Earners demonstrate their understanding of data collection with computational and non-computational tools and processes, including collecting, visualizing, transforming, and analyzing data about themselves and their world. The teacher will demonstrate how to effectively support student learning in the effects of collecting data with computational and automated tools. Earners will also show how they develop purposeful strategies to challenge unconscious bias and minimize stereotype threat in CS proactively.
To earn this microcredential, you will process through the ADDIE learning model producing evidence that demonstrates your knowledge of the Wyoming Computer Science Content and Performance Standards and the CSTA Standards for Teachers. Through the ADDIE learning model, you will analyze standards, design/develop and implement a lesson, collect student work artifacts, and evaluate your professional practices.
This microcredential is intended for teachers in grades K-6. If you teach middle school or high school grades, you will want to work on the secondary level computer science microcredentials. The Collection, Visualization, Transformation microcredential is one of three microcredentials that make up the Data & Analysis stack. The Data & Analysis stack is one of six microcredential stacks, which, when completed, will lead to a Computer Science Teacher Master Distinction.
A type of application software designed to run on a mobile device, such as a smartphone or tablet computer. Also known as a mobile application.Classroom Climate:
The prevailing mood, attitudes, standards, and tone that students and teachers feel when they are in the classroom.Computational Artifact:
Anything created by a human using a computational thinking process and a computing device. A computational artifact can be, b ut is not limited to, a program, image, audio, video, presentation, or web page file.Computational Thinking:
The thought processes involved formulating a problem and expressing its solutions so that a computer (human or machine) can effectively carry them out.Computer Science:
The study of computing principles, design, and applications (hardware & software); the creation, access, and use of information through algorithms and problem solving, and the impact of computing on society.Data:
Information that is collected and used for reference or analysis. Data can be digital or non-digital and can be in many forms, including numbers, text, show of hands, images, sounds, or video.Data Structure:
A particular way to store and organize data within a computer program to suit a specific purpose so that it can be accessed and worked with in appropriate ways.Data Type:
A classification of data that is distinguished by its attributes and the types of operations that can be performed on it. Some common data types are integer, string, Boolean (true or false), and floating-point.Encoding:
Encoding is the process of converting data from one form to another.Forms of assessment:
Forms of assessment include formative, summative, or student self-assessment.Inference:
A conclusion is reached on the basis of evidence and reasoning.Modalities of Assessment:
Modalities of assessment include written assessment, oral assessment, performance tasks, or visual representations.Model:
A representation of some part of a problem or a system. [MDESE, 2016] Note: This definition differs from that used in science.Reliability:
Consistently produces the same results, preferably meeting or exceeding its requirements.Stereotype Threat:
Being at risk of confirming, as a self-characteristic, a negative stereotype about one's social group.Unconscious bias:
Prejudice or unsupported judgments in favor of or against one thing, person, or group as compared to another, in a way that is usually considered unfair.Universal Design for Learning (UDL):
UDL is a framework for designing a curriculum to be broadly accessible to ALL students. Learn more about utilizing the UDL Framework in CS education.
This microcredential collection provides earners with the opportunity to document their knowledge and skills in teaching computer science to students in grades K-6. The resources offer support understanding.
Earners are encouraged to participate in additional learning opportunities if more extensive learning is needed. Other learning opportunities may include free online resources, postsecondary courses, and local courses.
The microcredential structure offers earners flexible pathways and timelines. Earners can complete the microcredentials in any order that aligns with their classroom timelines and availability. In addition, microcredentials offer earners the opportunity to submit evidence and receive evaluator feedback. Earners are encouraged to resubmit evidence until mastery is earned. Each resubmission will be reviewed, and updated feedback will be provided.
All instructions are included in the worksheet. Once you have completed the worksheet, upload it in the evidence section as a PDF.
This task requires an analysis of both computer science content standards and the CSTA Standards for Computer Science Teachers. Google Docs Template: https://bit.ly/3gmDjDJ
This lesson plan shows the planned instruction of your computer science focus standard.
Implement the set of activities or lesson plan you designed.
Submit evidence of student learning for your focus standard. Include evidence of students that have met the standard and students that have not met the standard. Examples include videos of students working, completed student worksheets, etc. Annotate each piece of evidence to demonstrate how you facilitated student achievement of the standard.
Evaluate how effective your activities were at promoting student learning of the standards. Use specific examples from the artifacts you submitted in the Implement activity.
Evidence submissions and reflections will be reviewed for alignment with the assignment guidelines and the proficiency scale. Proficiency scale: https://bit.ly/3ukNpgK
The checklist will help you review your submission materials to ensure you address everything that is expected for this micro-credential. Checklist: https://bit.ly/34czO0q
Please provide a self-assessment, a score from 1–4, on each component of the proficiency scale found here: https://bit.ly/3ukNpgK. Provide a few sentences stating where the pieces of evidence that support the scores for each component are located.
If you are resubmitting, please indicate what changes were made in the documents (e.g., highlight, text color) and include "Resubmission #" with the resubmission number in the file title when you upload.
Content knowledge – The teacher demonstrates accurate and complete knowledge of the content and skills of the standard being taught. CSTA 4a
Inform instruction through assessment – The teacher develops multiple forms and modalities of assessment to provide feedback and support. The teacher uses resulting data for instructional decision-making and differentiation. CSTA 4g
Supporting standards The teacher identifies and explains the connection of supporting computer science standards to the standard being taught in their lesson.
Vertical alignment – The teacher explains the relationship of the standard in the scope and sequence of computer science standards directly above and below chosen grade band. CSTA 4b
Minimize threats to inclusion – The teacher develops purposeful strategies to proactively challenge unconscious bias and minimize stereotype threat in computer science. CSTA 2b
Lessons aligned to each CS Standard for grades K-2.
Lessons aligned to each CS Standard for grades 3 -5.
Lesson for K-2 illustrating ways to collect and represent data in an unplugged way.
The lesson plan teaches students how to collect data and represent it in a circle graph to support a claim.
Students conduct a simple survey to collect, organize, and present data in this lesson. In doing so, they demonstrate their understanding of how to use patterns to represent data symbolically.
Background as well as example activities to do with adults or students to help them break their implicit bias.
Example activity that teaches primary students how to graph in google sheets.
This guide will help teachers fully understand inclusivity in the classroom and see examples of how to foster it.
Digital platform for students to use to create graphs to represent any data and make inferences based on their data.
Love Letters for Computers is a free resource including a series of videos, resources, classroom materials, and a teacher journal that will help you plan how to integrate computer science into your curriculum for children in kindergarten and first years of primary school.
Students measure each other’s heights to the nearest inch, keep a personal record of their own heights, and plot their own heights in the correct column on two graphs that differ in their format. They learn about and practice measuring, comparing measurements with others (are they greater or less than) to find the graph column, and analyzing the data.
In this context-setting lesson, students will run and collect data from a simple simulation in Sprite Lab. After running the simulation multiple times, students will have an opportunity to predict how changing a variable in the simulation might impact the outcome and test that hypothesis.
A resource to help teachers challenge their and their student's unconscious bias.
This course is designed to support you in completing the following Wyoming Elementary Computer Science (CS) micro-credentials in the Data and Analysis stack: (1) Storage; (2) Collection, Visualization, & Transformation; and (3) Inferences & Models The course is organized into three modules, one for each micro-credential in the stack. The course supports educators in better understanding the Computer Science Wyoming Content & Performance Standards, Computer Science Teachers Association (CSTA) Standards for CS Teachers, and how to complete the Analyze and Develop tasks associated with the micro-credential.
|Wyoming Department of Education
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