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October 2012






Policy Brief
Assessment is a large topic for Informaon and Communicaon
Technology (ICT), with important issues ranging from how to
evaluate the eecveness of an ICT to how to ensure that someone
receiving cercaon through an online course has proven mastery.
One approach to assessment with ICTs is to use technology to make
exisng forms of assessment more reliable and e cient. This brief
concentrates instead on how technology can change the way we
think about assessment, addressing two specic issues. First, how
can technology more eecvely enable assessments to be used
as a tool to improve student learning in ICTs? Second, how can
technology increase the range of learning outcomes we can assess
and what will these assessments look like?
IITE Policy Brief October 2012
Assessment is a big enterprise involving many possible stakeholders. For example, the
Programme for Internaonal Student Assessment (PISA) is an assessment given to
students in over 70 countries to provide a common metric of student performance.
It yields results at the naonal level. In contrast, teachers use informal formave
assessments in their classrooms every day to gauge individual student progress and
tailor their instrucon appropriately. Both of these assessments, as well as others, have
value for dierent purposes and stakeholders. It is important to understand the goals
and stakeholders of an assessment to make appropriate decisions about its design, the
intended use, and the interpretaon of the data it produces.
Below are a few examples of the potenal funcons of assessment and related
Ranking a country (state,
district) against others
The Programme for
Internaonal Student
Assessment (PISA)
policians, policy makers
Measuring progress of
a school (district, state)
over me
API growth indices in
California state standardized
policians, policy
makers, school
Ranking individuals IQ tests, the SATs Students, employers,
admissions o cers
Measuring an individuals
mastery of a subject
maer or skill set
Cercaon exams; IB tests Students, teachers,
granng instuons,
schools, employers
Understanding a
students current skill
and knowledge state
to more eecvely
tailor instrucon to that
Embedded formave
assessment in adapve
tutoring systems
Students, teachers,
instruconal designers
Measuring the
eecveness of an
instruconal intervenon
or learning environment
Randomized controlled trials,
such as in the What Works
Policy makers,
instruconal designers/
Iterave improvements
to an instruconal
intervenon or learning
A/B tesng, in which students
receive dierent versions of
an intervenon to test which
is more eecve
Teachers, instruconal
designers, soware
How Technology Can Change Assessment
This brief discusses innovaons in assessment made possible by technology that
can apply to all the levels of assessment described above. However, in relaon to
ICTs specically, the boom entry in the table the use of assessment for iterave
improvement is underulized. We highlight this new possibility as a major way to
transform the design, evaluaon, and delivery of ICTs.
Discussions of assessment in ICTs oen concentrate on increasing the precision of
assessments for evaluang individuals. One reason is that test makers are concerned
about issues of reliability (consistency) and validity (are they measuring what they
intend) of online assessments, and also about how to prevent cheang. These are
important issues. For example, as online programs increasingly confer cercaon, it is
crucial that instuons can be condent that the students have actually mastered the
material. A second reason for improving the precision of measuring individual learning
is that it enables adapve learning systems. Adapve learning systems ensure on-going
assessments of a learners knowledge state, so they can tailor problems and instrucon
appropriately. Cognive tutors (e.g., hp://pact.cs.cmu.edu), for instance, connually
assess students, updang a model of the students skills and knowledge. This model
of the learner is compared to a model of an expert, so the system can determine
discrepancies and deliver subsequent assignments accordingly.
A dierent goal of assessment is to evaluate learning experiences. Instead of making an
inference about how well a student is doing, the desired conclusion is about how well
instrucon is doing. This is not a new idea; educaonal research has been comparing
dierent instruconal condions for decades. However, when one explicitly shis the
focus of assessment to learning experiences rather than individuals, there is a shi in
priories. Quesons about the reliability and validity of assessment measures remain
important, but equally important is the ability to make inferences about the quality
of dierent specic learning experiences. If the goal of assessment is to improve
instrucon, then the measures need to yield aconable informaon at the level of the
learning environment.
IITE Policy Brief October 2012
Educaonal researchers and policy makers have recommended increased formave
assessment in classrooms and learning environments (e.g., Darling-Hammond, 2010;
US Department of Educaon, 2010). Oen considered assessment for learning (as
opposed to assessment of learning), the goal of formave assessment is to provide
feedback during the learning process to inform instruconal decision making. For
example, formave assessment might help a teacher realize which part of a math
formula students are struggling with during the lesson, so the teacher can shape
instrucon accordingly. In adapve learning environments, formave assessment allows
the environment to gauge the students current knowledge state to determine what
problems and instrucon to present next. This is in contrast to summave assessment,
where the goal is to provide informaon about the end product of learning did the
student learn to a standard or not? Standardized tests that report the performance of
students in a given school district are an example of summave assessments. These
tests are evaluave, and there is no assumpon that informaon from them will inform
the instruconal process for a parcular student.
Shiing the assessment focus from individuals to learning environments has implicaons
for the design of research. Large-scale randomized controlled trials, in which students
are randomly assigned to treatments to evaluate the eecveness of an educaonal
technology, are generally summave assessments. The goal of the assessment is not to
improve the learning environment; rather, the goal is to determine whether the learning
environment increased scholasc gains compared to no instrucon or a dierent form
of instrucon. The treatment comparisons for these types of studies are usually too
coarse to determine which aspect of an instruconal treatment made a dierence
one way or another. As such, they cannot serve a formave funcon, but rather, they
are designed to decide whether it makes sense to adopt a parcular technology or
Technology makes it possible to use large-scale tesng in a more formave way to help
shape and improve the eecveness of ICTs for learning. In parcular, the ability of
ICTs to reach a broad range of students, collect data, and present dierent variants
of the same material makes for a powerful research tool. Quesons as broad as the
best trajectory through the learning content or as narrow as the most eecve way
of visualizing informaon can be tested empirically, and the environment can be
shaped to reect the results. Under this formave model of research, students can
sll be randomly assigned to condions, but the goal of assessment is the connual
improvement of the ICT, not the nal proof of eecveness.
How Technology Can Change Assessment
One way ICTs enable large-scale formave research is through the use of A/B tesng.
A/B tesng is used in the soware industry to determine opmal features for products
and markeng. For example, Amazon.com might show a million customers a webpage
for a given product that it is selling. For half of the customers the webpage might show
a red buon, and for the other half of the customers there could be a green buon.
If more customers who see the green buon buy the item, Amazon has learned
that the green buon conguraon is more eecve. These ideas can be applied
to educaon in addion to markeng. Refracon (hp://games.cs.washington.edu/
Refracon) is a game-based approach to designing an opmal sequence of problems
for fracon learning. By using A/B tesng, the Refracon system can determine the
opmal sequencing of the curriculum. For example, aer compleng problem set
A, half of the students can be sent to problem set 1, while the other half works on
problem set 2. Aerwards, all the students can complete problem set B. One can
then compare which sequence leads to beer performance on problem set B: A1 or
A2. If the students do beer aer A1, this sequence can then be incorporated into
the design of the system.
As a second example of iterave design and tesng, the Learn Lab at the Pisburg
Science of Learning Center (hp://learnlab.org/) used A/B tesng to modify and
improve an online chemistry class over the course of a few semesters. Students
received versions of the course that diered in the presentaon of diagrams.
Embedded learning assessments, paerns of usage me and qualitave interviews
were analyzed. The results of these comparisons then fed into further course
redesign and addional tesng (see, for example, hp://evidenceframework.org/
rapid-controlled-experimentaon-to-facilitate-learning-di cult-conceptual-content).
While many decisions in the design of ICTs can be informed by exisng data and
learning theory, there are countless decisions to be made that will inuence learning,
and for which no empirical research currently exists (or the research is conicng).
Trying to run studies to address all of these decisions in advance of developing a
learning environment is intractable. A dierent approach involves an iterave process
of controlled experimentaon and connual improvement that is based on data
collected during the use of the ICTs. This type of boom-up design research requires
very large samples of students to test the many possible combinaons. Technology,
especially the internet, helps to solve this problem. Crowd-sourcing across thousands
of internet users has rapidly become a standard tool within the research kit of
behavioral sciensts. There are even commercial-grade plaorms, such as Amazons
Mechanical Turk (www.mturk.com), that help researchers connect with willing users
to conduct A/B experiments.
IITE Policy Brief October 2012
Technology has the power to transform what is assessed in ICTs and how. Assessments
can go beyond end-of-unit mulple-choice quesons to gather process data on how
students are going about learning and solving problems. This occurs by embedding
learning assessments within the learning experience and analyzing process data in log
les that capture every click and key stroke. In reference to computer learning games,
Shute (2011) uses the term stealth assessments, to refer to embedded assessments
that collect user performance data as part of the game. The performance data enables
inferences about the users knowledge state and learning. This kind of assessment can
reduce test anxiety, because the lines between learning and assessment are blurred;
the assessment is integrated with the learning process. It also has a benet for overall
me spent learning instead of tesng, because it is not necessary to close the store to
take inventory. For example, one task in a game about river ecology involved students
collecng virtual water samples from various parts of a river. The system logged the
accuracy and e ciency with which students collected the samples. The system further
used these data to inform the predicon of a students informaon gathering abilies.
Embedded assessments can help make ICTs adapve to individual student performance
and skill levels by allowing dynamic modicaon of instrucon and di culty based on
performance. They can also inform the design of learning experiences by comparing the
outcomes of the embedded assessments for students who received dierent versions
of the environment. By collecng data that indicates accuracy, hints needed, me on
task, resources used, and so forth, embedded assessments can provide designers with
evidence of the eecveness of design choices for learning and engagement, while
minimizing disrupon and test anxiety.
It is important to note that embedded assessments do not need to be hidden
assessments. In fact, there are examples where providing students with the results
of embedded assessments can drive greater learning and engagement. For example,
the popular online game World of Warcra connually assesses player progress and
presents feedback to the player in the form of heads up display that appears on the
game screen. The informaon is highly movang and points students to where they
should focus their aenon and learning eorts so they can do beer and open up new
levels within the game (Reeves & Read, 2009).
In addion to reconsidering when and where assessment can happen, we can also
rethink what is being assessed. While codied knowledge is important, equally crucial
How Technology Can Change Assessment
for 21st century job success is the ability to adapt and learn (e.g., Benner, 2002).
Bransford and Schwartz (1999) introduced the concept of preparaon for future learning
assessments. In this kind of assessment, rather than only measuring the students
codied knowledge (e.g., can they come up with the formula for momentum), students
are provided learning resources during the assessment. The assessment measures how
prepared students are to learn from those resources. For instance, they might be given
a worked example of a momentum problem within the assessment, and then asked
to solve a new kind of problem they had not yet encountered, but for which learning
from the worked example would be helpful. For iterave design of ICTs, the queson
becomes whether one experience (i.e., one version of the system) prepared students
to learn new, related content beer than another. These kinds of assessment measures
are more similar to what students will experience outside of school (e.g., learning on
the job) and can show dierences between learning experiences where tradional
measures of codied knowledge do not (e.g., Chin et al., 2010).
Technology can allow researchers and designers to collect data on students choices
about learning, which creates an interacve preparaon for future learning assessment.
For example, in a simulaon environment called River City, researchers used data about
what kind of informaon students chose to examine within the simulaon to assess
their inquiry skills (e.g., Ketelhut, 2007). Schwartz and colleagues (2012) used computer
log les to examine how students used crical thinking skills to learn. Students needed
to mix the primary light colors red, green, blue to create new colors. The online
environment provided several resources for learning including an experiment room
where students could mix light beams as well as a set of charts that held conicng
informaon for how to mix light to create dierent colors. Students who spent more
me evaluang the charts learned more about mixing light and were also doing beer
in school.
In the United States, the Naonal Assessment of Educaonal Progress (NAEP) is a
large naon-wide assessment of student performance. NAEP is pilong assessments in
which they track students use of resources, such as charts and graphs, during science
inquiry tasks (e.g., what causes phytoplankton growth). Measuring learning process
data can provide a powerful indicator of a given students inquiry and crical thinking
skills. Across students, collecng data about learning choices and outcomes makes
it possible to examine which learning paerns are more producve (lead to beer
learning outcomes). This informaon can then be used to inform design choices within
the ICT. For example, the system can intervene if students appear to be heading down
an unproducve learning path.
IITE Policy Brief October 2012
An important challenge in collecng process data from ICTs is determining how to
analyze it. One soluon receiving considerable aenon is data mining. Data mining is an
approach that leverages computers to discover paerns in large sets of data. Data mining
is used in business, for example, to nd paerns in shoppers buying habits. In educaonal
applicaons, an example might involve using click-stream data from computer logs of
student interacons along with informaon about the students prior achievement to
determine the best kind of student help to provide within an online system. For example,
using student data from interacons with an intelligent computerized tutor, Min Chi and
colleagues conducted data mining and machine learning to infer and design teaching
policies about which acon the computer tutor should take for a given problem-solving
event to opmize student learning gains (e.g., tell the answer to a problem or ask the
student to answer, Chi, VanLehn, Litman, & Jordan, 2010). While data mining is becoming
a hot topic in educaon, there are sll considerable impediments to its wide scale
adopon. The required computaonal and human resources are sll expensive. The
process requires a considerable amount of human intuion, knowledge, and eort on
the front end to determine the most eecve way to structure the informaon that goes
into the machine learning algorithms so they will generate meaningful and aconable
paerns. As data mining techniques advance in business and educaon, they will likely
become increasingly available and tractable for wide-scale applicaon in ICT assessment.
One example of an eort to reduce the expense and increase the tractability of
assessments of complex phenomenon comes from the autoscoring of wring samples
and the crowd-sourcing of answers. For example, the online course system, Coursera
(hps://www.coursera.org), can register more than 10,000 students in a class. They
have started using peer grading and crowd-sourcing to make grading of wring samples
possible. Students in the class are trained to use a grading rubric, and are assigned to
grade each others work. Mulple students are assigned to grade each work. This is
based on crowd-sourcing research, which found that taking the average of mulple
rangs will lead to an accurate score, even if the accuracy of any individual rang may
not be reliable. As another approach, the educaonal tesng service (ETS) is automang
the grading of wring and open-ended responses using natural language processing
(hp://www.ets.org/research/topics/as_nlp). Natural language processing refers to
using computer algorithms to make sense of and interact with human language. This
is being applied to students wrien responses to analyze the content in relaon to a
rubric or intended concept.
Tractability and the ability to scale-up are not the only challenges to the new wave of
technology-enabled assessments. With increases in the ability to seamlessly measure
learning with embedded assessments, there are new ethical concerns about the fairness
of the tests. Students may not be aware they are being evaluated or what aspect of their
performance is being evaluated, and therefore, they may not show maximal performance
(what they can do under opmal circumstances when they know they are being assessed).
This is an important concern when the goal of assessment is to sort and characterize
students, for example, to determine who gets into a university program or enrichment
program. Students may not put forth their maximum eort, because they do not know
that there is a high-stakes assessment occurring. As a result, the assessment may not
How Technology Can Change Assessment
provide a good characterizaon of student potenal or accomplishment. However, if the
goal of an assessment is to inform the design of instrucon rather than rank students,
then the fairness concerns are less pressing. For the goal of improving instrucon, it may
be more eecve to collect data on students typical performance (what they would be
likely to do in a new situaon in absence of explicit assessment cues). On the assumpon
that instrucon occurs under condions of typical performance, then assessments of
typical performance would be the most informave.
Finally, when using assessment for connual improvement of ICTs as proposed in this
brief, it is important to avoid hurng students by using instruconal condions that are
known to be ineecve or put student learning at risk. For instance, with A/B tesng,
it is important to avoid design decisions known from research to lead to less eecve
learning. However, for many design choices, there is no research available about which
is beer, or the research is incomplete or conicng. In these cases, tesng dierent
versions will be unlikely to hurt students relave to current pracce, because students
will oen receive standard pracce as the alternave condion. Moreover, given
the results of a comparison, the more eecve pracce can be rapidly incorporated
into the learning environment to help future learners. For these kinds of decisions for
which there is no directly applicable research, rather than making a command decision
and scking to it, as is oen the pracce, empirically tesng dierent opons leads to
ulmate improvement of the learning environment.
Technology can increase the e ciency of exisng models of assessment, but it can also
transform how assessment is used and conceptualized. This brief makes the following

Assessments should be designed to yield informaon that is aconable at the
appropriate level. Assessments have many dierent uses, from ranking naons
to characterizing a student to evaluang a learning experience. Dierent uses
require dierent kinds of assessment. For example, assessments designed to
rank schools do not generally provide informaon that is useful at the level
of making instruconal decisions for an individual student. One frequently
overlooked use of assessment is for the purpose of informing the design of
learning environments. In this case, assessment should be of a su ciently
ne granularity to isolate the factors in the learning environment that lead to
increased or decreased learning.

Formave assessment can be an important tool for making instruconal
decisions. Formave assessment is considered assessment for learning,
where the primary goal is to inform the learning process. This contrasts with
summave assessment, which measures the end product of learning, and is
primarily evaluave. Formave assessment can be used at an individual level to
decide what informaon or problems to present to a student given their current
level of understanding. It can also be used at the level of ICT development to
determine which design choices are most eecve for learning in a virtuous
cycle of tesng and improvement.
IITE Policy Brief October 2012

Assessments can be embedded in ICTs. They do not have to take me away
from learning. The embedded assessments can be integrated into the learning
process, such that students are not aware they are being assessed, which can
reduce test anxiety. Or they can be explicit, such that students are aware they
are being assessed and can see their progress and improvement.

One way ICTs can leverage assessments to inform the connual improvement
and renement of the ICT is by taking advantage of the internet, using A/B
tesng and crowd-sourcing to gather feedback and improve the design of the
learning environment. Varying features of an ICT in a controlled way allows for
evaluaon of which design and instruconal choices lead to the best learning.

In addion to collecng tradional measures of student knowledge, ICTs can
measure process data about how students go about learning and solving
problems in the ICT. This allows for assessment of student inquiry skills and
students preparaon to connue learning outside of the ICT, which is in line
with the 21
century demand to keep learning on the job. Looking at student
process data can also inform which paerns of interacons lead to beer
learning outcomes within the ICT, which can help ICT developers design
producve sequences of instrucon. Investment in new methods of data mining
will make it possible to analyze and evaluate important process outcomes, such
as how students go about learning, expanding what is possible to assess.
One approach to bridging technology and assessment involves using technology to make
exisng models of assessment more robust and e cient. Another approach is to use
technology to change how we think about the funcon and reach of assessment. This brief
takes the later approach. Technology-aided assessments can help in the design, delivery,
and improvement of learning environments. A common model of ICT assessment involves
large-scale randomized controlled trials, in which a large sample of students is randomly
assigned to a treatment and control group to test the eecveness of an educaonal
technology. This type of summave assessment may be useful for determining whether
to spend millions on a large-scale adopon of an exisng technology, but it is not an
e cient way to help in the development of new innovave technologies that can shape
the future of educaon. ICTs have a unique ability to reach many learners (for example,
Coursera has over 1.5 million users). Addionally, they can be designed to collect data
and selecvely present dierent informaon to dierent students. This can allow for the
empirical tesng of quesons about the best design choices for learning, and the results
can be incorporated into the learning environment for connual improvement. In terms of
reach, technology allows for new kinds of assessments, including assessments of students
learning and problem solving processes that are embedded in the learning context, and
assessments of how well prepared students are for future learning. Assessment has a
powerful eect on educaon, not only serving an evaluave funcon, but also shaping
what is considered important for students to know. Expanding the scope of assessments
from evaluang only end-state knowledge to evaluang learning processes themselves
has the potenal to transform what is taught and how.
How Technology Can Change Assessment
Benner, C. (2002). Work in the new economy: Flexible labor markets in Silicon Valley.
Oxford: Blackwell Publishing.
Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with
mulple implicaons. In A. Iran-Nejad & P. D. Pearson (Eds.), Review of Research in
Educaon, 24, 61-101. Washington DC: American Educaonal Research Associaon.
Chi, M. VanLehn, K. Litman, D., and Jordan, P. (2010). Inducing Eecve Pedagogical
Strategies Using Learning Context Features. Proceedings Eighteenth Internaonal
Conference on User Modeling, Adaptaon, and Personalizaon (UMAP2010).
Pp. 147-158.
Chin, D. B., Dohamen, I., Oppezzo, M., Cheng, B., Chase, C., & Schwartz, D. L. (2010).
Preparaon for future learning with Teachable Agents. Educaonal Technology
Research and Design, 58, 649-669.
high-quality learning. Washington, DC: Council of Chief State School O cers.
Ketelhut, D. J., (2007). The impact of student self-e cacy on scienc inquiry skills:
An exploratory invesgaon in River City, a mul-user virtual environment. The
Journal of Science Educaon and Technology, 16 (1), 99-111.
Reeves, B. & Read, J. L. (2009). Total engagement: Using games and virtual environments
Business Press.
Schwartz, D. L., & Arena, D. (in press). Measuring what maers most: Choice-based
assessments for the digital age. Cambridge, MA: MIT Press.
Shute, V. J. (2011). Stealth assessment in computer-based games to support learning.
In S. Tobias & J. D. Fletcher (Eds.), Computer games and instrucon (pp. 503-524).
Charloe, NC: Informaon Age Publishers.
U.S. Department of Educaon. 2010a. Naonal Educaon Technology Plan.
IITE Policy Brief October 2012

Many discussions of technology-based assessments concentrate on
automang current methods of tesng to save me and cost. However,
technology also changes what educators can assess, how and when
in ICTs, and have the potenal to measure learning processes, in
addion to end-of-lesson knowledge. Technology-aided assessments
are useful not only in the evaluaon of ICTs, but also as part of the
design process, leading to iterave improvement. This brief focuses on
assessment in ICTs, discussing how technology-enabled assessments
can be leveraged to improve ICT design and student learning.
Authors: Kristen Blair and Daniel Schwartz
Published by the UNESCO Instute
for Informaon Technologies in Educaon
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UNESCO, 2012
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