Learning
Analytics

WHAT IS LEARNING ANALYTICS?

The measurement, collection, analysis, and reporting of data about learners, learning experiences, and learning programs for purposes of understanding and optimizing learning and its impact on an organization's performance is termed as Learning analytics.

When we talk about analytics its mostly in the education space and the corporate space is not accounted. At liqvid, we have developed the definitions for the corporate world. Learning analytics in context of corporates serves the purpose of organizational development in terms of employee performance.

The grades of learning analytics

The grades of learning analytics

These analytics have different grades or stages - measurement, evaluation, advanced evaluation, and predictive and prescriptive analytics. Even though these stages are stated as analytics, however in terms of complexity, difficulty, and power they are quite different.

Measurement

Measurement

Analytics start with measurement, or the simple act of tracking things and recording values to tell us what happened. Measurement doesn't require complicated math or statistics, but you must start by gathering data. Otherwise, it's impossible to do any analytics.

Data Evaluation

Data Evaluation

After the data is captured evaluation and assessment starts to ascertain the'goodness to use' or validity of data. At this stage basic math like averages, means, modes, and basic statistics is applied to get cumulative data and establish benchmarks. In existing practice, most analytics fall into the basic data evaluation group, which is quite acceptable. There's tremendous value here, and opportunities for some huge wins.

Advanced Evaluation

Advanced Evaluation

The moment higher level math is applied, i.e. in advanced evaluation, enrapturing things start to unravel. In this stage we apply things like correlations and regression analysis. Statistical techniques are applied to ascertain not just what happened but also why it happened. This evaluation brings forth cause and effect theories which defines the further path of learning by underlining what works and what does not. In this process we eliminate the ineffective learning methods and sometimes also unlearn.

Predictive & Prescriptive Analytics

Predictive & Prescriptive Analytics

The most sophisticated stage of analytics are predictive and prescriptive analytics, which require graduate-level math and often rely on AI or machine learning powered by big data sets. Predictive analytics say, "based on what's happened in the past, here's what is most likely to happen next." Prescriptive analytics take that a step further and say, "based on what's most likely to happen next, here's the action we should take to optimize the outcome." Ultimately, when we get here, we rely on highly intelligent recommendation engines that deliver just the right learning, at just the right moment, in just the right way to significantly improve performance. As an industry, we're not there yet, but we can get there if we start measuring and work our way up.