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Innovate to Mitigate: Microgenesis of student design and rationale in a crowdsourcing competition to mitigate global warming

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Abstract

The Innovate to Mitigate project adapts crowdsourcing to support project-based STEM education, posing design challenges for secondary-school students. Students are charged with designing feasible innovative strategies to mitigate CO2 emissions and thus global warming. The paper draws on data from 3 project teams. The paper presents evidence that a web-mediated community of practice supports STEM learning of concepts and STEM practices and examines conditions under which the environment can enable an account of microgenesis of that learning.
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Innovate to Mitigate: Microgenesis of student design and rationale in a crowdsourcing
competition to mitigate global warming
Brian Drayton, Gillian Puttick and Santiago Gasca
TERC, Cambridge, MA.
Paper presented at the American Educational Research Association annual meeting
San Diego, CA
April 21-26, 2022
Abstract
The Innovate to Mitigate project adapts crowdsourcing to support project-based STEM
education, posing design challenges for secondary-school students. Students are charged with
designing feasible innovative strategies to mitigate CO2 emissions and thus global warming. The
paper draws on data from 3 project teams. The paper presents evidence that a web-mediated
community of practice supports STEM learning of concepts and STEM practices and examines
conditions under which the environment can enable an account of microgenesis of that learning.
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Introduction
When science education relates to problems students see as relevant, their funds of
knowledge and motivation can serve as resources for inquiry that both draws upon and increases
students' 3-D learning (sensu NRC 2012). Climate change is increasingly seen to affect
everyone's lives (Leiserowitz et al. 2021, Richels et al. 2020, NRC 2010, IPCC 2018), and many
young people are eager to respond (e.g., Thunberg et al. 2020). Engaging the issue constructively
("active hope") can counteract the anxiety and depression such an overwhelming problem elicits
(Hayes et al. 2018, APA 2009). The I2M project poses design challenges in climate change
mitigation for middle- and high-school students. Our initial research suggests that an open but
well-structured challenge to develop mitigation strategies can galvanize creativity and
engagement among young people, and support 3-D learning (Puttick et al. 2017).
This paper presents evidence that crowdsourcing and community dialogue led to changes in
students' design rationales and STEM knowledge during an Innovate to Mitigate (I2M)
competition to address reduction of CO2 emissions.
In the paper we address four research questions:
(i) What evidence is there that crowdsourcing led to changes in students' designs and
rationales, generated during an I2M challenge? What kinds of changes are seen?
(ii) How are these changes related to students' use of ideas generated in dialogue?
(iii) To what extent does the composition of the community of practice contribute to the
dialogue or to students' uptake of discussion inputs?
(iv) Finally, to what extent do the data allow investigation of microgenesis of students'
design and rationales?
Theoretical framework
Crowdsourcing for STEM learning
Crowdsourcing and social media are increasingly common media for participation in
society (Hossain & Kourainen 2015, Arelas & Ladrón-de-Guevara 2011). The crowdsourcing
communities in I2M (students, teachers, scientists) are designed as learning environments in
which students can iteratively improve design ideas and STEM knowledge. Such heterogenous
communities of practice offer a diversity of inputs, including insights about practice from more
experienced participants (Vygotsky 1978, Wenger 1998). Dialogic knowledge-construction is
facilitated by the division of labor within the team (Puttick et al. 2017, Drayton & Puttick 2018).
Dialogue within the community of practice comprising all teams and mentors, scientists,
and teachers, is central in our model. Students' use of ideas generated in dialogue can provide
insight into students' reasoning and understanding of concepts and practices (Ketonen et al. 2020,
Samarasekara et al. 2020, Anker-Hansen & Andrée 2019, Cheng & Tsai 2012). Other impacts
may include growth in quality and depth of student argumentation, and in metacognitive effects
such as students' capacity to evaluate their own reasoning and evidence (Anker-Hansen &
Andrée 2019).
Argumentation, rationales, and rhetoric
Because student-driven projects are likely to address a broad diversity of subject matters, in
this study we focus on the kind and structure of rationales that the students constructed in the
course of the project and examine whether and in what ways dialogue with peers or adult experts
might affect their argumentation. The NGSS, in advocating that students learn the science
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practices of argumentation and communication, several times suggest that students should learn
to communicate and argue about information and findings "clearly and persuasively" (NRC 2012
pg. 53). Instantiations of such standards overwhelmingly construe "argument from evidence" in
terms of question-claim-warrant-backing, or some variant of that, building both on the ideas of
such philosophers as Stephen Toulmin, and on the vast literature on scientific reasoning in
educational psychology and the learning sciences (McCarron 2021, González-Howard et al.
2018, Duschl & Osborne 2002). Other ways of thinking about scientific argumentation and
communication are generally not discussed in the literature — two important ones being rhetoric
and narrative. Both have prominent places in scientific discourse but little formal place in STEM
education (Wickman 2012, Nielsen 2012, Wilson 2002).
Yet we conjecture that rhetoric and argumentation, as methods of socially situated sense-
making, can serve pedagogically as pathways into effective, evidence-based reasoning about
research design, data collection, and data interpretation, as students advocate for a solution to a
problem, and then are challenged in dialogue to justify their claims, their methods, their
theoretical framing, and their data. This is thus a socially situated construction process, in which
the development and defense of a proposed design and its rationale are critiqued and developed
in the dialogue, drawing on the variety of knowledge and expertise present in the community of
practice.
A common "anatomy" of rhetoric, based in Aristotle's classic analysis (See
http://humanities.byu.edu/rhetoric/silva.htm) distinguishes three general strategies: logos
(arguments from reason), pathos (arguments appealing to emotion), and ethos (arguments
establishing the authority or credibility of the speaker). Such a simple categorization of
rhetorical strategies can provide a framework within which to identify instances of rhetorical
argumentation.
Microgenesis
In Vygotsky's model of how people learn higher-order thinking skills (Wertsch 1985), the
learning community plays an indispensable role: new skills and concepts emerge from social
activity around problems that require such capacities before they are internalized by individuals.
Thus, the acquisition of such higher-order thinking follows the familiar pattern in which
contextualized understanding, a group property, is internalized by individuals participating in the
learning group (scaffolded by more expert community members). When the new capacity is thus
internalized, the individual can apply it in new contexts (decontexualization) — it thus becomes
an individual property. When the individual, in a new social setting, applies the cognitive tool to
a new problem, it is thus recontextualized, and the constraints offered by the new use add
meaning to the cognitive tool, enriching the capacity of the group.
At each step along this process, individuals make progress by formulating provisional
accounts, provisional models, of the concept or skill in question. These provisional models are
typically incomplete and temporary, as continued thinking and activity within the learning
community lead to their revision or replacement. As Kuhn (1995) commented, in a review of
literature to date, "multiple strategies are present in an individual's repertory...Initial appearance
of a new strategy...does not mark its consistent application. Instead, less adequate strategies
continue to compete with it, and, indeed, the more formidable challenge appears to be
abandonment of the old, rather than acquisition of the new... (Kuhn 1995, p. 133)."
Vygotsky called the history of these intermediate understandings "microgenesis,"
and saw that if one could record and analyze such learning events, they could provide valuable
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insights into learning processes and learning challenges (Chinn & Sherin 2014, John-Steiner &
Mahn 1993, Vygotsky 1978). But it is evident that this research approach encounters important
challenges in methodology. How to capture fleeting thoughts, half-formulated conceptions? The
"think aloud" protocols, originating in the last century (Güss 2018) and used in a variety of fields
since the 1980s, as for example in artificial intelligence or cognitive research (van Someren et al.
1994, Weir 1987), represent a kind of clinical interview designed to capture the flow of thought
in the performace of a task, but they have primarily focused on a specific individual's thinking-
while-doing.
Text-based exchanges such as occur in an on-line discussion forum have the potential to
capture microgenetic data as they are generated within a community of practice, while preserving
the individual voices of participants. They emerge from shared design work and discussion and
debate about proposals and claims; they can occur at a suitably rapid rate to provide insight on
the necessarily short-term and fine-grained developments by which intermediate formulations
emerge, are evaluated, and either supplanted or refined (Siegler and Crowley 1991).
We have not found any evidence that such discussion forums have been used for an
investigation of microgenesis to date. We conjecture that I2M, situating discourse in an active if
short-lived community of practice where interaction is strongly incentivized, can provide
evidence of the value of such environments for microgenetic studies. In this paper, we examine
conditions under which the environment can enable an account of microgenesis of that learning.
Methods
Structure of the competition
Student teams submit the abstract for a proposed design based on a mitigation strategy
such as energy conservation, alternative energy generation, agricultural methods, or
social/behavioral change. Since abstracts scaffold the whole investigation, students are given a
rubric of elements to address (Table 1).
Innovate to Mitigate Abstract Rubric
Student teams submit a brief statement (250 words) describing their idea(s) for how to
reduce greenhouse gases. The statements are open for crowdsourced discussion on the
Edmodo community learning site. Students use feedback from peers to revise and resubmit
abstracts. Ideas presented in the abstract should be innovative (i.e., adapting an idea from
someone else rather than just copying it).
An innovation can:
· Be an entirely new idea
· Build on an existing idea in new ways
· Use common materials in new ways.
The abstract should:
· Go beyond a generic statement such as “everyone needs to save energy” to present a
specific idea that will become an investigation
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· Clearly describe what is innovative
· Include scientifically accurate information
· Describe an investigation that is feasible to execute within the timeframe of the
challenge.
Table 1. Abstract guidelines
Teams post their abstract to the project's EdModo page. All students can post questions
and comments on each abstract. TERC scientists also post comments & questions. Teams have 2
weeks to use the comments received, and then post revised abstracts. The teams conduct their
project over the next 3 months and produce a final paper and a short video presentation. When
the final paper is submitted, it is posted to the EdModo page, where all the students participating
in the competition can comment, as can the judges. Each team can reply to these comments or
questions. In the judging for the competition, points are awarded for participation in the
discussions in addition to points awarded for quality of submissions.
Participants
For this paper, we analyze the data generated by three I2M teams during the 2020-2021
competition: “LaGrazia” (four 8th graders), “Pedalpushers” (two high school students), and
“Pranav No-Till” (one high school student working alone).
Data sources
The data in this case study include:
• The teams' initial abstracts
• All discussion posts from students and scientists for initial abstracts
• The revised abstracts
• Final papers
• All discussion posts from students and scientists for final papers.
Analysis
Units of analysis. In the abstracts and discussions, researchers coded each sentence. For
the final research paper that each project had to submit along with their video, researchers coded
each paragraph.
Coding. In developing codes (Table 2), we took a grounded approach (except see
"Argumentation codes" below).
Code
Subcodes
Definition
Descriptive
School
Basic identifiers
Grade
Team demographics
Teacher/mentor
*Goal
Refers to intent or purpose of the proposed idea
*Design
Details about mechanism, process, or implementation
*Innovation
Claims about how innovative the proposal is, and what the
innovation consists of.
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*Advantages
Benefit forecast (e.g., ecological, social, economic)
*Scale
Refers to potential for scaling up the proposed idea
†Rationale
Authority
Explicitly cites an authority, whether teacher, text, article,
Web, etc.
Causal
Argument relies upon some explanatory theory or concept
Empirical
Argument is based on empirical data, cited from authorities
or students’ own data
Factual
Asserts the claim as fact without citation, reasoning, or data
Rhetorical
Aims to persuade, frame; engage reader; may include
figurative or descriptive language, etc.
Other
Affective
Affective comments on proposal quality, e.g. "Great job!"
Science content
Questions, or provides additional information about, science
content
Agreement/disagreement
Agrees or disagrees with claims being made
Table 2. Coding scheme. * indicates inductive codes. † indicates argumentation codes, adapted
from Sandoval & Millwood (2005).
Inductive codes identified statements referring to or articulating the goal of the project,
the ways it is innovative, technical/science content, design elements or strategies, scaling or
broader use, and affective statements (Table 2).
Argumentation codes. Other codes were more specifically intended to capture students'
rationale for their choices, and the kinds of statements that they used to make their case. We
adapted a coding scheme from Sandoval & Millwood (2005) to characterize categories of
argument in teams’ rationales. In addition to these, we developed a “rhetorical” code to label
statements that were judged to reach the reader's feelings or values, or to shape or direct attention
to some specific aspect of the argument. (In relation to the classic logos-ethos-pathos framework
alluded to above, our "rhetorical" code captures statements in the range of pathos-arguments
appealing to emotion.) As we discuss in the Conclusions section, coding of individual
statements (or even paragraphs) could not capture rhetorical structures or techniques at a larger
scale than the unit of analysis. These structures are simply noted where they occur.
Data were consensus coded by three researchers. Coded data were discussed by
researchers. After coding was stable, a researcher wrote an interpretive research narrative about
each team. Narratives were discussed by the research team to test inferences, identify issues
requiring further analysis, and maximize the value of the data (Merriam 1988). Finally, cases
were compared to identify differences and similarities of interest in relation to the research
questions, and to identify hypotheses and initial theories which can inform future research (Stake
1995).
Results and Discussion
Because the three teams varied in age, size, and constitution, we present the three cases
sequentially in what follows. After these three treatments, we compare the results across the
cases, identifying similarities and differences as they relate to the research questions.
1. The Pedalpushers
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This team of two high-school students proposed a design to generate power from
individual pedal-driven generators located in classrooms. This system would supply the
electricity needed by the classroom, reducing energy use one classroom at a time, and therefore
reduce CO2 emissions.
First abstract. Pedalpushers' initial abstract described a goal ("generate clean electricity in a
classroom setting"), and a proposed mechanism ("use a system of pedals to generate enough
electricity to keep plant grow-lights running and other light sources in the classroom"). They
claimed that their idea was innovative on two counts: the novel way of generating electricity, and
targeting plant grow-lights. Only one statement was coded as rationale and it was factual. One
statement was coded as rhetorical.
Dialogue with other students. Students from other teams made factual claims for potential
benefits, but none adduced data to support their claims. One student named a potential benefit
(for ADHD students). Another student asked about a technical detail (design). In response,
the Pedalpushers articulated more constraints that they would incorporate into their design. They
did not make appeals to an authority or make any empirical or causal claims other than that their
design would reduce that locale's fossil-fuel energy demands.
Scientist input. The scientists' questions addressed methods for gathering evidence in reference
to a causal model: about mitigation impacts, feasibility of the design, measuring energy output
and CO2 savings (empirical), and scalability of the design. In effect, all the questions assumed
that there was a causal rationale underlying the team's proposal and gave the team an opportunity
to supply or surface more of that rationale.
Second Abstract. In response to the community comments, the Pedalpushers added to their
rationale, connecting their idea to climate change and CO2 mitigation (causal, 2 coded
occurrences). Though they included two factual claims as part of their argument (2 coded
occurrences) they also specified empirical evidence (4 coded occurrences) they would need to
collect to test their claims and establish impact, promising to collect evidence on amount of
energy collected, and how it is used. Furthermore, they also introduced a new empirical
element: to make a quantitative comparison with their city's power use and its CO2 emissions as
a baseline to help estimate their innovation's impact on emissions. They also incorporated peer
questions about design and introduced additional design elements. They achieved a clearer and
more complete abstract, better positioning themselves to work out an implementation and
testing plan.
Final paper and discussion. The Pedalpushers' final paper reflects the impact of the questions
they were asked, and the resultant further R&D work they needed to undertake. They included
more information deriveed from studies cited from the literature (authority 3 coded occurrences)
about electricity generation by various means and estimates of potential power generation using
their proposed design. The calculations enabled them to make some quantitative comparisons, in
terms of rates of power generation, between their pedal-driven system and other green electricity
sources (e.g. wind or solar power). These comparisons and the claims derived from them
included causal claims, reflecting a more fully elaborated theoretical rationale (3 coded
occurrences). A notable additional feature in this document is the increased use of rhetorical
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methods, using repetition or iteration to frame the elements of their design and rationale to the
overall goal of their proposed invention. They repeated the key values and advantages of the
design, emphasizing potential scaling effects. They reiterated the relation of their design to the
need to mitigate CO2 emissions and invited the reader to imagine the system in operation, thus
presenting a way of prototyping a future with their design present. They bookended their whole
argument by pairing what we may call the proximal goal of their proposal in the context of a
distal or overarching goal, mitigating global climate change.
A detailed analysis of the sequence and structure of the discourse shows a temporal
development of this rationale, which was thus a collaborative achievement.
2. La Grazia
This was an 8th-grade team comprising 4 girls and 2 boys. Some of the team had
participated in the previous year's competition. Their team project in both years was a prototype
solar oven.
First abstract. Their abstract argued that reducing fossil-fuel use for cooking would reduce
CO2 emissions, with this causal argument: "Solar cooker can help with this problem because it
doesn’t use fossil fuels but uses the suns heat for energy." They briefly described the greenhouse
effect (science). They suggested that solar cookers would be cheaper than conventional ovens,
be compact, portable, and scalable; their one empirical statement mentioned the price of
conventional ovens, in contrast to their prototypes which were made with recyclable materials.
No authorities were cited, and no other data were adduced. Other rationale statements were
consequently coded as factual (3 occurrences); there was one rhetorical statement noted, which
invited the reader to imagine themselves weighing whether to try their innovation, as opposed to
staying with conventional systems: "Instead of using an oven, which costs about 300-1,000
dollars (extremely expensive) you can choose to create a cheaper one that can heat up food
through radiation."
Dialogue with other students. Student comments focused on design features — Did the team
see their invention as a convenience for, e.g., camping trips, or as a replacement for conventional
ovens? What about when sunshine was not available? Two mentioned batteries as supplemental
power, not mentioned by the La Grazia team (and not compatible with their design). La Grazia
did not respond to any of the student feedback.
Scientist input. One scientist commented, asking how the proposed design differed from their
design from the previous year, and how their innovation might increase the impact on CO2
emissions (causal). The scientist comments therefore addressed innovation, design, and the
rationale for the project. In contrast to the team's non-response to student feedback, La Grazia
did ask for clarification from the scientist about one comment about the innovative nature of that
addressed innovation.
Second Abstract. In revising their abstract, LaGrazia did not address students' design questions.
In response to the scientist's question about their innovation, they noted that they had to conduct
their project from home (owing to pandemic restrictions) but did not note any changes in design.
However, this change in method was not relevant to the question. They did add a new rhetorical
element to their rationale, which was that presenting their project to the community could raise
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awareness about climate change. Otherwise, all causal statements were factual (10 coded
occurrences). The increased number of factual arguments (3 coded instances in the first abstract,
10 in the revised version), and a marked increase in the number of statements asserting
advantages of their proposed system (5 in the first abstract, 9 in the revision) constituted a
heigthened attention to the need to persuade. In this case, one may say that the team employed
the rhetorical technique known as "accumulation."
Final paper and discussion. In their final paper, LaGrazia enriched their rationale by
increasing the use of statements coded as causal (from 2 to 4) or authority (from 0 to 10), that is,
adding backing to their claims from (a very few) published sources. They and also represented
their rationale as drawing on a causal theory of the phenomena. Moreover, they reported data
from a series of empirical trials of two different instantiations of their solar cooker design (6
coded occurrences). In this case, the required separation of the students from each other had the
benefit of making replication and comparison possible. Because there were several uncontrolled
variables in their comparisons, their data were inconclusive. Nevertheless, all data were
reported, and discrepancies were identifiable from their account. Thus, the proportion of
rationale elements coded as empirical, causal, and authority increased dramatically. Moreover,
the number of data items coded as rhetorical also increased slightly (3 instances).
The team's attention to empirical and causal rationale elements continued through the
discussion on-line of final products The changes were noted by the judges. For example, one
judge commented "I liked that you cited several references and provided the bibliography, and
that you stated hypotheses for your proposed innovation of the solar cooker design." Judges and
other participants continued to note open questions or unsolved problems, and the team
responded directly and appropriately. They introduced another thematic strand at one point
when they discussed the possible uses at scale of solar cookers (not necessarily involving their
own particular design).
3. Pranav No-Till
This “teamcomprised a solo high school student in his senior year. He proposed a design
for working with farmers to reduce emissions through a dual strategy of education about no-till
agriculture coupled with a shift to renewable energy, and payment with carbon credits for the
resultant carbon sequestrations and reductions in emissions.
First abstract. The initial abstract described a goal (“to motivate farmers to practice
sustainable farming practices” by “incentivizing no-till agriculture and on-farm renewable
energy sources with carbon credits"). The student proposed a mechanism ("farmers that practice
no-till agriculture and shift their energy sources from fossil fuels to renewables can earn money
for storing carbon and reducing emissions”) and provided a rationale that was causal (e.g.,
“Paying farmers to deliberately shift to renewable energy sources, along with no-till agriculture
(a concept that has been used in the past), can lower greenhouse gas emissions”). By stating that
no-till agriculture has been used in the past, it is implied that his idea is a novel adaptation of an
existing mitigation strategy. The rationale in this abstract was logically tight, with 3 causal
statements, 4 factual statements, and 1 empirical statement, and 4 advantages postulated.
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Dialogue with other students. One student from other teams posted a response to No-Till’s
first abstract (but see Second Abstract, below). The proposal focused on Florida only, and the
student queried why this was the case. No-Till did not respond to this query.
Scientist input. One scientist asked, "What is the innovation in your plan?” thus requesting a
response to an explicit requirement described in the rubric (Table 1). Another question focused
on a detail of design while the third asked how no-till methods reduce emissions. In effect, this
question asked the student to make the mechanism in his idea explicit and thus provide a causal
rationale for his proposal.
Second Abstract. In the revised abstract, the main improvement the student made was to
describe how he thought his idea was innovative: Although carbon credits and no-till agriculture
are not new ideas for carbon mitigation, combining them is. He also incorporated a more specific
statement than in the first abstract about the particular crops that could be targeted. Finally, in
response to the scientist’s question, he stated that no-till farming “works because it leads to
carbon being stored underground, not emitted,offering a partial causal element for this
rationale. He also added a few more empirical statements (1 in the first abstract, 4 in the second).
In all other respects his abstract was unchanged. The rest of his rationale included the same
number of factual claims (4).
At this point, another peer commented on the second abstract, asking two questions crucial
to the potential impact of his proposal: “How will you release your information to farmers, and
will it cost anything to them?” Pranav responded with a strategy for dissemination,
acknowledged the initial cost to farmers, and included a factual claim that his strategy would
benefit farmers later. As with the other two cases in this study, analysis of the sequence and
structure of the discourse shows a small collaborative improvement in the specificity of the ideas
in his proposal at this stage of the competition.
Final paper and discussion. Pranav No-till's final paper reflected the discourse about his
abstract. In elaborating on points raised in the discussion, as well as reporting on the completed
project, the author deployed a coherent rhetorical strategy in the composition. The elements in
this strategy include:
a. Rationale elements. The paper shows a consistent deployment of authority (coded in
every paragraph), along with causal or empirical elements. Every paragraph includes
rationale elements of various kinds: 5 instances of factual assertions, 4 empirical
statements, 5 causal statements, and 18 citations of authorities.
b. Goal or design elements. The overall aim of the design is reiterated frequently in
conjunction with elements of rationale (the code was used in 6 of the 9 paragraphs)
c. Innovation, advantage, scaling. The potential contribution in CO2 mitigation of a
widespread adoption of no-till agriculture are reiterated in every paragraph, either
explicitly connected with rationale or goal/design elements, or juxtaposed.
d. Rhetorical. Rhetorical elements (4 instances in 9 paragraphs) introduced affective notes
(e.g., "no-till agriculture is "environmentally friendly, and economical for farmers, too."),
or motivational/moral notes (e.g., "We still have a long way to go") that introduced the
final paragraph of the "conclusions" section. Moreover, the argument iterates the
“goal/design/rationale/benefits/rhetorical" elements. This repetitive, one might say
rhythmical, use was itself an effective rhetorical performance in posing a problem and
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advocating for a potential, innovative solution. This had an affective impact, increasing
the sense of cogency bolstered by empirical evidence.
Conclusions
(i) What evidence is there that crowdsourcing led to changes in students' designs and
rationales, generated during an I2M challenge? What kinds of changes are seen?
The three cases described above show several shifts in students' designs and rationales. In
each case, there were increases in rationale statements. Empirical, causal, and authority
statements are more salient in the final papers, and in the case of the Pedalpushers and Pranav
No-Till, they became more important in the second abstract, even before the paper was written.
As we laid out in the theoretical framework, students' use of ideas generated in dialogue
has been shown to support students' reasoning, understanding of concepts and practices (Ketonen
et al. 2020, Samarasekara et al. 2020, Cheng & Tsai 2012), growth in quality and depth of
student argumentation, and students' capacity to evaluate their own reasoning (Anker-Hansen &
Andrée 2019). However, La Grazia showed little change in regard to empirical, causal, and
authority statements in their second abstract, but in fact they dramatically increased their use of
factual rationales at that point. They increased their use of authority rationales in their final
paper, but their sources were relatively few. The Pedalpushers increased the use of empirical
rationale statements in both their second abstract and their final paper, but many of these were
statements of what kinds of data would be collected in a full development project.
All teams also showed increases in their uses of advantages and a small increase in
rhetorical elements. While these do not fit into the claim-evidence version of scientific
argumentation favored in the NGSS, such statements are in fact very much a part of scientific
writing and argumentation. Such argumentation often includes attention to methods of
persuasion (Wander & Jaehne 2002) and "informal argumentation" (Cecarelli 2001) to build a
case, since they frame the researcher's results in a way to increase the likelihood of impact (while
still maintaining integrity).
In this small data set, therefore, we see evidence that tends to support our conjecture that
the teams' need to make their presentations as persuasive as possible (in the context of the I2M
competition) motivated the refinement of "everyday arguments" (McCarron 2021) with attention
to appeals to logic (especially empirical, causal, and factual statements), to authority (authority),
to imagination (advantages), and to esthetic/moral/affective ideas and images. Thus the
"rhetorical triad" of logos, ethos, and pathos are deployed in the construction of the teams'
arguments. It is important to note, however, that in all cases the students did not only rely upon
more impassioned or imaginative appeals to their audience but saw the need to adduce
improvements in empirical or theoretical arguments, as is foundational to scientific
argumentation.
(ii) How are these changes related to students' use of ideas generated in dialogue? (iii) To
what extent does the composition of the community of practice contribute to the dialogue
contents or to students' uptake of these inputs?
As the analysis in the previous paragraphs shows, one can be reasonably confident that the
community's comments stimulated the teams' attention to details of their design, and especially
to their rationales. Students asked questions primarily about details of design, implementation,
and impact, and about proposed or suggested advantages. In response, the teams produced
answers acknowledging the question or suggestion, and evaluating, accepting, or refuting the
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points made. The revised abstracts reflected these exchanges, and in most cases material from
the dialogues appears in the final papers as well.
The scientists asked questions that were complementary to student questions. Scientists
sought clarification in causal rationale, both about the elements of the design, and its connection
to the mitigation of carbon emissions. Scientists also probed for empirical evidence about the
designs and arguments for innovativeness. Again, the teams acknowledged the points made, and
adjusted their arguments and reports, and this is reflected in the increase of empirical, authority,
causal, and factual statements. In several cases, responding to these questions required additional
research or re-design.
Taken together, the community input had a significant impact on the way the teams thought
about most aspects of their designs and rationales in Vygotskian fashion (Wertsch 1985), and the
diversity of the community composition made an important contribution. The scientists'
emphasis on causal and empirical rationale elements, in contrast to students' main focus on
design, is evident. But other differences came into play— most striking, when a student raised
the possibility that the Pedalpushers' design might benefit kids with ADHD. Diversity in a
community benefits science and engineering because it brings richness of experience and
attention as well as richness in expertise or other kinds of knowledge.
It may be of interest to note here that in post-competition conversations, student
participants noted that the responsibility to ask questions and critique other teams' proposals
stimulated them to learn about the other proposals (content, design, and methodologies), but
reflexively affected their critique of their own proposal. This reflexive dynamic has been noted
in studies of communities of practice of other kinds (Newman et al. 1989, Wenger 1998,
Palincsar and Brown 1984).
(iv) Finally, to what extent do the data allow investigation of microgenesis of students'
design and rationales?
This question is in a way a paraphrase of a methological one: Can an environment such as
I2M be used as a tool for microgenetic studies of students' learning of such higher-order skills as
argumentation?
The narratives presented show successive stages in the development of the designs and
rationales, but at the same time, they represent evidence of growth in students' understanding of
the processes of design and argumentation. Stimulation by the community of practice to
elaborate different kinds of rationale, for example, appeared to result in arguments that were
more coherent, and logically and conceptually consistent. Moreover, the student teams
strengthened their rhetoric by incorporating affective and aspirational elements, and by arranging
their arguments to keep the goals and potential advantages of their approach before their
audience (including peers as well as judges for the competition).
Given the small corpus of data, we can claim no more than suggestive evidence about the
usefulness of such a medium for microgenetic studies of student understanding of argumentation
and design. However, the environment has some important advantages in this regard. First, the
overall goal is clear since it is set by the competition structure. Within the general aim of
mitigation, the students choose for themselves a particular way of responding to the challenge,
and from the beginning are required to articulate and negotiate their ideas in a social setting.
Second, the conceptual field is rich — there's a lot to talk about, a lot of information to make use
of, a lot of scope for experimentation and for the adducing of new questions or new mediational
means to move the investigations and discussions forward. Third, the medium makes visible
AERA, 2022
13
much of the social environment — the discourse community— within which the students'
thinking takes place and develops, and because it is in written form it is available both to the
participants and to researchers for a period of weeks or months, thus facilitating reflection and
research. These advantages can be seen in the cases we have presented.
The I2M environment, however, does have some drawbacks as a medium for microgenetic
studies. Most important, perhaps, is the density of communication, essential for the tracing of
ephemeral intermediate formulations of concepts (Siegler and Crowley 1991). The schedule of
the competition requires students to share and discuss their work only a few times over the
course of the 8 months of the competition. Between these defined events, there is naturally a
large volume of communications, especially within teams, which is not recorded in the I2M
environment. Students use face to face meetings, telephone calls, and text messages day to day,
and even when some of these streams are captured for analysis, they tend to be fragmentary. The
project also requires several kinds of interim reports, and also requests permission to use student
notebooks or other documentation, as well as conversations and occasional classroom
observation. Such material can be rich enough to allow investigation into student learning from
various points of view (e.g., Drayton and Puttick 2018), but do not provide good data for
microgenetic studies.
We suggest, however, that it will be possible in future to co-design a study with one or two
teams to address the issues of density and inaccessible data, both by carefully specifying the
learning to be tracked, and by establishing clear protocols (and encouragement) to ensure
reporting that is frequent enough, and simple enough from the user's point of view that
compliance will be as little burdensome as possible.
Such an effort would be valuable if successful, since it would enable researchers to track
the interplay of student thinking (externalized in documents and designs), peer interaction, and
development of understanding in the context of a student-chosen task requiring factual,
conceptual, and instrumental learning, and its representation in narrative and in argument. A
further benefit would then be the use of such media for microgenetic studies of other learning
communities, for learners and practitioners of all ages, in the many subject domains now using
Internet tools for their collaborations.
Acknowledgments. We acknowledge the contributions of Renee Pawlowski. This material is
based upon work supported by the National Science Foundation under Grant Numbers 1316225
and 1908117. Any opinions, findings, conclusions or recommendations expressed in this material
are those of the author(s) and do not necessarily reflect the views of the National Science
Foundation.
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