Learning data is a new topic - quite big and still evolving. In the book a referal is made to 1000 self-assessments by learning professionals rating their skill set. Data interpretation was one of the lowest scoring skills of learning professionals!. Much higher scored presenting, facilitating skills etc. In other words, learning professionals are usually not the first to dive into numbers. Most betas are not learning professionals.
What is learning analytics? Learning analytics is about the use of data for learning and improving learning processes. In the cartoon above, for example, you see that the fortune teller used Facebook as a source of information to predict the the future. Smart of her ofcourse :). As a professional learning you can now do like the fortune teller using data (information) online. The book focuses on the use of big data in organizations to support, especially large-scale data learning. What I miss in the book is where you may practically start within an organization, even though they explain you can start with a training dashboard where you systematically collect data.
In this blog post I will try to propagate more use of data by learning professionals by making it small and practical and looking at three levels:
Level 1: the level of your Personal Learning Network (PLN)
At the level of your own online network you can measure a lot, depending on what your goals are. Think of it as a form of feedback to collect and analyze feedback. For example: you can measure the number of retweets on Twitter. We have an en_nu_online account on twitter where we share a tip everyday. I follow the number of retweets with the aim to see which tips are populair. I do this mainly to with the "my tweets, retweeting." column in Hootsuite. Every month I try to gather the totals and assemble those in an excel sheet. I have noticed that a lot of very practical tweets are retweeted - this helps me to focus the upcoming tips focus. See also my blog post "Do not follow your number of followers, but you mentions and retweets. New to Twitter is that you can also turn on your analytics. I did this yesterday and you get a lot of information about your tweets.
Level 2: the level of an online cursus or learning trajectory
For the course 'learning and changing with new media, we use an online platform, a Ning platform. Within this platform, you can also make use of data. For example you can see which topics were given a lot of responses. We use this type of information as we go through redesigns. But apart from the number of responses you can see the number of views. I use this information when I receive few reactions. Sometimes people read it but it is still a heavy topic to respond to. Most platforms do have data, and you want more than you could make use of Google Analytics. Qualitatively you might analyse the content of a course for instance with a wordcloud (eg. wordle or tagxedo). In Moodle I often monitor the participants who have not logged on for 5 days.
Level 3: the level of an organization or network
At the level of an organization or school learning analytics is a bit more complex. Within an organization or school is it really a major project since you also have to look at the performance data and dashboards which already exist. It is best when you can make a link between performance and assessment and training / informal learning. Who can play what role in such a project? Think of the Research and Development, Learning and Training (HRD), Data scientists and management departments. Perhaps a good start within an organization or school to see what data you actually use . Sometimes gathering data in an excel sheet can be a big step. Additionally, you can think about an organizational challenge to solve. How can you start collecting to progress in this issue? So start with a question. I actually think the book 'measuring the networked non-profit' van Beth Kanter en Katie Delahaye Paine might be a more practical book than 'big learning data'. A quote from that book is 'deciding what to measure is 90% of the process'.
What is learning analytics? Learning analytics is about the use of data for learning and improving learning processes. In the cartoon above, for example, you see that the fortune teller used Facebook as a source of information to predict the the future. Smart of her ofcourse :). As a professional learning you can now do like the fortune teller using data (information) online. The book focuses on the use of big data in organizations to support, especially large-scale data learning. What I miss in the book is where you may practically start within an organization, even though they explain you can start with a training dashboard where you systematically collect data.
In this blog post I will try to propagate more use of data by learning professionals by making it small and practical and looking at three levels:
- the level of your own online learning network (also called personal learning network PLN)
- the level of an online course or course
- the level of an organization
Level 1: the level of your Personal Learning Network (PLN)
At the level of your own online network you can measure a lot, depending on what your goals are. Think of it as a form of feedback to collect and analyze feedback. For example: you can measure the number of retweets on Twitter. We have an en_nu_online account on twitter where we share a tip everyday. I follow the number of retweets with the aim to see which tips are populair. I do this mainly to with the "my tweets, retweeting." column in Hootsuite. Every month I try to gather the totals and assemble those in an excel sheet. I have noticed that a lot of very practical tweets are retweeted - this helps me to focus the upcoming tips focus. See also my blog post "Do not follow your number of followers, but you mentions and retweets. New to Twitter is that you can also turn on your analytics. I did this yesterday and you get a lot of information about your tweets.
Level 2: the level of an online cursus or learning trajectory
For the course 'learning and changing with new media, we use an online platform, a Ning platform. Within this platform, you can also make use of data. For example you can see which topics were given a lot of responses. We use this type of information as we go through redesigns. But apart from the number of responses you can see the number of views. I use this information when I receive few reactions. Sometimes people read it but it is still a heavy topic to respond to. Most platforms do have data, and you want more than you could make use of Google Analytics. Qualitatively you might analyse the content of a course for instance with a wordcloud (eg. wordle or tagxedo). In Moodle I often monitor the participants who have not logged on for 5 days.
Level 3: the level of an organization or network
At the level of an organization or school learning analytics is a bit more complex. Within an organization or school is it really a major project since you also have to look at the performance data and dashboards which already exist. It is best when you can make a link between performance and assessment and training / informal learning. Who can play what role in such a project? Think of the Research and Development, Learning and Training (HRD), Data scientists and management departments. Perhaps a good start within an organization or school to see what data you actually use . Sometimes gathering data in an excel sheet can be a big step. Additionally, you can think about an organizational challenge to solve. How can you start collecting to progress in this issue? So start with a question. I actually think the book 'measuring the networked non-profit' van Beth Kanter en Katie Delahaye Paine might be a more practical book than 'big learning data'. A quote from that book is 'deciding what to measure is 90% of the process'.
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