Call for Papers and Abstracts: 5th International Big Data and Analytics Educational Conference
Call for Papers and Abstracts
5th International Big Data and Analytics Educational Conference
Date: 16th July 2017 (Sunday; 9am-5pm)
Conference Venue: Bayview Hotel, Langkawi Island, Malaysia (www.bayviewhotels.com/langkawi)
[*Note: PACIS2017 starts from 17th July 2017 in Langkawi Island, so this Conference is also convenient to those who will attend PACIS2017: http://www.pacis2017.org/ ]
Conference fee: AUD$200 (approx. US$150) and include attendance, morning tea, lunch and afternoon tea.
Accommodation (@ Bayview Hotel): Approx. US$80 per night
Paper/Abstract Submission: 15th April 2017
Author Notification: 22nd April 2017
Registration Deadline: 1st May 2017
Conference andRegistration website: https://goo.gl/HEdGRu
Abstracts (200-500 words) or papers (between 2000-7000 words) should be submitted to: https://easychair.org/conferences/?conf=bdaedconf2017langkaw
Three Best paper awards will be given to outstanding papers in the field of Analytics Education.
Accepted papers (for those between 5000-7000 words) will be considered for a special issue publication in International Journal of Business Intelligence Research:
*20-40 minutes of presentation time will be allocated for all accepted papers and abstracts.
About the Conference
The 5th International Big Data and Analytics Educational Conference (5th BDA EdConf) solicits papers and abstract presentations on the development and experience with educational programs in all areas of Analytics, Data Visualisation, Big Data, and Data Science. Educational programs can be undergraduate, postgraduate, in-employment or high school, and can focus on education and/or training. Topics of interest include but are not limited to:
- BDA curriculum design, development and delivery;
- Experience with teaching introductory big data, analytics and cognitive science concepts at university;
- Experience with developing degree and certificate programs in BDA. (What are the required knowledge and skills? What strategies are used for defining and developing new programs?);
- Lessons learned creating programs for cognitive science, BDA or teaching BDA at all levels;
- What challenges are being faced in building advanced courses around decision analytics, modelling, algorithms, machine learning, cognitive science and visual analytics? What other advanced topics need curriculum development?
- How can all students be trained in appropriate ethics for handling data and resulting analytics?
- How can all students be introduced to the fundamentals of analytics and to what extent is it necessary to meet the needs for very high numbers of analytics literate employees as customers of data scientists?
- How should using data as a business strategy and source of innovation be addressed?
- How is the need to train data policy professionals such as data curators and stewards, data security and privacy professionals, data quality, master data management for example being addressed?
- How should information systems, computer science, industrial engineering programs adapt to the world of big data analytics?
- How are cognitive science, big data and analytics impacting the professional fields, such as accountancy, law, medicine, medicine, education, etc? How should it be addressed within curricula?
- Novel approaches to teaching and assessing big data, cognitive science and analytics concepts;
- Empirical evaluations of big data and/or analytics skills, training, education or needs analysis;
- Inspiring, teaching and developing the “soft skills” of analytics (problem solving, creativity, communication, collaboration, story-telling, curiosity)
- The need for and delivery strategies of ongoing professional development;
- How can the curricula for ongoing professional development training programs be matched to the role/skills needs?
- Experience with teaching Big Data and Analytics in the Junior and High school system;
- The use of specific software for teaching BDA and cognitive science;
- How to address ‘train the trainer’ skill gaps at all levels?
- Ethics, Trust and Governance in Data and Analytics
- Regulatory and data compliance issues with Big Data, etc.
William Yeoh, Deakin University
Murthy V. Rallapalli, IBM & Colorado State University – Global Campus
Siew-Fan Wong, Sunway University
Richard Self, University of Derby
Randy Messina, IBM Corporation
Steven Miller, IBM Corporation
Aleš Popovič, University of Ljubljana
Bharatendra Rai, University of Massachusetts Dartmouth
Greg Richards, University of Ottawa
Don McCubbrey, University of Denver
Michael Gendron, Central Connecticut State University
Jie Ren, Fordham University
Elena Gortcheva, University of Maryland University College
Rens Scheepers, Deakin University
Michael Ben-Avie, Southern Connecticut State University
Ayse Bener, Ryerson University
Michael Bliemel, Dalhousie University
Jorge Medina, EGADE Business School
AAA Atayero, Covenant University
Tristan Chong, Xi’an Jiaotong-Liverpool University
Omar Hussain, Australian Defence Force Academy
Shah Miah, Victoria University
Kok-Leong Ong, La Trobe University
Yee Ling Boo, RMIT University
Peter Tong, Concordia International School
Suzanne Lema, IBM Corporation
Bernard Long, IBM Corporation
Laura Trouvais, IBM Corporation
William Yeoh, Deakin University
An initiative facilitated by Watson Analytics Global Academics Network and IBM Centre of Excellence in Business Analytics, Deakin University. http://www.waglobalacademicsnetwork.org/