Data is collected with both computational and non computational tools and processes. This micro-credential represents the knowledge of how to teach collection, visualization, and transformation in a secondary classroom to support student learning about how data about themselves and their world is collected and used in the early grades. As students progress, they learn the effects of collecting data with computational and automated tools. Data is transformed throughout the process of collection, digital representation, and analysis. In early grades, students learn how transformations can be used to simplify data. As they progress, students learn about more complex operations to discover patterns and trends and communicate them to others Please locate "01. PROFICIENCY SCALE – Data & Analysis – Collection, Visualization, Transformation" in the resources to view specific Wyoming Computer Science Content and Performance Standards and the CSTA Standards for Teachers included in this micro-credential.
To earn this micro-credential 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.
The collection, visualization, transformation micro-credential is one of three micro-credentials that make up the data & analysis stack. The data & analysis stack is one of six micro-credential stacks which when completed will lead to a Computer Science Teacher Master Distinction.
All of the skills listed in the proficient level of the Wyoming Computer Science Content and Performance Standards (see the resources) for the chosen standard.K–14:
Refers to computer science standards ranging from kindergarten into postsecondary education.Scope and sequence:
Scope refers to the topics and areas of development within a curriculum, and sequence is the order in which those skills are taught.Grade band:
The computer science standards are written in grade bands (K–2, 3–5, 6–8, and 9–12). The standard committee (CSSRC) determined the standard to be met by the end of the grade band. In grades 9–12, there are level 1 and level 2 standards. Level 1 standards include introductory skills. Level 2 standards are intended for students who wish to advance their study of computer science.Chosen grade band:
The teacher or earner can choose which secondary grade band and standard to focus their lesson around.Supporting computer science standard:
There is a difference between supporting standards and performance standards. All students are expected to be instructed on supporting computer science standards, taught within the context of the performance standards. Supporting standards do not need to be assessed through the district assessment system. If no supporting standards are listed on the "Micro-credential Map by Grade Band" in the resources, this area becomes N/A.Performance standards:
The Wyoming Content and Performance Standards serve several purposes. They articulate a set of expectations for what students should know and be able to do, enabling them to be prepared for college and career success; to live a life that contributes to the global community. These expectations are communicated to students, parents, educators, and all other Wyoming stakeholders, and provide a common understanding among educators as to what students should learn at particular grades. Standards do not dictate methodology, instructional materials used, or how the material is delivered. (See Wyoming Computer Science Content and Performance Standards in the resources.)Analog data:
The defining characteristic of data that is represented in a continuous, physical way. Whereas digital data is a set of individual symbols, analog data is stored in physical media, such as the surface grooves on a vinyl record, the magnetic tape of a VCR cassette, or other non digital media.App:
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, but is not limited to, a program, image, audio, video, presentation, or web page file.Computational thinking:
The thought processes involved in formulating a problem and expressing its solutions in such a way that a computer (human or machine) can effectively carry them out.Computer science:
The study of computing principles, design, and applications (hardware and 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 nondigital 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 a nd 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:
The process of converting data from one form to another.Inference:
A conclusion reached on the basis of evidence and reasoning.Integrity :
The overall completeness, accuracy, and consistency of data.Model:
A representation of some part of a problem or a system. [MO DESE, 2016] Note: This definition differs from that used in science.Reliability :
Consistently produces the same results, preferably meeting or exceeding its requirements.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.Stereotype threat:
Being at risk of confirming, as a self-characteristic, a negative stereotype about one's social groupUniversal design for learning (UDL):
A framework for designing curriculum to be broadly accessible to all students. (See UDL for Learning Guidelines + Computer Science/Computational Thinking in the resources)Modalities of assessment:
Modalities of assessment include written assessment, oral assessment, performance tasks, or visual representations.Forms of assessment:
These include formative, summative, or student self-assessment.
This micro-credential collection provides earners with the opportunity to document their knowledge and skills in teaching computer science to students in grades 6–12. The content provides resources to support understanding.
Earners are encouraged to participate in additional learning opportunities if more extensive learning is needed. Additional learning opportunities may include free online resources, postsecondary courses, and local courses.
The micro-credential structure offers earners flexible pathways and timelines. Earners can complete the micro-credentials in any order that aligns with their classroom timelines and availability. Micro-credentials 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.
Complete "02. ANALYZE – Data & Analysis – Collection, Visualization, Transformation" in the resources section below. All instructions are included in the worksheet. Once you have completed the worksheet, upload in the evidence section as a PDF. The resource can be found by following this link: https://bit.ly/3h5Ej0e.
Find "03. DESIGN/DEVELOP" in the resources section below. All instructions are included in the worksheet. Once you are finished with this task, upload your lesson plan in the evidence section as a PDF. The resource can be found by following this link: https://bit.ly/2Urhzik.
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.
Find "04. EVALUATE – Worksheet" in the resources section below. All instructions are included in the worksheet. The resource can be found by following this link: https://bit.ly/3xMingf.
Evidence submissions and reflections will be reviewed for alignment with the assignment guidelines and this proficiency scale, found here: https://bit.ly/3jqpqap. This checklist will help you review your submission materials to ensure you address everything that is expected for this micro-credential: https://bit.ly/3k4Vmki.
Please provide a self-assessment, a score from 1–4, on each component of the proficiency scale found here: https://bit.ly/3jqpqap. 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 – CSTA 4a The teacher demonstrates accurate and complete knowledge of the content and skills of the standard being taught.
Inform instruction through assessment – CSTA 4g 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.
Supporting standards The teacher identifies and explains the connection of supporting computer science standards to the standard being taught in their lesson.
Vertical alignment – CSTA 4b The teacher explains the relationship of the standard in the scope and sequence of computer science standards directly above and below chosen grade band.
Minimize threats to inclusion – CSTA 2b The teacher develops purposeful strategies to proactively challenge unconscious bias and minimize stereotype threat in computer science.
UDL is a framework for designing curriculum to be broadly accessible to ALL students. Learn more about utilizing the UDL framework in computer science education.
These standards are designed to provide clear guidance on effective and equitable computer science instruction in support of rigorous computer science education for all K–12 students.
This article discusses how computational thinking skills were integrated and assessed in New York City elementary schools.
This article discusses different types of assessments and what to consider when choosing an assessment.
Step-by-step guide showing teachers how they can change their lessons or classroom based on data.
The computer science standards are written in grade bands (K–2, 3–5, 6–8, and 9–12). The standard committee (CSSRC) determined the standard to be met by the end of each grade band. In grades 9-12, there are level 1 and level 2 standards. Level 1 standards include introductory skills. Level 2 standards are intended for students who wish to advance their study of computer science. The teacher or earner can choose which grade band and standard to focus their lesson on.
Use this resource for the design/develop step of the ADDIE model.
Evaluate how effective your activities were at promoting student learning of the standards. Use specific examples from the artifacts you submitted in Implement and suggest any changes in practice or approach that you might make in the future based on your experience with this micro-credential.
Performance Level Descriptors (PLDs) describe the performance expectations of students for each of the four (4) performance level categories: advanced, proficient, basic, and below basic. These are a description of what students within each performance level are expected to know and be able to do. All PLDs are found in this document.
CODAP is free educational software for data analysis. This web-based data science tool is designed as a platform for developers and as an application for students in grades 6–14. It includes classroom activities and downloadable resources.
DataBasic is a suite of easy-to-use web tools for beginners that introduce concepts of working with data. These simple tools make it easy to work with data in fun ways, so you can learn how to find great stories to tell. This project includes a hands-on learning program to kickstart your data culture.
This Code.org unit leads an exploration of data on a wide variety of topics. Students use App Lab to make use of a data visualizer.
This site provides information and discussion for educators and resource developers interested in effective teaching methods and pedagogical approaches for using data in the classroom.
This resource is a full Data curriculum that is free to download. It fuses math and computer science through the use of R/RStudio an open source programming language.
This research article looks at different technologies and their ability to automatically collect data for civil infrastructure projects. It discusses different collection tools and rates them according to specified criteria.
This resource provides graphic organizers to walk students through the collection, analyzing, and evaluating of data to answer a driving question.
This website includes a tip sheet and video about reducing bias in a CS classroom.
This article provides background experiences as well as example activities to do with adults or students to help them break their implicit bias.
Introduction to predictive analytics collection technique to forecast future outcomes based on historical data.
This scale is provided as a resource for learners to view micro-credential criterion and the performance descriptor levels for demonstration of mastery.
Analyze the student and teacher standards aligned with the Data & Analysis – Collection, Visualization, & Transformation micro-credential. Aligned standards and instructions for selecting a focus standard are outlined below the task description. There are two parts to this task.
This website will help teachers fully understand Data Transformation.
This guide will help teachers full understand inclusivity in the classroom and see examples of how to foster it.
This video focuses examples of how various cities around the world and how they are using data to solve problems. It showcases Seoul, Korea and how they use technology and data to improve their city. Also speaks with a company in Kenya who is creating an emergency contact system to save peoples lives. Lastly they visit with MIT in Boston and its effects on urban planning.
This video gives a quick summary of a variety of data tools for analytics and data science.
“Unwrapping” is a simple method that all teachers in all grade levels can use to deconstruct the wording of any standard in order to know its meaning inside and out.
This resource includes a sample response for analyze, design/develop, implement, and analyze as well as a sample reflection prompt response for the devices micro-credential.
This is a list of videos that support navigation of the Midas platform. Including how to submit micro-credentials for review.
This video helps for unpacking the Wyoming Computer Science standards as part of the micro-credential.
This video provides best practices in Google Drive organization for the micro-credentials.
This video gives pointers on completing the CSTA CS teacher standard analyze task for the micro-credential.
This checklist will help you review your submission materials to ensure you address everything that is expected for this micro-credential.
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