Exploring complexities

- I want to set the stage, here. This is a complex course with a lot of complex concepts, but I also want to reassure you that we're in this together, and I will be here along the way. Throughout the course, feel free to pause videos where you need to and even go back and rewatch in places. This course is for you, so make yourself comfortable and take the time you need. With that said, let's start laying the groundwork in defining our main themes and how these themes will be applied to determine what makes learning successful to individuals as well as organizations. First, let's define the term learning analytics. It's the measurement, collection, analysis, and reporting of data about learning in order to understand and optimize learning and learning's impact on an organization's performance. Beginning with the simplest form of analytics, measurement is the act of tracking activities and recording values. This is the foundational level that ensures success as you attempt more advanced analytics later on. There are a couple of different methods to gather this data, commonly either through passive or active data collection. Passive data collection is the act of accumulating data through usage of technology or a particular system. In other words, it's just measuring the activity that's already occurring without your intervention. Active data collection is just that, intentionally taking measurements to collect data. A more advanced level of analysis is evaluation, which is the process of deriving meaning from the data you've measured. In its simplest form, evaluation is the process of asking whether the data indicates something good or bad. For instance, two exams might have different definitions of passing. A score of 80% may not be good enough when the subject matter is drug safety, whereas receiving 80% on a leadership development pretest might be considered exemplary. The former has no room for error, so 80% is unacceptable, whereas the latter won't directly harm patient lives. Advanced methods of evaluation ask the more in-depth question of why the data indicates something good or bad. Expanding on our previous example, we can ask why our doctor needs to improve their leadership pretest score to inform what might be particular areas of focus during the leadership training. In that case, our hypothesis is that focusing on certain skill areas will lead to better leadership development, and we can test the validity of that hypothesis throughout the program. Once you have an understanding of why things might be trending positively or negatively, further analytic comprehension can be achieved through predictive and prescriptive analytics. We can apply our knowledge about why a thing is happening to potential future scenarios and begin predicting what will happen at a point in the future. Think about scenarios in your own situation in which you can conceptualize progressing from measurement to more advanced forms of evaluation. As you do so, document each level of measure or metric along the way, and more importantly, document or remember how you use that information to change something in the future.