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Inferences & Models - HS Students
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Microcredential ID : 81
Stack
Data & Analysis - HS Student
Credits
0 High School Credit

Description

Through a project, students demonstrate their understanding of inferences and models to support students in learning about using data to make simple predictions. Students must also show their learning of how models and simulations are used to examine theories and understand systems and how predictions and inferences are affected by complex and more extensive data sets.

Standards
  • Wyoming Content and Performance Standards > Inference & Models
    L1.DA.IM.01 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
How To Earn This Microcredential

Design and complete a project that proves your knowledge of the following High School Level 1 Inferences & Models Computer Science standard.

It is recommended you combine this work with the other Data and Analysis standards for Storage and Collections, Visualization, and Transformation. It is also recommended that you connect this work with Computer Science Practice 7, Communicating About Computing Micro-credentials. However, this MC may be earned on its own as well.

Fees
There will be no fee assessed for reviewing this microcredential.
Clarifications

Computational models predict processes or phenomena based on selected data and features. The amount, quality, and diversity of data and the features chosen can affect the quality of a model and the ability to understand a system. Predictions or inferences are tested to validate models. Students should model phenomena as systems with rules governing the interactions within the system. Students should analyze and evaluate these models against real-world observations.

Important Terms
Computational Model:

A simplification of a real system that can be analytically understood and/or run as a computer simulation

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, a show of hands, images, sounds, or video. [CAS, 2013; Tech Terms]

Inference:

A conclusion reached on the basis of evidence and reasoning.

Phenomenon:

A phenomenon, in a scientific context, is something that is observed to occur or to exist. It is simply a fact or event that can be observed with the senses, either directly or using equipment such as microscopes or telescopes.

Background Scenario / How This Will Help You

You can choose your own ideas for projects that demonstrate how hardware and software work together. Possible Data & Analysis project ideas could include:

  • Identify a problem in your community and suggest multiple solutions. Use or create software tools to store, collect, and transform data to back up your solution. Then create models to illustrate why this is the best solution. Assemble all of the new data into a presentation to present to desired audiences.
  • Work with a community partner to help them collect and analyze data that is relevant to the growth of their organization. Create a presentation that illustrates the collected data and presents ideas on ways they might change their current practices to increase their efficiency and grow their organization.
Evidence Options
Be sure to submit the type and number of pieces of evidence specified below.
Category: Evidence

This is where you will submit your evidence to demonstrate that your project meets the High School Level 1 Data and Analysis standard for Inferences and Models (L1.DA.IM.01).

Submission of this evidence is required. You must submit the Student Submission Data & Analysis CS Micro-credential Stack document.

Student Work:

You will want to submit the information on the Student Submission Data & Analysis CS Microcredential Stack document. You will need to complete the information on either the Google Docs or Word Version. Make sure you submit your documents as a PDF.

You can find the Google Docs Version of the template here: https://bit.ly/HS_Student_DA_SW.

Student Submission Data & Analysis MC Stack.docx


Review Criteria

Evidence submissions will be reviewed for alignment with the assignment guidelines and this proficiency scale:

Novice Developing Applying
The student’s project provides little to no evidence in addressing the expectation(s). (1) The student’s project contains computational models. (2) The models represent the relationships among different elements of data collected from a phenomenon or process. (1) The student’s project contains student-created accurate computational models. (2) The models represent the relationships among different elements of data collected from a phenomenon or process.

The full proficiency scale is available here: https://bit.ly/HS_Student_PS_DA.


This checklist will help you review your submission materials to ensure you address everything that is expected for this micro-credential: https://bit.ly/HS_Student_CL_DA.

Reflection Prompts

What new understanding, knowledge, or skill do you have around the use of inferencces and models in data analysis?

How did this experience develop your skills around making inferences and creating models?

What light bulb lit up for you?


Review Criteria

Student Reflection on Inferences and Models

Reflection Incomplete Complete
**** **** ****
Student Reflection The reflection needs more information. The reflection contains enough information.
Resources
SageModeler
https://sagemodeler.concord.org/

Web-based and open-source software to engage students in systems thinking through designing, building and revising models.


Videos: The Concord Consortium
https://www.youtube.com/channel/UCgY6_xM-sNBJUQMWh_p1jwQ/videos

A non-profit educational technology laboratory for science, mathematics and engineering. Our pioneering work brings technology's promise into reality for education. This site provides a series videos to support student learning about data and analysis.


Data Culture Project
https://www.databasic.io/en/culture/

Use our self-service learning program to facilitate fun, creative introductions for the non-technical folks in your organization. These are not boring spreadsheet trainings. The free tools and activites below are hands-on and designed to meet people where they are across your organization and build their capacity to work with data. Try to kickstart your data culture by running one activity per month as a brown bag lunch. The videos and facilitation guides below will lead you through running them yourself. People at more than 30 organizations have done so already - from a local public library, to the World Food Progam.

Earners
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