How Safe Is Your Identity?

2011 ◽  
pp. 84-106
Keyword(s):  
Data Mining ◽  
Private Information ◽  
Learning Experience ◽  
Security And Privacy ◽  
Global Community ◽  
Exchange Information ◽  
E Learning ◽  
Learning Platforms ◽  
Digital Fingerprints

Digitally mediated communications offer ease and flexibility to exchange information across a networked global community. All interactions could potentially be captured however, using different invasive technologies for spoofing, phishing, data mining, profiling, and tracking an individual’s digital fingerprints and footprints. Ultimately, the exposure of private information not only compromises an individual’s identity, security, and privacy, but also the security of organizations and governments. Nonetheless, these same technologies present unique opportunities for cyber educators to track and monitor, within e-learning platforms, the activities of students with the goal of using this data to improve the learning experience for the benefit of all learners.

How Safe Is Your Identity?

Cyber Crime ◽  
2013 ◽  
pp. 52-68
Author(s):  
Bobbe Baggio ◽  
Yoany Beldarrain
Keyword(s):  
Data Mining ◽  
Private Information ◽  
Learning Experience ◽  
Security And Privacy ◽  
Global Community ◽  
Exchange Information ◽  
E Learning ◽  
Learning Platforms ◽  
Digital Fingerprints

Digitally mediated communications offer ease and flexibility to exchange information across a networked global community. All interactions could potentially be captured however, using different invasive technologies for spoofing, phishing, data mining, profiling, and tracking an individual’s digital fingerprints and footprints. Ultimately, the exposure of private information not only compromises an individual’s identity, security, and privacy, but also the security of organizations and governments. Nonetheless, these same technologies present unique opportunities for cyber educators to track and monitor, within e-learning platforms, the activities of students with the goal of using this data to improve the learning experience for the benefit of all learners.

scholarly journals Design and Implementation of a Gesture-Aided E-Learning Platform

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8042
Author(s):  
Wolfgang Kremser ◽  
Stefan Kranzinger ◽  
Severin Bernhart
Keyword(s):  
Learning Outcomes ◽  
Field Test ◽  
Learning Experience ◽  
Learning Content ◽  
Learning Platform ◽  
System Usability Scale ◽  
E Learning ◽  
Learning Platforms ◽  
Reference Implementation ◽  
Generic System

In gesture-aided learning (GAL), learners perform specific body gestures while rehearsing the associated learning content. Although this form of embodiment has been shown to benefit learning outcomes, it has not yet been incorporated into e-learning. This work presents a generic system design for an online GAL platform. It is comprised of five modules for planning, administering, and monitoring remote GAL lessons. To validate the proposed design, a reference implementation for word learning was demonstrated in a field test. 19 participants independently took a predefined online GAL lesson and rated their experience on the System Usability Scale and a supplemental questionnaire. To monitor the correct gesture execution, the reference implementation recorded the participants’ webcam feeds and uploaded them to the instructor for review. The results from the field test show that the reference implementation is capable of delivering an e-learning experience with GAL elements. Designers of e-learning platforms may use the proposed design to include GAL in their applications. Beyond its original purpose in education, the platform is also useful to collect and annotate gesture data.

How e-Learning Experience Enhances the Social Presence in Community of Practice

2012 ◽  
pp. 75-120 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Radu Mogos ◽  
Maria-Iuliana Dascalu
Keyword(s):  
Data Mining ◽  
Project Management ◽  
Community Of Practice ◽  
Social Presence ◽  
Learning Experience ◽  
Main Research ◽  
The Social ◽  
Analytical Perspective ◽  
Management Area ◽  
E Learning

The chapter presents a study made in order to find out how the e-learning experience enhances the social presence in the community of practice. The study was carried out for the online master degree programme in project management, delivered by the Academy of Economic Studies, Bucharest. The main research method was a survey and the research instrument was a questionnaire. Statistics and data mining were applied. Statistics was applied to check hypothesis and quantify the correlation significance. Due to the large number of the variables and the indirect relationships, the analysis paths become very complex and it would be extremely difficult to manage the analysis workflow. So, the data mining approach was chosen. As a theoretical framework and analytical perspective for this research, Wenger’s theories of learning in Community of practice (CoP), and the social presence model of Garisson et al., are applied. The study revealed that the characteristics of the online social presence in learning environments enhanced the students’ interest for CoPs. Another finding of this study is that for project management area there is not a significant correlation between the learning domain and that of the CoPs chosen to get involved. The reason is that most of the project personnel hold a first degree in an area other than project management.

scholarly journals Promoting Students’ Global Perspectives Through a Gamified e-Learning Platform

2021 ◽  
Vol 6 ◽  
Author(s):  
Frankie Y. W. Leung ◽  
Martin Lau ◽  
Kelvin Wan ◽  
Lisa Law ◽  
Theresa Kwong ◽  
...  
Keyword(s):  
Learning Experience ◽  
Global Perspectives ◽  
Global Issues ◽  
Learning Platform ◽  
Before And After ◽  
E Learning ◽  
Learning Platforms ◽  
Cognitive Knowledge ◽  
Survey Responses ◽  
Development Of Students

With the rapid growth of internationalization in tertiary institutions worldwide, the development of students’ global perspectives has attracted the attention of many universities. However, this development is a challenging one due to the complicated nature of global issues and their incompatibility with traditional subject-specific boundaries of classroom teaching. Through two eTournaments organized on a proprietary gamified e-learning platform named “PaGamO,” this study examined participating students’ learning experience and their change of global perspectives due to their participation in the eTournaments. Data were collected before and after the two eTournaments, and 217 survey responses were considered to be valid and were further analyzed. The findings showed that participating students achieved the satisfaction level of enjoyment (M = 3.62) and their awareness of the United Nations Sustainable Development Goals (SDGs) (M = 3.96) had been improved. In addition, the findings also revealed that 1) students enjoyed and perceived a better understanding of the SDGs in terms of perceptual dimensions like value-oriented and partnership-oriented, rather than the global issues about substantial threats or environmental issues; 2) the “intrapersonal effect” of students had been significantly reduced after the eTournaments; 3) positive significant correlations were found between the level of enjoyment and frequency of question-attempt in relation to the change of cognitive knowledge and interpersonal social interaction. The findings of this study offered some possible insights into students’ gameplay experience concerning dimensions of global perspectives and also support the findings of prior research on how gamified e-learning platforms could contribute to the development of students’ global perspectives.

scholarly journals Student Engagement Level in e-Learning Environment: Clustering Using K-means

2021 ◽  
Author(s):  
Abdallah Moubayed ◽  
Mohammadnoor Injadat ◽  
Abdallah Shami ◽  
Hanan Lutfiyya
Keyword(s):  
Learning Experience ◽  
Machine Learning Techniques ◽  
American University ◽  
Performance Metric ◽  
Learning Techniques ◽  
Event Log ◽  
E Learning ◽  
Level Model ◽  
Learning Platforms ◽  
Undergraduate Science

E-learning platforms and processes face several challenges, among which is the idea of personalizing the e-learning experience and to keep students motivated and engaged. This work is part of a larger study that aims to tackle these two challenges using a variety of machine learning techniques. To that end, this paper proposes the use of k-means algorithm to cluster students based on 12 engagement metrics divided into two categories: interaction-related and effort-related. Quantitative analysis is performed to identify the students that are not engaged who may need help. Three different clustering models are considered: two-level, three-level, and five-level. The considered dataset is the students’ event log of a second-year undergraduate Science course from a North American university that was given in a blended format. The event log is transformed using MATLAB to generate a new dataset representing the considered metrics. Experimental results’ analysis shows that among the considered interaction-related and effort-related metrics, the number of logins and the average duration to submit assignments are the most representative of the students’ engagement level. Furthermore, using the silhouette coefficient as a performance metric, it is shown that the two-level model offers the best performance in terms of cluster separation. However, the three-level model has a similar performance while better identifying students with low engagement levels.

A Multi-Threshold Ant Colony System-based Sanitization Model in Shared Medical Environments

2021 ◽  
Vol 21 (2) ◽  
pp. 1-26
Author(s):  
Jimmy Ming-Tai Wu ◽  
Gautam Srivastava ◽  
Jerry Chun-Wei Lin ◽  
Qian Teng
Keyword(s):  
Data Mining ◽  
Private Information ◽  
Ant Colony ◽  
Security And Privacy ◽  
Legal Issue ◽  
Ant Colony System ◽  
Sensitive Information ◽  
Data Mining Algorithm ◽  
Useful Knowledge ◽  
Global Pandemic

During the past several years, revealing some useful knowledge or protecting individual’s private information in an identifiable health dataset (i.e., within an Electronic Health Record) has become a tradeoff issue. Especially in this era of a global pandemic, security and privacy are often overlooked in lieu of usability. Privacy preserving data mining (PPDM) is definitely going to be have an important role to resolve this problem. Nevertheless, the scenario of mining information in an identifiable health dataset holds high complexity compared to traditional PPDM problems. Leaking individual private information in an identifiable health dataset has becomes a serious legal issue. In this article, the proposed Ant Colony System to Data Mining algorithm takes the multi-threshold constraint to secure and sanitize patents’ records in different lengths, which is applicable in a real medical situation. The experimental results show the proposed algorithm not only has the ability to hide all sensitive information but also to keep useful knowledge for mining usage in the sanitized database.

scholarly journals Adaptive Upgradation of Personalized E-Learning Portal using Data Mining

2020 ◽  
Vol 10 (1) ◽  
pp. 224-227
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Clustering Algorithm ◽  
Learning Experience ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Development Phase ◽  
Data Mining Techniques ◽  
E Learning ◽  
Learning Portal

Implementation of data mining techniques in elearning is a trending research area, due to the increasing popularity of e-learning systems. E-learning systems provide increased portability, convenience and better learning experience. In this research, we proposed two novel schemes for upgrading the e-learning portals based on the learner’s data for improving the quality of e-learning. The first scheme is Learner History-based E-learning Portal Up-gradation (LHEPU). In this scheme, the web log history data of the learner is acquired. Using this data, various useful attributes are extracted. Using these attributes, the data mining techniques like pattern analysis, machine learning, frequency distribution, correlation analysis, sequential mining and machine learning techniques are applied. The results of these data mining techniques are used for the improvement of e-learning portal like topic recommendations, learner grade prediction, etc. The second scheme is Learner Assessment-based E-Learning Portal Up-gradation (LAEPU). This scheme is implemented in two phases, namely, the development phase and the deployment phase. In the development phase, the learner is made to attend a short pretraining program. Followed by the program, the learner must attend an assessment test. Based on the learner’s performance in this test, the learners are clustered into different groups using clustering algorithm such as K-Means clustering or DBSCAN algorithms. The portal is designed to support each group of learners. In the deployment phase, a new learner is mapped to a particular group based on his/her performance in the pretraining program.

scholarly journals Social, Personalized Lifelong Learning

2011 ◽  
pp. 90-125 ◽  
Author(s):  
Alexandra Cristea ◽  
Fawaz Ghali ◽  
Mike Joy
Keyword(s):  
Web 2.0 ◽  
Lifelong Learning ◽  
Theoretical Basis ◽  
Learning Experience ◽  
Group Interaction ◽  
Social Aspects ◽  
Learning Content ◽  
E Learning ◽  
Learning Platforms ◽  
The Web

This chapter discusses a challenging hot topic in the area of Web 2.0 technologies for Lifelong Learning: how to merge such technologies with research on personalizationand adaptive e-learning, in order to provide the best learning experience, customized for a specific learner or group of learners, in the context of communities of learning and authoring. The authors of this chapter discuss the most well-known frameworks and then show how an existing framework for personalized e-learning can be extended, in order to allow the specification of the complex new relationships that social aspects bring to e-learning platforms. This is not just about creating learning content, but also about developing new ways of learning. For instance, adaptation does not refer to an individual only, but also to groups, which can be groups of learners, designers or course authors. Their interests, objectives, capabilities, and backgrounds need to be catered to, as well as their group interaction. Furthermore, the boundaries between authors and learners become less distinct in the Web 2.0 context. This chapter presents the theoretical basis for this framework extension, as well as its implementation and evaluation, and concludes by discussing the results and drawing conclusions and interesting pointers for further research.

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

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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