Embedding Learning Analytics in Training to Assess Learner Performance

Capturing data throughout a learning experience helps learning and development (L&D) teams assess learners’ progress toward proficiency and identify opportunities to improve training effectiveness. By collecting data on what learners are thinking, feeling, doing and producing as they progress on their learning journey, L&D leaders can directly connect learning activity to job readiness and business outcomes and gain executive buy-in.
In this session, John Cleave shares a practical framework to incorporate learning analytics into training. You will see how decision points, surveys, scenarios and capstone simulations included in training give leaders insight into its potential impact. John will present a variety of learning analytics data points that can be included in training to predict workforce capability and will explore how L&D teams can use their existing learning technologies to capture meaningful data on learner progress and performance.
This leader talk will sharpen your ability to:
- Define high-value behavioral indicators before course creation so you can report on job readiness rather than simple course completion.
- Embed real-time decision checks and pulse assessments into modules to capture learner knowledge gain, reasoning, confidence and ability to apply skills.
- Use capstone simulations as diagnostic tools that generate hard evidence on how well employees perform in real-world conditions.
- Take advantage of your L&D tech stack to capture useful learning analytics data.
Speaker
![]() | John Cleave, Director of Systemic Solutions, SweetRush John Cleave, as director of systemic solutions at SweetRush, consults with SweetRush clients learning solution delivery and measurement. After earning an MA in corporate education and a Ph.D. in computer science at Northwestern University, John has enjoyed a long career designing and developing large-scale learning programs for hundreds of clients, as well as innovating in adaptive learning, simulation-based skill assessment, AI-based learning and learning path-level delivery. |
