pecial Issue on Big Data in Education
Renato P. dos Santos, ULBRA - Lutheran University of Brazil, Brazil
Aims of the special issue
Internet users worldwide are currently producing more and more data of various types such as emails, social media, search queries, etc. Such data are big data by nature. Big data techniques hold promise for disruptive innovations leading to better health care, cleaner environment, safer cities, more effective marketing strategies, and even new ways of doing science. The reach of big data and analytic knowledge is impressive and pervasive.
Much of the widespread discussion around big data has focused on surveillance, marketing, or other exploitation of data about people. The papers included in this special issue, however, should demonstrate the impact and potential for data science to improve education both from school administration and teaching/learning points of view. They should address new methodologies and new applications of big data in education, the challenges and possibilities that such enlarged scale brings for teaching and learning in this area.
This special issue “Big Data in Education” aspires to contribute to the further development and advancement of the dialogue among the research pioneers of big data in education at international level.
The topics of this special issue include but are not limited to:
· Learning and academic analytics in education
· The role and perspectives of big data in Information Systems programs curricula
· Applications and use cases of big scholarly data by teachers and students
· Strategies for learning and teaching with big data applications such as Google Trends and Google Correlate, among others
· Applications and use cases of big science by teachers and students
· Privacy, security, and ethical issues related to the use of students’ data
· Benefits and case studies of curriculum personalization through big data
· Critical perspectives on whether big data leads to big knowledge
· Adaptations needed by information systems, computer science, and industrial engineering programs to the world of big data
· The extent to which students shall be introduced to the fundamentals of analytics and appropriate ethics for handling data to meet the market needs
· Lessons learned creating programs for or teaching big data and analytics to all levels
· Lessons that can be applied from other research fields
These submissions can be:
· Papers describing and evaluating new and/or existing methods that can be used to solve educational challenges
· Position papers that describe data science challenges that need to be overcome in order to make methods more suited to solving large-scale educational challenges
· Papers describing implementations of data mining/analytics/big data/data science systems in school settings.
· Problem descriptions that highlight significant educational problems that could use data science expertise and skills, but are not being tackled today.
Submission deadline: October 30, 2015
Acceptance notification: November 30, 2015
Publication: Spring 2016 (Tentative)
Manuscripts should be submitted to the special issue’s section electronically via the journal’s webpage http://earthlab.uoi.gr/theste/index.php/theste/about/submissions.
Before submitting a manuscript, authors should advise the review checklist that reviewers use to provide feedback on the articles. Author(s) have to submit their article following the journal’s format.