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Now you have Data... how do we create efficiencies with data to enable effective learning?

  • Feb 13, 2019
  • 3 min read

As learning specialists, does anyone know efficient ways of using data? why should we be using data? or what data should we be using?  (please add to this post if you do?)

There a plenty of aficionados out there who claim they can help you create data sets or make sense of data to enable effective learning. Im not one of them. However, I do know that I need to embed potential systems and innovations, created during this and future data revolutions, in my everyday practice. Investments in learning has seen many educational suppliers develop platforms, algorithms, apps and texts which can decipher, calculate and group data sets in ways unimaginable. My concern with this abundance of data and suppliers available, is that learning specialists find it difficult to cut through the noise and are unable to find ways to embed data in learning practices they facilitate. 


Adding to the noise and confusion, leadership within learning institutions needs to improve when its comes to harnessing the effective use of data. Like other facets of learning, a clear, transparent direction is needed within various learning markets and institutions so those facilitating the use of data can enable self directed, collaborative and contemporary learning not just statistics to compare results. By having a direction, learning specialists can trial, take risks and play with data sets and platforms to create effective learning which align with the direction of institutions. ie. a concentrated effort with an allowance factored in for making mistakes. Ultimately, using evidence and feedback to guide learning so that learning specialists can find the best possible parts to build an effective data ecosystem.

Based on research, data can create efficiencies within organisations and guide decisions. But why are learning institutions too scared to invest time to try? As many learning institutions pay lip service to risk taking for their students, many are to scared to put their money where their mouth is when it comes to data.


Reflecting on my own practice, the conversations I have with my students about learning parallel questions and conclusions we should be asking ourselves about how we use data. Students are always asking how to improve their results. In reply, I ask, "have you tried doing it this way?" Students inevitably say “no I haven’t. I am comfortable with how I learn. I have always done it this way”. I then say “so are you happy with the results you are achieving. Is that the best you can do…”. Invariably, the coin drops at this point. I conclude with “so why would you be learning something a certain way if it clearly not working for you?” Learning institutions could be asked the same question...


Being adverse to experimentation also means explicit conversations between all stakeholders about what data is meaningful to enable effective learning for the end learner. Traditionally, data has been used in standardised testing, to create tests and to make sense of testing. Data mainly came in the form of numbers and scores. Usually it was up to the learning specialist to decipher what the data is for. Nowadays, algorithms can be built to collect and disseminate data to create learnings. Raw data is no longer numbers, percentages or scores. Raw data can be evidence of work and conversation pieces in which feedback can be generated. Learning specialists need to collaborate with suppliers and institution leaders in order to have the same understanding about what meaningful data looks like and how efficiencies can be created to enable effective learning. 


So how do we get buy in from our colleagues? A part from direction, the use of data needs to be complimented with; centralising data for easy access, creating efficient workflows with easy to use platforms, tailor the collection of data to suit what your colleagues see as meaningful data, demonstrating opportunities, just to name a few… 

More importantly, best practice is not doing things the way things have been done for years but best practic is creating efficiencies with data collection and analysis to enable effective learning.


 
 
 

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Excited by future challenges, my passion is to revolutionise learning, work practices and development so people, products, clients and organisations are assets in an ever changing Global community.

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