A learning analytics approach means tracking key data points across the entire learning experience in order to answer key questions and ultimately improve your learning outcomes.
Collecting data about your learning experiences helps you evidence success and make good decisions about future learning. Categorized and analyzed data leads to insights and a more robust understanding. This ultimately helps you improve your learning and create better learning experiences for your learners.
You may not be in the habit of considering your measurement needs for each learning project or course. That’s okay and quite common! We’re here to help define these needs.
So let’s dig into the 4 data dimensions we focus on…
This is all about applied knowledge. It’s essential to measure your learner’s ability to apply knowledge in realistic scenarios and/or exercises. You can assess competence by creating different levels of simulating the skills and knowledge application. The most common approach is a scenario-based assessment that provides the opportunity to evaluate learners using hypothetical or expected work situations.
Competence data can help us answer a range of vital questions, including:
- What do learners already know/do and where are the gaps?
- Is the learning filling those gaps?
- How well do learners apply knowledge in assessments and activities?
- What questions and activities do they find difficult?
- How well do they perform against different learning objectives?
This is about measuring a change in the learner. This could be through set outcomes or newly acquired skills. It could also be about an increase in confidence from the learner, from before and after the course. Or it could be a shift in thinking - for example, how comfortable a learner is about a certain subject.
Perception ratings are metacognitive—they require learners to report on their own thinking. This can include reporting their confidence around a subject or indicating how certain they are about judgments they make during activities within the learning. Confidence checks are useful in pre-and post-assessments to measure how confidence changes as a result of the learning experience.
This data helps us answer critical questions such as:
- Has the learning increased the confidence of learners?
- Where are the areas of low and high confidence with our learners?
- Even if they know the right thing to do, are learners confident to do it in practice?
This is all about measuring how the learner interacts with the learning. Engagement data measures how learners both interact with and complete the learning. The most common data in this category includes when the learner first opens the learning, when they complete it and the time they spend within the learning.
Engagement data helps us answer these important questions:
- How long do learners take to complete learning?
- Are they spending a lot of time in one area but skipping through another?
- Is their attention spread over multiple sessions?
- Are they coming back for more after completing the learning?
- Are they completing the learning on mobile or desktop devices?
This is an area most L&D teams are very familiar with, measuring a learner’s opinion or feelings about their learning. Reaction data is typically collected via “smile sheets”, asking for feedback from the learner on completion of the course. This data reflects the learner’s opinion or reaction to learning. Questions can range from generic satisfaction—“did you like it” to gauging helpfulness to the learner’s job, to likeliness to improve performance.
Reaction data helps us answer the following valuable questions:
- Did learners enjoy the learning?
- Did they find it useful and relevant?
- Do they intend to behave differently as a result of the training?
We are here to help you prioritize your learning needs and identify the best way to approach your learning analytics.
You may identify a need to focus on one of our data dimensions more than the others. That’s okay! Not all learning experiences will require you to capture confidence or competence, although most courses will have an opportunity to capture these in some form. For example, an awareness course might not include opportunities to measure competence. So, you may want to focus on measuring learners’ perceptions of the subject matter before and after the learning.