communication Networks ◽  
Recommendation System ◽  
Rapid Development ◽  
Personalized Learning ◽  
Research Literature ◽  
Research Progress ◽  
Personalized Recommendation ◽  
Future Research ◽  
Key Technology

With the rapid development of wireless communication networks and mobile terminal technology, it has an important significance for improving the cultural quality of farmers and the rural information level that researching the key technology of personalized learning for farmers based on wireless network. The research progress of personalized service was introduced based on reading a large number of domestic and foreign related research literature. The personalized recommendation technology, the recommendation algorithm and measure method was given in the research of personalized technology. The personalized recommendation system was designed and tested, the result could need the users need. Finally the problems and future research directions in the personalized system is summarized.

scholarly journals Understanding the drivers for the development of design rules for the synthesis of cylindrical flexures

2012 ◽  
Vol 3 (1) ◽  
pp. 25-32 ◽  
Author(s):  
M. J. Telleria ◽  
M. L. Culpepper
Keyword(s):  
Compliant Mechanisms ◽  
Current Knowledge ◽  
Research Direction ◽  
Future Research ◽  
Radius Of Curvature ◽  
Finite Radius ◽  
Element Analysis ◽  
Design Rules ◽  
Straight Beam ◽  
Current Design

Abstract. Cylindrical flexures (CFs), defined as flexures with only one finite radius of curvature loaded normal to the plane of curvature, present an interesting research direction in compliant mechanisms. CFs are constructed out of a cylindrical stock which leads to geometry, manufacturability, and compatibility advantages. Synthesis rules must be developed to design these new systems effectively. Current knowledge in flexure design pertains to straight-beam flexures or curved flexures loaded along the plane of curvature. CFs present a challenge because their mechanics differ from those of straight beams, and although their modelling has been researched thoroughly it has yet to be distilled into element and system creation rules. This paper uses models and finite element analysis to demonstrate that current design rules for straight-beam flexures are insufficient and inadequate for the design of CF systems. The presented discussion will show that CFs differ both at the element and systems levels, and therefore future research will focus on developing the three components of the building block approach: (i) reworking of element mechanics models to reveal the parameters which cause the kinematics of the curved beam to differ from those of the straight beam, (ii) development of a visual stiffness representation, and (iii) formation of system creation rules.

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