Interactive eBook on
“Technology-Enhanced Learning for Big Data Skills Development”
“Data is at the centre of the future knowledge economy and society”, the European Commission predicts (European Commission, 2014, p.4) and for the Big Data economy to flourish, “an adequate skills base” in form of a “sufficient number of domain experts” needs to be trained “to meet the strong demand in the labour market” (European Commission, 2014, p.6).
With demand soaring, Big Data Skills reel in the top salaries already today (Bednarz, 2014) with studies predicting a further increase in demand by 243% in the years up to 2017 (e-skills UK, 2013, p.6).
Big Data is a potential game changer, able to “unleash new organizational capabilities and value” (Davenport et al., 2012, p.22), helping to infer knowledge, and changing decision making. When applied right, Big Data can help increase performance and productivity in a wide range of sectors including industry, education, healthcare, and public administration.
Big Data skills subsume expertise along at least five dimensions, namely expertise in business or other specific domains (e.g., health, transportation, energy, smart cities, education), knowledge in math and statistics, new programming languages for data exploration and analysis, data storage and distribution technologies, and, finally, visualisation & data communication methods (PricewaterhouseCoopers, 2014).
There is a skill gap for Big Data in Higher Education at large (Carter, 2014). The changing environment in Business Intelligence requires continuous refinement of the curriculum (Wixom et al., 2014, p.1–13).
Learning Big Data skills engages multiple disciplines and requires a range of different capacities from students. There is a need of a more practical training (Wixom et al., 2014, p.1–13).
To prevent the existing skills gap from widening and to match up skills development with expected demand, specialists with Big Data Skills need to be educated and trained. To create upskilling opportunities at scale for the next generation of data jockeys and data scientists, technology enhanced learning for the development of Big Data skills is required. TEL can play a key role in improving the learning of Big Data skills and it can help educate and train more capable students in less time. TEL can make the learning and training process more attractive and engaging, by providing realistic examples at anytime and in anyplace.
This textbook is planned as an interactive ebook, some (!) of the chapters augmented with embedded ‘learning by doing’ data exploration and manipulation apps and shipping with Open Data (e.g. using R and Shiny: http://shiny.rstudio.com/). The main platforms for interactive eBooks do already support (iBooks) or are on the way towards supporting (ePub 3) integration of HTML5 widgets and apps. We foresee some of the chapters to be augmented by do-torial style live examples providing data with the book. Data provided need to be public and should not outdate quickly.
The book project aims to take stock of existing techniques and technologies, while driving methodological and technological development of what so far has been missing in the canon of TEL for Big Data skills development. It will bring together researchers and industry from different backgrounds to discuss and advance support of TEL for Big Data skills development. Naturally, it will serve as a forum for establishing new collaborations on a Horizon 2020.
Contributions are expected to not only provide a comprehensive description of the state of the art, the proposed technique or technology, and easy-to-follow example application cases, but also provide interactive widgets (e.g. R shiny apps) with interactive, try-out examples.
Topics of interest
Topics of interest include, but are not limited to contributing original theories, methods, evaluation studies, design and application (case) studies regarding TEL for Big Data skills in and on:
- Technology support for learning big data concepts such as:
- Data collection, relationship mining
- Predictive modeling, Inference of causes, Detection of behavioural patterns
- Personalization and adaptation, including recommenders
- Data-driven decision making
- Visual Analytics and static/interactive data visualisation techniques
- Data Storytelling and communication
- Streaming database and storage technologies (including Semantic Web)
- Real-time data warehousing
- Exploratory Data Programming, Processing, and Clustering
- Curriculum and course definition, sequencing of topics, modeling of objectives
- Technology-enhanced assessment of Big Data skills
- Pedagogies and methodologies of TEL for Big Data skills development (including social learning, gamification/game-based learning, augmented reality, MOOCs, mobile learning)
- Case studies in Health, Education (Learning Analytics), Finances, Smart Cities, Energy, Transportation and Logistics, Blue Growth, or other Horizon 2020 strategic focus areas
- Ethical and policy deliberations regarding, e.g., security, privacy, citizenship, liberty
- European Commission (2014): Towards a thriving data-driven economy, COM(2014) 442 final.
- Bednarz, A. (2014): Big Data skills pay top dollar, In: Networked World, February 7, online at: http://www.networkworld.com/article/2174178/software/big-data-skills-pay-top-dollar.html
- e-skills UK (2013): Big Data Analytics: Adoption and Employment Trends, 2012-2017, http://www.sas.com/offices/europe/uk/downloads/bigdata/eskills/eskills.pdf
- PricewaterhouseCoopers (2014): The 5 dimensions of the so-called data scientist, Anand Rao, March 5: http://usblogs.pwc.com/emerging-technology/the-5-dimensions-of-the-so-called-data-scientist/
- Carter, D. (2014): There’s a Big Data skills gap in higher education, http://www.ecampusnews.com/top-news/theres-big-data-skills-gap-higher-education/
- Wixom, B.; Ariyachandra, T.; Douglas, D.; Goul, M.; Gupta, B.; Iyer, L.; Kulkarni, U.; Mooney, J.; Phillips-Wren, G.; and Turetken, O. (2014): The Current State of Business Intelligence in Academia: The Arrival of Big Data, In: Communications of the Association for Information Systems, Vol. 34, Article 1, online at: http://aisel.aisnet.org/cais/vol34/iss1/1
Authors are invited to submit an abstract outlining their potential contribution (up to 1 page). Parallel to the open submission, editors will proactively approach suitable candidates for contributions.
Following acceptance of the idea purported in the abstract, the authors are then invited to submit an elaborated version of their chapter for peer review by at least two reviewers. It shall be noted that at this stage, contributions will be rigorously assessed for their suitability, even if that means that they may not make it into the final selection. This is to ensure originality, rigour, and significance of the contributions.
Submissions should use the Springer LNCS template
Please submit your abstract to:
f.wild [at] open.ac.uk
31.10.2014 Expression of interest (email: title + one paragraph + short bio)
31.01.2014 Submission deadline
15.02.2015 Notification of acceptance
28.02.2015 Final version
15.03.2015 Typesetting and proofs
31.03.2015 Publish to stores (iBooks 3, ePub 3; with ISBN number)
- Fridolin Wild, The Open University, United Kingdom
- Peter Scott, The Open University, United Kingdom
- Pedro J. Muñoz-Merino, Universidad Carlos III de Madrid, Spain
- Carlos Delgado Kloos, Universidad Carlos III de Madrid, Spain
- Hendrik Drachsler, Open University of the Netherlands
- Marcus Specht, Open University of the Netherlands