Using data to drive learning outcomes isn’t a new concept. For as long as teachers have been giving students assessments, both students and teachers have used the evaluations and results (even if only loosely) to determine how to move forward. What needs to be reviewed more? What was covered/studied well? Learning analytics takes this concept and kicks it up a notch more like a thousand cracks, especially if you’re considering things like adaptive computer-based testing that changes as students use it.
The handy infographic below takes a look at the four levels of learning analytics, which can be easily applied in your classroom, whether you’re using a ton of fancy-schmancy technology or none at all.
Checkout and share your view: Can you utilize the 4 learning analytics to drive change & learning into your classroom? Can you get it done with technology or not? Join the conversation by leaving a comment under.
The 4 Levels of Learning Analytics
- Descriptive: What’s happened? Look at facts, statistics, and any other data you have that give you a thorough picture. What concepts have been mastered, and what ones weren’t?
- Diagnostic: Why did it happen? Assessing the descriptive elements permits you to determine why an outcome happened badly? The student did ok on geometry queries but bombed the algebra-based material? Spending less class time on the algebra stuff? Were different kinds (or amounts) of assignments given? Start Looking for explanations.
- Predictive: What will happen? This is what you watch out for: What your expected outcome would be based on different elements. Consider it as a pick your adventure — will the student learn algebra-based material better if X, Y, or Z occurred?
- Prescriptive: What should I do? How can a particular outcome be accomplished through the use of specific components? Please take what you’ve learned through 1, 2, and 3 and use it to reach the learning outcome you’re looking for!
