scholarly journals Challenging Assumptions in Learning Analytics

2016 ◽  
Vol 2 (3) ◽  
pp. 1-3 ◽  
Author(s):  
Shane Dawson ◽  
Dragan Gasevic ◽  
Negin Mirriahi
Keyword(s):  
Big Data ◽  
Learning Analytics ◽  
Special Section ◽  
Scaling Up ◽  
Future Directions ◽  
Emerging Trends ◽  
Conference Theme

This final issue for 2015 includes a special section of invited papers from the recent Learning Analytics and Knowledge conference (LAK15). The collected papers connect with the conference theme of “Scaling up: Big data to Big Impact” and reflect the emerging trends and future directions of learning analytics research.

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

Environmental Issues and Social Responsibility: A Scientomeric Analysis Using Citespace

2020 ◽  
Vol 11 (1) ◽  
pp. 94-94
Author(s):  
Zixuan Zeng ◽  
Thammannoon Hengsadeekul
Keyword(s):  
Economic Development ◽  
Social Responsibility ◽  
Citation Analysis ◽  
Environmental Issues ◽  
Research Paradigm ◽  
Multiple Impacts ◽  
Quantitative Review ◽  
Future Directions ◽  
Emerging Trends ◽  
Strategic Perspective

Environmental issues and social responsibility have a significant impact on the natural ecological system and economic development. Hence, it is important to find a relative balance path between them. Previous studies have sought to explore environmental or social responsibility rather than seek solutions from a systematic perspective, and there seems to be a lack of a systematic, quantitative review of systematic solutions or details. To identify the multiple impacts and relationships between environmental issues and social responsibility and illustrate emerging trends and challenges, this article proposes a scientometrics review based on 1,336 articles published from 2001 to 2020, through co-occurrence analysis and co-citation analysis together with cluster and burstiness analysis to reveal the depth and breadth of emerging research. This research demonstrates the research paradigm of environmental issues and social responsibility extends from a single stakeholder level to a systematic strategic perspective of multiple organizations and stakeholders. The results provide researchers and practitioners with a deeper understanding of future directions and implications Keywords: Environmental issues; social responsibility; strategy; scientometrics; review

A bibliographic study on big data: concepts, trends and challenges

2017 ◽  
Vol 23 (3) ◽  
pp. 555-573 ◽  
Author(s):  
Deepa Mishra ◽  
Zongwei Luo ◽  
Shan Jiang ◽  
Thanos Papadopoulos ◽  
Rameshwar Dubey
Keyword(s):  
Big Data ◽  
Citation Analysis ◽  
Design Methodology ◽  
Future Directions ◽  
Content Type ◽  
Current Trends ◽  
Bibliographic Study

Purpose The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field. Design/methodology/approach To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals. Findings The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data. Research limitations/implications This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research. Originality/value To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective

2018 ◽  
Vol 30 (2) ◽  
pp. 381-399 ◽  
Author(s):  
Qing Li ◽  
Yan Chen ◽  
Jun Wang ◽  
Yuanzhu Chen ◽  
Hsinchun Chen
Keyword(s):  
Big Data ◽  
Stock Markets ◽  
Future Directions ◽  
Web Media

RELATIONSHIP IN IOT AND BIG DATA ANALYTICS: THE OPPORTUNITIES AND OPEN CHALLENGES

2021 ◽  
Vol 83 (4) ◽  
pp. 100-111
Author(s):  
Ahmad Anwar Zainuddin ◽  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Physical World ◽  
Future Directions ◽  
Wide Range ◽  
Time Utilization ◽  
Relationship Of ◽  
The Internet Of Things

Internet of Things (IoT) is an up-and-coming technology that has a wide variety of applications. It empowers physical objects to be organized in a specialized framework to grow its convenience in terms of ease and time utilization. It is to convert the thought of bridging the crevice between the physical world and the machine world. It is also being use in the wide range of the technology in this current situation. One of its applications is to monitor and store data over time from numerous devices allows for easy analysis of the dataset. This analysis can then be the basis of decisions made on the same. In this study, the concept, architecture, and relationship of IoT and Big Data are described. Next, several use cases in IoT and big data in the research methodology are studied. The opportunities and open challenges which including the future directions are described. Furthermore, by proposing a new architecture for big data analytics in the Internet of Things, this paper adds value. Overall, the various types of big IoT data analytics, their methods, and associated big data mining technologies are discussed.

scholarly journals Tracking the continuous dynamics of numerical processing: A brief review and editorial

2018 ◽  
Author(s):  
Thomas J. Faulkenberry ◽  
Matthias Witte ◽  
Matthias Hartmann
Keyword(s):  
Numerical Cognition ◽  
Mental Arithmetic ◽  
Special Section ◽  
Past Research ◽  
Number Processing ◽  
Hand Tracking ◽  
Single Digit ◽  
Future Directions ◽  
Numerical Processing ◽  
Computer Mouse

Many recent studies in numerical cognition have moved beyond the use of purely chronometric techniques in favor of methods which track the continuous dynamics of numerical processing. Two examples of such techniques include eye tracking and hand tracking (or computer mouse tracking). To reflect this increased concentration on continuous methods, we have collected a group of 5 articles that utilize these techniques to answer some contemporary questions in numerical cognition. In this editorial, we discuss the two paradigms and provide a brief review of some of the work in numerical cognition that has profited from the use of these techniques. For both methods, we discuss the past research through the frameworks of single digit number processing, multidigit number processing, and mental arithmetic processing. We conclude with a discussion of the papers that have been contributed to this special section and point to some possible future directions for researchers interested in tracking the continuous dynamics of numerical processing.

scholarly journals SUPPORTING AIR TRANSPORT POLICIES USING BIG DATA ANALYTICS: A DESCRIPTIVE APPROACH BASED EMERGING TREND ANALYSIS

2017 ◽  
Vol 8 (1) ◽  
pp. 51-72
Author(s):  
Jin-seo Park
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Text Mining ◽  
Research Methods ◽  
Qualitative Research Methods ◽  
Research Papers ◽  
Emerging Trends ◽  
The Future ◽  
Descriptive Approach ◽  
Core Issues

Qualitative research methods based on literature review or expert judgement have been used to find core issues, analyze emerging trends and discover promising areas for the future. Deriving results from large amounts of information under this approach is both costly and time consuming. Besides, there is a risk that the results may be influenced by the subjective opinion of experts. In order to make up for such weaknesses, the analysis paradigm for choosing future emerging trend is undergoing a shift toward mplementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The hange used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and the promising areas for the future from research papers pertaining to overall aviation areas through text mining method, which is one of the big data analysis techniques. This study has limitations in that its analysis for retrieving the aviation-related core issues and promising fields was restricted to research papers containing the keyword "aviation." However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and emerging trends regarding the promising areas for the future in the aviation industry through the application of a big data-based descriptive approach.

scholarly journals Desenhar e implementar um sistema de learning analytics no ensino superior

2013 ◽  
pp. 123-146
Author(s):  
Sérgio André Ferreira ◽  
António Andrade
Keyword(s):  
Big Data ◽  
Management System ◽  
Learning Analytics ◽  
Content Management ◽  
Content Management System ◽  
Learning Content ◽  
Academic Analytics ◽  
Learning Content Management

A utilização de plataformas tecnológicas com base de funcionamento online, com destaque para os Learning Content Management System(LCMS), tem ganho uma importância crescente nas Instituições de Ensino Superior (IES). Da atividade dos alunos e professores nestas plataformas resulta um imenso trilho de cliques, que se traduz no registo de um enorme volume de dados – Big Data – no sistema. A ideia do Learning Analytics (LA) é simples e tem associado um potencial transformativo muito elevado: o aproveitamento destes dados permite um processo de tomada de decisão mais informada, abrindo as portas a um novo modelo na gestão das IES nos campos pedagógico e da eficiência organizacional. Contudo, a abordagem à temática dos LA ainda está na infância e a operacionalização eficaz exige respostas a grandes desafios no domínio tecnológico, educacional e das políticas. O trabalho aqui apresentado insere-se neste contexto. Na Universidade Católica Portuguesa -Porto está em curso o desenvolvimento de um sistema LA alimentado com dados do LCMS institucional - Blackboard – que tem como objetivo posicionar cada unidade curricular (UC) e faculdade numa matriz de cinco níveis de integração do LCMS no processo formativo. A matriz foi construída com base em modelos internacionais e considerou-se as funcionalidades oferecidas pelo LCMS. Para dar resposta aos requisitos desta matriz, desenhou-se todo o backoffice do sistema de extração e análise de dados no LCMS. Adicionalmente, foi construída e validada uma escala que contempla as mesmas dimensões, para aferição da opinião dos estudantes sobre a integração e a importância do LCMS no seu processo de ensino e aprendizagem. Depois de concluída a construção deste LA é objetivo articular esta informação comos resultados académicos dos estudantes (Sistema de Gestão Académica) e avaliação dos docentes/ disciplinas (SIGIQ) - dando-se passos na construção de um Academic Analytics.

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