Introduction to the Skill Development Dashboards

These dashboards are designed to help you understand how your learning program is measurably helping to transform your employees’ skills. Below you will find an explanation of what each new graph is, why it matters, and how the information is calculated. 

The Skill Development Dashboards enable you to:

• Understand your organization’s level of skill mastery, with new and classic learning metrics
• Discover the top learners in your organization for different skills and proficiency levels 
• Understand how competitive your talent is compared to industry benchmarks 

In this product guide you will find information on the following: 

Distribution

• Learner Proficiency by Top Skill
• Median Hours to Mastery 

Mastery

• Skill Mastery Over Time
• Skill Mastery Leaderboard 

Learner Effort

• Total Assessments Completed 
• Average Assessments Completed
• Average Learning Hours 
• Most Relevant Courses Per Skill 
• Glossary

Skills Index 

• Current Industry Benchmark
• Industry Benchmark Over Time

Other Definitions

• Assessments 
• Proficiency Levels
• Mapping Courses to Skills

 

Distribution 

Learner Proficiency by Top Skill

What it does

Shows how many learners are at what level of skill mastery (Conversant, Beginner, Intermediate, and Advanced), for the most popular skills learned at your company. 

Why does this matter?

• Shows at a glance how program participants are developing skill proficiency: in what skills, and at what level. 
• Understand what skills or proficiency bands you want to focus your training on.

How is this calculated?

We measure the skill proficiency of employees using their performance on in-course assessments like multiple choice quizzes, programming assignments, and peer-reviewed projects. 

For the Coursera definition of “proficiency levels”, see Other Definitions below.

Median Hours to Mastery

What it does

This shows the median number of hours it has taken your learners to get to each level of skill mastery.

Why does this matter?

• Understand how much investment is required to develop [n] skills to [x] proficiency.  
• Identify “quick wins” in skills, and skills that may not be as ROI-positive for your organization. 

How is this calculated? 

You have learners who fall into each proficiency bucket. These numbers are calculated by the median # hours invested by each learner at that proficiency level.

For the Coursera definition of “proficiency levels”, see Other Definitions below.

Mastery

Skill Mastery Over Time

What it does

See how many of your learners have reached the Intermediate and Advanced levels of mastery over time, by skill.

Why does this matter?

• See if your learners are tracking toward mastering the skills they need
• Identify lagging skill areas that may need additional growth and investment
• Understand if your learning engagement strategy is effective
• Focus on opportunities to introduce new content to acquire the skills your business needs

How is this calculated? 

Total number of people who have reached, for a given skill, either an intermediate or advanced level, over time. 

For the Coursera definition of “proficiency levels”, see Other Definitions below.

Skill Mastery Leaderboard

What it does

Discover which learners have currently attained intermediate or advanced skill proficiency in the skills you care about.

Why does this matter?

• Find employees with intermediate or advanced proficiency based on their performance in course assessments
• Celebrate top learners and leverage social proof to drive further learning engagement
• Discover employees with relevant skills to allocate to new projects or opportunities

How is this calculated? 

We use an Elo model to measure skill proficiency across a continuous spectrum. Learners achieve a score based on their performance on in-course assessments. We group people based into BIA based on the value of their score.

Learning Effort

Total Assessments Completed by Skill

What it does

This shows the total number of assessments your learners have completed, by skill. 

Why does this matter?

• This is a more granular and aggregated view of the total amount of learning activity at your company
• Provides another option to measuring total organizational skill acquisition in addition to “hours spent” or “courses completed”

How is this calculated?

Total number of assessments completed across different skills. 

For the Coursera definition of assessments, see Other Definitions below.

Avg Assessments Completed by Skill

What it does

This shows the average number of assessments your learners have completed,
by skill.  

Why does this matter?

• Comparing this graph with the former (Total Assessments Completed by Skill) could help you infer how hard the average learner is working to get that skill. 
• Understand how your learners are building the skills they are building

How is this calculated? 

Total number of assessments completed across different skills divided by number of people. 

For the Coursera definition of assessments, see Other Definitions below.

Avg Hours Spent Learning Skill

What this is

This shows the average amount of time, in hours, it takes your learners to complete learning a skill. 

Why does this matter?

Classic learning metrics are used to see if the learning program meets the design you intended. 

How is this calculated? 

We add up the total # of hours all learners spend learning skills and divide those hours by the # of learners.

Most Relevant Courses per Skill Level

What it does

These are the most relevant courses your learners are taking to learn a skill (as measured against how pedagogically well that course teaches that skill at a particular level).

Why does this matter?

• See what content is relevant and popular with learners at intermediate and advanced levels in a particular skill
• Know which content to recommend to other learners that are just starting their learning program

How is this calculated? 

Courses displayed are restricted to the ones that your learners have taken. Of those, we process the recs based on relevance of each course to teaching that skill. Relevance is calculated from Coursera’s tagging model, which links the frequency of that skill reported as learned with whether the instructor reports that course teaches that skill.

For the Coursera definition of “mapping skills to courses”, see Other Definitions below.

Glossary

What it does

Definitions of the higher-level skills that appear in these dashboards, and example sub-skills. Learn more how Coursera maps skills to sub-skills and courses, here [link to EHC article].

Why does this matter?

• Anchoring on a standard set of definitions can help everyone get on the same page and bring strategic, targeted learning programs to learners faster.
• These domains and competencies are the most popular domains on Coursera. The BTD domains also represent the broad subject areas that contain skills which are critical to the future of work. 

How is this calculated? 

Coursera delivers learning in Business, Technology, and Data Science. At Coursera, we define each domain to include a set of 6 “canonical” competencies. These 6 competencies encompass the skills required to achieve expertise in the domain. 

Functionally, Coursera’s competencies and skills come from Coursera’s Skills Graph, which is a set of skills assembled through both open-source taxonomies like Wikipedia as well as crowdsourcing from Coursera educators and learners on what they are teaching/learning on the Coursera platform.

Skills Index

Current Industry Benchmark

What it does

This shows the fraction of companies you outperform among the selected reference group, by skill.

Why does it matter?

• Understand the competitiveness of your “skill portfolio” relative to your industry peers
• Decide how to allocate investment for the skills that matter to your org

How is this calculated? 

We average Elo scores of your learners and then compare you via percentile rank to the other companies on Coursera in the reference group you select (a combination of geo, company size, and industry). Ranking percentiles are divided into quartiles: 0-25% (lagging), 26-50% (emerging), 51-75% (competitive), 76-100% (cutting-edge).

Industry Benchmark Over Time

What it does

This shows the average skill level of your employees over time compared to that in the selected reference group.

Why does this matter to Enterprises?

Track your position (skill score) over time compared to the industry average—are you performing on par with other companies you might consider your peers or competitors?

How is this calculated? 

For your company’s average skill score, we take the skill score of your employees who are learning that skill. For the industry average skill score, we take the average of all companies in that industry. We do this at each date over time for you to see how your company’s skill score changes in relation to the industry’s average skill score.

Other Definitions

“Assessments”

We measure the skill proficiency of your employees using their performance on in-course assessments. Assessments on Coursera have varying difficulty levels and can take various forms: multiple choice quizzes, programming assignments, peer-reviewed projects, and essays.

“Proficiency Levels”

Learners are classified into four (4) levels: Conversant, Beginner, Intermediate, or Advanced based on their performance on in-course assessments on the Coursera platform. All new learners are initially classified as Conversant. An Intermediate level indicates the ability to apply basic concepts on the job; learners can typically reach the intermediate level by completing assessments in one or two courses. Advanced level indicates mastery of the material and ability to both teach others and identify novel applications of skills; learners typically move from intermediate to advanced after completing assessments across several intermediate-difficulty courses. New learners with pre-existing knowledge who begin with advanced content will progress more quickly.

“Mapping Skills to Courses”

Coursera's skills taxonomy maps every skill to a sub-skill or “competency.” These competencies and sub-skills are then mapped to courses using a machine learning model that combines occurrence data (how often a skill appears in things like the lecture transcripts) with feedback from instructors on what skills were taught in the course, and from learner generated feedback on skills learned.