# Understanding algorithms and big data in the job market

Understanding algorithms: algorithms and big data in the job market

This lesson was designed for students in the US grades 10-12 and UK Secondary 4-6.

Connected Standards

ISTE/WA Ed Tech 2.a. Students cultivate and manage their digital identity and reputation and are aware of the permanence of their actions in the digital world.

Scotland’s Curriculum for Excellence, TCH 4-07a:. I can present conclusions

about the impact of technologies on the economy, politics and the environment.

Learning Objectives

In this lesson, students will discuss and consider:

• How employers use applicant data from resumes and online activity to decide whom to hire.
• The ethics of using a digital algorithm.

Students can also craft and revise their own job resume based on ideas and activities in the lesson, which connects to future career readiness.

Lesson 1: Introduction to Hiring Algorithms

Before class, post and/or print materials students will need:

Have the two slideshows ready for class use.

Activity 1

Step 1: Students select 3 images on the job lottery card. Do not reveal what they are for. If any of the images may elicit a strong response from students, it would be wise to change it for an image with which they do not have any associated feelings.

Step 2: Use the job lottery slideshow to direct students to stand up or sit down based on their choices. If all of the students sit down before the end of the slides, have them stand back up when they have a desirable quality.

Step 3: Debrief. What did students question or wonder about the different criteria? Which ones seem fair or wise?

Activity 2

Step 1: Introduce the use of hiring algorithms using the Algorithms and Jobs slideshow.

• In small groups or pairs, students should brainstorm pros and cons of using hiring algorithms. What makes them appealing and/or problematic?
• Supplement: Reuters article on Amazon’s failed recruiting algorithm. Students can read/discuss the text before or after they brainstorm pros and cons.

Step 2: Distribute/publish a copy of Finding the Right Candidate to each group. Have them work together to identify top traits.

• Have students tally top picks between groups using Google forms, a tally on the white board, etc. If time allows, have them consider trends (most desirable, etc.) and how employers can identify who has those traits. Students can craft interview questions or consider other ways an employer can find someone with those traits.

Lesson 2: Algorithms and big data

Before class: post/print copies of the two sample resumes and the added info. Keep the added layers hidden until you begin the discussion/activity.

Step 1: Students read two resumes and consider which candidates stand out.

• Which candidate would they choose and why? Highlight key features they used to make a choice. Have them refer back to desirable qualities they are looking for from lesson 1.
• Discuss ideas as a class.
• Extension: Have students craft a simple hiring algorithm to explain their thinking/selection process. How many elements did they consider and which ones were used first, etc.

Step 2: Post/distribute data from each applicant’s web browser.

• Take time to read/highlight new information.
• Discuss: Does any information reinforce or change their hiring choice? Why? What assumptions do we make based on online habits?
• Do browsers track and sell user data?

Step 3: Social Media Data

• Again, distribute/publish social media statistics.
• Take time to read/mark and take note of details.
• Discuss ideas/reactions.

Extension: Students can revise their algorithms and/or craft one after considering various types of data that might be used.

Lesson 3: Resumes

Step 1: Show students how to use a resume template using a word processing program.

Step 2: GIve time for students to craft a draft of a resume. Encourage them to use the two practice resumes as examples.

Note: You can have them create drafts before discussing algorithms.

Step 3: Have students work with a partner to edit resumes.

• Use Finding the Right Candidate list of traits. Ask peers to highlight which details may show one of the traits. What can employers infer about them based on what they list?

Step 4: Revise resumes

Extension: Test the hiring algorithms developed by various groups. How many students would get to the job interview?

Algorithm Creation Resources:

BBC Bitesize info on algorithms

Lucid Chart Example of an Algorithm

Simple algorithm example

Simple algorithm example

Visme FlowChart maker

Basic Flowchart Symbols