Constructivist Dialogue Mapping Analysis of Ant Adaptation

Informatics in Education, 2020, Vol. 19, No. 1, 77–112

DOI: 10.15388/infedu.2020.05

Constructivist Dialogue Mapping Analysis

of Ant Adaptation

Kit MARTIN, Michael HORN, Uri WILENSKY

Northwestern University

e-mail: [email protected]u, {michael-horn, uri}@northwestern.edu

Received: October 2019

Abstract. This paper introduces constructivist dialogue mapping (CDM), a new type of concept

mapping. CDM encodes what people learn during a non-goal directed learning activity. CDM is

a practical means to outline the mini theories users uidly construct as they explore open-ended

learning environments. To demonstrate the method, in this paper we use CDM to track how two

modelers elaborate understandings during use of a constructionist learning game, Ant Adaptation.

Using the method, we show how two users contest and construct their idea of self-organization

in ant colonies. The method is rooted in constructionism, constructivism, concept mapping, and

conceptual change.

Keywords: keyword, constructivism, constructionism, evaluation, qualitative methods, complex-

ity research, agent-based models, concept mapping, conceptual change.

Introduction

To study open learning activities in a museum, we created a method to encode group

learning, through participant dialogue. The approach builds on Piaget’s method of clini-

cal interviews, which studied how people talk, and how their speech references the

mental models they are using (Piaget, 1926; 1929; 1952). We extended that approach

to pictorially represent how people understand complexity. The method creates hierar-

chical concept maps, we call constructivist dialogue maps, of what people say with a

focus on the temporality of when they advance new ideas. The method highlights the

possibility that concepts are not stable. The approach, constructivist dialogue mapping

(CDM), was innovated to study open-ended, constructionist (Papert, 1986; Papert &

Harel, 1991) learning activity in a museum (Martin, 2018; Martin, Horn & Wilensky,

2018; 2019). The approach was developed because of a weakness in evaluation meth-

odologies of open-ended environments (Ochoa & Worsley, 2016). We argue that a form

of deep learning with complex systems models can be documented by using our verbal

interactive tool: constructivist dialogue mapping. This tool was developed from con-

K. Martin, M. Horn, U. Wilensky

structivist and conceptual change theory. In this paper, we rst introduce constructiv-

ism, and our denition of concepts. Then, we differentiate CDM from similar methods

including concept mapping more broadly, as well as semantic mapping, dialogue, and

narrative mapping particularly and provide pictorial examples of those methods. From

that point, we narrate how museum studies focus on elaboration to track learning (Lein-

hardt and Crowley, 1998). In the methods and results sections, we show how CDM can

be used to follow how users elaborate their ideas. Then, we will discuss next steps in the

process of automating the approach in the discussion section. Finally, we argue CDM

allows us to show how it can be used to track theory development during learning in

groups, with a computer, and with a facilitator. To begin the discussion, we turn to the

theory that underpins each of these visualization methods, constructivism.

Constructivism

Piaget was a Swiss psychologist who focused on the development of the child’s under-

standing. He presented a notion of knowledge that is constructed. For example, he found

that young children, below 3 months old, could not remember where an object was. He

would hold his watch on a chain in front of the child, and slowly drag the watch out of

sight. When his children were young, they had no “object permanence”, and so, when

the watch came back, they were surprised, but as they aged he found they could track

objects. He concludes that humans “distinguish between these changes of position and

changes of state and thus contrast at every moment the thing as it is with the thing as it

appears to our sight; again, this dual distinction leads to the permanence characteristic

of the object concept” (Piaget, 1952, p. 7). In other words, as the child develops, she

becomes able to construct a notion of an object. The list of all objects a child has con-

structed in this way is her ontology. As a result, knowledge is constructed and a child

builds her ontology through action in the world.

To elaborate, consider if the child grew up in a gas cloud in some remote universe.

At rst, the twirling nebular shapes would appear to have no order, and she would have

no ontology for them. Therefore, when a particular eddy returned to the developing

child’s perception, she would not attach to it any particular permanence because the

learner would not have conserved its continuous change into an identied concept. As

the learner lived there longer, however, Piaget’s theory predicts she would combine the

gas cloud eddies into predictable movements based on experience to create concepts

of gas cloud objects through the process of assimilation and accommodation.

This

One interesting experiment that should be done on this theory is about whether it is that children construct

the idea of permanence, or whether it is that they construct the idea of time. That is, at rst they experience

all time simultaneously and so the object for them is technically in both places, all places, but through

experience come to see the object rst as here and then there, or in other words in linear time. Basically,

do children, only through experience, constructs linear time? This question would have implications for

physics, because some of the issues with the unied theory of the universe can be resolved by dropping the

notion of time from the equations. According the Julian Barber: “If you try to get your hands-on time, it’s

always slipping through your ngers people are sure time is there, but they can’t get hold of it. My feeling

is that they can’t get hold of it because it isn’t there at all” (Franks, 2011).

Constructivist Dialogue Mapping Analysis of Ant Adaptation

process would be conservation: seeing an object in spite of its changing features, or

ever-changing atomic arrangement as a continuation of its prior existence.

In this way,

all matter is more or less variant, but the child can construct an invariant version out

of this directionless, senseless mass, to form a theoretical frame. With this frame, the

child can operate on their constructed ontology to make predictions and take action.

This is like a biker, who sees a group of people walking, and can bike past them very

closely because she can assume they will continue to move the way she has observed

them up to now. This prediction of the pedestrians’ operation is prone to error, but as a

working hypothesis for riding a bike, is close enough. As a result, concepts are uid.

Concepts

While there are several perspectives on what a concept is, we take a physicalist theory

of mind: a concept is a mental representation the brain employs to mark a class of

things, like a nebular swirl or ock of pedestrians, in our world. Concepts are mental

representations that allow us to draw appropriate inferences about the type of entities

we encounter in our everyday lives (Murphy, 2004). Murphy argues concepts make

sense of our lived experience: “Concepts are the glue that holds our mental world to-

gether. When we walk into a room, try a new restaurant, go to the supermarket to buy

groceries, meet a doctor, or read a story, we must rely on our concepts of the world to

help us understand what is happening. We seldom eat the same tomato twice, and we

often encounter novel objects, people and situations. Fortunately, even novel things are

usually similar to things we already know, often exemplifying a category that we are

familiar with” (Murphy, 2004, p. 1). Thus concepts let us see the changing world as

consistent, and predictable.

Children construct conceptions. Adults do too. These conceptions have several uses,

such as that they can be placed in categories, that aid learners to make inferences. For

example, 4- to 7-year-old dinosaur acionados can generate many appropriate infer-

ences about an unfamiliar dinosaur after they categorize it on the basis of surface, and

taxonomic features (Chi & Koeske, 1983; Gobbo & Chi, 1986). This categorization al-

lows people to theorize about missing information in a theory (Chi, 2008).

For instance, in Fig. 1 a scientic illustration of Hadrosaurus shows a head. However,

there is no fossil record of Hadrosaurus heads. The artists have to extrapolate, deduce, or

imagine based on other hadrosaurids. The concept artists construct through experience

allows them to infer or deduce the missing information.

When learning from an agent based model, this could be described as learning an agents’ operators, or

rules.

One interesting possibility for constant construction and reconstruction of knowledge is the possibility

that there is no memory. In other words, like the original Macintosh, there is no onboard memory, instead

knowledge is always reconstructed from the assimilation, or of imposing one’s order on the world in the

context at hand. One’s order then would have to be maintained, but no need for memory, perhaps. This

possibility becomes particularly interesting in light of Ackermann (1996) that due to the 3D reconguration

task required for perspective taking, that some views are harder to reconstruct than others (p. 6).

K. Martin, M. Horn, U. Wilensky

Categorization is a crucial use of concepts in a physicalist view. We identify and

assign concepts to the category to which it belongs through categorization (Chi, 2008).

Once a concept is categorized it can inherit from where we assign the knowledge. For

instance, as long as we know that ants are insects and insects lay eggs, we can infer

that ants lay eggs even if we were never told that fact. When a student knows an ant is

a kind of insect, she can infer that ants inherit the properties of insects. Categorization

of concepts through assignment is powerful because a student can use knowledge of the

category to make many inferences and attributions about a novel concept/phenomenon

(Medin & Rips, 2005).

From Aristotle to the 1970s, philosophers argued that concepts have a denitional

structure. Concepts dened this way have a list of features, that are both necessary and

sufcient to determine membership into a class, i.e., a bachelor is an unmarried man.

There are several challenges to this view. For our current project, the biggest challenges

are that categories can be “fuzzy”, and that there is no psychological evidence for hu-

mans using concepts as strict denitions (see Laurence & Margolis, 1999 for a complete

Fig. 1. People form concepts about organisms. They learn the parts of the organism, its

functions, and properties, such as color or size. Based on what they know about similar

organisms they infer other unknown parts. (Right) Hadrosaurus interpretation. (Left) Al-

losaurus with taxonomic identication Hadrosaurus and Allosaurus drawn by Francisco

López-Bermúndez.

Constructivist Dialogue Mapping Analysis of Ant Adaptation

accounting of the challenges). Despite this challenge, however, categories remain very

useful heuristics for interaction in the world, letting us ll in the missing head of hadro-

saurids, and the pedestrian movement for bike riding, among many other daily tasks.

Piagetian Clinical Interviews

How are concepts formed? In our constructivist view, concepts are conserved ontologi-

cal entities a person builds through action in the world (Piaget, 1952). So, how do we

identify concepts that people hold? Piaget invented a method, the clinical interview, an

approach to documenting an open-ended conversation designed to illuminate the way a

child thinks or explains particular phenomenon. Even though Piaget widely employed

the clinical interview to examine how children construct their knowledge, there is sur-

prisingly little discussion of the method in his work (Posner & Gertzog, 1982). Piaget

elaborated on his data collection method most in the introduction to The Child’s Con-

ception of the World (1929), and also in the preface to The Language and Thought of

the Child (1926). His method of analysis of the development of cognitive constructions

involved observing children as they reason about unusual phenomena that he presented

in designed settings. The method involved the following four steps:

Design an activity. (1)

Let the child talk. (2)

Notice the manner in which the thoughts unfold. (3)

Do not just notice the answers the child gives to questions posed, but also follow (4)

the child’s line of thought.

Piaget argued that “If we follow up each of the child’s answers, and then, allowing

him to take the lead, induce him to talk more and more freely, we shall gradually estab-

lish for every department of intelligence a method of clinical analysis analogous to that

which has been adopted by psychiatrists as a means of diagnosis.” (emphasis added,

Piaget, p. 276 as in Claparéde’s preface to Piaget, 1926). This approach is a useful way

to follow children’s understanding of the world; it focuses on the knowledge, or mental

models, children construct during an activity. This process is the process of theory de-

velopment.

Theory Development

Theory development is the idea that students’ mental models are built out of theories of

how the world works, the sum of lessons learned from thinking that builds knowledge.

DiSessa and Cobb (2004) argue that from Newton, to Einstein, and Darwin, theories

embody generalizations to organize overly abundant data that is subsequently viewed as

part of a new theory. In this way, diSessa and Cobb (2004) posit theory as a lens, “teach-

ing us how to see” (p. 4). How we see the world, is the crucial part of these theories

since the lenses constructed through experience take actual form. Just as “[t]he world

is not just sitting out there waiting to be to be uncovered, but gets progressively shaped

K. Martin, M. Horn, U. Wilensky

and transformed through the child’s, or the scientist’s, personal experience” (Acker-

mann, 2001), constructivist thought highlights transformation and molding as the work

of mental models. These models can form into more stable theories. This transformation

happens through conceptual change which we turn to next.

Conceptual Change

There are at least three circumstances under which a person can learn (Chi, 2008). First,

they have no prior knowledge, so they add missing knowledge. Second, the person may

have incomplete, but correct knowledge so the learner is lling the gap. Third, the stu-

dent may need to amend prior knowledge. In this third case we refer to the process of

knowledge acquisition as conceptual change (Chi, 2008). Students undergo conceptual

change when they perceive a mismatch between previously constructed knowledge and

novel experience (Chi, 2008; Posner, Strike, Hewson & Gertzog, 1982). When the learn-

er experiences contradiction, he/she can reconstruct knowledge structures to account for

the novel experience. If the context of learning is aligned with conical scientic knowl-

edge, the resulting structure will more closely resemble scientic knowledge (Chi, 2008;

Posner et al, 1982).

There is a connection between observation, contextual knowledge construction,

and stable theories. These states convert from each other through conceptual change.

When a learner modies their knowledge, the knowledge-in-pieces framework (KiP)

indicates construction happens incrementally over time (sometimes years) (diSessa,

2018). The learner has subtle knowledge infrastructure, that yield conceptual under-

standing through knowledge structure synthesis. These knowledge structures only exist

in context, not as stable structures (Hammer, 1996). Instead context activates specic

cognitive building blocks known as phenomenological primitives (p-prims) (diSessa,

1993). These structures can re differently depending on context, so what may appear

as a misconception, may result from a re-ordering of p-prims from different contexts.

KiP indicates that instruction should activate p-prims in the context in which the in-

structors intends knowledge to be used. Even though knowledge structures are not

stable and they operate at different scales (Duit, Treagust, & Widodo, 2008), some

emerge and reinforce to become large, broad, stable knowledge structures stored in

memory, like a bikers understanding of pedestrian movement. We call these theories

(Darner, 2019). Theories are cohesive mental models that explain causal relationships

between several different contexts or phenomena. Our work attempts to track this

messy, context dependent path through a type of concept mapping called constructivist

dialogue mapping (CDM).

We developed CDM to study learning in an informal environment based on con-

structivist theory. In constructivist theory, a learner’s mental model drives his or her

construction of understanding and internal cognitive structures. This process includes

accommodation and assimilation (Piaget, 1952), maintaining a balance between stabil-

ity and change, continuity and diversity, and closure and openness (Ackermann, 2001)

Constructivist Dialogue Mapping Analysis of Ant Adaptation

when exploring the world. For Piaget, children are not just incomplete adults (1952).

Their ideas function very well for their current context and as a result, their mind chang-

es through experience. Even though knowledge is contextually bound, so are actions

and habits. As Ackermann (2001) said, children’s conceptual changes are like those of

scientists: they happen through “action-in-the-world” (p. 3) to accommodate for experi-

ences, and most likely through a host of internal cognitive infrastructures. “Knowledge

is not merely a commodity to be transmitted, encoded, retained, and re-applied, but a

personal experience to be constructed” (Ackermann, 2001, p. 7). Thus, with CDM we

hope to track these changes as elaborations of ontologies, as they happen, during knowl-

edge construction to facilitate the study of learning. An example of this sort of learning

environment can be found in constructionist microworlds (Edwards, 1995; Papert 1980),

or constructionist video games (Holbert & Wilensky, 2019) where the learning is inter-

woven into the gameplay.

Elaboration as Learning in Museum Studies

In Museum Learning as Conversational Elaboration: A Proposal to Capture, Code,

and Analyze Talk in Museums, Leinhardt and Crowley propose means to study how

learning actually occurs in museums (1998). Their work in museum learning motivates

their attempt to solve a core issue: lack of theoretical coherence in the museum learning

research. They suggest three problems: rst, a need for a learning denition; second,

the “univariate” issue where researchers attach a particular indicator, like terms, to a

single factor, such as age; third, the museum diversity problem, where research does

not span the different types of museums; and nally, the division between quantitative

and qualitative research methodologies. From these problems, the authors propose three

outcomes, the last of which is important for constructivist dialogue mapping. They argue

that their approach will provide a novel, stable, and disseminable methodology to con-

ceptualize, collect, and analyze conversations as a process and as an outcome of learning

in the museum context.

After introducing the problems and outlining them, Leinhardt and Crowley (1998)

describe a pragmatic approach to study learning in museums: dene learning as Con-

versational Elaboration. For their pragmatic approach and operational denition of

learning they chose how visitors elaborate conversations. They focus on conversational

elaboration because it is a naturally occurring part of the museum experience while also

being a product of the experience.

By elaborations they mean a particular kind of talk that occurs within a group. Con-

versation is important because it reects the “inter-twining of social with cultural pro-

cesses” (White, 1995, p.1). Sociocultural theories of voicing (Rogoff, 1990; Wertsch,

1997) emphasize that inter-twining of voices (in the Bakhtinian sense) is the primary

activity through which knowledge is constructed and appropriated across people. In this

paper we take this process of studying learning as conversational elaboration, and then

use concept mapping to study how these elaborations occur. Next we introduce the dif-

ferent types of concept mapping.

K. Martin, M. Horn, U. Wilensky

Concept Mapping

A concept map is a diagram that depicts relationships between concepts. It is a graphical

tool that instructional designers, engineers, technical writers, and others use to organize

and structure knowledge. In their overview of qualitative methodology, Miles, Huber-

man and Saldana (2014) describe concept mapping as useful for qualitative methods.

Joseph Novak invented concept maps to assist learners (1983). This theory was based

in the philosophical and epistemological origins of Conant (1947), Kuhn (1962), and

Toulmin (1972). Concept mapping is a process that focuses on a construct of interest or

a topic. The process generates input from one or more participants. As shown in Fig. 2,

from it the participant or a researcher produces an interpretable pictorial view (concept

map) of their ideas, concepts, and how these are interrelated.

These maps are usually organized hierarchically, and can have the links between

them labelled, or not. They can also be organized in other shapes, such as a radiating

pattern. There have been varied uses of concept mapping in education over the years.

Novak and coauthors’ method was used to bring concept mapping into students’ hands

to better understand science content. This work used concept mapping to improve stu-

dent learning. Trochim (1989) used concept mapping to guide action in planning and

evaluation. Novak (1990) and Kinchin (2014) reconceptualized concept mapping as an

educational tool through a wider literature on curriculum development. Jackson and

Trochim used concept mapping for analysis of open-ended survey responses, an alter-

native method to existing code-based and word-based text analysis techniques (2002).

There have been many types of concept mapping, including: mind maps, many forms

of note-taking, such as Cornell notes, the presentation software Prezi, and repertory

grids. Following in this tradition, constructivist dialogue mapping is a type of concept

Fig. 2. From Novak, 1983, and example of concept mapping of the concept of animals,

differentiating between animals with and without internal skeletal systems.

Constructivist Dialogue Mapping Analysis of Ant Adaptation

mapping meant to track the temporality of when people have ideas during an interven-

tion or interview, and how those ideas elaborate over time. There are several other types

of concept mapping that are closely related, which we will introduce next, including

semantic, dialogue and narrative maps.

Semantic Mapping

Semantic mapping is the study regarding the conceptual knowledge, where concepts are

represented as a semantic mapping of nodes and their related properties in a network

of nodes and links (Anderson, 1976; Collins & Loftus,1975; Collins & Quillian, 1969;

Norman & Rumelhart, 1975). As shown in Fig. 3, Attributes of the network structure are

assessed by the number of links between nodes, the strength of linkages, and the cohe- linkages, and the cohe-linkages, and the cohe-

siveness of the entire collection of concept nodes in semantic memory.

Chi, Hutchinson, and Robin used semantic mapping to represent children’s concep-

tions of dinosaur knowledge (1989). For Chi et al., semantic mapping is the structure

of the conceptual knowledge. It is the study of the properties of a representation in a

static state. It concerns what a representation is composed of (nodes), how the nodes in

the representation are clustered and related to each other, and the cohesiveness of the

structure. However, it is not about representational comparison between an adult and a

Fig. 3. Adapted from Collins and Loftus (1975), an example of

a semantic map of stereotypical map of human memory.

K. Martin, M. Horn, U. Wilensky

child, who are believed to have different representations. Likewise, comparison between

students of different ages may also be suspect. These hierarchies can be useful when

learners employ them to perform categorical reasoning. Categorical reasoning can allow

learners to infer that a particular kind of robin, can lay eggs, if they only know that birds

lay eggs, because robins are a type of bird (Chi, 2008).

Dialogue and Narrative Mapping

Concept mapping is related to narrative mapping. Veterans in a social situation, like

Alcoholics Anonymous, often use narrative maps to orient, inform and advise newcom-

ers (Pollner & Stein, 1996). Pollner and Stein describe narrative maps as having several

impacts, including:

Affecting recruitment into a social sphere through improving the attractiveness (1)

of a situation.

Contributing to socialization and social reproduction by transmitting values. (2)

Shaping action by changing probable activities in an area. (3)

In this way, narrative mapping is part of a process where a social world is talked into

being constituted. In other words, narrative mapping is both part of representing a social

world, and part of the process of reproducing that social world. Narrative mapping is

used to share cultural world resources from current members to new members through a

systematic, pictorial representation.

Concept mapping is also related to dialogue mapping. Dialogue maps can provide

a visual tool to promote shared understanding between novice (students) and expert

Fig. 4. Adapted from Pollner and Stein (1996) Example of a Narrative Map

about Constructivist Dialogue Mapping.

Constructivist Dialogue Mapping Analysis of Ant Adaptation

(teachers) to increase meaningful learning in biology education (Kinchin, 2003). Dia-

logue mapping can represent and scaffold students’ argumentation (Okada & Shum,

2008). The work to improve student argumentation with dialogue mapping has been

assisted by Compendium, a dialogue mapping software (Okada, 2008).

Uniqueness and Differences of the CDM Coding Method

Constructivist dialogue mapping is a means of producing a pictorial representation of

knowledge, but instead of trying to use the representation to improve student learning,

it is used to track the instability of knowledge as students learn and associate those

changes with learning interventions. We use it to represent players’ knowledge states at

different times as they develop. CDM is developed to show what learners believe exists

(i.e. their ontologies) as they come to understand a complex system modeling game.

We believe this method is useful in representing learning in systems where participants

elaborate their understandings in interaction with each other, teachers, and technology

to form new theories to account for novel experiences. While this method is particularly

useful in studying non-goal directed, constructionist learning – where it is difcult to

write summative tests, because the learning is not goal directed – constructivist dialogue

mapping can be useful for evaluating other learning activities.

The dearth of evaluation methodology for constructionist, open-ended learning envi-

ronments (Ochoa, Worsley, 2016) motivated our design of CDM. The lack of evaluation

can be easily explained by the lack of a learning trace; whereas computer-aided learning

systems, if designed for it or not, provide ample data of how the user interacts with the

environment. Open-ended people-to-people interactions provide far less data. Because

we want to know how participants structure the knowledge they learn in these environ-

ments, we developed CDM using constructivist theory, namely – following ideas as they

appear, are contested, and uidly change, based in a knowledge construction view.

Theory: Studying Learning in Informal Learning

through Constructivist Dialogue Mapping

This theory led us to construct a methodological innovation that is useful for the inter-

actions typical of constructionist learning environments. With CDM we track the con-

struction of conserved concepts through the available proxy of change and elaboration

of speech. We built on concept maps, methods as they emerged through transcript data

focusing on the moment to moment manifestations of ideas. The method is differentiated

from age specic representations as semantic maps have been. We do not attempt to use

the maps to improve student learning of the connections between material as much of the

concept mapping has. Instead, we attempt to track learners moment to moment manifes-

tation of conceptions as they construct them into less variable forms. At this level, these

maps represent how a player’s speech references the ontological entities they are form-

ing or have formed. We captured these ideas of the understanding players demonstrate

K. Martin, M. Horn, U. Wilensky

during play in a hierarchical map. We present concept maps of players elaborating their

ideas about agents through observation and interaction that accounts for the changing

nature of ideas through activity. Maps visually depict the ideas players share through

what they say and how they interact with the game.

We argue that we can research what people conserve (that is, learn through accom-

modation demonstrated by what they say) by lling in a map with what people say and

do during play. Constructivist dialogue mapping allows us to track how people’s words

and actions indicate learning through short play periods. The more we time stamp when

new concepts arise and change through talk, the closer we can track the messy contesta-

tion that develops new ontological entities and modies existing ones. However, these

maps, are only a proxy, built from the externally observable action of speech. In this

paper, we use transcript data, but the observable data could include log les of their

actions, or gestures that describe actions or entities. Importantly, we do not have direct

connection to the internal cognitive infrastructure. In other words, the maps are a method

to pay close attention to a participant’s words, a naturally occurring feature of museum

play, while keeping an eye on what it says about her ontology.

The advantage of this approach is we can present the smallest parts of what we ob-

serve. For instance, we can note when users rst identify an unknown activity, such as

“purple line” and then how they build out an understanding of the subprocesses of that

task such as “attracts ants” or “fades away.” As a result, we read the transcript word by

word to see how users construct such operational knowledge on the representations they

see. CDM demonstrates learning as concept elaboration over time through the proxy of

changes in speech. We will explain the implementation of CDM analysis in the methods

section below. First, we will explain our research questions. Then, following Piaget’s

method, we will explain the design of our unusual activity we used to understand how

people come to understand complexity.

Research Questions

In this paper we use CDM to explore how users make sense of the self-organization

of cooperation between ant colonies in competition with each other while they use an

agent-based model built in NetLogo (Wilensky, 1999).

We researched how users build knowledge when using the model of ant colony life

(Martin & Wilensky, 2019; Martin, Horn, & Wilensky, 2019). We were particularly in-

terested in how visitors made sense of the behavior of individual ants in the simulation

to in turn reason about aggregate-level outcomes of groups of ants.

Specically, we asked the following questions:

How can we capture visitors’ moment to moment sense-making while exploring 1.

complex systems notions such as emergence?

Can we see evidence that new knowledge structures emerge through game play? 2.

How do these knowledge structures shift over time, or remain stable emerg-a.

ing as theories explaining the context?

Constructivist Dialogue Mapping Analysis of Ant Adaptation

Design: Agent Based Modeling Game for a Museum

To show the environment we evaluated with CDM, we will describe our model/game

and discuss the design decisions we took as a result of implementing in the museum.

The Game: Ant Adaptation, Agent-Based Modeling in Museums

Ant Adaption (Martin & Wilensky, 2019), the game we use to demonstrate CDM, has

been used in studies on schema formation (Martin, Horn & Wilensky, 2019), and the

connection between affect and cognition (Martin, Wang, Bain & Worsley, 2019). In this

paper we used it to collect data and use CDM to analyze that data to demonstrate con-

structivist dialogue mapping. In order to provide context for our analysis, we describe

the game below.

In the game we created, Ant Adaptation, there is a fully functioning agent-based

model underneath. Ants go out to collect food and return to the nest. As they return to

the nest, ants lay down a pink pheromone that attracts others nearby. Other ants walk

toward the strongest chemical smell, which in most cases is where the rst ant just

passed. When ants nd a ower, their food source, they return, lay down a pheromone

trail, and thus construct and reinforce pink trails. This creates an emergent feedback loop

that routes more and more ants to successful sites of forage. As the ants exhaust a food

source, they must nd new locations, and thus repeat a cycle. When two or more ants of

opposing colonies encounter each other, they ght or scare each other away, also leaving

chemicals that attract more ants. For the winner, this works to protect the food source

from competing colonies. The ant queen reproduces when the ants in her colony collect

enough food. The player interacts with this complex system by adding pheromone trails

that the ants follow, as well as adding sources of food to the system, thus changing the

amount and distribution of food in the game. Through interacting with the system, stu-

dents form a functional understanding of the ants and their mechanisms of action (i.e.

agents and their rules) in the model.

This design scaffolds experimentation. Players must simultaneously make choices.

As shown in Fig. 5D, players can touch the screen to add pheromone the ants will fol-

low. At the ick of a switch, they can add more owers anywhere they like in the game.

Lastly, they can choose to apply vinegar, which erases trails. Erasing trails was used by

some game players (like Thomas discussed below) to get ants out of a feedback loop

that was leading them nowhere (a local optima). For players to achieve their goals in the

competitive environment, they need to understand the emergent consequences of simple

ant behavior.

Players can decide how big and aggressive ants are. When the size of ants increases,

they become slightly faster and stronger in a ght. Each increase in size/level adds up.

At the highest levels, the ants are thirteen times stronger. When players make their ants

more aggressive, it increases the radius in which ants detect opposing ants and thus the

probability that they will attack. Increasing either the size or the aggressiveness also

increases how much food is required to raise an ant, so the largest ant requires thirteen

K. Martin, M. Horn, U. Wilensky

times as much food to feed to adulthood. This gamication impacts how much food ants

must collect to make a new baby ant. Increases in either of these parameters reduces

the expected population of the colony, though it increases their likelihood of ghting

and winning through emergent interactions of parameters (size and aggressiveness) and

agent actions (collecting food, leaving trails, and ghting).

This sets up the main action of the game as a series of strategic choices: to de-

cide whether to passively collect food, thereby increasing the population, or, to go on

the warpath where big, aggressive ants conquer their opponents. Either method of play

could lead to high populations or the elimination of the opponent through better control

of food resources. After learning about the consequences of strategic choices through

gameplay, players strategize by increasing ants’ size, aggressiveness, or both. This might

lead them to win the game by annihilating the other group’s ant colony. However, bigger

and/or more aggressive ants consume more food to reproduce, and potentially reduce

the colony’s population size. Thus, a player might strategize by adding more owers and

pheromone tracks around the colony to help the larger ants survive. This learning and

strategy cycle interweaves the learning into the gameplay.

We built the game to be engaging while using the literature of designing digital

interactives for museums to have four affordances and two objectives (Martin, Horn,

and Wilensky, 2019). The game has four affordances that support two learning objec-

Fig. 5. John selects to add chemical (A) experiments with the touch user interface (B-C)

and then lays down his rst pheromone trail (D).

Constructivist Dialogue Mapping Analysis of Ant Adaptation

tives. In Ant Adaptation, playing with parameters allows the player to (1) construct their

colony in competition with an opponent, (2) share strategies through comparison, (3)

discuss what is happening through observer scaffolding such as parents’ intervention, or

interaction between players, including taunts, and (4) learn about the emergent impacts

of colony behavior arising from individual ant behavior in a complex system game. This

approach allows visitors to learn (1) the impacts of adaptation on ant colony life, and (2)

how attractants such as pheromones work in ants’ self-organization.

Method: Constructivist Dialogue Mapping

CDM presents learning as concept elaboration (Leinhardt & Crowley, 1998) through

transcript analysis. We innovate the method to record the agents, properties and actions

that users notice in two learning environments. We conducted two treatments: (1) 6,

hour-long individual clinical interviews in a lab setting; and (2) 38, approximately 4–20-

minute group interviews in a natural history museum.

Treatment One

In the clinical interviews we conducted a semi-structured interview protocol. This was

administered while people discussed their understanding of Ant Adaption. In treat-

ment one, there were 6 participants (50% White, 16.16% Black, 16.16% Asian, 16.16%

Latinx) being 50% male and 50% female. We recorded audio and video of participants,

transcribed the data from the audio, and analyzed both the media to construct CDMs.

Treatment Two

In the second treatment, participants were sampled over a six-day period, attracting

114 museum visitors in 38 groups (87% White, 4% Black, 5% Asian, 3% Latinx). This

contrasts with the museum-wide attendance demographics of 70% White (difference of

+16.61% points), 5% Black, (difference of -0.54% points) and 14% Latinx (difference of

-11.32% points). Of the players, 60 were male (51.57%) and 54 (48.43%) were female.

Players ranged in age from 2 to 55, with the age distribution skewed to lower ages. The

average length of time people played was 387 seconds, as opposed to a museum-wide

average interaction time with digital interactives of 105 seconds (as reported by internal

museum evaluations). In treatment two, we conducted a pre-post survey, and video and

audio recorded the play. From the audio we transcribed the data, and analyzed both me-

dia to construct CDMs.

Which Changes Are Conceptual Change

Leinhardt and Crowley (1998)’s approach particularly looks at four process that groups

will undertake. If there is learning, after an interaction with an exhibit, a coherent con-

versation group (CCG) will:

Refer to more items.1.

Include greater detail about those items.2.

K. Martin, M. Horn, U. Wilensky

Synthesize elements to elements from their prior knowledge.3.

Increase the level of analysis of the phenomena that they discuss.4.

This approach moves away from focusing on the amount of talk or types of talk

while building a strong foundation between amount, type and the process of learning. As

a result of this review, Leinhardt and Crowley suggest to investigate how conversation

as a socially mediating activity acts as a process and an outcome of museum learning

experiences.

In the end, the methods of collection and analysis offered by Leinhardt and Crow-

ley, (1998) are interesting, but they are not sufcient. With text capture, and direction

capture we can measure the learning much more fully than Leinhardt and Crowley

did.

Constructing Constructivist Dialogue Mapping

As shown in Fig. 6, CDM tracks the nouns that players mention during play with a sys-

tem (such as ants); the adjectives that modify those nouns, such as “six-legged” (6b); and

the verbs players use to describe those nouns’ actions, such as “follows trails” (6d). To

gather the transcripts, that we analyzed for CDM, we applied a mixed methods approach

to the observations (Clampet-Lundquist, Edin, Kling & Duncan, 2011).

We coded the transcripts by reading them. When we read a noun, we added a row

to our matrix. We then recorded the interview session, noting whether this interaction

was during the pre-interview, gameplay, or post-interview, noting who spoke, what

time they spoke, the question being answered, the exact quote of the response, and the

node label we coded the quote as. Finally, we recorded what parent box it modied and

counted the node’s depth in the hierarchy. For example, in Table 1: Kit asks Briana if

she has ever noticed anything about ants. She responds, “They carry fty times their

weight?” As shown in Fig. 6b, we would code this as a two-level deep hierarchy: Level

1-Ants and Level 2-Carry 50X weight.

To answer our second research question – How can we capture visitors’ moment to

moment sense making while exploring complex systems notions such as emergence –

we deployed this method to construct maps of ontological entities in the game (i.e.,

agents), their actions, and properties as described by players. The maps were construct-

ed by building a hierarchical map of players’ utterances using the coding method shown

in Fig. 6 and Table 1, before, during, and after play. The coders proceeded through the

transcript utterance by utterance. When an entity was named, such as “ant”, the coder

added a box. When other entities were named – such as the property “six-legged” or

action “carries a lot of weight” – coders determined what that concept modied. If, as

in Table 1, they modied “ant” unambiguously, then the coders added a subordinated

box below ant. In our coding during treatment two, we broke the transcript into a pre-

interview, a game play portion, and a post-interview. We analyzed the change in how

players thought about the actions ants take, and the functions of those actions. In Fig. 6,

we would look at how the person describes ants as six-legged (Fig. 6a), six-legged

Constructivist Dialogue Mapping Analysis of Ant Adaptation

Fig. 6. Constructivist dialogue mapping provides a simple interactive way of mapping

ontological entities, to their functions as demonstrated by players in short interactions.

Table 1

Coding Scheme for Constructivist Dialogue Mapping. Captures where in the interview

knowledge was mentioned, and by who, allowing for analysis of the development of knowl-

edge as it is mentioned

Session

Time Interaction

Type

Sequ-

ence

Speaker Question Quote Node Parent

Node

Depth

3 10:23 Pre-

Interview

0 Kit “Have You

ever noticed

anything about

ants?”

Ant 1

3 10:33 Pre-

Interview

1 Briana “Have You

ever noticed

anything about

ants?”

ìAbout ants?

They carry

fty times

their weight?î

Carry-Six

times their

weight

Ant 2

K. Martin, M. Horn, U. Wilensky

creatures that carry 50x their weight and follow trails to nd food (Fig. 6d). When these

entities, actions, and properties change over time is our measure of learning during the

intervention.

In treatment two, we color code change in ontological maps over time so we can

visually compare learning. When we add a box between the rst sample and the second,

we color it red. When we remove a box, either because the visitor has stopped mention-

ing the idea, or has explicitly contradicted themselves, we color the box grey and strike

through the text. When it is not clear if the item was there previously, or if it has gone

away, we leave them blue. We present two times scales of analysis in the results, show-

ing both the micro and macro changes. In treatment one, we analyzed the concepts par-

ticipants put forward as they emerged during the protocol and highlight the conicting

notions participants demonstrate.

The resulting representations have three affordances:

Evaluators can count the number of entities players notice during gameplay. We 1.

can note the number of actions and properties they ascribe to those entities. The

counting proceeds by adding the total number of node depth 1 items, then the total

number of actions, and then properties users ascribe to that entity.

If surveyors sample visitors at more than one point in time, we can track the 2.

changes over time of both the number of entities they notice and the number of

actions and properties player’s use to describe the agents’ actions and proper-

ties.

Researchers can track the conicting and changing nature of knowledge as dem-3.

onstrated through the maps.

In an open-ended environment people learn what is allowed in a system, rather than

memorizing. Therefore, we used mapping instead of measuring change through respons-

es in a more rigid classroom-style questioning.

“In this picture, the participants are active theorizers. They gather new evidence and

devise methods to test their theories. Instead of accepting classications as given, they

see these classications as provisional theories that are constantly reassessed and recon-

structed in light of the dialogue between theory and evidence” (Wilensky & Reisman,

2006, p. 172). We sought to capture how talk changes based on interactions with the

learning environment of Ant Adaptation. We propose that learning is demonstrated by

what students added to their discussions while playing. This elaboration in their discus-

sion is demonstrated by coding their interactions with CDM.

Results

During the activity, users learned about complex systems. We tracked this elaboration

of concepts through CDM. We test CDM in two treatments: (1) in a clinical interview

with Rebecca, where she develops ideas about ants during an hour-long interview, and

(2) with a group of students who played Ant Adaptation in a museum. These samples

were chosen because they demonstrated key aspects of constructivist dialogue map-

ping as a tool.

Constructivist Dialogue Mapping Analysis of Ant Adaptation

Dialogue Elaboration through Play

First, we present two demonstrative cases of the 44 interviews conducted with a complex

system model of ants (Martin & Wilensky, 2019) to illustrate how we use CDM to study

learning as elaboration.

We coded two people engaged with Ant Adaptation. First, conducted under treat-

ment one, we examined how an Asian woman, Rebecca (all names changed for anonym-

ity), age 28, engaged. Second, we examined a group from treatment two, noting how

Thomas, age 7, engaged in group of three other White youth: Ed, age 12; Mary, age 9;

and Sam, age 6. The ve players presented were more engaged than the average player

of Ant Adaption, along several measures: All of them touched the screen, and smiled

during play (showed their teeth while seperating their lips either with or without audible

laughter), and all worked to maximize their ants’ colony population count (Stating that

they wanted to maximize their population during play). This is in contrast to the wider

sample where 81% (92 people) touched the screen, 43% (49 people) smiled during play,

and 41% (46 people) tried to maximize their colonies’ population during play. As there

was no guidance on the goal of the interaction, it was surprising that so many of the

groups chose maximizing their population of ants as their primary goal. In the open-

ended environment, they could just have easily drawn smiley faces with their ngers or

planted a ower garden. Perhaps this goal was so popular in the museum because of the

competive arrangement of the exhibit. Yet this also occurred in the clinical interviews.

For the clinical interviews (which lacked this arrangement), we are not sure why this

happened. More study is required to investigate this outcome, which will be performed

in the analysis of the remaining groups in future work.

Treatment One, Assimilation of Pheromones:

Constructing a Trail Theory from Watching Digital Ants

Sitting on a couch in a quiet room, with just the interviewer and Rebecca we began the

interview. We started the interview by telling Rebecca we were interested in ants. We

Ant

Chemical

Channel Purple

Track

Connects

Fig. 7. Rebecca identies a purple track process that ants do.

K. Martin, M. Horn, U. Wilensky

asked her if she would mind if we recorded her while we asked her some questions.

After consenting to be interviewed we asked about her prior ant knowledge from which

it was clear she had minimal understanding of ants, or ant colonies. We then coded the

interview with CDM to observe moments of cognitive change, or synthesis. This coding

is the beginning of making a little mental machine we can use to dissect with transcript

analysis. The current process produces maps from transcripts, which are useful as sum-

maries, but the exact connection between cognitive change and the maps is still amor-

phous. In other words, we have only just stepped into this nebula, and are still nding

names for what we are seeing.

In this proto state, to demonstrate CDM we present the portions of the interview

where Rebecca constructs an understanding of the pink lines (pheromone trails), what

they do, and how they affect ant colonies in three parts. This construction occurs through

addition of ontological identities, such as trails, and ant queens, and synthesis of com-

peting ideas. Over time, it becomes clear she holds two separate notions of the same

concept, and then merges them. This is an example of concept formation, instability of

concepts, inference from theory, and the conservation of a new ontological entity. This

is also an example of deep learning as she adds functions and properties onto her idea of

ants, and pheromone trails. In the rst minute, she presents her confusion over “purple

lines.” Here she identies an action she observes, but has not yet conserved into a con-

cept she can operate with. At rst she identies a mysterious, unknown object. This is

pre-conservation:

Rebecca: I’m looking at... I’m looking at the red one in the middle. The red ant.

What are they doing here? What’s that line for?

Interviewer: What do you think?...

Rebecca: This purple line. Look there is another one. Its dropping here. The

red ants are dropping. Hold on, let me put this. What’s that? Is that

channel? Chemical Channel?

Interviewer: What do you mean?

Rebecca: Like when it releases chemical it left – it’s a new colony here – it left

a purple track. You see just the purple track you see just purple track

disappeared a couple of minutes ago when I ask you and, you asked

me ‘what do I think’. I thought it was just something like a track. A

chemical. Yeah.

Rebecca rst sees a purple line coming out of a red colony in the middle of the screen

and wonders what it is and what is it for. She sees that ants leave it. At this time, Rebec-

ca’s confusion with the purple lines rst comes up. As seen in Fig. 7, this identication

helps her identify a novel process: “Chemical channel/purple track” that ants do. She

immediately ties chemicals to the lines but does not seem to put forward why. In other

words, she creates an ontological entity to see the game, but has not added many func-

tions or properties to it yet. She attempts to test her ideas. When we probed her for what

she thinks the track does – that is what functions it has – she is seeing the connective

property of chemical tracks and then one of the potential impacts of that track. In other

Constructivist Dialogue Mapping Analysis of Ant Adaptation

words, she starts to hypothesize about the function of the line. This is the beginning of

a conceptual change where she begins to elaborate her understanding through dynamic

interaction with the complex system game. We then wanted to know what she thinks it

does, and so ask her. She suggests it connects ants:

Interviewer: What do you mean by a track or a channel? What’s the channel or the

track do?

Rebecca: It connects these two groups together. This chemical.

Interviewer: Okay. It connects them how?

Rebecca: Look they are connecting. And I guess it’s not a good thing because it

looks like it’s a war.

Interviewer: It’s a what?

Rebecca: It’s a war.

Interviewer: Where’s the war?

Rebecca: Here. [Points at the top right of screen where ants from both colonies

are massing]

Interviewer: How do you guess that?

Rebecca: Oh, there’s no turning back as there’s no turning around that red ants

back there. They are entering into the black ant colony. And the number

of escape, hold on. The reason it is not dropping because there’s owers.

So far. Look they keep going to this black ant colony. And I don’t know

what they’re doing are they trying to steal that food? They are stealing

food. They are stealing food. Come on! [talking to the ants] You have

food here [points at the screen where owers are].

This is an example of elaboration of her concept. After initially realizing she does

not know what the chemical does, through testing she constructs the idea that channels

“connect” ants and sees that this can cause “war” of some sort. The war seems to be an

example of prior knowledge about conict. But she also begins conceptualizing “war” as

a result of self-organization of ants connected by channels. After answering other ques-

tions, she brought the topic up again, conrming that she had kept thinking about it. She

at rst was confused, but then the interviewer provided her with some declarative knowl-

edge, that pheromones are a type of chemical which connects ants together. She then

takes that information and identies a new function, but does not merge it right away

with her previously constructed idea of chemical channels. Instead she holds to catego-

ries of activities ants do. In other words, she has constructed two models, one to do with

chemicals, and one based on the declarative knowledge provided. About pheromones

she has no reason to think they are the same concept from her previous experiences, as

in the real-world pheromones are invisible, and these purple lines are quite visible. As a

result, she holds two concepts at once:

Rebecca: I’m kind of confused by the purple zone. So, this purple zone is

something like this chemical released?

Interviewer: Why do you think that?

K. Martin, M. Horn, U. Wilensky

Rebecca: So, let’s see. I just created this chemical channel.

Rebecca: If this ower needs to be on this chemical to help. I’m gonna see. It

doesn’t eat them. Go eat them. Why don’t they eat them? It’s weird. It

doesn’t work that way. I should just make circles around the corners,

so they know there are chemicals.

Interviewer: Could you press pause? I have a long question to ask you.

Interviewer: So, E.O Wilson, a big ant researcher, discovered pheromones. He

realized that they are like perfume placed on the ground to mark the

food or when they encounter an enemy to encourage other ants in the

colony to come over and ght with the enemy. After hearing about

Wilson’s ndings how might you adjust your explanation of how these

ants are getting food?

With the probe, we wanted to see what the introduction of declarative knowledge

did to the understanding of the model. At rst, she took the declarative information in,

and made valid predictions based on it. But interestingly, at rst, she did not map this

onto the purple lines she’s been confused by. Instead of conceptual change she held two

concepts at the same time, as shown in Fig. 8.

Rebecca: Well when one ant senses the food around them they will leave this

pheromone to other ants. And so, they can be attracted to this, to the

owers.

Interviewer: Ok. So, what in here do you think is the pheromones?

Rebecca: I guess somewhere near this colony. Uhh. No no actually here maybe

here.

Interviewer: Here what?

Rebecca: Here when there is a group of [ants]. There is a huge group of ants.

Interviewer: Ok.

Ant

Chemical

Channel Purple

Track

Connects

Group of ants

Sense food

Leave

Pheromone

Begin grouping

Attract (to

flowers and each

other)

Ant

Pheromone

Begin

Sense food?

Turns ground

Colors

Whiting

(more ants)

Groups ants

PowerBall

Attracts ants

Purpling

(fewer ants)

End

Chemical

Dissappears

Spread in the

air

Ants

Carry 50X weight Scavenge

Eat a leaf

Find food on

ground

Animal Instinct

Recieve orders

from queens

Orders through

antenas

Make paths

to control Traffic

Smaller ants are

weaker and more

vulnerable

Aggressive

Increases your

likelihood of

getting hurt or

killed

Fig. 8. Rebecca sees two separate processes, Pheromones and Chemical.

Constructivist Dialogue Mapping Analysis of Ant Adaptation

Rebecca: Does that make sense?

Interviewer: So, you think this this what wouldn’t call it this perfume placed on the

ground. Do you think that’s just where ants are?

Rebecca: Yeah.

Rebecca: Because where there are ants there are pheromones are a way for them

to communicate. If there is somewhere like this [points at blank area]

… this, there’s no ants. How do we know there are pheromones? There

are not ants so there is no pheromone. This is a chemical released

by them. So, I guess that the denser a group of ants are, the more

communication they will have to with each other, the more pheromone

there will be. That’s my that’s my guess.

Interviewer: That’s your guess. Does that change your way you’re explaining how

they get food?

Rebecca: Can I say I don’t know.

Interviewer: Yeah that’s ne.

Rebecca: I don’t really know.

At this point, Rebecca has two ideas. As shown in Fig. 8, rst there is a purple chemi-

cal trail left by an ant, which connects ants. Second, she also imagines a mechanism

called “pheromones” learned from the researcher, that ants leave, and that attracts more

ants as communication gets denser. Interestingly, this sort of situation may come up in

many learning environments, where a learner may have her idea constructed from her

own experience in the world, and a set of declarative facts. For Rebecca, these two are

models she is simultaneously using to explain the simulation. Shortly, she will conserve

the two ideas into one through concept formation, inferring missing information from

that concept. At this point in the interview, she uses these dual models to answer some

questions about how congestions and disease work in ant colonies and applies both

ideas fairly differently, demonstrating the robustness of the two models. After seeing it

employed, we nally ask her if she can also see pheromones in the model. At that point,

she seems to assimilate by merging the two understandings into one ideas:

Interviewer: Can you see the pheromones in this model?

Rebecca: OH! Is this purple line pheromone?!

Interviewer: What do you think?

Rebecca: Hold on, let me just... cannot see clearly. My view is blocked by these

owers. [She examines the model for 16 seconds.]

Rebecca: Oh, I see. Did you notice that? You obviously noticed that.

Rebecca conserves her two understandings and then double checks her Eureka

moment:

Interviewer: Noticed what?

Rebecca: The denser the ants are the more ants, the more it is, you see that ants

are grouping here and the surrounding background color is like, bright

K. Martin, M. Horn, U. Wilensky

100

white. Because they are releasing a lot of pheromones. A single ant just

leaves this pheromones trails, like these purple trails, but when they

are grouping together there’s like a powerball.

Interviewer: Like a what?

We were not sure what a powerball was, but assumed she meant a massing and

wanted to be clearer on that. She claries that it means a lot of ants grouping together.

Rebecca: It’s a powerball.

Interviewer: Ok.

Rebecca: There’s a very strong powerball which means a lot of ants are releasing

the pheromone chemicals together.

Interviewer: So, what does that do?

Rebecca: Now? I don’t know what they are doing. There are just I guess, I know

what you mean, look you see these bright white places. that means they

are releasing chemicals aggressively. They are releasing chemicals to

let maybe friends their team players know there are food. ‘Come here.’

Interviewer: So why is the pink disappearing?

Rebecca: Disappearing? Maybe there are no food for them.

Like here.

Rebecca: Because it’s a chemical. It disappears.

Interviewer: What do you mean?

Rebecca: You release chemicals and chemicals kind of spread in the air.

As seen in Fig. 9, Rebecca assimilates the two branches of her understanding into

one, forming a concept. This is an example of conceptual change. She no longer holds

two separate ideas, one from declarative knowledge and one from experience, but in-

stead holds one model of how pheromones lead to agglomerations of ants that help them

self-organize to solve daily tasks. This is also an example of deep learning, where she

forms a process understanding. Then she adds a beginning state and an ending state to

the pheromones process. She also maps the action onto the coloration of the ground

process, seeing that whiter increases grouping resulting in a “powerball” where white

means higher concentrations of ants, and purple means lower concentrations of ants.

This is an example of forming a theory to better see the world. It also is an example

of discovering the meaning of representation through both declarative knowledge and

working with a computer- based model. Finally, she also invokes prior knowledge in

her theory to predict the chemical dissipate in air subprocess when released. It’s a break

through moment. After joining these processes together, she makes several predictions

with her newly conserved theory helping her see the world in a new way:

Rebecca: Now I know.

Interviewer: What do you know?

Rebecca: This chemical maybe [sends] the wrong messages.

Constructivist Dialogue Mapping Analysis of Ant Adaptation

101

Rebecca: Oh, I see. The stronger the messages the stronger this is, the more ants

will be attracted to this place.... Such a good game, I love it.

Rebecca: We’re kind of helping them to release the pheromones. Is that right,

about this chemical?

Interviewer: How would you know that?

Rebecca: Because there is a chemical option.

Interviewer: And how do you know that’s how you do. Why did you guess that?

Rebecca: Why do I get that?

Rebecca: Yeah.

Rebecca: Because once I press this button they are kind of attracted by this track,

this pheromone track.

At the end of the interview, Rebecca has constructed a model that the purple tracks

are called pheromone tracks, they are laid down by ants, and help the ants organize for

food gathering and war. At this point Rebecca has assimilated the two understandings,

observed and declarative, to form a new theory of how ants communicate. This new

theory led to conceptual change through play with a simulation that she can use to pre-

dict the ants movements, and trail placement:

Ant

Chemical

Channel Purple

Track

Connects

Group of ants

Sense food

Leave

Pheromone

Begin grouping

Attract (to

flowers and each

other)

Ant

Pheromone

Begin

Sense food?

Turns ground

Colors

Whiting

(more ants)

Groups ants

PowerBall

Attracts ants

Purpling

(fewer ants)

End

Chemical

Dissappears

Spread in the

air

Ants

Carry 50X weight Scavenge

Eat a leaf

Find food on

ground

Animal Instinct

Recieve orders

from queens

Orders through

antenas

Make paths

to control Traffic

Smaller ants are

weaker and more

vulnerable

Aggressive

Increases your

likelihood of

getting hurt or

killed

Fig. 9. Rebecca accommodates her observation of chemical trails with her declarative

knowledge about pheromone trails. She then adds beginnings and endings to her processes.

K. Martin, M. Horn, U. Wilensky

102

Interviewer: Do you know anything now, after playing this game that you didn’t

know before?

Rebecca: Yeah. Like this pheromone. Chemical I was confused. Like creating

channels for them. You know actually not, its leaving chemicals for

them to follow.

Rebecca went from visual confusion to a theory. She now mapped a system of com-

munication onto what was once visually confusing through assimilating while using a

simulation through a process of conceptual change.

As a caveat, from this interview there are two key factors we do not know: (1) the

length of time this new conception will last. Without reinforcement, or better yet, a delay

study to query how long the idea persists, we can say nothing about the durability of these

ideas. Worryingly, because they arose quickly, they may fade quickly. And (2) we should

consider the situationally contingent nature of the learning, it happened in interaction with

a model, and an interviewer. If other researchers use the method, some thought should be

given if such situational constraints should be inserted while coding with CDM.

Rebecca’s Agent-Based Knowledge Construction

To answer our second research question – can we see evidence that new knowledge

structures emerge through gameplay and how these structures shift through time – we

examined how Rebecca builds her knowledge about ants. We answer this by following

through her elaborations during the play. As shown above, Rebecca rst identies a

phenomenon she does not understand, namely, purple tracks. Then through observation,

she makes a conclusion that that phenomenon attracts other ants. From this prediction

she expresses its function, but maintains some confusion. This confusion is the affec-

tive state that accompanies her process of accommodation, that pushes her to account

for greater and greater parts of the complex phenomenon. As she forms the concept, she

operationalizes it to make conclusions about excessive aggregation of ants in large rein-

forcing groups caused by pheromone’s attractive qualities categorizing them as “power-

balls”. She then explains, with here newly constructed theory, how the phenomena end

through the dissipation of the chemical through air, like perfume.

This interaction also suggests an answer to our rst question, how can we cap-

ture visitor’s moment-to-moment sense-making? Moment-to-moment expressions stick

together as they account for what Rebecca sees. She even connects the declarative

knowledge we probe with. When they no longer helped her understand, she dropped the

ideas. Like her idea of pheromone just being on or off, rather than a continuous vari-

able where more concentration is lighter colored and more attractive. This process of

pruning suggested that in future uses of CDM, we should set a heuristic where we say

something like “if an idea isn’t used for X minutes, we can drop it from the tree.” As

a result, we used a version of this heuristic in the second treatment. Thus, her knowl-

edge is assimilating over time to accommodate to what she has seen. In other words,

she is constructing an idea of the parts of the representation she at rst is confused by,

and forms trees of processes that account for ever greater complexity of action in the

Constructivist Dialogue Mapping Analysis of Ant Adaptation

103

agent-based model. At times, the process looks like unstable concepts. However, as she

gathers more evidence, the concepts become helpful, predictive theories, helping her

learn how to see the model.

CDM is a useful way to document this change and stability. The interaction had two

main effects that CDM demonstrates. First, through the process of playing in the open-

ended environment Rebecca created a theory of how feedback, represented by phero-

mone trails, in the complex system leads to self-organization. Second, our analysis of

Rebecca’s use of Ant Adaptation demonstrates constructivist dialogue mapping’s utility

in tracking learning as concept elaboration. Ant Adaptation scaffolds her development of

theory to see how trails work in the microworld. The process of discovery engaged her

as she constructed this understanding of pheromone trails.

Treatment Two:

Constructing a Strategy to play Ant Adaptation through Feedback

While CDM can be used to track concepts as they develop, as in Rebecca’s case, you can

also use it to monitor what people know before and after an intervention. In this second

use, researchers can code the changes, before and after. New additions are coded in red

in this treatment.

While playing in a large natural history museum with his brothers and

sister, Thomas and his siblings were recruited to the study. Often times in open-ended

learning environments, interactions are not individual. Instead a whole family can in-

teract with the exhibit simultaneously. Thus, Thomas’ case demonstrates what one user

developed through interacting with the game and his siblings. This section addresses

our third question: How do these knowledge structures shift over time, or remain stable

emerging as theories explaining the context?

Thomas Pre-Interview

During the pre-interview, we established Thomas’ prior understanding of ants through

a semi-structured interview. As shown in Fig. 10, during the pre-questions he said ants

carry 50x their weight. In response to probes about how ants control trafc, he offered

the explanation that ants make physical paths, to control trafc, which he later rescinded

after playing the game. Protocol questions on aggressive roles prompted Thomas to

guess aggression simply increases an ant’s likelihood of getting hurt or even killed – a

claim he later amended:

Interviewer: Okay. And then imagine you’re an ant. How do you think being

aggressive affects your life?

Thomas: Uh, if you’re aggressive, like, you- you’re ... Uh, how do we explain

While in this case we only present before and after, researchers could pictorially represent the changes at

ner grain sizes to show more of the construction of concepts, and also set heuristics for when ideas no

longer inhabit a CDM.

K. Martin, M. Horn, U. Wilensky

104

this? If you’re aggressive, like, you- since you go for more things you

have a greater risk of getting hurt or killed, I guess.

In response to questions about how ants know what to do, Thomas offered animal

instinct, or, a command and control understanding when ants get orders through antenna

and/or from their queen.

Interviewer: Okay. That’s fair. And then, how do ants know what to do? So, you said

they- they pick up leaves, or they scavenge, but how do they know to

do that?

Thomas: Um, animal instincts.

Interviewer: Animal instincts.

Thomas: Or the queen tells them to, if there’s a queen ant. We don’t know.

Interviewer: So, what’s the diff- How does the queen tell them to?

Thomas: It’s something with their antennas.

Interviewer: Okay.

Thomas: I think. Additionally, he said that ants scavenge food from the ground

or maybe eat leaves.

Thomas: Like, they can like go hunt for food. They can like, um, try like, get to

some, like, maybe some food on the ground like in the city or like in a

park, or they can just eat a leaf.

After we explained how the game worked, players broke into two teams, Thomas and

Ed versus Mary and Sam. At the beginning, the younger Sam and Mary chose to have

maximum-sized, not very aggressive ants (10% aggressive). Thomas and Ed chose to

have medium sized though not very aggressive ants (2% aggressive).

Near the beginning, players agreed through conversation on what strategies matter.

Soon, Thomas informed the older Ed how to play: “add owers close to the nest.” From

this we add a new ontological entity, owers, into the group’s constructivist dialogue

map and put a mechanism ‘close to the nest’ under it because the players started planting

Ant

Chemical

Channel Purple

Track

Connects

Group of ants

Sense food

Leave

Pheromone

Begin grouping

Attract (to

flowers and each

other)

Ant

Pheromone

Begin

Sense food?

Turns ground

Colors

Whiting

(more ants)

Groups ants

PowerBall

Attracts ants

Purpling

(fewer ants)

End

Chemical

Dissappears

Spread in the

air

Ants

Carry 50X weight Scavenge

Eat a leaf

Find food on

ground

Animal Instinct

Recieve orders

from queens

Orders through

antenas

Make paths

to control Traffic

Smaller ants are

weaker and more

vulnerable

Aggressive

Increases your

likelihood of

getting hurt or

killed

Fig. 10. Pre-play constructivist dialogue map of four players’ understanding of ants.

For instance, they think ants (entity) can carry 50 times their own weight (mechanism).

Constructivist Dialogue Mapping Analysis of Ant Adaptation

105

owers in close proximity. Notably, they did not mention owers in the pre-interview,

but also did not seem surprised by them. We think this is a case of constructing knowl-

edge through action in the game world, as it is unlikely they knew that some types of At-

tini ants collect owers for fungus farming. The players then established the connection

between two complex systems ideas – the proximity of food and increasing ant popula-

tion – through dynamic play with the model.

Post- Interview

Thomas seemed to develop an understanding of the feedback cycles inherent in Ant Ad-

aptation. Thomas offered this understanding as the way to play the game.

Thomas: Yeah, you had to gure it out and the- you have to have some owers,

see, and then you put the chemicals and lead it to there, then they’ll

bring it back, and like, if you want to get rid of the chemicals you use

the vinegar. So, um, you put some sunowers down, then you get the

chemicals and lead it to the sunowers and if- if there’s too much then

the ants aren’t getting the sunowers and you- then they’ll just like,

then you use the vinegar and erase it. But if- if you just do one path that

leads to the sunowers it’ll just get the energy and just keep going back

and forth and back and forth. And that’s how we got 21 [ants].

As shown in Fig. 11, in the post-test the players’ concept maps became more elabo-

rate. Thomas takes on a more cyclical understanding of the role of ants’ paths to attract

Fig. 11. Post-play constructivist dialogue map map of four players. Came to the cyclical under-

standing of pheromones in food foraging and a more contextual understanding of the role of

adaptions utility. Red Boxes indicate elaboration. Grey ideas that were not mentioned again.

K. Martin, M. Horn, U. Wilensky

106

each other to owers. Thomas argued that ants follow chemicals to bring food to “get

to 21” ants. He also saw that sometimes ants can get trapped in their own chemicals or

“white spots.” He used vinegar to clear excess chemicals. One of the primary motiva-

tions of the learning environment was to teach that the simple rules of agents could

lead to complex, social patterns that sustain a population of ants. Thomas’s descrip-

tions indicates he came to understand the impact of ants simple rules on complex social

behaviors. Thomas leveraged this new understanding to use the macro-level effects of

population size to lead his ants to victory by making predictions based in his theory

of the agent based model’s simple rules. Thomas employed complex systems thinking

learned in the short interaction to reach his goal of maximizing population. He set this

goal in communication with his teamate in the open-ended constructionist learning

environment afforded by our design of Ant Adaptation.

Through play, Thomas learned that entities like ants have a mechanism such as lay-

ing trails to attract other ants to owers in a cycle by recursively following the chemi-

cals. He also realized that sometimes this process can lead ants astray as shown by his

use of vinegar to redirect them out of deleterous local optima. He also constructed the

concept that the food source’s proximity to the colony increased the ant population by

increasing food intake. After he stated as much, the other side started placing owers

close to their nest, indicating they also understood agents’ actions led to the macro-level

effect of population growth.

Discussion

We developed CDM (Martin, Horn and Wilensky, 2018; Martin and Wilensky 2018)

to study learning in an informal environment (Martin, 2018) based on constructionist

theory (Martin, Horn and Wilensky, 2019). Piaget presented a notion of knowledge that

is constructed. Since he believed knowledge is constructed, Piaget invented the clinical

interview to document how people construct these understandings during open-ended

conversation and question-asking designed to illuminate the way the child thinks of or

explains a phenomenon. The next step in this research for informatics is to automate it.

This work poses some challenges, but will be feasible in the near-term and so is worth

discussing here.

It is difcult to automatically transcribe what children say. But, the barriers are

dropping. According to a report by RideOut Media (2017) Most American Children

(98%) are now in a home with a tablet or smartphone. These provide ubiquitous access

to microphone, voice search enabled devices (Lovato & Piper, 2019). Otter.ai, an AI

powered transcription service, along with REV.com, are dropping the cost and increas-

ing the accuracy of automated transcription. As this revolution takes place, the need to

understand and identify key learning moments in children’s speech will rise.

Consequently, we should focus on natural language processing to identify ontologi-

cal entities, the functions they employ and their properties. In a naïve search, if all con-

Constructivist Dialogue Mapping Analysis of Ant Adaptation

107

cepts had a specic name, this would be a search for nouns, the verbs those nouns take,

and the adjectives and adverbs that that modify them. Unfortunately, language is less

clear than that. Not all entities have names. Antecedents are not always clear. At the mo-

ment, bridging these connections is the work of humans.

Previous research, however, suggests that an automated ontological concept map-

ping can help us track theory development. For example, the OntoClean (Guarino, Ni-

cola and Chris Welty, 2002) methodology supports the construction and evaluation of

taxonomic relationships based on the use of a number of philosophical meta-properties

– namely, unity, rigidity, identity and (notional) dependence – as well as on constraints

limiting relationships that can be established between concepts (types, properties)

tagged with these different meta-properties. It was the rst attempt to formalize ontol-

ogy for information systems. Moreover, Newsreader (Vossen et al., 2016) provides

aggregation of information over massive amounts of online text to build stories that

decision makers can use. In Newsreader there are four steps:

Identication, that extracts what happened to whom, and where. (1)

DeDuplication, which makes sure that similar information across a corpus is (2)

available only once, while referencing each article referring to it.

Aggregation show complementary articles across the corpus.(3)

Perspectivation, makes sure different viewpoints and perspectives in the corpus (4)

are traceable in the nal narrative.

While the previous work with Newsreader and OntoClean examines large corpus

understanding, we want to track how children develop their ideas in informal learning

environments. They develop their ideas rapidly, and sometimes hold two conicting

notions simultaneously. CDM suggests potential for the context. The advantage of this

approach is we can present the smallest parts of what we observe with large samples of

transcript data. For instance, we can note when users rst identify an unknown activity

and then how they build out an understanding of the subprocesses of that task (Martin,

Horn and Wilensky, 2018). Currently, we read the transcript to see how users construct

such operational knowledge on the representations they see.

Future Work

As a purely qualitative method, CDM demonstrates learning as concept elaboration

over time through the proxy of changes in speech (Leinhardt and Crowley, 1998).

Through automating this process, in future work, we will scale the process, and there-

by test our methods on larger n-samples of data. Mixed Human-Articial Intelligence

solutions, like Quantitative Ethnography (Shafer, 2017), Ncoder (Shafer et al., 2015)

or DeepLabCut (Mathis, 2018 et al., 2018) are methods that we will use in the near

term. These approaches mix researchers’ hand-coding a small number of bits of text,

that can scale to larger corpuses of text or behavior data using regular expressions or

machine learning.

K. Martin, M. Horn, U. Wilensky

108

Conclusion

The system of tracking players’ conceptual development illustrated how their thinking

changed throughout the interaction. Thomas learned that the chemical trails lead ants

through a cycle that feeds the whole colony. He also learned that sometimes this process

can lead ants astray, and how to intervene with vinegar in the self-organized system to

optimized it. Meanwhile, Rebecca’s knowledge emerged through play. Using CDM, we

tracked how Rebecca created a theory of how feedback between ants in a colony leads to

self-organization of food foraging. These two examples showed CDM’s utility in track-

ing learning as concept elaboration through interaction with technology and a facilitator.

The method highlights two parts of the theory:

Elaboration of discussion improves understanding. And by tracking the paths of (1)

elaborations and its sometimes dual conceptions we can observe conservation of

concepts in transcripts.

Knowledge uidly grows through action taken in a complex system. The dynam-(2)

ic interaction with the model enabled users to learn through mediation with the

computer system, each other, and a facilitator. This sort of dialectic interaction

could be scaled to other learning environments, and tracked through CDM.

The game facilitated its learning objectives by scaffolding theory development. When

people engage part of a complex system, they attempt their best theories in real time, and

received dynamic feedback from the computer and each other. The overly abundant data

gets t into a theory that people test in mediation with the machine and themselves and

nds regularities and breaks in their system of thought.

The constructivist dialog mapping approach introduced in this paper was used to

capture changes in a player’s understanding of agents, functions and properties. We

demonstrated the process of learning complex ant systems through playful interaction

with our agent-based modelling game, Ant Adaptation. In future work, CDM could also

be useful in tracking what people learn in other learning environments where they inter-

act with peers, technology and teachers.

Elaboration of discussion improves understanding. Through the presentation of

Thomas’ case, we show that a player added to his understanding of ant colonies through

the elaboration of discussion about a complex system. He started using vinegar to elimi-

nate local optima where the ants got trapped. Simultaneously, he used the feedback of

pheromone trails to organize his colony’s foraging and adjust adaptations to changing

circumstances.

Knowledge uidly grew through action taken in the complex system. From Rebecca,

we notice that her knowledge is fairly uid – namely, she came up with theories on the

y. She took a while to decide on one, and it seems her notions were a bit more xed

and situational. Our research question was how users are building their knowledge when

using the model of ant competition. She understands the rules of the behavior of indi-

viduals to make sense and meaning from agent-based models. Thus, we nd this agent-

based model a felicitous way for students to freely construct. Working with each other

and mediated by the tabletop game, they build understanding of the complex systems

Constructivist Dialogue Mapping Analysis of Ant Adaptation

109

processes by conserving change and forming operational concepts – like children grow-

ing up in a gas cloud.

Effortful problem-solving activity is the process of science, and that is the process

constructivist dialogue mapping tracks. The CDM approach introduced in this paper

was able to capture changes in a player’s understanding of agents, functions and prop-

erties (entities’ mechanisms), while they learned complex ant systems through playful

interaction with our agent-based modelling game, Ant Adaptation. CDM captured the

changes as utterances occurring during a short interaction. By analyzing changes in

talk pre- and post- play, we found that a player learned about feedback, and employed

that learning at multiple levels to maximize an ant population. The elaboration took

place by forming and testing theories with Ant Adaption. This method could be used

in other learning environments to inquire about the association between elaboration of

talk about concept formation.

Acknowledgements

We would like to thank Paul Won, Gabby Anton, Bill Hoover, Rui Han and Melissa

Perez for their feedback. We would like to thank Francisco López-Bermúndez for his

illustrations. We also would like to thank the Center for Connected Learning and Com-

puter Based Models, TIDAL, and TIILT labs for their feedback throughout the project,

and the supportive Northwestern Community. Additionally, Marcelo Worsley and Emily

Wang provided copious insight. Finally, we would like to thank the National Science

Found (NSF Stem+C: Award # 1842375) and the Multidisciplinary Program in Educa-

tion Sciences (IES: Award # R305B090009) for funding the project.

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Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a rey: Learning biology through

constructing and testing computational theories – an embodied modeling approach. Cognition and Instruc-

tion, 24(2), 171–209.

K. Martin is a doctoral student at Northwestern University in Learning Sciences and

a Multidisciplinary Program in Education Sciences fellow. He completed the Thomas

Watson Fellowship, studying the simulation of ants through naturalistic observation in

South America, the Middle East, and Africa. His research considers the pedagogical val-

ue of constructing agent-based models in complexity education, often times with ants.

Some of his recent work has focused on restructuring history and creative writing classes

through the use of digital environments. Find more about him at kitcmartin.com.

K. Martin, M. Horn, U. Wilensky

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M. Horn is an associate professor at Northwestern University with a joint appointment

in Computer Science and the Learning Sciences. Mike runs the Tangible Interactive

Design and Learning Lab. His research considers the intersection of human-computer

interaction and learning, with a focus on thoughtful uses of emerging technologies in

diverse learning settings. Some of his recent projects have included an investigation of

multi-touch tabletops in natural history museums, and the use of tangible programming

languages in kindergarten classrooms and science museums.

U. Wilensky, Lorraine H. Morton Professor of Learning Sciences, Computer Science

and Complex Systems, is the founder and current director of the Center for Connected

Learning and Computer-Based Modeling. He is also a faculty member in Cognitive Sci-

ence, philosophy, the program in Technology and Social Behavior, the CIERA center

and the Segal Design Center research council. He co-founded the Northwestern Institute

on Complex Systems (NICO). He is the author of the NetLogo agent-based modeling

software, which his lab actively maintains and improves.