Measuring outcomes is only one step in the assessment process.
It is common for the term “assessment” to be used synonymously with
“measurement,” but using the terms interchangeably is misleading.
Outcomes assessment is a process and measurement is a single step within
How to measure outcomes
When measuring outcomes, please follow this one bit of advice: Strive to collect information that will be useful.
“You don’t need to collect data you don’t use; it’s much
more important to collect a small amount of useful data than to
proliferate data that sit in a drawer or on a computer file. If you are
collecting information you are not using, either start using it or stop
collecting it. Instead of focusing on compliance, focus on the
information you need for wise action.” (Walvoord, 2010)
SLO Assessment is Action Research, Not Empirical Research
“Assessment is action research, not experimental research. While it is
systematic, it is context specific rather than generalizeable, informal
rather than rigorous, and designed to inform individual rather than
general practice” (Suskie, 2009).
Our goal is not to conduct empirical research that is scientific or
experimental that is generalizable to all students in the US or
worldwide. We do not have the time, resources or support to conduct
such research. Plus, no one expects it.
What is expected is that we systematically gather enough reliable
information (given the amount of time, resources and support available)
about Southwestern College students to make reasonable conclusions about
how and what they are learning, what supports their learning, and what
additional support they may need.
The goal of SLO assessment is to gather reliable information about
teaching and support services in order to guide data-based
decision-making. The information collected needs to be specific to our
classrooms, programs, student services, and administrative units at
Southwestern College so we can support our local student population.
With SLO assessment, “You are not trying to achieve the perfect research
design; you are trying to gather enough data to provide a reasonable
basis for action” (Walvoord, 2010).
How Much Data Should Be Collected?
It is important to consider the amount of time, resources and support
available for conducting assessments. It would be great if we could
track every single assignment produced by every single student in every
single one of his or her classes, and to track every single interaction
every single student has with campus services, but the truth is we
cannot. Further, according to the Academic Senate for California Community Colleges (ASCCC) we should not.
An important issue regarding outcomes assessment is the matter of
student privacy. If course-level assessment focuses no areas in which
learning can be improved by changes in the instructor’s practice and
methods, then the names of individual students are not relevant.
Indeed, compiling data for individual students might even prove
detrimental to assessment processes, as it could shift attention to the
performance of specific students, each with their own needs and personal
obstacles, rather than to the overall effectiveness of the teaching and
learning in the course. In addition, even in regard to SLO assessment,
the information regarding individual student performance remains
subject to student privacy rights. Recording of data for individual
students is therefore unnecessary to student learning outcomes
assessment, and colleges need to exercise great care regarding the ways
in which student information is compiled and stored. As software tools
become more powerful and subject increasingly to control by external
vendors, colleges must take all necessary precautions to safeguard
When collecting data, Walvoord (2010) offers two pieces of advice:
If you and your colleagues are going to put in the time and effort
to measure outcomes, only collect data you will use. If you collect
data just for the sake of collecting data (to meet program review and
accreditation standards), you’re wasting your time and the collection of
data is futile. Only collect information that will be useful.
It is better to collect a small amount of useful and reliable data
than to collect a lot of unreliable data that you won’t use.