What this means for research on integrating computational thinking and modeling with science curricula in elementary grades. My goal is to study the complex interaction and negotiation within and across these dimensions involved in the development of computer literacy in elementary science classrooms.
The work reported in this chapter examines the close-interplay between the material and cognitive dimensions of literacy by investigating the forms of reasoning fourth
Additionally, this chapter extends the work done in Chapter 2 and investigates how computational modeling is enhanced through its integration with other material forms, particularly scientific modeling, including embodied modeling and the development of mathematical representations of change over time. time. This work provided a possible avenue for integrating computational modeling to support the development of science and computer literacy in the elementary classroom.
Chapter 4 further develops the work conducted in Chapter 2 and 3 and focuses on how computational modeling and programming are integrated into existing
For example, in Activity 1, the movement of birds and butterflies on the screen registered in students' minds as "butterflies change color". The aim of the first activity was to elicit the students' initial interpretations (registrations) of what is happening in the model. Manishe: “The population will also decrease because some birds have eaten most of the butterflies.”
Embodied Modeling Activity
Another example of functional equivalence can be understood in terms of the equivalence of both short and long flowers as possible food sources for the long-throated butterfly. In this context, reasoning about functional equivalence involves reasoning about the variation of the structure, behavior, and function of the relevant attributes among the agents, and their implications for system-level outcomes such as population dynamics. For reference, Table 3 provides a summary of the sequence of activities and the relevant learning objectives.
Students' actions during this activity represented "agent-level" rules of the ABMs that were later introduced in Phase 3, described below. As Danish (2014) pointed out, correcting this situation involved redesigning the activity by changing the rules of the game so that students worked in pairs to hide nectar and then create a dance around the location communicating some of the nectar to their mates, who would then search for the indicated location. Based on this assignment, we designed actions, performed by student-as-agents, interwoven with reflection that supported the intended learning objectives to familiarize students with the various "agent-level" elements of the ecosystem such as flower location, nectar sac depth , and proboscis length (Table 2).
Some rules and actions (e.g. losing energy through travel and gaining energy through food intake) are designed to stimulate the students. This clarification helped frame the notes of their energy gains and losses as an integral part of the embodied modeling experience.
Generating Foraging Maps and Bar Graphs of Energy During Foraging
Inquiry with ABMs
On their worksheets, students were asked to provide three types of explanations: a) Explanations of the population-level results of. A summary of the instructional schedule, the time spent on each activity and the relevant learning objectives is provided in Appendix A. Typicality means that the selected case(s) should potentially provide insights that are likely to have broader relevance for the remainder of the learning process. participants in the study.
The class-level analysis provides evidence on the typical nature of responses evident in the respective individual cases. Our first research question analyzes the development of mechanistic explanations in phases I and II of the learning sequence. After completing each iteration of the embodied modeling and graphing activity, students were asked to provide written explanations of how their foraging actions were represented in the graphs.
Our final research question examines the forms of reasoning about some of the key features of complexity in the system, specifically in the form of looseness. In order to analyze student thinking around population survival, student interview data were coded for explanations of the role of camouflage in population change.
Development of Mechanistic Explanations Across Phases I and II
This indicates that she was aware of the target phenomenon (Category 1) that she was modeling. However, she did not consider the length of her proboscis as a factor in deciding which flowers to visit; instead, her foraging decision was guided by the goal of “just trying to reach all the flowers,” indicating that the purpose of the activity was the most important factor in her decisions about where and how to forage. The use of the word 'could' implies that she had now taken into account the similarity between her proboscis length and the flower's nectar depth as a factor in deciding which flowers to visit.
Her statement that her energy kept getting "greater and greater" shows that she continued to go to flowers from which she was able to drink, which is also evident in line 6 of the transcript. Similar to her responses in Iteration 1, she continued to demonstrate an understanding of the entities (Category 3), activities (Category 4), and setting conditions (Category 2) of the target phenomena (Category 1). We found that, similar to Dontavia, all students demonstrated an understanding of the target phenomena and setting conditions of the activity (categories 1 and 2 in Table 1 ) during both iterations.
This would suggest that these explanations were not "given" from the design of the activity, but emerged through the course of engagement in the activity. A summary of the type and prevalence of specific mechanistic explanations across all students is provided in Table 3.
How students’ embodied experiences in Phases 1 and 2 shaped their interactions with the ABMs in Phase 3
For example, when her first attempt failed, she again used her knowledge of the causal structure of the phenomena to make a new claim that it would occur in the model given the new parameters she had set (line 6) . Dontavia then went on to derive a causal mechanism for her explanation of the phenomenon. line 13): camouflage (or blending) leads to survival. This is evident in the use of the pronoun "I" as he describes the movement of the butterfly (lines 3, 4 and 5).
She explained that the graphs represent how her energy - ie. property of the agent (category 5) – changes over time. She also identified an agent activity (category 4) – death – to explain why the graph showed a value of zero. This episode therefore shows that her mechanistic understanding of the mathematical notation in the simulation and the change in butterfly energy over time represented by that notation was directly based on her previous experiences of embodied modeling and graphing.
We found that 13 students (86%) used backward or forward chaining to explain the effect of new variables introduced in Model 1. Known system variables were accounted for by changing parameters to settings that favored butterfly survival . .
Reasoning about Complexity
Thus, a majority of students (60%) were able to identify camouflage, proboscis length and flower lengths as key factors for butterfly survival. We begin our analysis by examining the early thoughts of two students, Brian and Monikia, about the role of camouflage in relation to system health. When the researcher is asked to explain why this was important to the butterflies' survival, Brian and Monikia give explanations from the butterflies' perspective.
Following the exchange shown in extract 8, Brian and Monikia recorded on their activity sheets that 'blending' and being 'the same colour' as flowers were important mechanisms in butterfly survival. At this point, similar to their interaction with Brian and Monikia, the researcher prompts Julius and Shalaya to take the birds' perspective (lines 38 and 39). Another related measure is the ratio of the number of agents presented by students to the number of connections between agents.
Students' understanding of energy flow and the matter cycle in the context of the food chain, photosynthesis and respiration. Of the six links in the photo, two are the correct link between producer and consumer: from the sun to the berry bush and from the sun to the daisies. Initially, “good” measures were socially defined, with students choosing how to measure a “step size” for reasons unrelated to the purpose of the measure.
One student in particular suggested to the class that "11" was the best choice because it appeared "the most times" and was "in the middle" of the data set.
We believe that the illustrative cases we presented show that the development, establishment, and refinement of sociomathematical norms led to repeated improvements in the quality of students' models as progressively more authentic representations of the phenomena they modeled. Interestingly, students' constructs with ViMAP as a modeling tool progressed in a way that was initially interpreted by researchers to be at odds with the epistemic goals of the research. Additionally, we also believe that the teacher's emphasis on using physical and embodied modeling as a way to complement modeling and computational thinking played an important role in students' acquisition of the norms.
Furthermore, the teacher's focus on the measure command as a way to "see" individual steps of the ViMAP turtle helped students to discretely represent the motion of the turtle agent and correctly interpret the resulting graphs. The state of the simulation represents a single event at any time in the form of spatial representations of agent actions and interactions. In our study, the formulation of “accuracy” was by the teacher – i.e. students' models must be "mathematically accurate"—based largely on her intuitive conceptualization of the term.
Our decision stems from the fact that researchers should essentially position teachers as principals of the partnership – rather than equal to the researcher. So if our goal is to make computer programming and modeling ubiquitous in the K-12 science classroom, we assume that researchers and designers of programming languages for K-12 classrooms must learn to see the world through the eyes of teachers, especially. when it involves conceptual dissonance between researchers and teachers.
