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drawing causes students to engage in more generative processing
during learning.
Taken together, the results suggest that the drawing strategy encourages
students to engage in generative processing during learning,
as is indicated by their higher learning outcomes. Thus, the data
provide further evidence for the generative drawing effect consistent
with the results of Schwamborn et al. (2010). Additionally,
results indicate that students in the drawing condition seem to invest
more mental effort than students in the control group, without perceiving
higher levels of difficulty.
2.4.4. Is there support for the prognostic drawing effect?
Mean proportion correct on drawing accuracy during learning
was .59 (SD = .23). A correlation analysis revealed that the drawingaccuracy
score of learner-generated drawings correlated significantly
with the comprehension posttest score, r = .620, p < .001, and with
the drawing posttest score, r = .623, p < .001. Additional correlation
analyses revealed that the drawing-accuracy score of learnergenerated
drawings correlated significantly negatively with the
perceived difficulty score, r = −.489, p = .015. There were no significant
correlations between the drawing accuracy score and either
the invested mental effort score, r = −.134, p = .533, the prior knowledge
test score, r = −.004, p = .984, the spatial ability test score, r = .072,
p = .739, or the motivation test score, r = .086, p = .690. Thus, as predicted,
the data provide further evidence for the prognostic drawing
effect consistent with the results of Schwamborn et al. (2010).
In sum, the results of Experiment 1 are consistent with the prediction
that students learn better from a science text when they are
asked to draw illustrations representing the main ideas of the text
and that the quality of the generated drawings during learning correlates
positively with students’ text comprehension (e.g.,
Schwamborn et al., 2010; van Meter, 2001; van Meter et al., 2006).
However, it might be argued that the reported results are due
to the way we supported the strategy use. In other words the reported
positive effect of the learner-generated drawing strategy might
not be caused by students’ engagement in generative learning activities
during reading (de Jong, 2005; Mayer, 2004, 2009; Wittrock,
1990) but rather by the additional pictorial information given in the
drawing prompt. Additionally, looking at students’ learning outcomes,
our results indeed show positive effects of drawing; however,
mean scores of learning outcomes for the drawing group are
medium-sized. Thus, it might be argued that the way we supported
the strategy use was not fully sufficient. In other words, the
reported positive effect of the learner-generated drawing strategy,
i.e., the generative drawing effect, might be increased by giving students
instructional support in addition to the drawing prompt (van
Meter, 2001; van Meter et al., 2006; van Meter & Garner, 2005). To
address these issues, we added two experimental conditions by
implementing author-generated pictures in the design in Experiment
2.
3. Experiment 2
One possible issue with Experiment 1 is the type of control group
used. In Experiment 1, following Schwamborn et al. (2010), we used
a reading only control group, in which the control group learned
with verbal information only. In the drawing group, however, students
not only learned with verbal information but also with pictorial
information given by the drawing prompt. Based on theories of multimedia
learning the use of different forms of representations such
as texts and pictures can promote learning in that “people learn
better from words and pictures than from words alone” (i.e., multimedia
principle; Mayer, 2009, p. 223) because in this case,
both, a (verbal) propositional representation as well as a (pictorial)
mental model are built up and are optimally integrated into one
schema that can be stored in long-term memory (Schnotz, 2005).
This assumption is also in line with the dual-coding approach stated
by Paivio (1986). In this regard, it might be argued that the reported
drawing effect is actually a multimedia effect that is based
on the presentation of text and picture rather than a generative
drawing effect that is based on students’ active engagement in
drawing activities during reading. In other words, instead of asking
people to draw pictures representing the main ideas of the text,
giving them text and author-generated pictures representing the
main ideas of the text might be as good or even better. Thus, we
included a condition in Experiment 2, in which we added authorgenerated
pictures to the text.
An additional issue with Experiment 1 is whether the reported
generative drawing effect can be enhanced by using various forms
of supporting the strategy. First, there is evidence that using a
drawing prompt during learning seems to be effective in supporting
the learner-generated drawing strategy by minimizing the
creation of extraneous processing (cf., Schwamborn et al., 2010; see
also Exp. 1). Second, research has shown that instructing students
to compare their own drawing with an author-generated picture
might be also effective in supporting the learner-generated drawing
strategy as self-monitoring processes are enhanced (cf., van Meter,
2001). Up to now, however, there is no empirical evidence whether
the combination of both ways to support the drawing strategy has
an additive effect on learning outcomes. Thus, we included a further
condition in Experiment 2, in which we combined both forms of
strategy support.
The main purpose of Experiment 2 was to test the generative
drawing and prognostic drawing effects of learner-generated drawing
as in Experiment 1, but, this time also compared with another control
group (i.e., author-generated pictures). Additionally, we were interested
in testing whether the benefits of the learner-generated
drawing strategy can be increased when we instructionally support
students not only with a drawing prompt but also with an authorgenerated
picture after the drawing process. In this new treatment,
we instructed students to draw a picture of the text content, and
then to compare their own drawing with an expert picture.
3.1. Participants and design
The participants were 168 German eighth graders from higher
track secondary schools. The mean age was 13.8 years (SD = 0.6),
and there were 112 girls and 56 boys. The study was based on a
2 × 2-between-subjects design, with learner-generated drawing (yes/
no) and author-generated picture (yes/no) as factors. Forty students
served in the drawing group, 44 students served in the authorgenerated
picture group, 41 students served in the drawing + authorgenerated
picture group, and 43 students served in the control group.
3.2. Materials
The materialswere identical to those used in Experiment 1, except
that we used a shortened version of the comprehension pretest that
consisted of 19 rather than 25 items (Cronbach’s alpha = .70) and
slightly extended versions of both the comprehension posttest (28
items, Cronbach’s alpha = .84) and the drawing test (four items with
a maximum score of 21 points; Cronbach’s alpha = .78). The pretest
was shortened, because the first experiment showed that the respective
items were either much too easy or much too difficult and
thus unsuitable to differentiate between successful and unsuccessful
learners; thus we deleted these items in the second experiment.
Furthermore, we decided to add some items to the comprehension
posttest in the second experiment, because during data analysis
of the first experiment, and after receiving some feedback from
experts in the domain of biology, we recognized that a few items
assessing transfer ability could be added. These transfer items,
however, would have been unsuitable to be included in the pretest
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