responded the way he or she did,
helping to identify gaps in skill level,
conceptual misunderstandings, or
other information that could inform
instruction.
Instead of just one data point—a right
or wrong answer—technology-enabled
assessments can produce hundreds of
data points about student actions and
responses. One of the major research
challenges at this time is developing and
validating statistical algorithms to
analyze and distill these data into usable
information.
Real-Life Applications
Simulated exercises are useful for
assessing students’ knowledge of interactions among multiple variables in a
complex system, such as an ecosystem.
But because these models assess both
process and content, they require assessments that are closely linked with classroom instruction.
This presents a problem for the broad
use of these models. The TRE project,
for example, restricted its assessment to
scientific problem solving with technology—rather than science content—
because NAEP cannot assume that
students in the United States’ 14,000
school districts have all covered the
same science content.
In contrast, the Calipers project,
funded by the National Science Foundation, seeks to develop high-quality,
affordable performance assessments that
can be used both for large-scale testing
and in classrooms to inform instruction.
Focused on physical science standards
related to forces and motion, along with
life sciences standards related to populations and ecosystems, Calipers engages
students in such problem-solving tasks
as determining the proper angle and
speed to rescue an injured skier on an
icy mountain (see fig. 2). Similar to
Technology-Rich Environments, Calipers
captures descriptive data, describing the
approach that a student took to solve the
problem (choice of experimental values,
choice of formulas), along with multiple-choice and open-ended responses. These
descriptive data, along with student
reflection and self-assessment activities,
can provide to both students and
teachers information to guide learning
and instruction.
Fully immersive simulations, such as
those found in medical education and
military training, point to further applications of technology. iStan, a lifelike,
sensor-filled mannequin that can talk,
sweat, bleed, vomit, and have a heart
attack, is used for medical training to
simulate patient interactions and
responses. The U.S. Army has “
instru-mentalized” many of its war games and
other performance exercises, using video
John Bransford, a professor at the
University of Washington and a leading
expert in cognition and learning technology, is designing assessments that
enable students to demonstrate not only
what they can recall, but also how they
can use their expertise. Technology-enhanced environments and virtual
worlds, such as those found in medical
training, are necessary for students to
practice and gain feedback in real-life
situated environments.
Putting It All Together
But technology alone cannot transform
assessment. We first need to overcome
logistical and funding challenges that
often impede efforts to maintain, administer, and update schools’ technological
Simulated exercises are useful for assessing
students’ knowledge of interactions among
multiple variables in a complex system.
cameras and sensors to gather multiple
sources of data about what is happening
and when. These extensive data can
illustrate multiple interactions among
team members and lead to productive
conversations about what happened,
why, and how to improve. These types
of assessments and simulated experiences are becoming more prevalent in
higher education and the workplace.
This focus on situated assessment—
assessing behavior in realistic situations—is increasingly important when
people need to be able to communicate,
collaborate, synthesize, and respond in
flexible ways to new and challenging
environments. However, assessing the
ability to approach new situations flexibly is challenging in the current paper-and-pencil testing environment.
infrastructure. Also, new assessment
models must not erode efforts to
promote high expectations for all
students, nor should they disadvantage
low-income schools and students with
limited access to technology.
Successful changes to assessment will
also require equally challenging revisions
to standards, curriculum, instruction,
and teacher training. Without deliberate
attention to these areas from policymakers and educators, there is no guarantee that technology will fundamentally
change core practices and methods in
education, a field that is notoriously
impervious to change. According to
education historian Larry Cuban (1996),
just adding technology and hoping for
education transformation, without
considering the content and practice of
