Integrated STEM in Elementary Grades Using Distributed

Agent-based Computation

Pratim Sengupta, Gokul Krishnan and Mason Wright

Mind, Matter & Media Lab, Vanderbilt University, Peabody College, Nashville, TN, U.S.A.

Keywords: Computational Thinking, Tangible Interfaces, Agent-based Programming, Visual Programming,

K-12 Science Education, User-centered Design, Integrated STEM, Engineering Education.

Abstract: We investigate how the integration of visual agent-based programming and computationally augmented

physical structures can support curricular integration across STEM domains for elementary grade students.

We introduce ViMAP-Tangible, a socio-technically distributed computational learning environment, which

integrates ultrasonic sensors with the ViMAP visual programming language using a distributed computation

infrastructure. In this paper, we report a study in which 3

and 4

grade students used ViMAP-Tangible to

engage in collaborative design-based activities in order to invent “drawing machines” for generating

geometric shapes. The curricular activities integrate engineering practices such as user-centered design,

mathematical reasoning about multiplication, rates and fractions, and physical science concepts central to

learning Newtonian mechanics. We identify the key affordances of the learning environment and our

pedagogical approach in terms of the relationship between the structural elements of students’ physical

constructions and computational models, and their learning outcomes, both in terms of computational

thinking, and the domain-specific, mathematical and scientific knowledge that they began developing.

1 INTRODUCTION

Integration of the individual domains of science,

technology, engineering, and mathematics (STEM)

is now recognized as a central pedagogical aim of

engineering and science education reform at the K-

12 level (Nathan, Srisurichan, Walkington,

Wolfgram, Williams, & Alibali, 2013; Berland,

2013). STEM integration is considered in the US

education policy statements to be necessary for

several objectives: a) supporting STEM education,

including the preparation of future STEM

researchers; b) for developing informed citizens;

and, c) for supporting workforce development in an

increasingly complex economy (Katehi et al., 2009;

NRC, 2007, 2010; Nathan et al., 2013).

Integrated STEM necessitates integrating diverse

domains by highlighting big ideas that transcend

these different domains (Nathan et al., 2013;

Schunn, 2009; Roehrig, Moore, Wang, & Park,

2012). However, the generation of these ideas

involves material agency, conceptual agency and

participation in a community of practice. For

example, engineering educators have argued that

some of the most essential “skills” in engineering

“arise out of engagements not only with formal

representations, but also with tools, materials, and

other people” (Johri & Olds, 2011, p. 163).

Similarly, historians, philosophers and sociologists

of science have shown that the development of

scientific knowledge (e.g., big ideas such as laws of

physics) is deeply intertwined with the invention of

representational systems and tools, as well as the

development of communicative representational

practices (e.g., modeling) (Giere, 1999; Pickering,

1993). The representational systems and tools

include both semiotic systems (e.g., calculus and

computational modeling languages) and mechanical

devices (e.g., bubble chambers and particle

accelerators). This is known as the Science as

Practice perspective, and has been adopted as a key

pedagogical framework for K-12 science education

in the US (NRC, 2008).

Given this background, we believe that the focus

on Integrated STEM is synergistic with the recent

focus on computational thinking (Wing, 2006, 2010;

NRC, 2010). Wing (2006) described computational

thinking as a general, analytic approach to problem

solving, designing systems, and understanding

human behaviors. Sengupta, Kinnebrew, Basu,

Sengupta P., Krishnan G. and Wright M..

Integrated STEM in Elementary Grades Using Distributed Agent-based Computation.

DOI: 10.5220/0004949500670078

In Proceedings of the 6th International Conference on Computer Supported Education (CSEDU-2014), pages 67-78

ISBN: 978-989-758-021-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Biswas & Clark (2013) argued that computational

thinking is evident in the form of epistemic and

representational practices such as problem solving,

design, programming, and modeling.

Pedagogy that supports the development of

representational practices associated with

computational thinking can bring together different

domains in science, such as biology and physics, in

middle school classrooms through the use of agent-

based, visual programming languages designed

specifically for modeling scientific phenomena

(Sengupta et al., 2013). In agent-based

programming, a user creates a computer program by

using simple rules to command the movement and

behavior of computational agents, e.g., the Logo

turtle (Papert, 1980; diSessa, Abelson & Polger,

1991; Repenning, 1993; Kelleher & Pausch, 2005;

Resnick et al., 2009; Sengupta & Farris, 2012;

Sengupta et al., 2013). In this paper, we extend this

argument and propose that a particular form of

agent-based programming and modeling, in which

control of a computational agent is socio-technically

distributed, can be leveraged to integrate diverse

STEM domains for children in elementary grades.

The learning environment we present here can be

best understood as a socio-technically distributed

activity system. This is because ViMAP-Tangible

distributes the control of a single computational

agent socially between two collaborating users, and

technologically, between two physical machines and

a virtual algorithm. The goal of each student dyad in

this study was to design, build, test and refine a

hybrid (computational and physical) computational

machine for generating geometric shapes. Our

pedagogical approach emphasized User-Centered

Design (UCD), i.e., students were asked to design

their machines with usability (Norman, 1998) as a

key focus, which has been shown to be a crucial

element of product engineering but challenging to

implement pedagogically.

Our paper makes three contributions. First, we

present a pedagogical framework for integrating key

engineering practices – UCD, collaboration and

computational thinking - with math and science

education. To this end, we present a theoretical

framework that integrates relevant literature from

multiple domains: computational thinking,

Integrated STEM, Agent-based and Tangible

Computation in education, User Centered Design,

Design-based Learning and Collaboration in

engineering practice and education. Second, we

present a technological innovation in the form of

ViMAP-Tangible, and a set of curricular activities,

which were designed to bring about such integration.

Third, while recent efforts have focused on

designing and implementing Integrated STEM

curricula at the college level (Sanders, 2009), middle

school (Berland, 2013) and high schools (Nathan et

al., 2013), we demonstrate that younger children (3

and 4

graders) can be brought into the fold of

Integrated STEM education that also includes a

focus on developing computational thinking.

2 THEORETICAL FRAMEWORK

2.1 Computational Thinking in K-12

Computational thinking is an increasingly ubiquitous

epistemic and representational practice in all fields

of science and engineering (Wing, 2006; NRC,

2010). As Sengupta et al. (2013) pointed out,

computational thinking draws on concepts that are

fundamental to computing and computer science, but

also includes practices (e.g., modeling, abstraction,

reformulation, simulation, verification) that are

central to a large number of scientific, engineering,

and mathematical disciplines. This sentiment is also

reflected in the model ACM K-12 computer science

curricula for middle schools (Tucker et al., 2003),

and the recently concluded National Academy of

Education panel on computational thinking (NRC,

2010), which argued for integrating computational

thinking with existing K-12 curricula in other

domains such as mathematics and science.

Wing (2006) argued that a key characteristic of

computational thinking is design-based thinking.

Design is a form of problem solving in which

thinking, tool manipulation, and materials are

reflected in the iterative construction of an artifact

(Bucciarelli, 1994; Simon, 1969; Perkins, 1986).

From a pedagogical perspective, design challenges

provide learners opportunities for testing and

revising their developing conceptions and

understanding, and interweave action and

development with reflection and refinement to

facilitate deep learning (Kolodner et al., 2003).

Researchers have shown that students’ construction

failures, when scaffolded appropriately, provide

additional opportunities for learning (Kolodner et

al., 2003; Papert, 1980; Harel, 1990; Penner, Lehrer

& Schauble, 1998).

2.2 Integrated STEM as Pedagogy

Integrated STEM has been defined as technological

and engineering design-based learning approaches

that intentionally integrate concepts and practices of

CSEDU2014-6thInternationalConferenceonComputerSupportedEducation

science and/or mathematics education with content

and process of technology and/or engineering

education (Sanders, 2009; McCulloch & Ernst,

2013).

As Nathan et al. (2013) pointed out, integration

necessitates a pedagogical approach in which fields

are integrated, rather than merely combined (cf.

Dyer, Reed, & Berry, 2006; Satchwell & Loepp,

2002). The emphasis on “integration” implies that

diverse fields of knowledge and practice should be

merged in a manner that reveals big ideas that

transcend specific disciplines (Nathan et al., 2013;

Schunn, 2009; Roehrig, Moore, Wang, & Park,

2012).

Nathan et al. (2013) argued that big ideas that are

regarded as invariants in math and science are

represented using different inscriptions, both

material and semiotic, as well as in different social

interactions, such as in lectures or group work. As

such, Nathan et al. (2013) argued that STEM

integration in a high school level project-based

engineering classroom can be viewed as the

production and maintenance of cohesion of invariant

relations across the broad range of representations

that exist in the engineering classroom.

Similarly, Berland also argued that STEM

integration can be brought about by a particular class

of activities that she termed STEM-design

challenges (Berland, 2013). In such activities,

students are posed an engineering design challenge

that can only be completed when relevant math and

science concepts are applied (e.g., Coyle, Jamieson

& Sommers, 1997; Fortus et al., 2004; Kanter, 2010;

Kolodner et al., 2003). In such activities,

pedagogically, these concepts represent domain-

specific learning goals in science and math.

One of our central goals in this paper is to

demonstrate that computational representational

practices that are supported by agent-based

programming and the design and development of

usable physical control mechanisms for controlling

agent-behaviors, can integrate representational

practices and conceptual development across

multiple STEM domains.

2.3 Agent-based Visual Programming

& Tangible Computation for

Children

The literature on designing agent-based

programming languages and environments for

novice programmers highlights the following

affordances. First, agent-based programming has

been shown to be intuitive for novice programmers,

as it leverages learners’ embodied intuitions about

movement in space (Papert, 1980). Second, agent-

based programming can also help children learn

scientific concepts in physics and biology (diSessa,

Abelson & Ploger, 1993; Repenning, 1993;

Sengupta & Farris, 2012; Sengupta et al., 2013).

Third, agent-based programming can help develop

computational literacy through the design of self-

expressive digital narratives and games using visual

agent-based environments such as Scratch (Maloney

et al., 2004) and Alice (Conway, 1997).

Our choice of visual programming as the mode

of programming is grounded in the literature on

computer science education. Children find it difficult

to understand the syntax and semantics of

programming (Spohrer & Soloway, 1986; Perkins,

1988). They also find it challenging to effectively

control the flow of a program using loops and

conditionals (du Boulay, 1989). Researchers have

also found that alleviating syntax problems helps

students focus on the semantic ones (Hohmann,

1992; Soloway, 1993; Anderson, 1989; Mannila,

Peltomaki & Salakoski, 2006). Visual programming

– in which students construct programs using

graphical objects in a drag-and-drop interface– has

been shown to be effective in alleviating these

difficulties (Kelleher & Pausch, 2005). Examples of

agent-based visual programming environments are

AgentSheets (Repenning, 1993), StarLogo TNG

(Klopfer, Yoon, & Um, 2005), Scratch (Maloney et

al., 2004), ViMAP (Sengupta, Farris & Wright,

2012) and Alice (Conway, 1997).

Researchers have also started focusing in on the

integration of tangible computation with agent-based

modeling and programming for novice learners

(Suzuki & Kato, 1995; Blackwell, 2003; Horn &

Jacob, 2007; Blikstein & Wilensky, 2009). Blikstein

and Wilensky (2009) showed that linking multi-

agent computer models with real-world phenomena

by using sensors could enable undergraduate

students to learn authentic scientific and engineering

concepts and practices. Tangible programming

languages such as AlgoBlock (Suzuki & Kato, 1995)

and Tern (Horn & Jacob, 2007; Horn et al., 2011)

have also been used to teach young children

programming.

In AlgoBlock, lexical elements of the Logo

language are assigned to sealed metal boxes, about

20 cm on each side. These blocks can be assembled

by plugging each block, via connectors on the sides,

into neighboring blocks. These blocks are connected

to the computer using a wired interface. A complete

Logo algorithm could be constructed by assembling

a sufficient number of blocks on a tabletop, and the

IntegratedSTEMinElementaryGradesUsingDistributedAgent-basedComputation

results of running the program are visible in the form

of an animated submarine on screen (Suzuki & Kato,

1995). Horn and colleagues developed Tern, in

which users construct programs by arranging and

organizing wooden blocks with computer-vision

fiducials (black and white symbols), which are then

scanned by a program to generate a Logo algorithm.

Tern has been used effectively to teach young

learners programming and robotics, both in informal

and formal settings (Horn, Crouser & Bers, 2011;

Horn & Jacob, 2007).

While tangible and visual programming offer

two different interactional modes of programming

for the learner, Horn, Crouser & Bers (2011) have

argued for introducing a hybrid approach in which

learners (users) can elect to work either using

tangible blocks, or by using a visual (graphical)

programming interface, to generate the same

algorithm. They found that such a hybrid approach

was more advantageous than using either approach

individually.

2.4 User Centered Design: Practice and

Pedagogy

User Centered Design (Norman, 1998; Norman &

Draper, 1986) emphasizes the importance of

understanding the needs of the users in order to

design usable systems. Norman (1998) argued that

central principles of designing for people to support

understandability and usability are: a) providing a

good conceptual model to the user, which will allow

them to predict the effects of their actions on the

designed system; and b) making things visible, i.e.,

by carefully considering the relationship between the

design of “controls” of a system and its “placement”

(location) so that it makes the function of the control

intuitively available for the user.

However, there is variability in how design

researchers have defined and operationalized UCD:

while there is a general agreement that UCD

requires paying attention to the needs of the user,

and involving the user in the system design process,

there is relatively less agreement on how user

involvement can be accomplished (Vredenburg,

Mao, Smith, & Carey, 2002; Gulliksen, Göransson,

Boivie, Blomkvist, Persson, & Cajander, 2003;

Karat, 1996). Vredenburg et al. (2002) defined UCD

as the practice of the following principles: the active

involvement of users for a clear understanding of

user and task requirements, iterative design and

evaluation, and a multi-disciplinary approach.

Gulliksen et al. (2003) further elaborated on the

principles and argued that in addition to involving

the user throughout the design process, the following

principles are necessary to be enacted in practice in

order to support UCD: 1) rapid prototyping during

the early phases of the design process; 2) a cyclic

iterative process of designing solutions interwoven

with evaluation; 3) multi-disciplinary design teams

that bring together distributed expertise for the

various design components; and 4) an integrated

design process, in which

the system, the work

practices, on-line help, training, organization, etc.

should be developed in parallel.

In this paper, we adopt Gulliksen et al.’s (2003)

definition of UCD as a key element of our

pedagogical approach. There is some evidence that

designing for instructional use can act as a

productive pedagogical model for K-12 science

education (Carver et al., 1992; Harel, 1990; Brown

and Campione, 1993). Brown and Campione (1993)

noted that fifth and sixth graders developed a deeper

understanding of science concepts while they sought

causal explanations to incorporate into HyperCard

documents they developed to teach their classmates.

A few studies have also focused on children

designing agent-based instructional software for

mathematics (e.g., Harel, 1990) and instructional

games in science and mathematics (e.g., Kafai et al.,

1998). In all of these studies, students not only

developed a deeper understanding of the target

science or math concepts, but also developed

substantial expertise in programming.

An interesting finding across these studies is that

children find the consequentiality of their design

projects in terms of designing for use to be quite

motivating, but at the same time, they do not regard

the involvement of users as a useful component of

their design process (Carver et al., 1992; Kafai et al.,

1998). Carver et al. found that "getting someone to

try out the presentation" was regarded by middle

school students as one of the least important tasks to

accomplish during their design process; instead, they

believed that the designers themselves could act as

users during the design process. Similarly, in Kafai

et al.’s work, children who designed educational

software did so largely without involving real users

in their design process. This in turn resulted in the

design of user interfaces that were confusing for the

real users (Kafai et al., 1998). This is in striking

contrast to Norman’s (1998) famous dictum that

because the interface guides the interactions between

the user and the product, it should therefore guide

the design of the rest of the product.

These studies suggest that the involvement of

users during the design process therefore requires

explicit instructional scaffolding by the teacher. In

CSEDU2014-6thInternationalConferenceonComputerSupportedEducation

fact, once “real” users tested the children’s designs,

Carver et al. (1992) found that the design documents

with their designs increased greatly in terms of

making explicit the connections between the

different aspects of their design, as well as

explaining how to use the designed artifact. In

Norman’s terms (Norman, 1998), one can therefore

conclude that involvement of user feedback during

the design cycle enhances the usability of the

product, as it makes clear to the designers the need

to make things visible to the user.

2.5 Collaboration in Engineering

Practice & STEM Education

Collaboration and teamwork hold a significant place

in engineering practice (Bucciarelli, 1994). As Johri

(2012) pointed out, engineers increasingly work

collaboratively around the globe; technology is a

primary driver of such arrangements. Anderson,

Courter, McGlamery, Nathans-Kelly and Nicometo

(2010) studied engineering work across six

engineering firms that examined engineers’ self-

valuation of important work characteristics. They

found remarkable similarities across settings in

which most engineers saw their work as

collaborative problem solving, and greatly valued

communication skills and working in a team.

In the field of education research, collaboration

and cooperation have been defined as conceptually

distinct processes. Roschelle & Teasley (1995)

defined collaboration as “a coordinated,

synchronous activity that is the result of a continued

attempt to construct and maintain a shared

conception of a problem” (Roschelle & Teasley,

1995; p. 70). Dillenbourg (1999) defined the

distinction between collaboration and cooperation as

follows: “In cooperation, partners split the work,

solve sub-tasks individually and then assemble the

partial results into the final output. In collaboration,

partners do the work ‘together.’” (p. 8).

Productive collaborative tasks create positive

interdependence among learners, which can be

understood as the coordinated activity of multiple

people for accomplishing a specific learning

objective (Kreijns, Kirschner & Jochems, 2003;

Antle & Wise, 2013). White & Pea (2011) argued

that collaborative tasks are likely to be most

effective when they are sufficiently open-ended and

complex to necessitate contributions from each

member of a group (e.g., see Cohen, 1994), and

when participants engage the task and one another in

ways that sustain their diverse contributions (e.g.,

see Barron, 2003). In the literature in computer

supported collaborative learning, collaboration is

often fostered through variations on the “jigsaw”

script in which each student only has access to part

of the information (i.e. one piece of the puzzle)

needed to solve a collaborative task (Miyake,

Masukawa & Shirouzou, 2001).

Research suggests that collaboration can indeed

enhance science and math learning through the

creation of productive opportunities for shared

inquiry and discourse. Pathak, Kim, Jacobson &

Zhang (2010) showed that dyadic discourse in an ill-

structured inquiry activity when exploring a

scientific phenomenon using agent-based

simulations, creates opportunities for reflective

reasoning. Specifically in the context of learning

kinematics using agent-based programming,

researchers have showed that collaborative design of

graphical, mathematical and computational

representations of motion creates productive

opportunities for within-group “conversations for

conceptual change” (diSessa et al., 1991; Sherin et

al., 1993). Similarly, in math education, researchers

have focused on how learning in collaborative

settings leads students to develop mathematical

discourse by productively appropriating their group

members’ ways of talking or acting (Carlsen, 2010;

Moschkovich, 2004; Radford, 2006; Lai & White,

2011).

Of particular relevance to our paper is the

argument that meditational tools – that include both

tangible and computational artifacts – can support

collaborative learning by creating opportunities for

each group member to attend to what the other is

doing, by making actions visible and gaze

observable in supporting collaborative meaning-

making (Antle & Wise, 2013; Fernaeus &

Tholander, 2006; Hornecker, 2005; Suzuki & Kato,

1995; Baker et al., 1999; Suthers et al., 2008). These

studies showed that the presence of tangible artefacts

in a shared transaction space (Hornecker, 2005)

grounds the interaction between group members by

providing a referential anchor for conversation,

which can be referred to by using both verbal and

gestural communication channels. Of direct

relevance to our study, Fernaeus & Tholander

(2006) found that when students worked

collaboratively using a tangible programming

language for learning agent-based programming,

they formed subgroups dynamically, and

furthermore, and these subgroups further

collaborated with one another on the different

activities to accomplish their goals.

IntegratedSTEMinElementaryGradesUsingDistributedAgent-basedComputation

3 THE VIMAP-TANGIBLE

LEARNING ENVIRONMENT

3.1 The Representational

Infrastructure

Three key elements of ViMAP-Tangible are:

(1) Agent-based visual programming: ViMAP

(Sengupta, Farris & Wright, 2012) serves as the

agent-based, visual programming language for our

learning environment. ViMAP consists of library of

graphical programming primitives designed

specifically to support learning of mathematics,

kinematics and biology, a construction zone where

learners can generate an algorithm by using a drag-

and-drop interface that is easy to understand for

children, and a NetLogo environment (Wilensky,

1999) to allow learners to visualize the results of

their algorithm. Similar to Alice and Scratch,

ViMAP provides a drag-and-drop interface for

constructing programs that is easy to use and

understand for children. Commands in ViMAP

include both domain-general abstractions (e.g.,

loops, conditionals), as well as domain-specific

commands (e.g., for controlling speed, distance and

acceleration of the turtles).

(2) Tangible and gestural representation of digital

information: In ViMAP-Tangible, students can use

gestures and/or mechanical devices in order to

control different variables by linking them to sensors

through their ViMAP programs. The ultrasonic

distance sensors we used in this study measure the

distance from the sensor itself to the nearest object

in its field of operation. Some of the ViMAP

programming primitives were designed specifically

to allow users to set the values of different agent-

variables (e.g., color, speed, acceleration, pen-width,

horizontal and vertical displacements) based on the

sensor-readings. For example, the “set <step-size>

equal-to <sensor-reading>” command sets the

distance the agent on the screen travels equal to the

reading of the ultrasonic sensor. That is, when a

learner uses this command in her/his algorithm,

moving a hand towards the sensor (i.e., closer) will

cause the turtle to travel a shorter distance.

(3) Distributed control of virtual agent: In ViMAP-

Tangible, multiple students can simultaneously

control the behavior of a single agent. In the version

of ViMAP-Tangible we report here, control of the

behavior and attributes of a single computational

agent is distributed across two ultrasonic sensors,

which are connected to ViMAP via an Arduino

microcontroller (Figure 2). We believe that such a

setting can foster positive interdependence (Antle &

Wise, 2013), since it requires the coordinated action

of both the group members in order to

computationally implement and physically enact a

successful and non-redundant control mechanism.

3.2 Curricular Activities

Figure 1: Domain-Specific Learning Goals for STEM

Integration Using ViMAP-Tangible.

The curricular activities consisted of three phases,

and were designed to integrate multiple domain-

specific learning goals. These learning goals are

shown in Figure 1.

During the first phase of our curriculum, students

were introduced to agent-based programming using

ViMAP. Students learned to generate “open” and

“closed” geometric shapes (e.g. squares, circles,

spirals) using ViMAP. In the second phase, students

used these shapes to represent models of phenomena

involving continuous change over time. We watched

segments of the movie The Lorax in class, after

which students identified various events depicted in

the movie (such as Lorax running and accelerating,

objects in free fall, etc.) that could be modeled using

ViMAP shapes. This phase was designed to

establish shape drawing as a consequential (Gresalfi

& Ingram-Noble, 2008) activity that is not only

valuable for its visual aesthetics, but that can also be

used as a computational model of change over time.

In both these phases, each student worked

individually. Our previous work has shown that

these two phases can effectively introduce students

to the basics of agent-based programming, and

students also learn to use their programs (i.e.,

CSEDU2014-6thInternationalConferenceonComputerSupportedEducation

geometric shapes) as models of motion in Phase 2

(Sengupta & Farris, 2012).

Figure 2: The ViMAP-Tangible Distributed Computation

Infrastructure.

The third phase used a STEM design challenge

(Berland, 2013), in which students worked in dyads

to design and develop a tool for shape drawing using

ViMAP. The nature of the tool that the dyads were

asked to build was a hybrid computational machine

consisting of a physical control structure (i.e., a

simple machine), and a ViMAP program, which

would together control the movement of the virtual

agent (ViMAP turtle) on screen. Each student was

asked to design a mechanical control for one sensor,

but the dyad was responsible for jointly designing

the ViMAP program. Emphasis on User Centered

Design was maintained throughout this phase as

follows: First, students themselves acted as the

users; thereafter, students from other groups acted as

users (this is the main user-testing phase for the

purposes of our analysis); and during the final day of

the class, students’ parents were invited to visit the

class to test their children’s designs.

Our pedagogical approach emphasized several of

Gulliksen et al.’s (2003) core principles of UCD.

These included the following: an emphasis on rapid

prototyping during the early phases of the design

process; a cyclic iterative process of designing

solutions interwoven with evaluation; and within

group distributed expertise for different design

components.

4 RESEARCH QUESTIONS

We investigate the following research questions:

1. How are students’ computational and

mechanical representations shaped by the

following curricular foci:

1.1. User-Centered Design?

1.2. Collaboration between learners?

2. What is the relationship between the structural

characteristics of students’ physical and

computational inventions and the development

of their scientific and mathematical knowledge?

5 SETTING & METHOD

The study took place in a metropolitan city in the

form of an enrichment program for elementary

school children, conducted in a classroom on the

campus of a large private university in the mid-

southern USA. Classes met once a week (9:00 a.m.

to 11:30 a.m.) on six consecutive Saturday

mornings. Students were recruited through an online

solicitation sent to the local elementary schools.

None of the students in this course had any prior

programming experience, and we particularly

encouraged female students to apply. Students were

admitted on a first-come, first-served basis. There

were 16 participants, out of which eight students

were in 3rd grade, and eight were in 4th grade. Five

of the students were female, and three of them were

in 3rd grade. The ethnic composition of the students

whose work we analyzed for this paper is as follows:

White (7), Asian American (4), and African

American (3). Two students were absent for multiple

days, and their work has not been analyzed.

The first author acted as the lead instructor for

the study. Both the authors also collected data in the

form of videotaped in-depth interviews with the

participants, the software and hardware artifacts

(i.e., ViMAP programs and physical machines)

interviews were conducted while the learners were

engaged in the modeling and programming

activities. In some cases, the interviews ensued when

the learner called upon the researcher in order to

help him or her with a difficulty. In other cases,

researchers conducted interviews in order to ask

learners to explain their programs or models.

Similar to Carver et al. (1992), we believe that

the role of the teacher in such a design-based

classroom can be best described as using the

cognitive apprenticeship framework (Collins, Brown

& Newman, 1987). That is, the teacher(s) supported

students' development of representational practices

in some cases by explicitly modeling certain

elements of the design practices (e.g., by acting out

the ViMAP commands physically in class), and in

other cases, prompting students to reflect on the

changes they are making to their designs in terms of

their affordances.

Our data consisted of student-generated artifacts,

field notes, and video-recorded interviews of

IntegratedSTEMinElementaryGradesUsingDistributedAgent-basedComputation

students with the researchers. We analyzed this data

by identifying themes and sub-themes using the

double coding method (Miles & Huberman, 1994).

To present our analysis, we used a case study-based

approach. Our selection of cases was guided by the

following criteria: representativeness and typicality.

Representativeness implies that the selected cases

should aptly represent key aspects of the

instructional and learning processes. These key

aspects, in turn, are defined based on the putative

research question(s). Typicality implies that the

selected case(s) should potentially represent aspects

of the process of learning experienced by the

majority of the student population.

We answered the research questions (RQs) as

follows. In order to investigate the role of UCD and

collaboration (RQ1), we compared the intermediate

and final products of the students' design activities

before and after user testing (during Phase 3) to

check for improvements. In order to investigate the

affordances of the students’ designs in terms of the

scientific and mathematical concepts and discourse

that the students engaged in their designs (RQ2), we

analyze the structure of each group’s physical

machines, their ViMAP program (i.e., programming

commands), and their instructions for users in terms

of the type(s) of mathematical measures generated

by each group. The children generated these

measures in order to provide explicit instructions to

users for operating and understanding their

machines. These measures in turn were categorized

either as a) mathematical i.e., involving either

multiplicative or proportionality-based reasoning, or

reasoning involved in understanding geometric

coordinate systems; or b) physics-based, i.e.,

indicative of reasoning about the measurement of

speed and distance. 20% of the data reported here

was blind-coded by an additional coder not involved

with our study (Cohen’s Kappa = 0.95).

6 FINDINGS

6.1 Pedagogical Affordances of UCD &

Collaboration

Across all the groups, we found that (1) the

distributed computing infrastructure resulted in

positive interdependence (Kreijns et al., 2003)

among students, where members of each group

made coordinated, non-redundant contributions

towards a common goal; and (2) a focus on User-

Centered Design resulted in students refining both

the software and hardware components of their

programs by engaging in iterative design cycles.

Figure 3A (Top Left): Jerry’s pulley mechanism for

controlling turn of the turtle via Sensor 1; Figure 3B (Top

Right): Chuck’s machine for controlling the speed of the

turtle via Sensor 2. Figure 3C (Bottom) is a screenshot of

their ViMAP program for generating a square, and shows

how it responds to the sensors.

An illustrative case is the work of Chuck and

Jerry (Figure 3A, 3B & 3C). During Phase 3, they

built two separate machines to represent specific

hand movements over the sensors. Jerry’s machine

consisted of a flat LEGO plate that could be lowered

or raised above an ultrasonic distance sensor (Sensor

1 in Figure 3), using a pulley mechanism, to control

the turn angle of the computational agent. Chuck’s

machine also comprised of a horizontal LEGO

surface that could be lowered or raised above a

distance sensor (Sensor 2 in Figure 3), using a

manually operated crank lift, to control the step-size

of the turtle.

In order to facilitate better collaboration between

group members, during Phase 3, students’ ViMAP

programs became iteratively more refined with

fewer bugs and redundancies. For example, in their

initial versions, Chuck and Jerry had linked multiple

turtle variables to each of the sensors, and after a

few attempts, realized that their coordinated actions

would make such a design redundant. Instead, they

decided to divide their responsibilities: one person

would control the turn, while the other would control

the speed of the turtle, thereby, creating positive

interdependence. Another effect of collaboration

was the shared development of mathematical

measures within each group, which in turn fostered

CSEDU2014-6thInternationalConferenceonComputerSupportedEducation

mathematical and physics-related discourse and

representational practices. This is discussed in

Section 6.2.

We also found that students’ ViMAP programs

and instructions for users, post user-testing, were

more generative, and communicative. We found that

the final designs of six out of seven groups allowed

users to draw multiple shapes, whereas their initial

designs during Phase 3 were more constrained and

could only generate a specific shape (typically, a

circle). This improvement was a direct result of user

testing, as nearly every user demanded to be able to

draw more than one shape using the same tool. In

some cases we also noticed that the reliability of the

output of their ViMAP program also improved as a

result of improvement of their physical structures.

For example, both Chuck and Jerry realized that they

had to improve the flatness of the surfaces that were

generating the sensor-readings, because their users

were unable to generate reliable outputs. This

resulted in Chuck introducing a flat paper strip to

cover the bottom of the LEGO plates, while Jerry

created a wider plate to control sensor readings more

reliably.

6.2 Relationship between Children’s

Inventions & Learning Math and

Physics

6.2.1 Designs That Primarily Supported

Learning Rate and Kinematics

Figure 4 shows an example of student work in which

two students, Seana and Curly, built two separate,

manually operated, wheeled cars, with flat surfaces

in front (stacked LEGO bricks) to represent the palm

of a hand. The operating mechanism involves

pushing the car towards or away from the sensor,

where distance of the flat surface (representing the

“palm of a hand”) from the sensor generates the

reading of the ultrasonic sensor. The distance of one

of the cars from the sensor controls the speed of the

ViMAP turtle, while distance of the other car from

the second sensor controls the rotation of the turtle.

A total of two groups of students developed two cars

as their drawing machines.

After user-testing, Seana and Curly realized that

they had to provide instructions to the user on how

fast they would have to move each car in order to

generate the desired shape(s). This is because, in

their design, moving the two cars at different rates

resulted in different shapes. Neither student had any

experience in calculating rates or speed prior to this

study, nor were they formally instructed during this

class to calculate the rates. However, they used the

computer clock and cell phones as timers, and

figured out by trial and error how fast they had to

move each car in terms of the time taken by their

cars to travel specific distance(s) in order to generate

the desired shape(s). This also resulted in making

their designs more communicative by annotating the

track at specific positions, along with some written

and verbal instructions for users regarding how fast

they needed to move the cars between these

annotated positions. Although this was a rough

measure of rate, we believe that this is a productive

entry point into learning kinematics through the

development of consequential physics talk, as well

as mathematical discourse on rates – i.e., through

engaging in “conversations for conceptual change”

(diSessa et al., 1991).

Figure 4A: Seana and Curly’s ViMAP program for shape

drawing.

Figure 4B (Left) and 4C (Right): Seana (dressed in black)

and Curly (dressed in blue and white) take turns in moving

their “cars” towards the ultrasonic sensors. Seana controls

the rotation, and Curly controls the step-size of the Turtle.

6.2.2 Designs That Primarily Supported

Multiplicative and

Proportionality-based Reasoning

An example of this type of design is Chuck and

Jerry’s work described in Section 6.1. Jerry’s

machine controlled the angular turn, and the user

could generate readings from 1 – 10 (inches) by

using the pulley mechanism. However, the user

could also alter the numerical parameter of the

“Repeat” command in the ViMAP program, which

in turn would effectively multiply the sensor reading

by that parameter. Chuck used a similar strategy to

let users control the step-size of the turtle; he had

created several visible marks on his towers at

increments of a third of the maximum height, so that

IntegratedSTEMinElementaryGradesUsingDistributedAgent-basedComputation

the user can generate shapes of three levels of

magnification.

Chuck and Jerry, who were both beginning to

learn multiplication tables in their regular math

class, thus got an opportunity to use and further

develop their multiplicative and proportionality-

based reasoning in order to make their designs work.

We found that a total of three groups of students also

invented this type of design.

6.2.3

Designs That Primarily Supported

Learning the Cartesian Coordinate

System

Two groups of students developed machines similar

to Chuck and Jerry, but using a Cartesian coordinate

system in their ViMAP program. One of their

machines controlled the translational displacement

of the turtle, while the other controlled the vertical

displacement of the turtle. To do so, they used

ViMAP commands such as “jump-X-by <Sensor-

reading>” and “jump-Y-by <Sensor-reading>”,

respectively. An illustrative example is the case of

Ken & Yang. As they iteratively refined the various

combinations of translational and vertical

displacements that would generate the different

shapes, they became familiarized with the NetLogo

XY-coordinate space. They realized during user-

testing that it is much easier for users to control their

machines if they are provided with coordinates for

the vertices of target shapes such as equilateral

triangles and rectangles. This was evident in their

instructions for users on the final day.

7 CONCLUSION & DISCUSSION

In this paper, we have presented a socio-technically

distributed pedagogy for Integrated STEM in

elementary grades. Integrated STEM is a necessarily

interdisciplinary enterprise, and given the nature of

elementary classrooms (K-4), where the same

teacher is responsible of teaching all disciplines, we

believe that K-4 is a great setting for such curricula.

The cases we presented show that 3

and 4

grade

students were indeed able to successfully participate

productively in an engineering design process that

highlights User Centered Design and collaboration –

key elements of the practice of engineering.

Furthermore, we also showed that in the process,

students were able to iteratively develop expertise in

agent-based programming, as well as mathematical

and scientific discourse and representational

practices such as measurement and modeling.

Our paper also represents the first attempt to

integrate agent-based computational thinking with

engineering, curricular science and math within a

single curriculum in elementary grades. To this end,

we presented a pedagogical innovation in the form

of new socio-technically distributed computational

environment - ViMAP-Tangible - and a set of

curricular activities in the genre of STEM Design

Challenges (Berland, 2013). The computational

abstractions in the form of ViMAP programming

language, as well as the associated epistemic and

representational practices such as measurement and

modeling - act as invariants that facilitate cohesion

across the STEM domains (Nathan et al., 2013).

Our curricular design proposes a mechanism for

bringing about such cohesion using a socio-

technically distributed computational infrastructure.

Following Berland (2013), we maintained focus on

engineering practices from the beginning (Phases 1

& 2) by engaging students in design, in the form of

iterative development and refinement of their

computational models. Furthermore, we leveraged

the synergy between tangibility and collaborative

learning in a manner that introduced students to

another key engineering practice (i.e., collaborative

design). This in turn supported the development of

scientific and mathematical knowledge by creating

opportunities for reflection and positive

interdependence.

ACKNOWLEDGEMENTS

Financial support was provided by the National

Science Foundation (NSF Early CAREER #

1150230).

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