Learning experience analytics often answer questions about usage patterns for specific activities—such as e-learning courses, social learning boards, mobile apps, MOOCs, informal information access, or even classroom-based courses. This blog post explains how you can use this type of learning analytics to fine-tune your learning programs.
Before we dive in, it’s important to remember that learning analytics has two dimensions:
- Complexity—or the sophistication of the analytics that ranges from basic measurement and data evaluation to advanced evaluation and predictive and prescriptive analytics
- Category—which identifies the specific area or type of learning data that’s being analyzed and covers learner analytics, learning program analytics, and learning experience analytics
Use Learning Experience Analytics to Improve L&D Programs
You invest time, resources, and energy when creating learning programs, but how do you ensure that investment pays off? That’s where learning experience analytics can help, as they can answer questions like these:
- How much/often is a learning experience used?
- Is there “scrap learning” that you can remove?
- When is the experience used, and for how long?
- How do learners interact with the experience?
- What resources or topics do learners search for most frequently?
- What are the details of learners’ interactions?
- How do learners navigate the experience?
- In what contexts do people access training resources?
- What workplace experiences do employees find to be meaningful?
They also incorporate learning content analytics and user experience analytics to help organizations fine-tune L&D offerings to ensure learners receive the best possible experiences, which then helps maximize the effectiveness of specific learning activities.
How to Apply Learning Experience Analytics
Say, for example, you want to understand more about an elearning assessment. Here's how you could apply all four levels of complexity in the learning experience analytics category:
1) Learning Experience Measurement
Record each learner's response to a multiple-choice question, the result (i.e., whether that response was correct), and the overall score the learner received on the assessment.
2) Learning Experience Evaluation
Present a chart showing the distribution of selected answers for a given question. Is there an incorrect answer that's selected more frequently than expected? Could that indicate the question or answer choices are poorly worded or confusing?
3) Advanced Learning Experience Evaluation
Run question result data and the assessment score data through a correlation engine. Are there any question results that are inversely correlated with the assessment score?
In other words, are the learners who received the highest scores more likely to answer certain questions incorrectly? If so, does that mean these learners have incorrect information or are the questions confusing or misleading?
4) Predictive Experience Analytics
Run question result data and assessment score data through a correlation engine. Are there questions that show a very high positive correlation with assessment scores?
In other words, when learners get the question wrong, they will most certainly fail the test. Or, when learners get the question right, they will most certainly pass the test. So, if you can predict the overall assessment results after only a few questions, can you alter that learning experience to provide a more efficient and engaging learning path?
Up Next: What Is Learner Analytics?
Next, we'll discuss learner analytics, which helps you understand more about a specific person or group of people engaged in learning-related activities.
About the author
As an innovative software developer turned entrepreneur, Mike Rustici has been defining the eLearning industry for nearly 20 years. After co-founding Rustici Software in 2002, Mike helped guide the first draft of xAPI and invented the concept of a Learning Record Store (LRS). In 2013, he delivered on the promise of xAPI with the creation of Watershed, the flagship LRS that bridges the gap between training and performance.
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