The growing attention for natural sciences in primary education is typically aimed at the development of practical experimental skills. Natural scientific reasoning, in which thinking back and forth between phenomena and explanations forms the connecting factor, remains underexposed. The focus of our study is on how interactive diagrams and matching experiments can support scientific reasoning and thus scientific literacy. We start with three design studies, which successively investigate how (1) the complexity of diagrams can be tailored to the developmental level of students, (2) the interactivity of diagrams can be adapted to the developmental level, and (3) the dialogue that arises during collaboration influences diagram construction. Finally (4), an effect study is carried out to examine the effects of the interactive diagrams on scientific literacy in school practice. Results of a preliminary study show that the approach enables several types of scientific reasoning, in a more autonomous way than in traditional science classes. Therefore, it is intended that this approach leads to an innovative teaching method that better meets the cognitive needs in upper primary education.
Thinking back and forth between the world of
phenomena and the world of ideas is essential for the
development of scientific literacy [
Current Science and Technology teaching practice
(S&T) in primary education is often limited to doing
individual 'experiments', i.e. mainly hands-on activities
[
One way to promote the learning of higher thinking
skills, such as scientific reasoning, is to use diagrams as
external representations for creating knowledge [
This allows students to create and manipulate
knowledge constructs on the computer screen [
We have created lessons on various types of
scientific reasoning which combine short, hands-on
practical activities with minds-on learning through
interactive diagrams. These lessons are made available
as web-based applications [
The central question is how our approach can be used to promote scientific reasoning among students in upper primary education.
The aim of this PhD research is to gain new insights into
how the use of interactive diagrams as a learning
technology for science education can lead to an
improvement in this type of reasoning, resulting in an
increase in the effectiveness of science and technology
education [
The PhD research consists of four phases. The first three phases are design studies, which successively investigate: 1.
how the complexity of diagrams can be tailored to the developmental level of students, 2. how the interactivity of diagrams can be adapted to the developmental level, and 3. how the dialogue that arises during collaboration influences diagram construction.
These three phases then culminate into an intervention (study 4), in which the effects of the interactive diagrams on scientific literacy are examined in school practice.
In order to explore how interactive diagrams and matching experiments can support scientific reasoning and thus scientific literacy, the following problems need to be addressed: 1. What are adequate levels of complexity of interactive diagrams, taking into account individual differences in developmental level of students? 2. What are effective forms of adaptive feedback, when the learning needs of individual students are taken into account? 3. What is the effect of collaboration on the quality of the diagrams to be constructed? 4. What are the effects of working with interactive diagrams on the scientific reasoning skills and knowledge level of individual students? In the next sections, these problems are discussed in more detail.
To represent a particular content, combinations of visual
symbols and written language offer opportunities to
scaffold students in the development of their scientific
reasoning [
Concept diagrams lend themself well to being digitized,
as for instance done with the Cmap software [
Study 1 investigates how the complexity of diagrams can be defined and determined so that diagrams used in education can be tailored to the developmental level of the students.
In this research, complexity is operationalized on the basis of (a) the average level of familiarity with the vocabulary on which the reasoning and the associated diagram is based, and (b) the number of concepts and mutual relationships between these concepts. To tailor the diagram complexity to the development level, the complexity levels of the diagrams are categorized. By measuring at different levels of diagram complexity, it can be determined how the quality of the diagrams to be constructed relates to the complexity of a certain type of diagram and adjustments can be made accordingly.
Study 2 investigates how the interactivity of the diagrams can be adapted to the development level and learning needs of individual students.
Automated feedback takes place via automated evaluation of student-computer interactions, subsequently presented to the student via visual cues in the diagram. This software evaluation takes place (1) when the student selects, positions and/or connects diagram elements, and (2) during task performance and after task completion.
By varying the interactivity in the ways described above, the effectiveness of the different forms of automated feedback can be determined by measuring the quality of the diagrams that are constructed.
Study 3 investigates how the dialogue that arises during collaboration among students influences the quality of the diagrams they construct.
Research shows that conversational activities
stimulate the development of scientific reasoning [
In general, working in heterogeneous groups offers a more challenging learning environment than working in homogeneous groups [22]. To investigate whether the composition of pairs based on developmental level has an effect on the quality of the diagrams that are constructed, students are clustered in homogeneous or heterogeneous pairs. By determining the diagram quality per student and analyzing the vocabulary and the quality of the reasoning of the dialogue, the effect of mutual cooperation is examined.
Study 4 is an effect study on a larger scale in which the effects of working with the interactive diagrams on both the development of scientific reasoning skills and the subject-matter knowledge level of students are investigated.
Through the previous studies, insights have been gained about (1) complexity, (2) interactivity, and (3) collaboration. In study 4, these insights are combined into an experimental pretest/posttest design with a retention measurement.
Potential contributions to solving the problem on technology enhanced learning are: 1. 2. 3. 4. 5.
A validated measure for determining the complexity of diagrams.
Interactive diagrams that are tailored to the performance level of students in terms of complexity.
Insights into the effects of different forms of adaptive feedback.
Insight into the effect of collaboration on the quality of the diagrams constructed.
Insights into the effect of working with interactive diagrams on the individual development of scientific reasoning skills and professional knowledge.
By using representations of scientific thinking and
working methods, also called crosscutting concepts [23,
24], we investigate different types of diagrams
associated with (1) reasoning in patterns, (2) causal
reasoning, and (3) reasoning in systems. To realize this,
the predict-observe-explain routine of primary science
education is translated into a web-based application
called Minds-On [
The Minds-On application shows a section of the diagram with its ingredients (Figure 1), consisting of nodes (individual concepts) and lines (relationships). The appearances of both the nodes and the lines depend on their type (e.g., circular nodes denote objects, thin arrows denote ‘has/is’ relationships and bold arrows denote causal relationships). Before filling in a diagram, students conduct an experiment relating to the concepts in the diagram (e.g. about the properties of ‘sound’). As an initial scaffold, one or more concepts are already in place in the diagram. Next, students can drag and drop concepts into diagram nodes, during which automated feedback is presented in the form of evaluative statements. Until all concepts are placed correctly, students also receive automated feedback by means of a check function. After successfully completing a diagram step, students progress to the next experiment. Throughout this stepwise progression, the complexity of the diagram increases, until culminating in its final form.
A study was carried out with primary school students
(9-12 years, n=490) in the Metropolitan Region of
Amsterdam, Netherlands, showing that most students
successfully complete the task within standard lesson
time. The approach enables the effective application of
several types of scientific reasoning, in a more
autonomous way than in traditional science classes.
Results indicate that successful task completion is
associated with less dependency on the interactive
functions. However, since a minor proportion of the
students did not use the interactive functions as
envisioned, we conclude that additional forms of
interactivity, focusing on vocabulary and reasoning
abilities, are necessary [
Diagrams for scientific reasoning have already been implemented successfully, although most studies concern middle-school students (or higher), who create diagrams from scratch [25]. The pre-formal developmental stages that characterize students in Primary Science Education (PSE) are less advanced, and ask for a more object -related, scaffolded approach. Therefore, a unique design element is the stepwise building up of the concept, by combining short hands on experiments with the corresponding part of the interactive diagram. Interactive functions, such as the presentation of inference statement to the student during construction of the diagram, and a check function after completing each diagram step are considered essential for the scaffolding of scientific reasoning at the primary level. The built in datalogger allows for detailed measurements of relevant user parameters.
The research presented here is co-funded by The Netherlands Organization for Scientific Research (NWO), PhD-grant for teachers, grant number 023.0I7.002 and the Dutch Regie-orgaan SIA, project Minds-On, grant number RAAK.PUB06.033.
[21] Osborne, J. (2010). Arguing to Learn in Science: The Role of Collaborative, Critical Discourse. Science 328, 463. [22] Oostdam, R.J. (2009). Tijd voor dikke leerkrachten. Over maatwerk als kern van goed onderwijs. [Time for big teachers. About customization as the core of good education].
Amsterdam University Press. [23] Van Graft, M. & Klein Tank, M. (2018).
Wetenschap & technologie in het basis- en speciaal onderwijs: richtinggevend leerplankader bij het leergebied Oriëntatie op jezelf en de wereld [Science & technology in primary and special education: guiding curriculum framework for World Orientation]. SLO. [24] National Research Council (2012). A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. The National Academies Press. [25] Stevenson, M.P., Hartmeyer, R. & Bentsen, P. (2017). Systematically reviewing the potential of concept mapping technologies to promote self-regulated learning in primary and secondary science education. Educational Research Review, 21(1), 1-16.