Monday, 8 June 2015


The current model in training analytics is "small data" - data based on reports, assessments, and so on from small numbers of learners. This "big data" can be used to model learner and organizational characteristics and outcomes and, most importantly, to predict future trends and patterns. It can help organizations identify which programs are working and which are not, where additional training is required, and the best way to deliver that training.

In a 2012 report on educational data mining and learning analytics, the U.S. Department of Education's Office of Educational Technology identified several questions that big data can help educators answer. Here are a few of them:

  • What sequence of topics is most effective for a specific learner? When are learners ready to move to the next topic? 
  • What learner actions are associated with more learning? What actions indicate satisfaction, engagement, learning progress, etc.? 
  • What features of an online learning environment lead to better learning? What will predict learner success? 
  • When is intervention required?

Here are a few major areas where big data from MOOCs can inform training practice:
  • Improving results. This is the obvious one. Of course the goal of all training is to increase employees' skills and effectiveness. MOOC data can be analyzed on both micro and macro levels to improve individual and organizational results. 
  • Clustering and relationship mining. These two concepts have to do with discovering relationships between variables. The data can be used in many ways, such as for organizing employees with complementary skills into teams and work groups. 
  • Customizing programs on a large scale. MOOCs started out as a one-size-fits-all solution, but they are rapidly evolving into adaptive learning environments tailored to individual learners. In the near future, the learning experience will be optimized individually, business-unit, and organizational success.

New in Malaysia

National research and development agency, Mimos, has launched the Big Data Analytics (BDA) Digital Government Lab (DGL), which is part of the wider Digital Government Innovation Network (DGOIN), an initiative to accelerate BDA adoption in Malaysia.

The launching ceremony was officiated by Science, Technology and Innovation Minister Datuk Dr Ewon Ebin at the Mimos campus here yesterday. The BDA-DGL is an initiative resulting from a tripartite memorandum of understanding between Mimos, the Modernization and Management Planning Unit ( Mampu ) and Multimedia Development Corporation (MDec) signed in January 2015.

Dr Ewon said the Government was serious in implementing BDA in line with expanding the country’s science, technology and innovation agenda.

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