As learning practitioners, we must understand attributes in defining skills and how they relate to each other. This is particularly relevant as we use skills to track learning through time and match skill maps to learning programs. Consider the following table:

Root Skill


Related Skills




Python, Javascript, C#

File Management, Object oriented programming…

Verbal Communication


Presenting, Listening

Conversations, Meetings…

Written Communication


Report Writing, Grant Writing

Composition, Grammar…

Website Development



Architecture, Client side Interaction, Server side Interaction…

Critical Thinking


Problem Solving, Reasoning

Induction, deduction, summarization, ranking, sense making…


For tracking purposes, it is worth marking root skills with synonymous skills in a database (skills that can be replaced with each other). The solution is simple. A learning practitioner puts either ‘programming’ or ‘coding’ on a learning label – either representation. On a Skills Emblem (dynamic badge), a skills map, or other summarization, use only the root skill and summing both representations together with Skill Points.

Rather than show a learner accumulated 100 Skill Points ® in Coding and 200 Skill Points ® in Programming from learning labels, say a learner accumulated 300 Skill Points ® in Programming.

Root skills and related skills should be treated as separate line items. This is how they appear on a learning label. This is how they appear in a LinkedIn profile. Though, it is worth understanding the relationship between a root skill and related skills.

For example, a college graduate is taught to program and might learn a few related skills – programming languages. When applying for jobs, the skill of programming is valuable regardless of the required language. How do we allocate proper attribution between what might be considered a transferable skill with a technical skill – a ‘sub-skill’?

Once there is a large data set of learning labels to work with, understand how skills cluster together.

Methods are not ‘related skills’ but are worth tracking. For example, the paramount skill of critical thinking is too difficult to track by itself, so makes sense to track the methods and frameworks behind applying the skill. Education and training standards are largely a representation of methods. The above-mentioned relationship of a root skill and its synonymous skills is also important in matching standards to skills.

Consider the Skills Map for an early stage Web Developer in the graphic:

Programming should be interchangeable with coding. On a Skills Emblem, resume, portfolio, personal website, or LinkedIn profile, a substitution should be made automatically.

Critical thinking should be included, but a reference to the related methods tightens the map.

Does the programming requirement include Javascript, Java, or C#? How do we negotiate differences in required languages (i.e. experience in Python, Java, etc.)?

This is a second part of a study I am looking to move forward on. Here is a link to the study: Skill Coefficients. Please contact me if interested: .