3.3 Data analysis
The analysis of drawings was based on the holistic evaluation of the emotional
classroom climate as suggested by Laine et al. (
2013
,
2015
). Here each drawing was
analyzed one content category at a time. Specifically, the evaluation was based on both
the students’ and the teacher’s moods as well as on their speech and thought bubbles
illustrated in the drawings. According to Koike (1997, cited in Gramel,
2008
, p. 36)
feelings can be divided into five categories of expression in drawings, namely facial
expression, gestures, the facial schema, the representation of situations triggering
emotions, and symbols. In the study, different facial features, and speech and thought
bubbles were analyzed based on the coding manual developed by Zambo (
2006
),
which was expanded with physical body features (i.e., body posture, arm position) as
suggested by Glasnović Gracin and Kuzle (
2018
), to achieve a more accurate
representation (see
Table 2
).
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855
Table 2.
An excerpt from the coding manual
Feature and thoughts
Nature and ranking
Clues
Physical facial features
eyes
positive (+1)
wide open
mouth
neutral (0)
drawn as a straight line
symbols drawn on face
negative (-1)
tears, tongue stuck out, teeth in
a growl
Physical body features
arms
positive (+1)
arms in the air, open arms
arms
neutral (0)
arms on the table
arms
negative (-1)
crossed arms
Thoughts
symbols, signs, words, emotional words
positive (+1)
hearts, peace signs, thumbs up,
“easy”, “fun”, “I like”
symbols, signs, words
neutral (0)
no expression
symbols, signs, words, emotional words
negative (-1)
dark scribbles, sad, “blah, blah”,
“hate”, “too hard”
Concretely, in each drawing, both the depicted students as well as the teacher were
examined according to the developed inventory. The analysis of the latter was
necessary because the teacher influences the affective climate of the class (e.g.,
Harrison et al.,
2007
). I use
Figure 1
for the purpose of explaining the coding process.
The drawing does not represent a prototypical drawing, but rather has been selected
on the basis of the scan quality. As shown in
Tables 2
and
3
, if a child’s rating of a
category was emotionally positive, a counter (+1) was noted. If the assessment was
negative, a negative counter (-1) was noted, and if the assessment was neutral, the
symbol 0 was noted (Zambo,
2006
). If none of the categories was drawn, it was
classified as unidentifiable and received a dash (-). After rating each feature, the
“counters” were balanced against each other. If the score was 0, the emotional state
of the respective child was rated as neutral; if the score was positive, it was rated as
positive; and if the score was negative, it was rated as negative. If an individual
contained both positive and negative characteristics, it was coded as ambivalent.
Thus, Kevin, depicted in
Figure 1
, was assigned +2 counters, his emotional feeling was
coded as positive. The same procedure was then used for all the other protagonists in
the drawing (i.e., Jessica, Lucas, Leonie, the teacher).
LUMAT
856
Figure 1.
Exemplary coding of the emotional feeling of the drawn children
Following the rating of the children drawn, the holistic evaluation of the emotional
climate in a geometry lesson was assessed. This was based on five categories reported
in Laine et al. (
2013
,
2015
) which was slightly adapted. These include the following
emotional classroom climate categories: positive (i.e., the majority of persons smile,
or think or behave positively, although some of the expressions can be neutral),
ambivalent (i.e., there are both positive and negative facial/body language
expressions or thoughts), negative (i.e., the majority of persons are sad or angry or
think/behave negatively, although some of the expressions can be neutral), neutral
(i.e., all facial/body language expressions or other thoughts are neutral, although
some of the expressions can be either positive or negative), and unidentifiable (i.e., no
facial/body language expressions or thoughts). If identifiable and non-identifiable
persons were illustrated, only the non-identifiable ones were identified in the overall
image analysis but were scored as neutral. To facilitate the interpretation of the
children’s drawings, the semi-structured interviews were transcribed. Multiple data
sources (i.e., data triangulation) were used to assess the consistency of the results, and
to increase the validity of the results (Kuzle & Glasnović Gracin,
2020
; Patton,
2002
).
Furthermore, they gave precise indications of the two temporal aspects of affect (i.e.,
state, trait). Going back to
Figure 1
, the interview confirmed that Kevin was in a
positive mood and that he enjoyed geometry lessons very much. In addition, it
provided information about the emotional state of two students, namely Lucas and
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857
Leonie, who were drawn from the back. In the drawing, only arm posture is visible
(i.e., arms open downward) which would imply neutral counters in each case, and
hence neutral emotional feeling. The interview, however, revealed that both Lucas and
Leonie disliked geometry lessons, were not happy, and for that reason the drawer did
not want to draw their faces. In this case, the interview added new information – or
even gave a completely different picture of the emotional state of both children than
one would take from the drawing itself. Thus, both children did not reflect a neutral
emotional state but rather a negative one.
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