Collaborative Mobile Learning

2015 ◽  
pp. 73-87
Author(s):  
Nor Fadzleen Sa'don ◽  
Noorminshah A. Iahad
Keyword(s):  
Data Mining ◽  
Educational Technology ◽  
Social Networking ◽  
Mobile Learning ◽  
Personalized Learning ◽  
Research Trends ◽  
Assessment Tools ◽  
Main Research ◽  
Art Research ◽  
Wireless Mobile

Mobile learning (ML) fosters engaging personalized learning where students can optimize their understanding and learning gratification via wireless mobile devices. Although there are studies conducted on the research trends on ML, not much studies were conducted on the cutting-edge researches pertaining to Collaborative ML. Collaborative ML is significant because it promotes active learning and cooperative skills. This paper aims to analyze the state-of-the-art research conducted in the span of five years for identifying the trends and focuses in Collaborative ML. The systematic literature review is conducted based on Knowledge Discovery Database model capitalizing data mining as the main research methodology. Findings were based on top ten Impact Factor Journals on Educational Technology indexed in the ISI Web of Knowledge. The SLR discovered ten main areas that were discussed pertaining to Collaborative ML which are Motivation, Students' Acceptance, Pedagogy, Assessment, Tools, Social Networking, Gaming, Knowledge Sharing, Special Needs and Communications.

Collaborative Mobile Learning

2016 ◽  
pp. 676-690 ◽  
Author(s):  
Nor Fadzleen Sa'don ◽  
Noorminshah A. Iahad
Keyword(s):  
Data Mining ◽  
Educational Technology ◽  
Social Networking ◽  
Mobile Learning ◽  
Personalized Learning ◽  
Research Trends ◽  
Assessment Tools ◽  
Main Research ◽  
Art Research ◽  
Wireless Mobile

Mobile learning (ML) fosters engaging personalized learning where students can optimize their understanding and learning gratification via wireless mobile devices. Although there are studies conducted on the research trends on ML, not much studies were conducted on the cutting-edge researches pertaining to Collaborative ML. Collaborative ML is significant because it promotes active learning and cooperative skills. This paper aims to analyze the state-of-the-art research conducted in the span of five years for identifying the trends and focuses in Collaborative ML. The systematic literature review is conducted based on Knowledge Discovery Database model capitalizing data mining as the main research methodology. Findings were based on top ten Impact Factor Journals on Educational Technology indexed in the ISI Web of Knowledge. The SLR discovered ten main areas that were discussed pertaining to Collaborative ML which are Motivation, Students' Acceptance, Pedagogy, Assessment, Tools, Social Networking, Gaming, Knowledge Sharing, Special Needs and Communications.

scholarly journals Research Trends in the Use of Mobile Learning in Mathematics

2015 ◽  
Vol 7 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Helen Crompton ◽  
Diane Burke
Keyword(s):  
Learning Outcomes ◽  
Mobile Learning ◽  
Mobile Phones ◽  
Mathematical Concept ◽  
Research Trends ◽  
Exponential Rate ◽  
Main Research ◽  
School Settings ◽  
New Findings ◽  
Year 2000

The use of mobile learning in education is growing at an exponential rate. To best understand how mobile learning is being used, it is crucial to gain a collective understanding of the research that has taken place. This research was a systematic review of 36 studies in mobile learning in mathematics from the year 2000 onward. Eight new findings emerged: (1) The primary purpose of most studies was to focus on evaluating mobile learning. (2) Case studies and experimental design were the main research methods. (3) Most studies report positive learning outcomes; (4) Mobile phones were the mobile device used most often. (5) Elementary school settings were the most common research context. (6) The majority of researchers did not identify a specific mathematical concept being studied. (7) The majority of the studies took place in formal educational contexts; and (8) research on mobile learning in mathematics is geographically diverse.

scholarly journals Research Trends in the Use of Mobile Learning in Mathematics

2016 ◽  
pp. 2090-2104 ◽  
Author(s):  
Helen Crompton ◽  
Diane Burke
Keyword(s):  
Systematic Review ◽  
Mobile Learning ◽  
Mobile Phones ◽  
Mathematical Concept ◽  
Research Trends ◽  
Exponential Rate ◽  
Main Research ◽  
School Settings ◽  
New Findings ◽  
Year 2000

The use of mobile learning in education is growing at an exponential rate. To best understand how mobile learning is being used, it is crucial to gain a collective understanding of the research that has taken place. This research was a systematic review of 36 studies in mobile learning in mathematics from the year 2000 onward. Eight new findings emerged: (1) The primary purpose of most studies was to focus on evaluating mobile learning. (2) Case studies and experimental design were the main research methods. (3) Most studies report positive learning outcomes; (4) Mobile phones were the mobile device used most often. (5) Elementary school settings were the most common research context. (6) The majority of researchers did not identify a specific mathematical concept being studied. (7) The majority of the studies took place in formal educational contexts; and (8) research on mobile learning in mathematics is geographically diverse.

Textual analysis and visualization of research trends in data mining for electronic health records

2017 ◽  
Vol 6 (4) ◽  
pp. 389-400 ◽  
Author(s):  
Jingfeng Chen ◽  
Wei Wei ◽  
Chonghui Guo ◽  
Lin Tang ◽  
Leilei Sun
Keyword(s):  
Data Mining ◽  
Electronic Health Records ◽  
Textual Analysis ◽  
Research Trends ◽  
Health Records ◽  
Electronic Health

scholarly journals Influence of Cloud Based Mobile Learning Applications on User Experiences: A Review Study in the Context of Jordan

2017 ◽  
Vol 11 (4) ◽  
pp. 202 ◽  
Author(s):  
Osama Harfoushi
Keyword(s):  
Cloud Computing ◽  
Mobile Learning ◽  
Mobile Applications ◽  
Educational Institution ◽  
Research Area ◽  
Traditional Learning ◽  
Learning Methods ◽  
User Experiences ◽  
Main Research ◽  
Innovative Tool

Beside the increasing trend of cloud computing and mobile applications, the use of cloud based mobile learning applications is also mounting. Almost every e-commerce service provider offers cloud based mobile learning applications so that they can target more visitors and ultimately increase their sales. The usability of cloud based mobile applications is not only grounded in e-commerce platforms but it also ease out mobile learning processes. Most of the educational institutes are now offering cloud based mobile applications so their students can navigate to their knowledge portal more easily and download relevant material or submit assignments respectively. The main research area of this article is to explore how cloud based mobile learning applications can be utilized more effectively and what impact they imply on its users. Also, this research compares the mobile learning methods versus traditional learning methods. The study is evidence from Jordan and the major part of the research will be carried out through surveying literature, reports, content, and national and international databases in order to critically discuss the interactions between clouds based mobile learning application and user experiences. Published researches, published reports, books and articles has been included in the review.  Review of literature shows that mobile cloud computing is rising in Jordan and have significant impact on mobile learning of Jordanian students. Further, M-learning indeed an innovative tool for learning and it helps the users in many ways. In traditional learning, students used to spend their money on books and other written content. Findings of this study are helpful for the educational institution so they will come to know about user experiences of utilizing these cloud based mobile applications.

A comprehensive analysis of the 21st century's research trends in English Mobile Learning: a bibliographic review of the literature

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rouhollah Khodabandelou ◽  
Masood Fathi ◽  
Mohammad Amerian ◽  
Mohammad Reza Fakhraie
Keyword(s):  
Bibliometric Analysis ◽  
Mobile Learning ◽  
Research Trends ◽  
Current Status ◽  
Future Research ◽  
Review Of Literature ◽  
Content Type ◽  
The Past ◽  
Learning Research ◽  
Points Of View

PurposeThis study examines the importance of English Mobile Learning research as a foundation for lifelong and sustainable education from different points of view, including those of technology innovation experts, psychologists and educators. It aims to explore the current status and relevant research trends through the application of bibliometric mapping and bibliometric analysis.Design/methodology/approachFor this study, all Web of Science records (in total 5,343) from 2000 to 2020 in the field of English Mobile Learning were analyzed using the VOSviewer and CiteSpace software tools. The WoS built-in functions, including “Refine” and “Analyze,” were employed to perform the bibliometric analysis. The study further analyzed a sample of the five most-cited articles to identify the previous studies with the highest quality or impact.FindingsThe results showed that research in English Mobile Learning is growing quickly and steadily with a noticeable emphasis on various device-based technologies and applications. The study also discusses the key implications for research institutions, education policymakers and academicians, and identifies the most prominent avenues for future research on English Mobile Learning. Moreover, the results shared in this review highlight the most important and emerging areas of research in the field.Originality/valueThis article is the most recent bibliographic review of literature that particularly addresses the English Mobile Learning research during the past two decades.

Host–Based Intrusion Detection Systems

2014 ◽  
pp. 184-213
Author(s):  
Vít Bukač ◽  
Vashek Matyáš
Keyword(s):  
Intrusion Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Research Trends ◽  
The State ◽  
Intrusion Detection Systems ◽  
Future Directions ◽  
Detection Systems ◽  
Research Findings ◽  
Art Research

In this chapter, the reader explores both the founding ideas and the state-of-the-art research on host-based intrusion detection systems. HIDSs are categorized by their intrusion detection method. Each category is thoroughly investigated, and its limitations and benefits are discussed. Seminal research findings and ideas are presented and supplied with comments. Separate sections are devoted to the protection against tampering and to the HIDS evasion techniques that are employed by attackers. Existing research trends are highlighted, and possible future directions are suggested.

Social Interactions and Automated Detection Tools in Cyberbullying

2014 ◽  
pp. 1175-1195
Author(s):  
Michael J. Moore ◽  
Tadashi Nakano ◽  
Tatsuya Suda ◽  
Akihiro Enomoto
Keyword(s):  
Social Networking ◽  
Digital Media ◽  
Instant Messaging ◽  
Future Perspective ◽  
Chat Rooms ◽  
Face To Face ◽  
New Form ◽  
Art Research ◽  
Automated Tools ◽  
E Mail

Face-to-Face bullying is a traditional form of bullying in which bullies attack victims through physical, verbal, or social attacks. Cyberbullying is a new form of bullying. Cyberbullies abuse digital media to attack victims (such as attacks through websites, social networking services, blogging, e-mail, instant messaging, chat rooms, and cell phones). Cyberbullying and face-to-face bullying share many similarities. For example, bullies achieve power over a victim in both cyberbullying and face-to-face bullying. On the other hand, cyberbullying has differences from face-to-face bullying that arise from characteristics of digital media such as anonymity and rapid spreading of attacks. This chapter highlights key concerns of cyberbullying stemming from the use of digital media and discusses existing models of face-to-face bullying which may aid in model cyberbullying. This chapter then introduces state-of-the-art research in automated tools to detect cyberbullying. Finally, this chapter concludes with future perspective of research in automated tools to detect cyberbullying.

Need for Dynamicity in Social Networking Sites

2014 ◽  
pp. 64-87
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

Sign in / Sign up

Export Citation Format

Share Document