Contemporary Educational Psychology 39 (2014) 275–286
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Contemporary Educational Psychology
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / c e d p s y c h
Theoretical Analysis
Drawing pictures during learning from scientific text: testing the
generative drawing effect and the prognostic drawing effect
Annett Schmeck a,*, Richard E. Mayer b, Maria Opfermann a, Vanessa Pfeiffer c,
Detlev Leutner a
a
Department of Instructional Psychology, University of Duisburg-Essen, Berliner Platz 6-8, D-45127 Essen, Germany
Psychology, University of California at Santa Barbara, Santa Barbara, CA 93106, USA
c
Didactics of Biology, University of Duisburg-Essen, Universitätstraße 2, D-45117 Essen, Germany
b
A R T I C L E
I N F O
Article history:
Available online 23 July 2014
Keywords:
Text comprehension
Drawing
Generative learning activities
Generative drawing effect
Prognostic drawing effect
A B S T R A C T
Does using a learner-generated drawing strategy (i.e., drawing pictures during reading) foster students’
engagement in generative learning during reading? In two experiments, 8th-grade students (Exp. 1: N = 48;
Exp. 2: N = 164) read a scientific text explaining the biological process of influenza and then took two
learning outcome tests. In Experiment 1, students who were asked to draw pictures during reading (learnergenerated drawing group), scored higher than students who only read (control group) on a multiplechoice comprehension test (d = 0.85) and on a drawing test (d = 1.15). In Experiment 2, students in the
learner-generated drawing group scored significantly higher than the control group on both a multiplechoice comprehension test (d = 0.52) and on a drawing test (d = 1.89), but students who received authorgenerated pictures in addition to drawing or author-generated pictures only did not. Additionally, the
drawing-accuracy scores during reading correlated with comprehension test scores (r = .623, r = .470) and
drawing scores (r = .620, r = .615) in each experiment, respectively. These results provide further evidence for the generative drawing effect and the prognostic drawing effect, thereby confirming the benefits
of the learner-generated drawing strategy.
© 2014 Elsevier Inc. All rights reserved.
1. Introduction
Suppose you want to enable students to study a scientific text
by themselves for deep level understanding. In this case, you will
have to ensure that students engage in generative learning processes during reading, such as organizing material into coherent
mental representations, and integrating the representations with
each other and with relevant knowledge activated from long-term
memory (de Jong, 2005; Mayer, 2004, 2009; Wittrock, 1990). A possible way to accomplish this goal is to encourage students to use a
learner-generated drawing strategy (Alesandrini, 1984; Schwamborn,
Mayer, Thillmann, Leopold, & Leutner, 2010; van Meter & Garner,
2005), in which they receive a text to read and are instructed to draw
pictures that reflect the main elements and relations described in
the text. The goal of the present study is to examine a generative
drawing effect (i.e., engaging in appropriate drawing activities during
learning from text improves performance on tests of learning
* Corresponding author. Address: Annett Schmeck, University of Duisburg-Essen,
Berliner Platz 6-8, D-45127 Essen, Germany. Fax: +49 2011834350.
E-mail address: annett.schmeck@uni-due.de (A. Schmeck).
http://dx.doi.org/10.1016/j.cedpsych.2014.07.003
0361-476X/© 2014 Elsevier Inc. All rights reserved.
outcomes) and a prognostic drawing effect (i.e., the quality of drawing
during learning from text predicts performance on subsequent tests
of learning outcomes).
1.1. Theoretical framework for the learner-generated
drawing strategy
A straightforward way to encourage students to use a learnergenerated drawing strategy when learning from verbal instruction
is to ask them to generate an external visual representation of a tobe learned content. The drawing that is generated has a
representational quality, similar to the characteristics of a representational illustration (cf., Alesandrini, 1984; van Meter & Garner,
2005). By representational, we mean that learners make drawings
which are intended to show what depicted objects look like (Carney
& Levin, 2002). This requirement excludes nonrepresentational
graphic constructions such as diagrams and concept maps. Thus,
our definition of drawing is that the learner creates a visual representation intended to depict what is described in text.
Drawing can be seen as a learning strategy intended to influence how learners process information during learning (Pashler
et al., 2007; Weinstein & Mayer, 1986). By drawing, learners are no
longer passive consumers of information and knowledge; they are
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A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
actively involved in the cognitive processes of selecting, organizing and integrating the information to be learned. Thus, learnergenerated drawing is a cognitive learning strategy that is aimed to
foster learning from text, and if used adequately drawing can increase learning outcomes (Ainsworth, Prain, & Tytler, 2011;
Alesandrini, 1984; van Meter & Garner, 2005).
The processes underlying drawing are described in van Meter
and Garner’s (2005) generative theory of drawing construction
(GTDC), which is based on Mayer’s (2005) model of multimedia
learning. It is assumed that learners benefit from using the drawing
strategy as drawing requires them to engage in generative learning processes during reading. First, learners select the relevant key
information from the text. Second, the selected key information is
organized to build up an internal verbal representation of the text
information. Third, learners construct an internal nonverbal (visual)
representation of the text information and connect it with the verbal
representation and with relevant prior knowledge. To construct the
visual representation, which is the basis for the external drawing,
the learner has to rely mainly on the verbal representation, and thus
learner-generated drawing demands an integration of the verbal and
nonverbal representation.
Additionally, van Meter and Garner (2005) describe metacognitive
processes fostered by the drawing activity: “Attempts at constructing the nonverbal representation can send learners back to either
the verbal representation or the text as difficulties building the internal image are encountered” (van Meter & Garner, 2005, p. 317).
That is, as the drawing process itself is not linear, metacognitive processes of monitoring and regulation are stimulated by drawing (cf.,
van Meter, 2001; van Meter, Aleksic, Schwartz, & Garner, 2006).
1.2. Empirical framework for the learner-generated drawing strategy
Following the GTDC (cf., van Meter & Garner, 2005), the drawing
strategy is beneficial as it fosters deep cognitive processing including organizing and integrating material (which can be called
generative processing; Mayer, 2009) as well as metacognitive selfmonitoring and regulation processes. Research on drawing, however,
has produced somewhat mixed results (see Alesandrini, 1984; van
Meter & Garner, 2005, for overviews) in which some studies reported positive effects of drawing on text comprehension (e.g.,
Alesandrini, 1981; Hall, Bailey, & Tillman, 1997; Leopold & Leutner,
2012; Lesgold, DeGood, & Levin, 1977; Lesgold, Levin, Shimron, &
Guttman, 1975; Schwamborn et al., 2010; van Meter, 2001; van
Meter et al., 2006), whereas others did not (e.g., Leutner, Leopold,
& Sumfleth, 2009; Rasco, Tennyson, & Boutwell, 1975; Tirre, Manelis,
& Leicht, 1979). Benefits of drawing appear to be related to the
quality of students’ drawings during learning: Students, who produce
high-quality drawings during reading, tend to score better on
posttests of learning outcome than do students who produce lowquality drawings during reading (e.g., Greene, 1989; Hall et al., 1997;
Leopold, 2009; Lesgold et al., 1975, 1977; Schwamborn et al., 2010;
van Meter, 2001; van Meter et al., 2006).
1.2.1. Effectiveness of learner-generated drawings
Following van Meter and Garner (2005), reasons for the mixed
empirical results concerning drawing can be seen attributed to the
type of test used for assessing learning outcomes as well as in the
form of support that assists learners in the drawing process. First,
benefits of drawing are more likely to be revealed on tests that assess
higher-order knowledge of to-be learned content, for example, tests
on comprehension and transfer (e.g., Alesandrini, 1981; Leopold &
Leutner, 2012) or problem solving (van Meter, 2001; van Meter et al.,
2006). Leutner et al. (2009), for example, found no positive effect
of drawing compared with a control group on a multiple choice test
on factual knowledge. Leopold and Leutner (2012), however, showed
superior effects of the drawing strategy on transfer test perfor-
mance. van Meter et al. (2006), accordingly, found no effects of
drawing activity on a multiple choice recognition test; however, students in the drawing group scored significantly higher on a problemsolving test. With regard to the GTDC (van Meter & Garner, 2005),
it seems that benefits of drawing can be found if the learning
outcome test complies with characteristics of the verbal and nonverbal representations, which are generated by drawing.
Second, positive effects of drawing often appear under the condition that instructional support is provided to constrain and
structure the drawing activity (e.g., Lesgold et al., 1975, 1977;
Schwamborn et al., 2010; van Meter, 2001; van Meter et al., 2006).
That is, drawing is more effective when the learners’ generation of
the drawing is assisted by some kind of additional information. van
Meter (2001) and van Meter et al. (2006), for example, showed that
the provision of author-generated pictures after drawing enhanced the benefits of the drawing strategy. By comparing their own
drawing with a provided one, learners get to know what their
drawing should look like, and this might lead them back to revise
their own drawing and thus, their mental model. Following the GTDC
(van Meter & Garner, 2005), this should improve comprehension.
Lesgold et al. (1975), in turn, supported first grade students with
cutout figures and instructed them to organize these into an accurate pictorial representation while listening to a prose story. This
learner-generated illustration activity facilitated prose learning as
indicated by higher recall of story propositions only when students were given the correct pieces for the illustration or had the
illustration done for them. When students had to select the pieces
for each illustration out of a pool of cutouts, the learner-generated
illustration activity had either a negative or no effect (cf., Lesgold
et al., 1977). Following these results, Schwamborn et al. (2010) proposed that a pure, unsupported drawing instruction might bear the
risk that managing the mechanics of drawing itself is difficult for
the learners, resulting in insufficient remaining capacity for making
sense of the text through generative processes of organization and
integration, which might diminish the benefits of drawing defined
by van Meter and Garner’s GTDC. To counter this risk in the study
of Schwamborn et al. (2010), students in the drawing groups received baseline instructional support while learning a lesson on
washing, which provided them with a drawing prompt that included a legend showing all the relevant elements for drawing and
a partly pre-drawn background for their paper-pencil based drawings. That is, students could use the presented elements as prototypes
for their own drawings and integrate them by pencil in the given
pre-drawn backgrounds. Results showed that students, who were
instructed to generate drawings during learning, scored significantly higher on the subsequent comprehension tests than students
who only read the text.
Using cutout-figures (cf., Lesgold et al., 1975, 1977) or a drawing
prompt (cf., Schwamborn et al., 2010) during drawing seems to
provide sufficient constraints and leave enough cognitive capacities for learners to benefit from the drawing strategy. Thus, cognitive
processing including selecting, organizing and integrating material should be encouraged, resulting in an improved mental model,
which in turn should improve comprehension (cf., GTDC; van Meter
& Garner, 2005).
In line with the GTDC and the reported results derived from research on drawing Schwamborn et al. (2010) proposed a generative
drawing effect, that is, students gain a better understanding of a scientific text when they are asked to draw illustrations representing
the content of each paragraph they read. This work highlights the
importance of drawing support, such as the provision of drawings
of all key elements and a background for the drawing.
1.2.2. Quality of learner-generated drawings
Previous studies that measured the quality of students’ drawings during learning all showed positive correlations between
A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
the quality of students’ drawings during learning and their learning outcomes (e.g., Greene, 1989; Hall et al., 1997; Lesgold et al.,
1975, 1977; Schwamborn et al., 2010; van Meter, 2001; van Meter
et al., 2006). The quality of learner-generated drawing is also referred to as the drawing accuracy (e.g., van Meter, 2001; van Meter
et al., 2006), and is defined as “the degree to which completed
drawings resemble the represented object(s)” (van Meter & Garner,
2005, p. 299). In a study by Hall et al. (1997), for example, college
students learning a mechanics lesson with the instruction to draw
produced better learning outcomes on a transfer test than a text
only control group, but only if they produced higher quality drawings. In the study by Schwamborn et al. (2010), students who were
able to generate high accuracy drawings scored significantly higher
on learning outcome tests than did those who generated lower
accuracy drawings. In addition to this, Schwamborn and colleagues also found that the quality of the generated drawings during
learning correlated positively with the comprehension test scores.
Based on these results Schwamborn et al. (2010) proposed the prognostic drawing effect: Students, who produce high-quality drawings
during reading a scientific text, tend to score better on posttests
of learning outcome than do students who produce low-quality
drawings during reading.
1.3. Overview of the experiments
Research on drawing – i.e., the generative and the prognostic
drawing effect – is promising; however, at the present time there
is a need for a more solid evidence base and for a closer examination of theoretical issues. First, the generalizability is limited at
this point as replication studies using learning outcome tests that
are sensitive to the underlying process of drawing as well as new
learning materials other than the washing lesson (e.g., Schwamborn
et al., 2010) or the birds wing (van Meter, 2001) are yet missing.
In their report for the U.S. National Research Council, entitled
Scientific Research in Education, Shavelson and Towne (2002, p. 4),
for example, highlighted the need to “replicate and generalize
across studies” as one of the six essential scientific principles of
educational research. It has to be mentioned at this point, that when
generalizing results to new domains or lessons, one should carefully consider whether these are comparable at all. In our
experiments, we aimed at generalizing results by Schwamborn et al.
(2010), who worked with a science text explaining the causal
steps regarding the chemistry of washing, to a new lesson that is,
however, comparable in that the text we used also described
causal steps of a process, in this case regarding the infection with
influenza and regarding the immune response. That is, although
there were differences between the two domains (chemistry versus
biology), the lessons showed structural similarities and thus
allow for comparing results and drawing conclusions regarding
generalizability.
Second, research on drawing indicates that some form of support
is needed to assist learners during drawing. Schwamborn et al.
(2010), for example, introduced a drawing prompt as helpful support
for learners to benefit from drawing. They proposed that the resulting positive drawing effect is based on students’ engagement
in generative learning activities during reading due to drawing (consistent with the GTDC of van Meter & Garner, 2005; see also de Jong,
2005; Mayer, 2004, 2009; Wittrock, 1990). However, the results reported by Schwamborn et al. (2010) might rather reflect a
multimedia effect (Mayer, 2005, 2009) than the proposed drawing
effect as the learning lesson used – a scientific text and a drawing
prompt consisting of pictorial elements and backgrounds – created
a multimedia lesson. In other words, the results of Schwamborn et al.
(2010) might not be due to the drawing activity but rather due to
the multimedia effect that students “learn better from words and
pictures than from words alone” (Mayer, 2009, p. 223). In this case,
277
the words are presented in the lessons and pictures are generated
by the students, so a control group that receives author-generated
pictures is warranted.
Third, research on drawing mostly used only one way to support
the drawing strategy at a specific time. That is, instructional
support was added during learning (i.e., by using cut-out figures or
a drawing prompt; (cf., Lesgold et al., 1975, 1977; Schwamborn et al.,
2010) or after learning (i.e., by providing pictures, van Meter, 2001;
van Meter et al., 2006). Less is known about whether
adding instructional support not only during learning but also after
learning can additionally enhance the benefits of the drawing
strategy.
Fourth, research on drawing should include motivational and
cognitive aspects that may have an impact on the effectiveness of
the learner-generated drawing strategy. Students’ current motivation, for example, is a one condition for successful learning. A student,
for example, who has low motivation to learn, may invest less
effort in learning than students who are highly motivated to learn
(cf., Vollmeyer & Rheinberg, 2000). Students’ spatial ability may
be a further condition for successful learning when working
with visualizations (cf., Höffler, 2010; Höffler, Schmeck, & Opfermann,
2013). A high-spatial-ability student, for example, may have
advantages in learning with visualizations compared with a lowspatial-ability student. That is, preexisting motivational and cognitive differences between students before learning might have
an influence on the learning outcome, and thus should be
controlled.
In addition, recent research has shown that not only experimental conditions (such as the kind of picture) and the above
mentioned “classical” covariates can have an impact on how successful learning takes place, but that these effects can be mediated
by the amount of mental effort someone invests while learning or
working on a lesson; and by how difficult someone perceives a
domain or lesson to be (cf., Leutner et al., 2009; Schwamborn,
Thillmann, Opfermann, & Leutner, 2011). These aspects of cognitive load (invested mental effort and perceived task difficulty) were
thus included as additional variables in our studies as well. Thus,
we conducted the following two experiments using a science text
explaining the biological process of influenza. In Experiment 1 we
implemented an experimental drawing condition and a reading only
control condition, in order to determine how both the generative
and the prognostic drawing effect would extend to a new context.
Analogous to the study of Schwamborn et al. (2010), students in
the drawing condition received a baseline instructional support by
means of a drawing prompt that included a legend showing all the
relevant elements for drawing and a partly pre-drawn background for their drawing (as shown in Fig. 1).
In Experiment 2, we again implemented an experimental drawing
condition and a reading only control condition, and we additionally implemented author-generated pictures, in order to test whether
the generative drawing effect was caused by the simple presence
of illustrations rather than the generation of illustrations. That is,
we implemented a text plus picture condition (which we called the
author-generated picture condition), to test whether the reported
generative drawing effect of Schwamborn et al. (2010) is based on
students’ engagement in generative learning activities during reading
rather than on the pictorial representations given by the drawing
prompt. In addition, we implemented a drawing plus picture condition (in which students both draw and are given a picture), to test
whether the reported generative drawing effect can be enhanced
by instructing students to compare their own drawing with an
author-generated picture. In short, we tested whether combining
different forms of support to the drawing strategy additionally enhances the benefits of the drawing strategy. In both experiments,
learning outcome tests that are sensitive to the underlying process
of drawing were used.
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A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
Fig. 1. Screenshot of the drawing prompt for the first paragraph in the drawing versions of the learning booklet. Note: Translated from the German original.
2. Experiment 1
four students served in the control group, and 24 served in the
drawing group.
2.1. Participants and design
2.2. Materials
Forty-eight German eighth graders in higher track secondary
schools participated in this study. The mean age was 13.7 years
(SD = 0.6), and there were 22 girls and 26 boys. The study was based
on a between-subjects design, with two levels of text learning
(learner-generated drawings and control) as the single factor. Twenty-
All materials were paper-pencil based. The materials consisted
of five adjunct questionnaires, two learning booklets, two cognitive load rating scales, and two posttests. The five adjunct
questionnaires were intended to determine whether the groups were
A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
equivalent on basic characteristics. They included a participant questionnaire, a comprehension pretest, a spatial ability test, and a
motivation questionnaire. The participant questionnaire solicited information concerning the students’ age and sex. The comprehension
pretest consisted of 25 multiple-choice items and was intended to
assess students’ prior-knowledge of information covered in the text.
Students’ spatial ability was measured with a 10 multiple-choice
paper-folding items taken from a battery of cognitive tests developed by Ekstrom, French, and Harman (1976). The motivation
questionnaire assessed students’ current motivation for doing the
learning task after reading the instructions before the lesson. It consisted of nine items from the challenge and interest subscales of the
Questionnaire on Current Motivation (QCM) developed by Rheinberg,
Vollmeyer, and Burns (2001). Cognitive load by means of invested
mental effort was measured using the 7-point subjective rating scale
developed by Paas (1992), which ranges from (1) very low mental
effort to (7) very high mental effort. Cognitive load by means of perceived task difficulty was measured using the 7-point subjective rating
scale developed by Kalyuga, Chandler, and Sweller (1999), which
ranges from (1) very easy to (7) very difficult. These subjective measures have been criticized for assessing cognitive load with only single
items (e.g., Brünken, Plass, & Leutner, 2003). However, several studies
showed the effectiveness of the rating scale by showing that the variation in learners’ cognitive load ratings depended on variations in
task complexity or instructional design (for overviews see Paas,
Tuovinen, Tabbers, & Van Gerven, 2003; Van Gog & Paas, 2008). In
this regard, Sweller, Ayres, and Kalyuga (2011) conclude that “the
simple subjective rating scale, regardless of the wording used (mental
effort or difficulty), has, perhaps surprisingly, been shown to be the
most sensitive measure available to differentiate the cognitive load
imposed by different instructional procedures” (p. 74). For that reason
and due to the economic applicability we decided to use this kind
of cognitive load measurement, while acknowledging the limitations of a short, self-report instrument.
The two learning booklets each included a science text on the
biology of the influenza. The text explained the causal steps regarding an infection with influenza and regarding the immune
response, which is an unfamiliar subject for eighth graders in higher
track secondary schools due to the German curriculum. The text consisted of approximately 850 words (in German) and was divided into
seven paragraphs (as shown in Table 1).
The drawing version of the booklet contained seven pairs of facing
pages with a text paragraph on the left page and a two-part drawing
prompt on the right page. The first part of the drawing prompt included a legend showing all the relevant elements (in total eight
elements) for drawing a picture for that text paragraph (as shown
in the top of Fig. 1). The second part of the drawing prompt included a partly pre-drawn background for students’ drawing (as
shown in the bottom of Fig. 1). Overall, students had to make seven
drawings, i.e., one drawing to each paragraph.
The control version of the learning booklet contained four pairs
of facing pages with one of the seven text paragraph on each page.
Table 1
Text from the second paragraph of the influenza lesson.
How the influenza virus replicates
Once inside the influenza virus uses your somatic cell to produce new particles
of the influenza-virus. The glycoproteins move toward the membrane of the
somatic cell and stick out into the outside of the cell. The capsules of the
virus, however, are assembled inside the somatic cell. Next, these new
assembled capsules of the virus leave your somatic cell. By moving through
the somatic cell membrane the capsules are enveloped with the membrane
and its glycoproteins, which then plays the role of the virus membrane.
Thus, several new influenza viruses are located outside your somatic cell.
Note: Translated from the German original.
279
Students in both groups learned with exactly the same text material. To make sure that students in the control group learned
with the same amount of information as students in the drawing
group, all elements of the drawing prompt as well as the spatial
relations between these elements were also described in the science
text.
The two posttests intended to assess the learning outcomes were
a comprehension posttest and a drawing posttest. The comprehension posttest (Cronbach’s alpha = 0.83) consisted of 25 multiplechoice items (the same items as in the comprehension pretest) and
was intended to assess students’ comprehension of the factual and
conceptual information covered in the text as well as their ability
to transfer what was presented to new situations. An item example
is “T-helper cells do not only recognize viruses, but also agents that
are extraneous to the body. Which medication would you administer to a patient, who has received a new kidney? (a) a medicine
that suppresses the immune response of the body, (b) a medicine
that activates the immune response of the body, (c) a medicine that
contains antigens, or (d) a medicine that contains blood of the kidney
donor” [(a) is the correct answer]. The drawing test (Cronbach’s
alpha = 0.81) was intended to assess students’ comprehension of the
conceptual information presented in the science text by means of
drawing. That is, students had to reproduce the main ideas given
in the text by drawing. It consisted of three drawing items, in which
students were asked to draw sketches depicting key concepts of the
text and their spatial relations. An item example for the drawing
test is “How does an influenza virus invade a cell, and how is it reproduced?” The science text, the drawing prompt and the learning
outcome tests were constructed by the first author in cooperation
with a biology teacher. The materials were adapted from
Schwamborn et al. (2010); however, using another science domain,
and including measures of individual learning times and cognitive
load.
2.3. Procedure
Participants were tested in the schools’ classrooms. Within their
classes, they were randomly assigned to one of the two groups.
Groups were tested in separate classrooms, in order to insure that
students in the drawing group did not feel rushed when students
in the control group completed the task early. Each student was
seated at an individual desk. First, students were given the participant questionnaire and the comprehension pretest to complete at
their own rate. Second, students filled in the paper-folding test with
a 3 min time limit. Third, students were given instructional booklets corresponding to their assigned group. After they had read the
instructions for reading the booklets, students’ current motivation
for doing the learning task was assessed. Next, students started learning with the text material corresponding to their treatment group.
Students were instructed to carefully read the text on the biology
of the influenza in order to comprehend the material. Students in
the drawing condition were instructed to read the text and additionally to draw pictures for each text paragraph using the drawing
prompt representing the main ideas of each text paragraph. That
is, students had to use the pictorial elements given in the legend
such as the virus as templates for their own paper-pencil based
drawing across the pre-dawn background. Students in the control
group were instructed to read the text for comprehension, but were
not instructed to engage in drawing. Students in both groups learned
at their own pace, whereby individual learning time was measured by the instructors in the classrooms. Fourth, in order to ensure
comparable testing procedures after finishing learning with the
whole learning material, students in both groups directly rated the
amount of mental effort he or she had invested during learning and
the amount of difficulty he or she had perceived during learning.
Fifth, students received the comprehension posttest consisting of
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A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
the comprehension posttest and the drawing posttest. Students had
20 minutes time for completion, and did not have access to the
science text or their drawings. Finally, students were thanked and
debriefed. As students learned at their own rate, the whole procedure took about 70–90 minutes, depending on the individual testing
times.
2.4. Results and discussion
2.4.1. Scoring
The dependent variables were students’ scores on the comprehension and drawing posttests, students’ rating on the mental effort
and the difficulty scales, and the drawing accuracy score indicating the quality of learner-generated drawings produced by students
in the drawing group during the learning phase.
The comprehension test score (pre- and posttest) for each student
was computed by awarding 1 point for each correct answer, and by
adding up the points to obtain the total comprehension score (out
of a total possible of 25 points). Actual scores ranged from 3 to 24
points, with a mean of 13 points (SD = 5.3). Following Schwamborn
et al. (2010), scoring of the drawing test was carried out by counting the total number of correct main ideas in each learner’s answer
across the three drawing items. The main ideas were drawn out from
both expert visualizations and a checklist specifying important relational features. Students could earn a maximum of 19 points on
the drawing test. Two student assistants (teacher trainees in biology)
scored the quality for each of the three drawings for each student
with an acceptable inter-rater agreement of Goodman–Kruskal
gamma of 0.90. Actual scores ranged from 0 to 18.5 points, with a
mean of 7.7 points (SD = 4.7). Total scores of both the comprehension and the drawing test were transferred into z-standardized scores
to make them comparable across studies.
The drawing accuracy score (concerning drawing during learning in the drawing group) was computed by using a coding scheme
adapted from Schwamborn et al. (2010), which was based on expert
drawings and a checklist specifying important relational features
of the drawings. Students could earn a maximum drawing-accuracy
score of 22 points. Again the two student assistants scored each of
the seven learner-generated drawings for each student with an acceptable interrater agreement of Goodman–Kruskal gamma of .92.
Both coding schemes were constructed by the first author and a
biology teacher. Actual scores ranged from 4 to 21 points, with a
mean of 13.3 points (SD = 5.0). The total drawing accuracy score was
again transferred into a z-standardized score.
In addition the spatial ability test was scored by tallying the
number correct out of 10, and the motivation questionnaire was
scored by tallying the nine ratings on both subscales to a total score
of motivation. Finally, for comparing performance across the different tests, the proportion correct on each test was computed by
dividing the student’s obtained score by the total possible score.
2.4.2. Are the groups equivalent on basic characteristics?
Before looking at treatment effects on the dependent variables,
we analyzed whether the two groups differed on several control variables. A chi-square analysis indicated that there were no significant
differences regarding gender (p = .562). Separate univariate analyses of variance (ANOVAs) revealed that the groups did not differ
significantly on age, F < 1; on spatial ability, F < 1; or on motivation, F(1, 46) = 3.60; p = .064. However, groups differed significantly
on prior knowledge, F(1, 46) = 38.90, p < .001, partial eta2 = .46, in
that students in the drawing group scored significantly lower on
the comprehension pretest (M = .10, SD = .15) than students in the
control group (M = .34, SD = .12). Thus, we included students’ prior
knowledge in the following analyses.
Table 2
Mean proportion correct on the comprehension test and drawing test for two groups
– Experiment 1.
Group
Type of test
n
Drawing
Control
24
24
Comprehension test
Drawing test
M
SD
M
SD
.61*
.44
.20
.20
.52*
.28
.27
.11
Note: Asterisk (*) indicates significant difference from control group at p < .05.
2.4.3. Is there support for the generative drawing effect?
Mean proportion correct and SDs on the comprehension and
drawing posttests for both groups are presented in Table 2. Repeated measures univariate analyses of variance (ANOVA) with the
comprehension pre- and post-test scores as the within-subject factors
and group (drawing versus control) as the between-subject factor
showed a main effect over time indicating that overall, participants reached significant knowledge gains between the
comprehension pretest and the comprehension posttest, F(1,
46) = 98.97; p < .001; partial eta2 = .68. An interaction additionally
showed that these knowledge gains were significantly higher for
the drawing group than for the control group, F(1, 46) = 46.17;
p < .001; partial eta2 = .50.
For the drawing test, a repeated measures ANOVA was not possible, since these items were only used in the posttest. In this case,
a univariate analysis of covariance (ANCOVA) predicting the drawing
test score with group (drawing versus control) as the factorial independent variable and prior knowledge as a covariate showed that
the drawing group scored significantly better than the control group
on the drawing posttest, F(1, 45) = 13.49, p = .001, partial eta2 = .23.1
Cohen’s d favoring the drawing group over the control group was
0.85 for the comprehension posttest, and 1.15 for the drawing
posttest, all of which are considered large effects. Thus, there is strong
support for the generative drawing effect, as predicted.
Additionally, results revealed that the drawing group needed significantly more learning time (M = 21.08 min., SD = 4.24) than the
control group (M = 17.38 min., SD = 3.33), F(1, 46) = 11.34, p = .002,
partial eta2 = .20. Thus, to test whether learning time mediates the
positive effect of drawing on text comprehension, additional mediation analyses (Baron & Kenny, 1986) were calculated by including
learning time as an additional predictor in the aforementioned linear
model. A mediation effect would be detected if, in this case, effects
of drawing on text comprehension would significantly decrease.
Results of the mediation analyses showed that the effect of drawing
on both comprehension test scores and drawing test scores was not
fully mediated by learning time. That is, including learning time still
revealed the interaction between group (drawing versus control)
and time (pre- versus post) in that the drawing group had significantly higher knowledge gains than the control group on the
comprehension test items (p < .001). Furthermore, the drawing group
also still outperformed the control group on the drawing posttest
after controlling for learning time (p = .009).
Furthermore, results revealed that students in the drawing group
rated their invested mental effort during learning significantly higher
(M = 5.04, SD = 1.12) than students in the control group (M = 3.96,
SD = 1.65), F(1, 46) = 7.05, p = .011, partial eta2 = .13. There was no
difference between the two groups on the perceived difficulty item
(drawing group: M = 4.08, SD = 1.50; control group: M = 4.25,
SD = 1.22; F < 1). Thus, consistent with predictions concerning the
generative drawing effect, there is partial support for the idea that
1
The exclusion of students’ prior knowledge in the reported analyses does not
change the reported pattern of results.
A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
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).
281
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 materials were 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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A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
Fig. 2. Author-generated pictures for the seven paragraphs in the author-generated picture versions of the learning booklet. Note: Pictures are scaled-down from the original format.
because they are too difficult to answer without prior training in
the topic. Additionally, author-generated pictures were used in the
new conditions. The author-generated pictures were static functional pictures representing the main ideas of each paragraph and
consisted of pictorial elements identical to those provided in the
drawing prompt (as shown in Fig. 2). These pictures were constructed by the first author in cooperation with a biology teacher.
The drawing version of the booklet was identical to that used
in Experiment 1 (as shown in Fig. 1). The control version of the learning booklet was identical to that used in Experiment 1. The authorgenerated picture version of the booklet consisted of seven pairs
of facing pages with a text paragraph on the left page and a corresponding author-generated picture (such as in Fig. 2) on the right
page. The drawing + author-generated picture version of the booklet
contained the material from the drawing version consisting of seven
pairs of facing pages with a text paragraph on the left page and a
two-part drawing prompt on the right page. In addition, attached
to each page there was an additional page that students could fold
out after having generated their drawing. When unfolding this additional page, a picture of that text paragraph right aside the drawing
prompt was provided, and there was an additional instruction to
compare the learner-generated drawing with the author-generated
picture. Author-generated pictures were the same as in the authorgenerated picture version of the booklet.
3.3. Procedure
The procedure was identical to that used in Experiment 1, except
that there were two additional groups learning with authorgenerated pictures. Students in the author-generated picture
condition were instructed to read the text and additionally to look
at pictures representing the main ideas of each text paragraph. Students in the drawing + author-generated picture version of the
booklet were instructed to read the text, to draw pictures for each
text paragraph using the drawing prompt representing the main
ideas of each text paragraph, and finally to compare their picture
with an author-generated picture representing main ideas of each
paragraph correctly. Students in all groups learned at their own pace,
whereby individual learning time was measured by the instructors in the classrooms. Again, to ensure that students’ in both drawing
groups did not feel rushed when students in the non-drawing
A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
group completed the task early, groups were tested in separate
classrooms.
3.4. Results
3.4.1. Scoring
All tests instruments were scored with the same procedures used
in Experiment 1. Again two student assistants (teacher trainees in
biology) scored each of the drawing test items and each of the seven
learner-generated drawings for each student with acceptable
interrater agreements (drawing test: Goodman–Kruskal gamma of
.90; drawing-accuracy: Goodman–Kruskal gamma of .94). Actual
scores ranged from 1 to 28 points (M = 15.3 points, SD = 5.8) for the
comprehension test, from zero to 21 points (M = 10.9 points, SD = 5.3)
for the drawing test, and from 2.75 to 21.5 points (M = 14.1 points,
SD = 4.8) for drawing accuracy. Again, total scores of comprehension, drawing and accuracy were transferred into z-standardized
scores.
3.4.2. Are the groups equivalent on basic characteristics?
Before looking at treatment effects on the dependent variables,
we analyzed whether the four experimental groups differed on
several control variables. A chi-square analysis indicated that there
were no significant differences regarding gender (p = .097). Separate univariate analyses of variance (ANOVAs) revealed that the
groups also did not differ significantly on age, F < 1; on spatial ability,
F(3, 164) = 1.20, p = .312; or on motivation, F(3, 164) = 1.22; p = .305.
However, groups differed significantly on prior knowledge, F(3,
164) = 1.04; p = .010, partial eta2 = .07, in that students in the control
group scored significantly higher on the comprehension pretest
(M = .25, SD = .17) than students in both (p < .05) the authorgenerated picture group (M = .17, SD = .16) and the drawing + authorgenerated picture group (M = .15, SD = .12); the drawing group
(M = .20, SD = .14) did not differ significantly from the other groups.
Thus, we included students’ prior knowledge as a covariate in the
following analyses.
3.4.3. Is there support for the generative drawing effect?
A major goal in this experiment was to determine whether asking
students to generate drawings to represent science text is a more
effective learning strategy than asking students to learn with text
alone or with text and author-generated pictures. In other words,
we wanted to determine whether we could replicate and extend
the learner-generated drawing effect. Additionally, we were interested in whether giving students an author-generated picture after
drawing can increase the benefits of the learning strategy. Mean proportion correct and standard deviations on the comprehension and
drawing tests for the four groups are presented in Table 3.
The left portion of Table 3 summarizes the mean proportion
correct on the comprehension test. A two-factorial analysis of
covariance (ANCOVA) predicting learning outcomes (comprehension posttest score) with learner-generated drawing (yes/no) and
Table 3
Mean proportion correct on the comprehension test and drawing test for the four
groups – Experiment 2.
Group
Type of test
n
Learner-generated drawing
Author-generated picture
Learner-generated drawing +
author-generated picture
Control
Comprehension test
Drawing test
M
SD
M
SD
40
44
41
.63*
.53
.51
.22
.19
.20
.66*
.50
.63
.22
.25
.19
43
.52
.20
.30
.16
Note: Asterisk (*) indicates significant difference from control group at p < .05.
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author-generated picture (yes/no) as the factorial independent variables and prior knowledge as a covariate showed a significant
positive main effect of learner-generated drawing, F(1, 163) = 3.98,
p = .048, partial eta 2 = .02, a significant interaction effect, F(1,
163) = 6.26, p = .013, partial eta2 = .04, but no main effect of authorgenerated pictures, F < 1. In addition, multiple pairwise comparisons
(with p < .05) showed that the drawing group performed significantly better than each of the three other groups, which did not differ
significantly from each other. Cohen’s d favoring the drawing group
over the author-generated picture group was .49, over the learnergenerated + author-generated picture group was .57, and over the
control group was .52.
The right portion of Table 3 summarizes the mean proportion
correct on the drawing posttest. Again, a two-factorial analysis of
covariance (ANCOVA) predicting learning outcome (drawing test
score) with learner-generated drawing (yes/no) and author-generated
picture (yes/no) as the factorial independent variables and prior
knowledge as a covariate showed a significant positive main effect
of learner-generated drawing, F(1, 163) = 62.60, p < .001, partial
eta2 = .28, a significant positive main effect of author-generated pictures, F(1, 163) = 11.04, p = .001, partial eta2 = .06, and a significant
interaction effect, F(1, 163) = 16.58, p < .001, partial eta2 = .09. In addition, multiple pairwise comparisons (with p < .05) showed that
both the drawing group and the drawing + author-generated picture
group performed significantly better than the author-generated
picture group (d = .68; d = .59) and the control group (d = 1.87;
d = 1.88). In turn, the author-generated picture group performed significantly better than the control group (d = .95). The drawing group
and the drawing + author-generated picture group did not differ significantly from each other (d = .15).2 Overall, these results are
consistent with Experiment 1 and provide additional support for
the generative drawing effect.
In accordance with Experiment 1, we were interested in whether
differences in learning time among the experimental groups mediate
the positive effect of drawing on text comprehension. First, an ANOVA
predicting learning time with learner-generated drawing (yes/no)
and author-generated picture (yes/no) as the factorial independent variables showed a significant main effect of learner-generated
drawing, F(1, 164) = 392.26, p < .001, partial eta2 = .71, a significant
main effect of author-generated picture, F(1, 164) = 16.85, p < .001,
partial eta2 = .09, and a significant interaction effect F(1, 164) = 4.90,
p = .028, partial eta2 = .03. Linear contrasts (with p < .05) revealed
that the drawing group (M = 19.38 min, SD = 3.80) and the
drawing + author-generated picture group (M = 23.40 min, SD = 5.51)
needed significantly more learning time than the author-generated
picture group (M = 9.60 min, SD = 3.97) and the control group
(M = 8.34 min, SD = 2.51). Thus, to test whether learning time mediates the positive effect of drawing on text comprehension,
additional mediation analyses (Baron & Kenny, 1986) were calculated by including learning time as an additional predictor in the
aforementioned linear model. Results of the mediation analyses
showed that the effects of drawing on the comprehension posttest
and the drawing posttest scores (see multiple pairwise comparisons) are mediated by learning time to some extent. That is, including
learning time in the linear model for predicting comprehension test
scores still revealed a positive effect of the drawing group compared with the drawing + author-generated group on the
comprehension test (p = .012). However, including learning time in
the linear model for predicting comprehension posttest scores
reduced the positive effect of the drawing group compared with the
author-generated picture group (from p = .034 to p = .281) as well
as compared with the control group (from p = .002 to p = .087), being
2
The exclusion of students’ prior knowledge in the reported analyses does not
change the overall pattern of results.
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A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
no longer statistically significant. Regarding the drawing posttest
score, including learning time does not change the reported pattern
of results, except that the positive effect of the drawing + authorgenerated picture group compared with the author-generated picture
group is no longer statistically significant (from p = .004 to p = .223).
There were neither main effects of learner-generated drawing
and author-generated pictures on the mental effort item (drawing
group: M = 4.55, SD = 0.25; author-generated picture group: M = 4.59,
SD = 0.24; drawing + author-generated picture group: M = 4.44,
SD = 0.26; control group: M = 4.81, SD = 0.24; F < 1) nor on the
perceived difficulty item (drawing group: M = 3.63, SD = 0.23; authorgenerated picture group: M = 3.71, SD = 0.22; drawing + authorgenerated picture group: M = 3.95, SD = 0.23; control group: M = 3.93,
SD = 0.22; F < 1).
Taken together, the drawing strategy apparently fosters students to engage in generative activities, indicated by their higher
learning outcomes. Thus, the data provide further evidence for the
generative drawing effect predicted by Schwamborn et al. (2010).
In Experiment 2, benefits of the drawing activity, however, are mediated by learning time and do not involve higher mental effort.
Additionally, there was no increased benefit when additional drawing
support was available in the form of author-generated pictures.
3.4.4. Is there support for the prognostic drawing effect?
A second major goal of this study was to determine whether the
prognostic drawing effect could be extended to a new context. Mean
proportion correct on drawing-accuracy during learning was .60
(SD = .04) for the drawing group and .68 (SD = .03) for the
drawing + author generated picture group. This difference between
the two drawing groups is not significant, F(1, 79) = 2.52, p = .116.
This lack of group differences allowed us to pool the data of both
drawing groups for subsequent correlation analyses. Correlation analyses based on the combined data from the two drawing groups
revealed that the drawing-accuracy score of learner-generated drawings correlates significantly with the comprehension posttest score,
r = .470, p < .001, as well as with the drawing posttest score, r = .615,
p < .001. Additional correlation analyses revealed that the drawingaccuracy score of learner-generated drawings did not correlate
significantly with the prior knowledge test score, r = .095, p = .400,
the spatial ability test score, r = .127, p = .257, the motivation test
score, r = .033, p = .769, or the mental effort test score, r = .042,
p = .712. The correlation between the drawing-accuracy score and
the perceived difficulty score was only slightly statistical significance, r = −.218, p = .053. Thus, the data provide further evidence
for the prognostic drawing effect consistent with the results of
Schwamborn et al. (2010).
In sum, results of Experiment 2 are partly consistent with the
results of Experiment 1 in that students learn better from a science
text when they are asked to draw illustrations representing the main
ideas of the text, and 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).
4. Discussion
4.1. Empirical contributions
The present set of experiments makes three empirical contributions to the field. First, this study shows strong and consistent
evidence that students who are asked to generate drawings (with
sufficient support) during reading a scientific text that describes a
causal sequence perform better than students who read without
drawing, both on a comprehension test (d = 0.85 in Experiment 1
and d = 0.52 in Experiment 2) and on a drawing test (d = 1.15 in Experiment 1 and d = 1.87 in Experiment 2). Thus, the generative
drawing effect can be extended to a new domain, and therefore
corresponds to Shavelson and Towne’s (2002, p. 4) recommendation to “replicate and generalize across studies” as one of the six
essential scientific principles of educational research.
Second, this study shows strong and consistent evidence that the
quality of drawings that students generate during learning with a
scientific text that describes a causal sequence is positively related
to subsequent performance on tests of learning outcome including a comprehension test (r = .623 in Experiment 1 and r = .470 in
Experiment 2) and a drawing test (r = .620 in Experiment 1 and
r = .615 in Experiment 2). Thus, the prognostic drawing effect can
be replicated and extended to a new domain, consistent with standards for scientific research in education prescribed by Shavelson
and Towne (2002).
Third, this study shows that asking learners to draw pictures
during reading a scientific text (i.e., learner-generated drawing group
in Experiment 2) is more effective than simply providing drawings (i.e., author-generated picture group in Experiment 2) both on
a comprehension test (d = 0.49) and a drawing test (d = 0.68). Similarly, adding author-generated drawings (i.e., learner-generated
pictures + author-generated pictures group in Experiment 2) does
not improve the learning outcomes of students who also draw pictures during learning (i.e., learner-generated pictures group in
Experiment 2) either on a comprehension test (d = −0.57) or a
drawing test (d = −0.15). In short, the act of drawing during learning (with sufficient support) improves learning beyond the simple
provision of drawings.
4.2. Theoretical contributions
The results are consistent with the idea that drawing during learning serves as a generative activity (Mayer & Wittrock, 2006;
Schwamborn et al., 2010; van Meter & Garner, 2005; Wittrock, 1990).
That is, the act of drawing encourages learners to engage in generative cognitive processing during learning such as organizing the
relevant information into a coherent structure, and integrating it
with relevant prior knowledge from long-term memory. In the
present study, positive effects of drawing were indicated with a comprehension and a drawing learning outcome test, and therefore are
in line with the theoretical assumption derived from the GTDC that
benefits of drawing can be found if learning outcome tests are used
that are sensitive to the underlying process of drawing (cf., van Meter
& Garner, 2005). Additionally, in our study the drawing activity was
supported in a way that was intended to help learners carry out the
underlying cognitive processes of drawing (i.e., selecting, organizing and integrating) successfully. In this regard, results of the present
study might supplement the theoretical framework of learnergenerated drawing by providing further evidence that benefits of
drawing defined by van Meter and Garner’s GTDC can diminish, if
no instructional support is given to constrain and structure the
drawing activity. However, a fuller understanding of the underlying cognitive processes of drawing, and how these processes can
be influenced via drawing support, requires more direct measures
of cognitive processing during learning. Additionally, following the
idea that metacognitive processes of monitoring and regulation are
automatically activated by drawing (van Meter & Garner, 2005), a
fuller understanding of the metacognitive effects of drawings is also
required.
4.3. Practical contributions
The present study encourages instructional designers and instructors to incorporate drawing activities into venues involving
learning from text, which we call the generative drawing effect. One
important feature of a successful drawing strategy that is present
in this study and in a previous study by Schwamborn et al. (2010)
is that the drawing activity was supported by providing a
A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
background scene and a legend showing how to represent each
element to constrain and structure the drawing activity. Thus, an
important practical implication is that students may need support
in their drawing activity, so they do not need to draw from scratch.
The present study also suggests a potentially useful diagnostic
tool to gauge the depth of student learning, namely, the quality of
the drawings created by students during learning, which we refer
to as the prognostic drawing effect. Incorporating a measure of the
quality of a learner’s drawing during learning can be a useful tool
in developing remedial instruction to give learners individual support.
It may be important to use materials that explain a cause-andeffect process and give learners drawings of the elements they need
to represent the process pictorially. Asking learners to simply draw
pictures of elements is unlikely to be helpful, whereas asking them
to generate drawings that show the relations among the elements
in a schematic form is more likely to be helpful.
4.4. Limitations and future directions
Some limitations and future directions of our study should be
addressed. As noted in the theoretical contributions subsection, we
did not have direct measures of cognitive processing during learning; so it is not possible to pinpoint how the drawing activity affected
specific cognitive processes such as attending to relevant information, organizing it, and integrating it with prior knowledge. We also
did not assess metacognitive processing during learning, thus it is
not possible to pinpoint how the drawing activity affected specific
metacognitive processes such as monitoring and regulation.
Furthermore, results of the cognitive load rating scales (invested mental effort and perceived task difficulty) are inconsistent.
Whereas in Experiment 1 an effect on mental effort but not on perceived task difficulty showed up (i.e., students in the drawing group
rated their invested mental effort during learning significantly
higher), no effects on mental effort and task difficulty were found
in Experiment 2. Additionally, in both experiments only a negative correlation of perceived task difficulty with the quality of learnergenerated pictures appears, but no correlation of mental effort with
the quality. Following de Jong (2010) those cognitive load rating
scales might have the disadvantages that they do not give a concurrent measure of cognitive load and do not measure an essential
concept in cognitive load theory, namely cognitive overload (p. 125).
Future studies on learner-generated drawing might also use other
cognitive load measures, such as physiological measures as more
direct indicators of cognitive load.
As noted in the practical contributions subsection, we showed
the drawing effects by using a scientific text describing how a causeand-effect system works, that is the causal steps regarding an
infection with influenza and the immune response. It might be possible, however, that for other types of text, producing drawings might
harm rather than promote text comprehension. Thus, to test whether
the reported drawing effects can be extended, future research has
to focus on other types of text such as descriptive texts as well as
on other types of relations that can be conveyed with other types
of representations, such as compare and contrast relations which
can be shown in a matrix. Additionally, students’ learning outcomes were tested immediately after reading; thus, future work is
needed to investigate the longer-term effects of generative drawing
on learning outcomes.
Furthermore, we only compared drawing with control groups
that received no further learning strategy instructions. However, engaging in generative learning activities such as drawing requires a
considerable amount of time. Accordingly, results showed that for
Experiment 2, the positive effect of the drawing group on text comprehension compared with the author-generated picture group and
to the control group was mediated by learning time. To rule out that
the effects of drawing result only from additional time on task instead
285
of the generative activity future research should also compare the
drawing strategy with other time demanding generative learning
strategies such as summarization (cf., Leopold & Leutner, 2012).
Another point that should be noted is that students in both experiments received some kind of multimedia materials in that, even
when they had to draw and did not see presented pictures, they were
at least provided with the basic (visual) elements for their drawings, which they had to do on the given background, which thus
also contained information. In other words, when students are presented with important elements of the drawings, which they can
use to draw themselves, they will not have to put as much effort
into summarizing visually what they have just read compared with
students who have to draw without any instructional help. Future
studies might also compare the drawing group with a summarization group, in which students receive a set of verbal key terms that
are similar to the drawing elements, and are asked to make a textual
summary.
Additionally, future research is needed to validate the prognostic drawing effect. So far, we know that the quality of learnergenerated pictures is related to students’ learning outcomes (i.e.,
the higher the learning outcome, the higher the drawing accuracy
and vice versa) and their perceived difficulty (i.e., the lower the perceived difficulty the higher the drawing accuracy and vice versa),
and that it is not related to students’ prior knowledge, motivation,
spatial ability or mental effort. However, less is known about what
this might mean. That is, less is known regarding the causal direction of this relation or the presence of a possible further moderator
variable. Do students’ efforts to produce accurate drawings lead to
better comprehension and lower perceived difficulty? Or do students who are more adept in drawing benefit more from the strategy
and thus perceive the difficulty of the learning materials as being
lower? Both arguments seem convincing.
Finally, more work is needed to determine the level of support
that makes the drawing strategy most effective for various kinds of
learners. As noted in the empirical contribution adding authorgenerated drawings (i.e., learner-generated pictures + authorgenerated pictures group in Experiment 2) does not improve the
learning outcomes of students who also draw pictures during learning and were supported by a drawing prompt. In other words, the
combination of two ways of supporting the drawing strategy (i.e.,
giving a drawing prompt during reading plus an author-generated
picture after reading) did not improve students’ learning outcomes compared with students in the drawing group as well as
compared with students in the control and author-generated pictures only groups. This result is inconsistent with previous research
(e.g., van Meter, 2001; van Meter et al., 2006) which found that comparing own drawings to author-generated pictures normally helps
learning. van Meter and colleagues (2001, 2006), however, provided author-generated pictures plus prompting questions after the
drawing process. That is, students answered prompting questions
to guide the comparison process between their self-generated
drawing and the author-generated drawing. In our study, students
were only instructed to generate a drawing, to inspect an authorgenerated one, and to check whether their own drawing in
comparison with the author-generated one really represented the
main ideas of the text paragraph correctly. In other words, we did
not guide the process of comparing self-generated drawings with
author-generated ones. As a potential consequence students performed the intended comparison process inadequately or even not
all, and thus did not benefit from it. One reason for this inadequate comparison process might be that students need guidance
in doing the comparison process. Another reason might be the fact
that students do not seriously engage in generating drawings once
they notice that there are author-generated drawings. Thus, future
research should also use additional guidance to test whether the
combination of different ways of supporting the drawing strategy
286
A. Schmeck et al./Contemporary Educational Psychology 39 (2014) 275–286
(i.e., giving a drawing prompt during reading plus an authorgenerated picture after reading) helps learning as well as
observational measures of the drawing process itself (i.e. think aloud
protocols) to shed more light on the cognitive processes underlying the drawing activities.
Overall, drawing during learning from text appears to be a potentially powerful strategy for improving students’ learning from
scientific text when certain boundaries and prerequisites are taken
into account.
Acknowledgments
This article is based on a research project funded by the German
Research Foundation (DFG; LE 645/9-3 as part of FOR 511). We would
like to thank Angela Sandmann for her assistance in developing the
learning materials.
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