
Data has become a bit of a dirty word given the recent exposé of how social media platforms vacuum up your interactions and the ‘less then beneficial’ ways these insights can be used! Big data has never had an easy ride in the education world especially given education’s often slow adoption of technology driven or enabled trends.
So what is the elusive recipe for the successful use of data to drive outcomes in schools? I would never lay claim to having THE recipe for success, but I will venture to suggest some possible vital ingredients below. Bearing in mind that all the best cooks will say that the success of the cake depends largely on the quality of the ingredients!
- Individualized, detailed, diverse and real-time (where possible) digital learner data is now possible and readily available thanks to the deployment of EdTech on a large scale in classrooms.
- This essential ingredient must be complemented by non-digital data sets. They encompass information such as contextual issues, human insights and observations of both learners and learning.
- Key metrics need to be decided upon for the analysis and outcome indicators – What is the question that YOU are asking? What do YOU need to know from the data? Prioritize and target the analysis provided! Far too often valuable trends and warning signs are drowned in a sea of unnecessary information.
- Data literacy should be addressed. Don’t assume that everyone is a data savant and knows how to interpret the huge quantity of analysis available. What am I looking for? What should I expect to recognize if there is progress? What are the warning signs I should look out for? Interpretation is often the hardest aspect to grasp. This is the vital link between a point on a graph and what it is showing about what is actually happening within classroom.
- Collaborative reflections. We often end up dealing with our particular data in isolation, especially when it is generated electronically. There is such a benefit in sharing our interpretations of the analysis, comparing trends and issues and collaborating on possible solutions.
- Learner engagement – data is not just about learners, it should be for them too! We are missing out on an important step if learners are not part of this reflective process. We are hoping to enable learners to analyze and respond dynamically to situations – this is one way to cultivate this type of thinking.