Establishment of Problem E-learning Behavior Scale

2020 ◽  
pp. 394-403
Author(s):  
Junyi Zheng ◽  
Wenhui Peng
Keyword(s):  
Learning Behavior ◽  
E Learning ◽  
Behavior Scale

Web 3.0 and E-Learning

2015 ◽  
pp. 101-123 ◽  
Author(s):  
Amit Chauhan
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Learning Goals ◽  
Learning Behavior ◽  
Defining Moment ◽  
Web 3.0 ◽  
E Learning ◽  
Future Of Education ◽  
The Web

The annals of the Web have been a defining moment in the evolution of education and e-Learning. The evolution of Web 1.0 almost three decades ago has been a precursor to Web 3.0 that has reshaped education and learning today. The evolution to Web 3.0 has been synonymous with “Semantic Web” or “Artificial Intelligence” (AI). AI makes it possible to deliver custom content to the learners based on their learning behavior and preferences. As a result of these developments, the learners have been empowered and have at their disposal a range of Web tools and technology powered by AI to pursue and accomplish their learning goals. This chapter traces the evolution and impact of Web 3.0 and AI on e-Learning and its role in empowering the learner and transforming the future of education and learning. This chapter will be of interest to educators and learners in exploring techniques that improve the quality of education and learning outcomes.

Web 3.0 and E-Learning

2016 ◽  
pp. 41-62
Author(s):  
Amit Chauhan
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Learning Goals ◽  
Learning Behavior ◽  
Defining Moment ◽  
Web 3.0 ◽  
E Learning ◽  
Future Of Education ◽  
The Web

The annals of the Web have been a defining moment in the evolution of education and e-Learning. The evolution of Web 1.0 almost three decades ago has been a precursor to Web 3.0 that has reshaped education and learning today. The evolution to Web 3.0 has been synonymous with “Semantic Web” or “Artificial Intelligence” (AI). AI makes it possible to deliver custom content to the learners based on their learning behavior and preferences. As a result of these developments, the learners have been empowered and have at their disposal a range of Web tools and technology powered by AI to pursue and accomplish their learning goals. This chapter traces the evolution and impact of Web 3.0 and AI on e-Learning and its role in empowering the learner and transforming the future of education and learning. This chapter will be of interest to educators and learners in exploring techniques that improve the quality of education and learning outcomes.

Mining Students' Learning Behavior in Moodle System

2014 ◽  
Vol 7 (4) ◽  
pp. 12-26 ◽  
Author(s):  
K. Touya ◽  
Mohamed Fakir
Keyword(s):  
Data Mining ◽  
Web Application ◽  
Educational Data Mining ◽  
Educational Environment ◽  
Learning Behavior ◽  
System Database ◽  
Hidden Knowledge ◽  
E Learning ◽  
Mining Algorithms ◽  
Interesting Area

In the last few years, Educational Data Mining has become an interesting area exploited to discover and extract hidden knowledge of students from educational environment data. During the establishment of this work an attempt was made to manage the extracted information using mining techniques. These methods took place in order to get groups of students with similar characteristics. The application of classification, clustering and association rules mining algorithms on the data stored on the e-learning (Moodle system) database allowed to extract knowledges that help to understand students' behaviors and patterns. Additionally, the development of a Web application for the educators is a tool to monitor their students learning behavior by monitoring the number of assignments taken, the number of quizzes taken, the number of forum post and read by students, etc. The knowledge obtained can help the instructors to make decision about their students' interacting with the courses activities in Moodle system, and to create an efficient educational environment. In this research, a Data Mining tool called RapidMiner was used for mining the data from the Moodle system database, and a web application written in PHP was established to aid teachers with statistics.

scholarly journals Online Behavior Analysis-Based Student Profile for Intelligent E-Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Kun Liang ◽  
Yiying Zhang ◽  
Yeshen He ◽  
Yilin Zhou ◽  
Wei Tan ◽  
...  
Keyword(s):  
Online Education ◽  
Jaccard Coefficient ◽  
Learning Behavior ◽  
Online Behavior ◽  
Online Learners ◽  
Student Profile ◽  
Learning Attitude ◽  
Learning Platform ◽  
E Learning ◽  
Behavior Characteristics

With the development of mobile platform, such as smart cellphone and pad, the E-Learning model has been rapidly developed. However, due to the low completion rate for E-Learning platform, it is very necessary to analyze the behavior characteristics of online learners to intelligently adjust online education strategy and enhance the quality of learning. In this paper, we analyzed the relation indicators of E-Learning to build the student profile and gave countermeasures. Adopting the similarity computation and Jaccard coefficient algorithm, we designed a system model to clean and dig into the educational data and also the students’ learning attitude and the duration of learning behavior to establish student profile. According to the E-Learning resources and learner behaviors, we also present the intelligent guide model to guide both E-Learning platform and learners to improve learning things. The study on student profile can help the E-Learning platform to meet and guide the students’ learning behavior deeply and also to provide personalized learning situation and promote the optimization of the E-Learning.

scholarly journals Influence of Attitudinal belief on e-learning behavior Among Academics in State Universities of Sri Lanka

2019 ◽  
Vol 8 (3S3) ◽  
pp. 8-14
Keyword(s):  
Higher Education ◽  
Sri Lanka ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Behavioural Intention ◽  
Model Fit ◽  
Perceived Ease Of Use ◽  
Learning Behavior ◽  
Actual Behaviour ◽  
E Learning

The objective of this paper is to investigate the influence of attitudinal belief of “Decomposed Theory of Planned Behavior” (DTPB) on actual e-learning behavior among university academics in Sri Lanka. The study applied a quantitative method by using questionnaire to collect data among university academics. Explorative and Confirmatory Factor analysis was used to examine the reliability and validity. And this study carried out Structural Equation Modeling to assess model fit and through path analysis, the association between variables was investigated. The study resulted that perceived ease of use, perceived usefulness, compatibility, attitude, behavioural intentions and actual behaviour met the indices for model fit. The findings showed perceived usefulness and perceived ease of use associated with attitude while compatibility is not. Attitude significantly and positively associated with behavioural intention. While behavioural intention positively associated with the actual usage this mediates the relationship between attitude and actual behaviour. The e-learning usage of academicians will help to enhance the quality of higher education system in Sri Lanka as well as to magnify the number of university intake as the country seeks to achieve its vision to be “the knowledge hub in the region and a leader in higher education in Asia by 2020”. Though the e-learning adoption studies are growing trend of education and system researchers as evidenced by extant literature, studies on attitude and intention of academicians especially in an emerging economy like Sri Lanka is still lacking. This study attempts to establish the attitudinal factors that contributes actual e-learning behavior of academicians.

scholarly journals An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches

2020 ◽  
Vol 10 (19) ◽  
pp. 6804
Author(s):  
Àngela Nebot ◽  
Francisco Mugica ◽  
Félix Castro
Keyword(s):  
Latin American ◽  
Inductive Reasoning ◽  
Educational Communication ◽  
Evaluation Process ◽  
Learning Behavior ◽  
Linguistic Rules ◽  
Grouping Students ◽  
E Learning ◽  
Student’S Performance ◽  
Rules Extraction

In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the linguistic rules extraction algorithm (LR-FIR), which extracts comprehensible and consistent sets of rules describing students’ learning behavior, and (ii) the causal relevance approach (CR-FIR), which allows to reduce uncertainty during a student’s performance prediction stage, and provides a relative weighting of the features involved in the evaluation process. In addition, the presented toolbox enables, in an incremental way, detecting and grouping students with respect to their learning behavior, with the main goal to timely detect failing students, and properly provide them with suitable and actionable feedback. The proposed toolbox has been applied to two different datasets gathered from two courses at the Latin American Institute for Educational Communication virtual campus. The introductory and didactic planning courses were analyzed using the proposed toolbox. The results obtained by the functionalities offered by the platform allow teachers to make decisions and carry out improvement actions in the current course, i.e., to monitor specific student clusters, to analyze possible changes in the different evaluable activities, or to reduce (to some extent) teacher workload.

Preschool Learning Behavior Scale--Greek Version

2014 ◽  
Author(s):  
Efthymia Penderi ◽  
Konstantinos Petrogiannis ◽  
Paul McDermott
Keyword(s):  
Learning Behavior ◽  
Greek Version ◽  
Behavior Scale
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