Disclosure: Blog Author may be part of
a submission of papers to this conference
LAK 2013 is a mechanism for combining the expansion of
technologies in supporting learning, with large volumes of data from the
learning process. Learning Analytics research combines several domains of study
to ensure that interventions and organizational systems meet the needs of all
The Society for Learning Analytics Research (SoLAR) is an
international group that oversees this conference, and a range of other activities in Learning Analytics.
A definition of big data in terms of size and/or complexity:
"Big Data” is a term used to describe data
sets so large and/or complex that they become awkward to work with using
traditional database management tools. An example is the use of Learning Analytics techniques to find trends in comments on a course in a Learning Management System (LMS) in conjunction with
comments in Social Media.
A useful reference:
Franks, B 2012, Taming the
Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced
This book has
some good sections on analytics practices and teams, and isn't just about Big
Data. It also has sections on Enterprise Analytical Datasets (ADS), creating secure data, Model
and Score Management, statistical tools, and Analytic Sandbox types.
Learning Analytics is an area set
to grow rapidly:
Learning analytics is the measurement,
control, collection, and analysis of data about learners, educators, and their environments.
Initiatives based on an understanding of these elements and how they interact
can lead to superior outcomes.
The following text provides a thorough coverage of most Analytics,
Predictive Modeling, and Data Science techniques:
Ratner, B 2011, Statistical and Machine-Learning Data Mining: Techniques for Better
Predictive Modeling and Analysis of Big Data", CRC Press
Interesting chapters include Principal Components Analysis (PCA),
Smoothing, Segmentation with Chi-squared Automatic Interaction Detection
(CHAID), look-alike profiles, data treatments, and the principles of genetic
Science Fiction becomes reality:
Most enterprises don’t have the amazing power of Watson yet,
but capability can be built across time.
BUSINESS INTELLIGENCE, BUSINESS ANALYTICS, AND DIGITAL ANALYTICS
A focus on Students
and Tutors drives the need for Analytics, and an entrepreneurial approach to
Governance, Reporting, and Business Intelligence, underpin development in
Analytics. A useful
reference is the following data and information visualization classic:
2012, Show Me the Numbers: Designing
Tables and Graphs to Enlighten, Analytics Press.
text provides useful material on technical architecture, data modeling, and the
implementation and application of scoring models. Secure Data Systems for
Analytics and Predictive Modeling can drive recommendations and value for learning
and teaching initiatives:
Haertzen, D 2012,
The Analytical Puzzle: Profitable Data
Warehouse, Business Intelligence and Analytics, Technics Publications.
and data from digital environments are also necessary for Analytics in the
online space, and require appropriate measurement and interpretation. The
following guide includes data integration, online metrics, campaign tracking,
Clifton, B 2012, Advanced
Web Metrics with Google Analytics, Sybex.
This Case Study combines these elements to drive initiatives in an
Education context to boost student performance and add commercial value: