Tracking and Analyzing Assessment Results Butler & McMunn (2006)…
Tracking and Analyzing
Butler & McMunn (2006)
: Observing patterns and trends
assessed more formatively to make inferences about student learning
Data analysis process
Look for patterns
Collect useful data so that qualitative
quantitative data can be derived
Values of assessment data
configure student groups
examine strengths/ weaknesses,
help learners set goals
differentiated instruction - if it benefited learners
were valid assessments used to derive grades
set grade-level expectations for students
Examining individual assessments
What did the student learn and how well
did they learn it?
was it due to lack of information or
lack of understanding of the directions given?
Is this assessment evidence valid?
does it accurately measure what it is
designed to measure?
can it be added to other evidence
of the student's learning?
Is this assessment evidence relevant?
i.e. does it address the learning target(s) directly/indirectly?
Clear and precise feedback given to student?
Can the student and teacher review the evidence together?
Is there sufficient evidence to make inferences of the student's learning?
Criteria of good assessments/rubrics
requires cognitive challenge/intellectual work
that students are regularly exposed to
aligns with important learning targets
requires written, constructed responses
provides clear descriptions of expectations
relevant, reliable (consistency), and valid (measures what they are intended to measure)
to use when making decisions about
Looking for pattern/trends:
Change over time data
-student's portfolio of essays
-running records - work samples, videos, audiotapes
-learning analysis sheet
Anecdotal data (observations)
-descriptions of student behavior/performance
(score range, item analysis)
-used to determine if assessments
need to be readjusted / revised / concepts
need to be retaught
Creating valid inferences
Portfolio use - contains valuable sampling
of students' work
Worksheet analysis - e.g. observations
Electronic data - e.g. discussion board participation