Interactive eBook on ‘Technology-Enhanced Learning for Big Data Skills Development’

Interactive eBook on
‘Technology-Enhanced Learning for Big Data Skills Development’

*** Call for contributions ***

*Rationale*

“Data is at the centre of the future knowledge economy and society“, the
European Commission predicts (EC, 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” (EC, 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* 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

*References*

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

*Submission*

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
(http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0).

Please submit your abstract to:
f.wild@open.ac.uk

*Important dates*

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)

*Editors*

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