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