The document summarizes a study on modeling-based learning (MbL) with pre-school children. The study found that during MbL activities: 1. Students started by collecting data on a phenomenon, then proposed analogical models to explain it and revised their models based on discussion. 2. Students engaged in elements of mechanistic and analogical reasoning to develop representations of the phenomenon and how it occurred. 3. For a model to be accepted, students felt it needed to be possible, plausible, and explain their observations of the phenomenon.

1. Pre-school Children Talking
About the Models They Have
Constructed:
An Analysis of Children Constructed
Models and Their Presentation
Loucas T. Louca

2. • Science is a complex, multifaceted activity that includes the
process of formation and justification of new knowledge as
well as the new knowledge itself, in an effort to explain natural
phenomena.
• In this view, science is viewed as a dynamic network of
models embedded in a system of theoretical principles.
• In this context, models are the core components of any
scientific theory and they take a central role in the justification
and formation of knowledge (Koponen, 2007).
• Models are conceptual maps of the physical systems,
constructed within the context of a specific theory, to reliably
represent a specific pattern in the real world (Halloun, 2006).
Modeling in Science

3. • Models and modeling are considered as integral parts of scientific
literacy (Gilbert & Boulter 1998, Gilbert, 1991, Linn & Muilenberg
1996), reflecting educators’ efforts to introduce and engage students to
authentic scientific inquiry.
• Modeling can provide the context in which the construction and
refinement of models can achieve better quality outcomes than
what is possible through other learning environments/tools.
• Modeling can provide students opportunities to think and talk
scientifically about physical phenomena (Penner, 2001), to share,
discuss and criticize their ideas, and to reflect upon their own
understanding (Jonassen, Strobel, & Gottdenker, 2005).
Modeling-based Learning in Science Education

4. What do we know about Modeling-based
Learning for young learners…

5. • Representation of physical objects
• Representation of physical entities (e.g., velocity)
• Representation of physical processes (e.g.,
accelerated motion)
• Representation of various interactions between
physical objects, physical entities and physical
processes
Student-constructed models
(Louca et al, 2011)

6. • Discourse type I: Phenomenological description of the physical
system under study
– Describe the overall physical system or talk about the story of individual physical objects
involved
– Support their ideas with everyday experiences
• Discourse type II: Operationalization of the physical systems’ story
– Describe the story of physical processes (e.g., relative motion, accelerated motion)
– References to the overall phenomenon as a reality check
• Discourse type III: Construction of algorithms
– Describe the story of physical entities
– Identify and investigate the relationships among physical entities
Different types of modeling discourse
(Louca et al, 2011)

7. • Student MbL may take several different forms,
depending on how students frame their work. The
process may become:
– technical (about the use of the modelling medium)
– conceptual (the way causal agents are represented in the
model)
– procedural (by describing how something happens over
time)
– causal (by describing how an agent affects a physical
process)
Various modes of student modeling work
(Louca et al, 2008)

8. • My purpose in this paper was to investigate in detail
MbL in preschool science education by providing
rich, detailed descriptions of how MbL looks in young
ages.
– Focused on the model formation and the
characteristics of models constructed in pre-school
ages
– Followed a discourse-based perspective
– Used student-constructed models as prompts for
discussions
Purpose

9. Methods
Context, data sources &
analyses

10. • Participants:
– 18 pre-school students (age range: 5-6)
– working in a STEM education afternoon club (1 hr per week)
– the author was the instructor
• Data collection: over a year
• Data sources
– A unit on solution of substances in water
– Three (3) 60-minute lessons were collected from each topic, covering a
complete investigation of a phenomenon and subsequent development of
models
The study’s context

11. • Drawing-based modeling approach (Ainsworth,
Prain, & Tytler, 2011; Brooks, 2009 van
Joolingen, Schouten, & Leenaars, 2019)
– working with young children aged 5-6,
– children use annotated drawings to represent their
model, including the behavior of the physical
objects, physical entities, physical processes and
physical interactions in their models.
Drawing-based Modeling Approach

12. • Two analyses based on our previous work (Louca et al,
2011a; Louca et al, 2011b) were used for primary data:
– Analysis of transcripts of student conversations, specifically
focusing on student descriptions of their constructed models.
– Analysis of the corresponding student constructed models (all
in paper-and-pencil format).
• Both analyses focused on elements of models that
students included either in their constructed models or
discussed during their modeling group work.
Data analyses

13. Findings
The case of “solution of
substances in water”

14. The experiment and the data collection
• We placed a drop of
food color at the
bottom of a glass of
water
• In groups, children
recorded 5
observations every 2
minutes

15. • We then displayed the drawings in sequence on the white board.
• We made observations about the changes in the water
• And then the question: “suppose you are in the water. What would
you see happening?” (aka: how does this process take place)
Identifying the problem – Interpretation of data

16. • After a short discussion, mostly
around the types of answer that this
question required
• Children drew on a new piece of
paper what they though happened in
the water.
• We posted drawings (models) on the
white board
• Students briefly talked about their
models
• We talked about the differences and
the similarities of the drawings
(models)
Proposing, discussing and evaluating models

17. • ”The infectious disease”
– The substance in the water infects (like an infectious disease) the water “pieces” (aka molecules)
around it, which in turn infect other water molecules and at the end all the water gets coloured
• ”Getting bigger and exploding like a bomb”
– The substance in the water becomes bigger and bigger, explodes like a bomb, spreads all its “pieces”
(aka molecules) in the water
• The substance pieces were “stuck” on water pieces, which carried them
allover the water
• ”Growing up, giving birth to babies etc
– Like living organisms
• ”Getting bigger and dying”
– The substance in the water becomes bigger and bigger, gets old, dies and breaks up into small pieces
which were carried around in the water by the water “pieces”
5 types of models

18. • Sequential approach
3 types of model depiction
• Representation of all the
“scenes” of the phenomenon
in one drawing
• Representation of the last
scene of the phenomenon

19. • In their efforts to explain their static paper-and-pencil model, students
were actively describing the story of the physical system and how that
occurs using two types of reasoning.
• Analogical reasoning was used to create links between what happens
in the system under study with another known system (May et al, 2006;
Gentner & Colhoun, 2010)
• We observed students in various cases to engage in
– creating analogies
– using analogies
– evaluating analogies used in their models,
• which are different elements of the analogical reasoning.
• Studies of analogical reasoning in MbL (e.g., Shemwell & Capps, 2019)
Analogical Reasoning

20. • A possible difficulty: constructing models for phenomena of the
microcosm that are difficult to observe the underlying mechanism
• Despite that difficulty, students were observed to engage in various
elements of mechanistic reasoning clearing viewing their work as
developing representations of how the particular phenomena worked.
– “Water does not change”
– Talked about water ”pieces” and substance “pieces”
– Talked about substance “pieces” being able to swim…
• Developing and revising models that address the mechanism of
phenomena lies at the core of the scientific endeavour (Ke & Schwarz,
2019)
Mechanistic Reasoning (Russ et al, 2008)

21. Components of the mechanistic
reasoning
Representation
Limited Partly Fully
Description of the target phenomenon (what we see happening)
0% 0% 100%
Identification of the set-up conditions that are necessary for the
phenomenon to happen
0% 32% 68%
Identification of entities (conceptual or physical objects that
play a particular role in the phenomenon)
0% 34% 66%
Identification of the entities activities that cause changes in the
surrounding entities
10% 25% 65%
Identification of the entities’ properties
10% 34% 54%
Identification of the entities’ organization (how entities are
located, structured or oriented within the phenomenon) 19% 81% 0%
Chaining, that is making claims about what had happened prior
to a phenomenon and what will happen 10% 60% 30%

22. Discussion

23. • During their MbL work students…
– Started from data collected
– Identified the main players in the phenomenon
– Looked for analogical scenarios/resources from their
past experiences to describe the situation/mechanism
they observed.
– Added/invented new objects involved in the
phenomenon
– Described and/or represented the mechanism that
caused the phenomenon.
Overall…

24. During the discussion and evaluation of their models, students agreed that
in order for a model to be an accepted representation of the phenomenon,
it had to share 3 important characteristics.
1. The model had to be possible: students thought that a model was
accepted if they could feel that it was possible to exist. “Magic” or
“fairy tale”-like models were quickly dismissed.
2. The model had to be plausible and logical: that is, the model
should be able to provide a tangible and logical, causal description of
the mechanism that underlie the phenomenon, in a way that would
make sense.
3. The model should explain the data collected or observed:
Students felt strongly that acceptable models had to account for the
observed phenomenon.
3 characteristics of model acceptance

25. • Schwartz (2019)
• The practice of modeling is a standard practice that is used by the
scientific community to build, test and elaborate on new ideas about
how (new) phenomena take place. These epistemic considerations
include 4 important characteristics.
– The nature of knowledge product
• provide explanation as to what kind of answer should the knowledge product would provide
– The justification of the knowledge product
• how do we justify the ideas used or incorporated in the model
– Generalization
• explain how the model is related to other scientific phenomena and ideas
– The target audience of the knowledge product
• for whom is the model used for
Epistemic considerations within the disciplinary
norms of the scientific community

26. • We contend that the findings are aligned with the idea of modelling
resources
• The idea of modeling resources, mostly derived from physics education
research, is used to identify student knowledge, abilities, or reasoning
skills related to various modeling tasks.
• In this study we have seen students exhibiting some (advanced) modeling
abilities and reasoning
– Despite the fact that this was their first engagement in a MbL activity
• A pedagogical implication would be that instead of seeing the absence of a
particular modelling knowledge or abilities as a need to help students develop
modelling abilities they lack, it might be more productive to view this as a need
to help students develop more reliable access to modelling resources they
might have and might be context dependent.
The idea of Modeling Resources

27. Thank you for watching!
You may contact me at
L.Louca@euc.ac.cy