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Integrating Computational Thinking Into the Middle School Science Classroom

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Listen and learn : Research paper
Lecture presentation


Monday, June 24, 1:00–2:00 pm
Location: 121AB

Presentation 3 of 4
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meridith bruozas   Dr. Nanette Dietrich   Carolyn Staudt  
Funded by the National Science Foundation, this curriculum design project integrates computational thinking (CT) practices into an inquiry-driven middle school weather unit in order to better understand how the integration of CT impacts students’ understanding of weather concepts and influences students’ abilities to adopt a CT mindset.

Audience: Curriculum/district specialists, Teachers, Teacher education/higher ed faculty
Attendee devices: Devices useful
Attendee device specification: Smartphone: Windows, Android, iOS
Laptop: Chromebook, Mac, PC
Tablet: Android, iOS, Windows
Focus: Digital age teaching & learning
Topic: Computer science and computational thinking
Grade level: 6-8
Subject area: Computer science, Science
ISTE Standards: For Students:
Computational Thinker
  • Students break problems into component parts, extract key information, and develop descriptive models to understand complex systems or facilitate problem-solving.
  • Students formulate problem definitions suited for technology-assisted methods such as data analysis, abstract models and algorithmic thinking in exploring and finding solutions.
  • Students collect data or identify relevant data sets, use digital tools to analyze them, and represent data in various ways to facilitate problem-solving and decision-making.

Proposal summary

Framework

Currently there is no single agreed upon definition of computational thinking (CT). The field varies in their operational definition of CT.

- “Computational thinking is taking an approach to solving problems, designing systems and understanding human behaviour that draws on concepts fundamental to computing" (Wing 2006).
- International Society for Technology in Education (ISTE, 2016) views computational thinking as algorithmic thinking with automation tools and data representation with the use of simulation.

- Dave Moursund, educator, mathematician, & computer scientist, (2009) suggests that “the underlying idea in computational thinking is developing models and simulations of problems that one is trying to study and solve.”

One expected outcome of this project is a robust definition of computational thinking that guides our work. Getting to this singular definition, one that meets the needs of meteorologists, computer scientists, educators, curriculum designers and researchers is challenging. Below we outline the similarities, conflicts and the resolution which is used as the framework for our curricular design.

Similarities:
-Computational thinking is evident in computational models
-Underlying science knowledge is essential to model understanding

Conflicts:
Should students central goal be to:
-Create conceptual models
-Create the rules and algorithms for computational models
- Evaluate and assessing existing models

Resolution:
The project teaches weather through simulation where students learn both content and the attributes of models. This is a springboard into deeper study of computational weather models as students use a series of simulations to engage in: decomposition, pattern recognition, abstraction and algorithm design

The project goal is to teach computational thinking skills through a study of weather; to bring about integrated learning of science, mathematics, and computational thinking, and how the learning environment designs effectively scaffold this learning. (Our goal is not to create a better curriculum to teach weather.)

Methods

Our project is guided by the following research questions.
RQ1. What enacted experiences lead to science, mathematics and CT content understanding and practices?
-How do middle school students employ CT practices to generate scientific explanations about current and future weather events?
-How do middle school students’ explanations of current and future weather events evolve over time? How does the integration of CT practices into students’ learning experiences help students develop more sophisticated explanations?

RQ2. What learning environment designs foster and scaffold these enacted experiences effectively?
-What aspects of the instructional cycle (Collect → Verify → Model → Predict → Evaluate) are influential in bringing about students’ engagement with CT practices?
-How does the integration of the design elements (i.e., classroom embedded phenomena, experimentation with real-world phenomena, computer modeling and simulation) within a unit of instruction impact middle school students’ abilities to reason about current and future weather events?

We employ an iterative design research methodology, in which students experience all four phases of the instructional model (Collect, Verify, Model, Predict, Evaluate) within each of the four instructional sequences . This design allows for us to simultaneously investigate 1) the student development and use of computational thinking practices while exploring the real-world phenomena of weather forecasting and 2) the impact of our instructional model at both the instructional learning sequence and the unit level. The curriculum was implemented in the winter/spring of 2018 with three teachers in a total of fourteen classrooms in 2 states, the curriculum will be implemented again in the winter/spring of 2019 with six teachers in 25 classes in two states, we used the data gathered and insights gained from the 2018 implementation to inform the curricular design and to modify the curriculum.

During 18 day unit, we collected video footage of the enactment of targeted classroom activities. Video footage followed one small group of students per class in order to clearly capture student conversation, and video was taken at stages of the instructional sequences in which actions and verbalizations related to CT practices were likely to be most prominent. (A standing classroom-scale video feed during these was available for reference related to larger classroom context.)
REFLECTION PROMPTS. Throughout the unit, students responded individually to a series of written reflection prompts designed to draw out student thinking about the CT Practices within the context of weather content.
STUDENT SCREENCASTS Three times during the unit each group of 3-4 students prepared a brief (3-5 min.) screencast following a prescribed format. This screencast—consisted of student voiceover onto computer screen video as students narrate their manipulation of computational models, and present relevant photographs and data displays
PRE-POST TESTS. At the beginning and conclusion of the project, students completed an assessment of their weather content knowledge and their computational thinking skills.

Results

THEORY OF LEARNING

-Students experience growth in computational thinking after each design sequence, suggesting that the development of computational thinking skills requires repeat exposure and numerous opportunities for practice before students obtain a given level of competence.

-Identified attributes of curricular design (e.g., instructional sequence, use of specific tools, integration across curricular elements) specifically influence growth in computational thinking. Using an iterative design approach, it is possible to identify aspects of the curriculum that lead to improvements in students’ computational thinking, suggesting that future curriculum can be specifically designed to improve students’ computational thinking.

DESIGN HEURISTICS

-Successful curriculum design principles can be identified to engage students with Practice 1 collecting, interpreting and representing data and Practice 2 evaluating and predicting with computational models, suggesting that these design principles can translate to approaches looking to reinforce CT in other STEM content areas.

-These two identified CT Practices can be mutually reinforcing. Visualizations are an important mediator for helping students interpret data from the world and models and making connections between the two. Through a structured sequence of instruction, students come to view models as tools for making predictions for real world data.

Importance

Through this work, we aim to empower students to understand and apply weather-related science and mathematics by employing core computational methods and thinking involving data, models and simulations. By enacting the curriculum unit and conducting research in distinctive contexts, the project seeks to develop knowledge and build environments that can be realized across a broad range of learner communities, geographical regions, and technology infrastructures.

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Presenters

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meridith bruozas, Argonne National Labs
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Dr. Nanette Dietrich, Millersville University of Pennsylvania
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Carolyn Staudt, The Concord Consortium Inc

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