Science for Life - development of a multi-concept instrument to
study the impact of socio-scientific issues on student interest in
science.
Mikael Winberg, Umeå University
Britt Lindahl, Kristianstad University
Background
According to Ramsden (1998) interest in questions of attitudes towards science has decreased
since each study gives the same results and nobody knows what to do to change the students’
attitudes. The current importance of this question needs to be even more emphasized as young
people’s interest in choosing a scientific career is declining.
One way to increase students’ interest in science can be to bring in a humanistic perspective
(Aikenhead, 2006). The relative absence of right answers and the high degree of autonomy
and ‘real world’ connection that characterize socio-scientific issues (SSI) (Ratcliffe and Grace
2003) are factors that have been suggested to influence the quality of students experiences
during learning science (Osborne et al, 2003). Thus, studying the impact of socio-scientific
issues on students’ affective reactions during learning and attitudes toward learning science is
highly relevant.
The relation between the characteristics of the learner, the situation and outcomes is complex
and there is a need for research on how personal and situational factors interact in the forming
of learning experiences and outcomes. To achieve that, we need to simultaneously take into
account several different models and concepts. This paper concerns the development of
instruments necessary to assess the multivariate characteristics of students, learning situations,
and affective outcomes that are central in the understanding of how socio-scientific issues
might contribute to high quality learning and positive attitudes to learning science.
Framework
Several surveys of studies on attitudes towards and interest in science have been completed
and most of them, like Osborne et al (2003), conclude that earlier research shows a complex
picture as the concept of an attitude is somewhat nebulous, often poorly articulated and not
well understood. Koballa and Glynn (2007) claimed that we now have to take a
multidimensional approach to understand students´ experiences during learning. It has been
suggested that much of the ambiguity in education research is due to a failure to account for
the complexity of factors that influence learning. Examples of such factors are; student
emotions (Pekrun et al, 2006), the instructional design, student attitudes toward learning
science (Osborne et al, 2003), epistemological beliefs (Hofer, 2001), and interest, self efficacy
beliefs, and sense of autonomy (Ryan & Deci, 2000). Windschitl and Andre (1998) found that
student epistemological beliefs functioned as predictors of learning outcomes only if the
degree of autonomy in the learning situation was considered simultaneously, arguing that the
match between the characteristics of the situation and students’ epistemological beliefs
elicited affective reactions that regulated student behaviour during learning and, eventually,
learning outcomes. Another example comes from research on cognitive load and mental effort
(Paas et al, 2005). While cognitive load is the proportion of an individuals working memory
processing capacity that is required to solve a task, mental effort also takes into account the
learners motivation to do so, i.e., a measure of the cognitive capacity that is actually invested
in the task. Hence, mental effort results from an individual’s interaction with a learning
situation and reflects the learner’s knowledge in the domain, his/her motivation to engage in
the task (influenced by task value and complexity, self efficacy and sense of autonomy) and,
at least in the case of more ill defined tasks, how the task is interpreted (involving the pupil’s
epistemological beliefs). If the complexity of the task is too high in relation to the learner’s
prior knowledge, effective processing of the information is hampered, ultimately affecting the
learner’s ability to solve the task - and the affective experiences during learning.
Aims
The aim is to develop an instrument that simultaneously considers the characteristics of the
student, situation, and outcomes. We want to investigate this instrument’s ability to indicate
which personal and/or situational characteristics are the most important to describe variation
in student behaviour during learning as well as affective and perceived cognitive outcomes.
Method/Results
Construction and categorization of items
A large number of items were collected from extant questionnaires and, when necessary,
adapted to Swedish conditions. Additional items were constructed, based on theory within the
relevant fields of research. Items were categorized through Principal Component Analysis
(PCA) of 1276 responses to the questionnaire, paralleled by discussions between researchers.
The final categories, and the descriptive (R2) and predictive (Q2) ability of the PCA models
describing the categories, are found in table 1.
Table 1. The categories, the contextual aspects they relate to (Personal, Situational, and
Outcome), and number of components and statistical performance of the corresponding PCA
model.
1
2
3
4
5
6
7
Categories
Items
Attitudes and goals (P)
Beliefs about learning (P)
Self-efficacy / locus of control (P)
Ordinary work forms (P)
Work forms during SSI-work (S)
Affective outcomes (O)
Cognitive outcomes (O)
43
20
16
7
17
19
11
Comp.
3
1
2
2
3
1
1
R2 (%) Q2 (%)
39
23
55
45
40
38
33
28
14
39
-6
3.8
31
20
All models showed intelligible distribution of items on the different components (describing
the underlying “features” that connect items to each other) and correlation patterns.. The
descriptive and predictive ability ranged from moderate to good, with the exception of the
“work form”- models that had poor predictive ability – possibly indicating the lack of an
underlying construct that link the different work forms.
Prediction Model
The large number of variables, although improving reliability of the instrument, makes
interpretation of the relations between them difficult. Therefore, hierarchical PLS analysis
was applied to items in categories 1-5 respectively to investigate their relation to outcomes
and to “condense” items (i.e., personal and situational characteristics) into latent components.
Then the latent components from all categories were pooled and their relations to affective
and cognitive outcomes were examined in a “top model”. Table 2 shows number of
components and performance of the PLS models on separate categories and the top model.
Table 2. Number of condensed variables in each category, percentage of described variation
in the independent variables (R2X) and outcomes (R2Y), and predictive ability (Q2) of models
Category
1
2
3
4
5
Attitudes and Goals
Beliefs about learning and knowledge
Self-efficacy / locus of control
Ordinary work forms in science class
Work forms during SSI-work
Comp.
R2 X
R2 Y
Q2
2
1
2
1
2
28
22
50
29
36
43
16
25
6
44
37
14
22
4
38
2
47
56
56
Top model
Components from category 1-5 vs.
outcomes
The single categories “Attitudes and Goals” and “Work forms during SSI work” seems useful
for predicting outcomes. However, the top model that includes components from all
categories is better at describing and predicting variation in outcomes than any singlecategory model, which supports the idea that we need to consider several variables
simultaneously. Response permutation testing, and external dataset validation of the top
model supports its validity (data not shown). Details on the validation process and the specific
original variables that were most influential in the prediction of affective and perceived
cognitive outcomes are available and will be reported on the conference.
Conclusions and Implications
Initial PCA models (table 1) support the categorisation of items. The top model was able to
indicate the relative impact of the categories on cognitive and affective outcomes (figure not
shown). Most categories contributed, although in varying degree, to the predictive ability of
the top model, supporting the validity of the multivariate approach. Although there is a need
for a more in-depth investigation of patterns of causality (to be performed within the project
during 2008), the potential implications of a deeper understanding of these relations are
obvious for everyone interested in instructional design.
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