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Using Data to Improve student Learning

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  1. Using Data to Improve student Learning State Assessment Meeting June 21, 2013

  2. Using Data to Improve student Learning • Think – Pair – Share • In your work in higher education, what is the best piece of data you have ever seen? • Why? • An example of Great Data • What is different about data from learning assessment?

  3. Bias:The Purpose of Assessment (Data) is to improve student learning • Assessment of learning creates the possibility of better conversations • Course Level Assessment • Faculty – Student • Student – Students • Program Level Assessment • Faculty – Faculty

  4. Bias: Data from Learning Outcomes Assessment at the Program Level is Messy and the Messiness is an Important Part of the Process An Example What should we expect from Students exiting Comp1?

  5. Lessons Learned about Data Collection and Use

  6. Lesson 1 • A “culture of evidence” requires the development of an institutional practice that • gives careful consideration to the question being asked • gives careful consideration to the data needed in order to answer the question.

  7. Lesson 2 • Structured reflection and dialogue allows for data to be transformed into meaningful (actionable) information • The “meaning” of data in Higher Education is not generally self-evident and requires the benefit of the intersection of multiple perspectives • The more meaningful learning data is to an outside audience the less actionable it is to those who work with students

  8. Lesson 3 • Meaningful information promotes consensus about lessons learned and a shared vision / plan for the future • No data should be shared as information until it has been processed in a collaborative and thoughtful way. 

  9. Lesson 4 • A culture of evidence is one that seeks data supported decisions. • Data driven decision making runs the risk of over looking / underestimating the human factor which is very often concealed by the desire for statistical significance • Data driven decision making runs the risk of underestimating the role / significance / importance of evidence informed hunches to inform our decisions

  10. Lesson 5 • Meaningful information from data does not generally emerge from a single data point but from the intersection of multiple and varied (quantitative and qualitative) sources of data • There is rarely a silver bullet (and if there is, then the question being asked is probably not particularly interesting) • What data can be add to learning data to make it more meaningful • Student Assessment of instruction data • CCSSE • Grade distribution report • ??

  11. Lesson 6 • The best insights often come as an unintended result of simple questions asked about things you were not planning to question.

  12. Lesson 6 (An Example) • How are our new students doing? • Data was provide on FTIC Degree Seeking Students • Who are our new students? • Development of our Philosophy statement on the New Student • All Students with less than 15 College-level credits at Valencia • Who are our new Students?

  13. Who are our New Students?

  14. How are our New Students Doing?

  15. Lesson 7 • Simple assessment measures does not necessarily produce less meaningful data. • Checklist • 4 question multiple choice “test”