Learning's Big Data Problem: Measuring & Analyzing Impact Part 2

Editor's Notes

• #20: There are so many things required to achieve scalable learning We’re not just talking about dissemination of information we know, but also about the acquisition and discovery to new knowledge in a scalable way How do we get there. To achieve scale, we need measurement, we need a culture of constant improvement and we need alignment with business objectives Because this isn’t just about scaling learning, it’s about scaling an organization’s ability to adopt to the exponential rate of change the digital economy is creating To achieve scale, we need analytics, we need data
• #21: There are so many things required to achieve scalable learning We’re not just talking about dissemination of information we know, but also about the acquisition and discovery to new knowledge in a scalable way How do we get there. To achieve scale, we need measurement, we need a culture of constant improvement and we need alignment with business objectives Because this isn’t just about scaling learning, it’s about scaling an organization’s ability to adopt to the exponential rate of change the digital economy is creating To achieve scale, we need analytics, we need data
• #22: You can’t manage what you can’t measure. You can’t measure what you can’t define. Learning and development analytics don’t have to be complicated, as long as you stay organized. Start with the basics and work your way out from there. The simplest way to think of learning analytics is to divide it up into categories and complexities.
• #23: Learning experience analytics seek to help you understand more about a particular learning experience or set of learning experiences. Learner analytics seeks to understand more about a specific person or group of people engaged in activities where learning is one of the outputs. Learning program analytics seeks to understand how an overall learning program is performing.
• #24: The most basic form of analytics is measurement—the simple act of tracking things and recording values. Evaluation is the process of trying to make meaning out of the data you’ve measured. And does the data mean something good or bad? Advanced evaluation techniques—such as exploratory and inferential analytics— search for relationships within your data and discover powerful insights. The first three levels help you understand what’s already happened. Predictive and prescriptive analytics help you understand what will happen in the future.
• #25: When categories and complexities are combined, it forms the matrix (or pyramid) of learning analytics. Many organizations master the lowest level of complexity within a specific category and then work their way outwards. Some organizations may reach high levels on one category, but remain in the lower levels for the others. As long as you can identify where you fall within the pyramid, you’ll know where you’re going, which helps you to set goals, determine metrics, and evaluate the maturity of your program. So doesn’t this contradict the evaluation methods Michael discussed before? Nope—they fall nicely in the middle. Evaluation is result of measuring data and the cause for predictive and prescriptive analysis.
• #27: During the last webinar we talked more in depth about xAPI and LRS. As a quick recap, xAPI is a technical specification that makes it easier and cheaper to connect learning technologies and an LRS is the place where information from all of those systems can easily be analyzed to drive a culture of continuous improvement.
• #28: During the last webinar we talked more in depth about xAPI and LRS. As a quick recap, xAPI is a technical specification that makes it easier and cheaper to connect learning technologies and an LRS is the place where information from all of those systems can easily be analyzed to drive a culture of continuous improvement.
• #36: HOW WE DO IT We Put Your Business First We Listen and Respond to Your Needs We Keep Our Approach Simple, Flexible, Principled We Communicate