The Difference between Academic Analytics and Learning Analytics

ACADEMIC ANALYTICS AND LEARNING ANALYTICS - is there a difference?

Phil Long and George Siemens wrote an article in 'Educause Review' in September/October 2011 that provides a handy definition of Academic Analytics and Learning Analytics. The article states that “learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs". Academic analytics is the "application of business intelligence in education and emphasizes analytics at institutional, regional, and international levels". The table below from page 34 of the publication, delineates the level of analysis and the end user of these analytical approaches:

Here is a link to the source article:
https://er.educause.edu/~/media/files/article-downloads/erm1151.pdf%20

I think that both play a key roe in education analytics, and consider academic analytics to be more focused on the student and outcomes, and learning analytics to consider the behaviours and interactions that occur throughout the process. 

Here's some criteria that I apply to determine whether an institution has advanced beyond academic analytics to learning analytics:

(1) are they using data from a learning management or related system?
(2) are they applying statistical methods, e.g. clustering or regression? and
(3) have they linked learning behaviours to characteristics of the student, and their outcomes?

Disclaimer: This disclaimer informs readers that the views, thoughts, and opinions expressed in the text belong solely to the author, and not necessarily to the author’s employer, organisation, committee or other group or individual. https://www.termsfeed.com/blog/disclaimer-examples/#8220Views_expressed8221_disclaimer

SEASONS GREETINGS

Have a safe and happy holiday, and all the best for 2014!

Big Education Analytics

Thought Leaders and Events in Learning Analytics

KEY EVENT

A key conference in Learning Analytics is the international Learning Analytics and Knowledge (“LAK 2013”) Conference:

http://lakconference2013.wordpress.com/for-authors/

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 stakeholders.

THOUGHT LEADER

The Society for Learning Analytics Research (SoLAR) is an international group that oversees this conference, and a range of other activities in Learning Analytics.

http://www.solaresearch.org/

Disclosure: Employer of Blog Author has an affiliation with SoLAR.

Big Data and Learning Analytics Resources

BIG DATA AND 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 Analytics, Wiley

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 set to grow in Australia from 2013

Acknowledgement: New Media Consortium

A definition of Learning Analytics:

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 modeling.

Science Fiction becomes reality:

Most enterprises don’t have the amazing power of Watson yet, but capability can be built across time.

http://www.networkworld.com/news/tech/2011/050611-watson-big-analytics.html?page=1

Acknowledgement: Network World

Quote:

“Any sufficiently advanced technology is indistinguishable from magic.”, Arthur C. Clarke

Building Analytical Capability

BUSINESS INTELLIGENCE, BUSINESS ANALYTICS, AND DIGITAL ANALYTICS

A focus on Students and Tutors drives the need for Analytics, and an entrepreneurial approach to initiatives.

Information Governance, Reporting, and Business Intelligence, underpin development in Analytics. A useful reference is the following data and information visualization classic:

Stephen, F 2012, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press.

The following 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.

The interactions 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, and benchmarking:

Clifton, B 2012, Advanced Web Metrics with Google Analytics, Sybex.

CASE STUDY

This Case Study combines these elements to drive initiatives in an Education context to boost student performance and add commercial value:

http://public.dhe.ibm.com/common/ssi/ecm/en/ytc03480auen/YTC03480AUEN.PDF

Disclosure: Blog Author, Employer, Supplier, and Implementation Partner, are referenced in the Case Study. This is also a current Top 5 candidate for this Award:

http://www.cio.com.au/mediareleases/15059/business-intelligence-finalists-listed/

Photo Source: Wikipedia

Educating and Learning in Online Environments

The Science of Education (Pedagogy)

The application of Analytics in the Education context requires a sound understanding of teaching, learning, and environments. The following references are particularly useful in the Online space:

Harasim, L 2011, Learning Theory and Online Technologies, Routledge, New York.

This text covers Behaviorist, Cognitivist, Constructivist, and Online Collaborative Learning (OCL).

Crawley, A 2012, Supporting Online Students: A Practical Guide to Implementing, and Evaluating Services, Jossey-Bass.

Provides detailed descriptions of a range of student support initiatives. Includes rating systems for services, useful frameworks, and the measurement of online student engagement and persistence.

The Transformation of Education and Big Analytics

In a session at the ‘Current/Future State of Higher Education, An Open Online Course’ 

Jeff Selingo (Vice President of ‘The Chronicle of Higher Education’) 

identified five general factors that are likely to disrupt Education including: 

1) skilled jobs, 

2) diversity, 

3) cost, 

4) lifelong learning, and 

5) learning outcomes.

http://www.jeffselingo.com/; @jselingo

http://www.edfuture.net/; #CFHE12

http://www.chronicle.com/

Data, Technology, and Pedagogy enable Big Analytics to create value and generate positive outcomes from this transformation. A journey we can take together.