Week 00

Guidelines

Assessments are meant to give you and Operation Spark staff an idea of how well you understand, or can figure out, the material that you covered recently.

  • Turn off all communication devices such as your phone, email, Slack, etc.
  • Fork this repo and clone down your fork to your laptop.
  • Commit working code early and often (at least after every prompt). You are graded on your commit messages in addition to the code that you write

[When this code is committed it will] Complete the basic-iteration prompt

  • Use the Chrome Console Snippets feature to author, test and debug your code before committing. An approved alternate tool is JSFiddle.
  • Do at least a little work on every prompt, even if you only leave comments that describe your intent. Leaving a prompt blank is tantamount to failing a prompt. You'll get much more credit even for writing comments or pseudo-code that describe your intent
  • You must submit a pull request for all assessments on time (guidelines for how to submit a pull request are below). Give yourself several minutes to do this at the end of the time allotted for the assessment
  • @help_desk is available to you during assessments.
  • You are allowed to Google.
  • After completing the assessment, if there are any prompts you felt you did not do well on, or, that you would not know how to assess whether or not you did well on, make plans to improve your skills on that topic as soon as possible.

Using and Referencing Outside Resources

Each prompt contains a list of outside resources you are allowed to use to support your work on that prompt. Using any previous class materials, or, communicating through any mechanism with anyone other than Operation Spark staff during the assessments is academic dishonesty and is cause for immediate removal from the course. If you have any questions about whether a resource is available for use, or if you are in need of support, use @help_desk.

Self-Grading Process

After you complete each prompt, assign a grade to each item in the Grading Outline, using the Grading Scale. Note your grade by editing this file (README.md), and writing your grade next to the corresponding item in the Grading Outline. Please use the exact terms shown in the Grading Scale; do not modify or improve them.

Grading Outline

  • [Significant progress] filter-family-members

Grading Scale

Grade Meaning
Complete You believe your solution to be fully complete and meeting the specified requirements.
Mostly complete Your solution is well on its way to being complete, but you ran out of time or can't remember exactly how to do one particular aspect. You believe anyone who understands the problem well would endorse your solution as the right one, and know pretty clearly how to finish up any last touches.
Significant progress You have the right idea and were heading in a good direction. Covers everything between Mostly Complete and Attempted.
Attempted You were very challenged by the prompt and had trouble making any significant progress on the problem, but wrote at least one meaningful line of code that appears to be a genuine attempt.
Not attempted Whether you've thought much about the problem or not, you have no lines of code to show for the problem. (Note, you should avoid ever getting into a situation where this is the grade you'd give yourself. Make a passing attempt at each problem before going back to complete any one problem.)

Submitting Solutions

Solutions are submitted via Pull Request. Follow these steps:

  1. From your fork, select Pull Requests and then create a New pull request.
  2. The pull-request heading should look like this:

OperationSpark:master ... username:master

  1. Copy and paste the completed Grading Outline into the comment block of your Pull Request.
  2. Click Create pull request

NOTE: Only submit one pull request per assessment. You can feel free to continue working on the content and can submit another PR after our incredible Instruction Team has completed reviewing your work (which takes about a week), but submitting multiple PRs greatly complicates our review process and subverts our ability to observe your work in the context of specific time constraints.