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2. Theoretical Framework 45
able to complete this task by explaining the results on a descriptive level, but they
fail to infer from the data they collected. Students are not automatically prepared to
explain observations by relating to underlying principles. They are stuck with
writing up descriptions of what they observed. One reason might be the nature of
inquiry itself: During the process of making inferences, they are left alone as the
common inquiry method provides no structure to support developing inferences
(Smith & Reiser, 2005). In sum, students have problems to generate explanations
that serve as justifications or inferences during the inquiry cycle.
Problems Concerning the Quality of Explanations
As described above, generating explanations during an inquiry cycle poses a
challenge for inexperienced learners. Even if learners are able to generate
explanations, they still have problems to develop explanations of high quality.
Explanations of high quality during inquiry learning include justifications (before
the experiment has been run) and inferences (after the experiment has been run and
data has been collected). Learners have problems concerning developing
justifications. If learners predict outcomes of their experiment runs, they often do
this without justifying their predictions at all, or they provide superficial
justifications. Instead of backing up claims by referencing findings from previous
experiments or existing knowledge about scientific principles, learners tend to
provide superficial justifications, including statements such as “It is just the way it
is”. Davis and Linn (2000) referred to this as “no problem behavior”. Learners also
have problems concerning developing inferences. The pre-condition of developing
an inference is to relate to experimental data. One problem is that students fail to
reference their experimental data and therefore are not able to develop inferences
properly (Sandoval & Reiser, 2004). If learners are able to relate to experimental
data, they often do not do it properly, which leads to inferring problems. Kuhn and
colleagues (Kuhn et al., 1992) refer to problems of inferring from experiment data as
inferential error. The authors distinguish between inclusion and exclusion errors
while developing inferences. False inclusion (e.g. “The more CO2, the more
photosynthesis will take place”) is based on a false causal implication from the co-