It is no surprise that data analytics is one of the high priority skills identified for the future of L&D. According to the Global Sentiment Survey report for 2020, in answer to “what do you think will be hot in workplace L&D next year”, learning analytics ranked number 1.
While the desire is there to dive deeper into learning analytics, L&D people in general have a rather rudimentary understanding of it. Some consider the reporting of training activities such as course completion rate as sufficient data while others know little of how to turn data into actionable insights.
If we were to up our game on data, we would first have a deeper understanding of why learning analytics matter and why should we care about it.
What is learning analytics?
In general, learning analytics are the tools that collect and analyze data generated by our learners and their learning interactions. For examples, within a Learning Management System (LMS), one can view, collect, export, and manipulate data such as students’ attendance pattern, how long they spend on a particular learning activity, what kind of questions did they ask, what are their test scores, and if they downloaded any reading material. The ultimate goal of learning analytics is to be able to interpret and make meaning from these data so we can better support our learners, improve business performance and workforce effectiveness.
Historically, learning analytics emerged as an academic field, converging subject matter knowledge from educational research, data science, and human-centered design. Nowadays, interests and applications have grown in the corporate sector, with many vendors flourishing in the market selling learning dashboard visualization, xAPI and learning record store integration, predictive analytics, and network analysis for social content. While some of these things are built into LMS, most LMS will produce data but lack the tools that analyze it. However, some newer and better featured LMS provide dashboards with some basic learning analytics functions.
Why should you care about learning analytics?
As learning practitioners, regardless of approaches we took to design learning, a series of questions consistently arises: Was the training effective? And if so, what more can we do? What area can be improved? How do you know if the learner can transfer what she learned onto the job? Did the training benefit the organization as a whole?
Meanwhile, across organizations, we regularly deal with an increasing numbers of technologies. In addition to LMS, many organizations have e-libraries, web conferencing systems, learning experience platforms (LXP), online survey tools, internal social networks, and employee knowledge sharing repositories. Collectively, these technologies continuously generate a large and growing amount of data. With the amount of “data exhaust” we get from these systems, we can interpret and gain an understanding of how people learn. It is worth noting that implementing learning analytics does not mean collecting raw data – data collected from a source without any processing. Raw data by itself is of little value until you do something about them. Learning analytics involves selecting, capturing, processing, interpreting, and acting on data that can support learners and improve learning and on-the-job performance.
One of the main reasons for learning analytics is that it helps us to make better decisions based on evidence and provide relevant interventions.
Some examples are:
- Where are the tricky spots for your learners? Where are people having difficulties?
- Where are opportunities to provide more support and feedback?
- Are the learning materials aiming at the right knowledge and skill level?
- What are the sentiments about a certain topic? How do your learners feel collectively as well as individually?
Moreover, learning analytics focus on creating a feedback loop that allow us to adapt content, learning trajectory, level of support, and other personalized services in real time and on an ongoing basis. This contrasts with the current process whereby course evaluations or learner feedback come at the end of the training. In some instances, learning analytics is used to predict learning behaviors, act on the predictions, and feed those results back to the process in order to improve the predictions over time and provide a more personalized learning environment.
Why should you care about learning analytics NOW?
According to Statistic Canada, nearly five million Canadians started working from home to control the spread of COVID-19. When we add this on top of the number of people who normally work from home, it accounts for about 40 per cent of the Canadian workforce. Similarly, in other countries, the social distancing mandate is causing more people to work from home than ever. The implication is that now we have an unprecedented numbers of digital process, digitized content and abundance of data storage already exist; there is no excuse to not analyze the data.
What makes learning analytics more compelling now is the fact that lean operations will be the norm for many companies. L&D departments will need to justify expenditures and optimize resources even more. Learning analytics can help you decide if you need to redesign the training material based on data collected about how learners interact, or if you need to renew licenses for curated learning content based on usage pattern, or introduce additional L&D supports such as coaching and mentoring based on sentiment analysis.
As job responsibilities and tasks become more in flux (be it the results of a pandemic or an introduction of automation and AI, or combinations of various factors), I truly believe that reskilling and upskilling will take center stage. People are going to need to learn fast because their jobs or components of their jobs are changing so quickly. A recent example in Ontario, Canada, illustrated just how extreme this shift could be. As long term care homes and retirement facilities are scrambling to find extra support amid COVID-19 outbreak, local government is recruiting temporarily out-of-work librarians and museum workers to be personal support workers in senior homes. It is not necessarily that people would rush into another job in droves, especially one that might be perceived as less desirable. In fact, only seven of 54 workers have accepted the offer. However, it is important to note that some jobs will experience decline while other jobs will flourish at a more rapid pace. As learning professionals, how do you support this transition? How do you ensure that people are actually learning what they need to apply to the new work? How would you identify and bridge transferable skills? These are some of the questions that perhaps we should look to learning analytics for answers.
In summary, the opportunity afforded by learning analytics empowers L&D leaders and their organizations to make use of the wealth of data related to learning, and to be able to use this information to understand more about their staff and the context in which they learn and develop. With its potential benefits in mind, I hope you will start exploring more about learning analytics and its applications.