Using Data to Improve student
Learning
State Assessment Meeting
June 21, 2013
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?
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
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?
Lessons Learned about Data
Collection and Use
Lesson 1
• A “culture of evidence” requires the
development of an institutional practice that
1. gives careful consideration to the question
being asked
2. gives careful consideration to the data
needed in order to answer the question.
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
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.
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
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
• ??
Lesson 6
• The best insights often come as an
unintended result of simple questions asked
about things you were not planning to
question.
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?
Who are our New Students?
How are our New Students Doing?
Lesson 7
• Simple assessment measures does
not necessarily produce less
meaningful data.
• Checklist
• 4 question multiple choice “test”