3. Collect and analyze formative data on the instance. Next, you begin data collection
by conducting a formative evaluation of the design instance (see e.g., Dick & Carey, 1990). The
intent is to identify and remove problems in the instance, particularly in the methods prescribed
by the theory. In some situations, design and implementation of the instance occur
simultaneously, in which case the data are collected during the design process (or alternatively
design occurs during the data collection process). In other situations, design and development of
an instance are completed before implementation begins, in which case data collection comes as
a separate phase of activity. In still other situations, you can do a combination—some smallscale
testing of parts as you design the instance, then larger-scale testing of the whole when it is
completed. First, you should prepare the participants, so that they will be more open in providing you
with the data you need. This can be done by explaining that you are testing a new method, that
you want them to be highly critical of it, and that any problems encountered will be due to
weaknesses in the method, not to deficiencies in themselves. Try to establish rapport with them,
and in one-to-one formative evaluations, try to get them to think aloud during the process (in this
case, the instructional process).Three techniques are useful for collecting the formative data: observations, documents,
and interviews. Observations allow you to verify the presence of elements of the design theory
and to see surface reactions of the participants to the elements. Documents on both elements
(methods of instruction, in this case) and outcomes can help you to make judgments about the
value of elements of the theory. For example, test results can help you to gauge how much
learning occurred and what types of learning occurred. Newspaper reports of effects on the
community can provide new insights about the value of certain elements or triangulation for
elements on which you already have some outcome data, assuming the effects reported in the
newspaper reflect the criteria you have established for assessing preferability, as discussed
earlier.
But usually the most useful data come from interviews with the participants. Both
individual and group interviews, or interactions, allow you to probe the reactions and thinking of
the participants (such as teachers and students). They help you to identify strengths and
weaknesses in the design instance, but they also allow you to explore improvements for elements
in the design instance, to explore the likely consequences of removing elements from, or adding
new elements to, the instance, and to explore possible situationalities (ways that methods should
vary for different situations, such as kinds of learning, learners, learning environments, and
development constraints for research on instructional-design theories). Although such data, as
conjecture from the participants, are always suspect, they can also be highly insightful and
useful. At a minimum they will likely provide some hypotheses worthy of testing with subsequent participants and situations. Interviews can be done during or after the
implementation of the instance, or both.
Interactions with the participants during the implementation of the design instance should
be guided by a set of questions that progress from very open-ended ones to very targeted ones.
These questions should be tailored to the design theory under investigation, and should strive to
collect data about how to improve the specific guidelines in the theory, including adding new
guidelines that may better attain the goals targeted by the theory. Therefore, for instructionaldesign
theory the questions should focus on identifying particular aspects of the implementation
of the design instance that helped or hindered learning and finding ways to improve weak
elements. The questions should be used flexibly and responsively, as they are prompted by such
cues as facial expressions (e.g., a quizzical look), and used at break points in the implementation
of the instance. If participants experience difficulties with certain elements of the instance, it is
usually wise to help them overcome those difficulties before they proceed, so that future data
will not be tainted by earlier weaknesses in the instance.