What is Natural Language Processing?
Simply put, NLP leverages the power of computers to operate on natural language data in ways that can inform many practical downstream applications. Using this approach, computers analyze, understand, and generate “natural language” akin to the speech and text that humans use to communicate.
How can NLP help credentialing organizations?
Traditionally, credentialing organizations rely on subject matter experts (SMEs) and other stakeholders to understand their industry, workforce, and their certificants’ needs. However, engaging SMEs or stakeholders in any extensive effort to understand the entire industry could become unwieldy quickly, and ensuring such SME input remains up to date is both difficult and costly. Relying on SME and stakeholder judgment for this undertaking would likely over-tax the organizations’ certificant and potential customer base, thereby detracting from other critical activities such as content development for credentialing exams.
NLP has the potential to reduce the reliance on SME input and preserve these valuable human resources by taking advantage of the rich job information that already exists “in the wild.” In addition, many NLP applications can be updated and redeployed at low cost once the tools are developed, making them far less brittle than solutions and approaches that center on SME input.
What are some specific applications of NLP for credentialing?
One application of NLP that has the potential to revolutionize the way credentialing organizations consider their target market and certificant base is through workforce mapping. The sea of job vacancy announcements housed online contains a plethora of information including the tasks, knowledge, skills, and abilities (KSAs) required for the job, along with meta-data such as position level, salary, and location.
However, vacancy announcements rarely follow any uniform structure across platforms, jobs, or organizations. As such, it would be a nearly impossible exercise to manually wade through hundreds or thousands of job postings to identify the tasks and KSAs required of the jobs within a given workforce. NLP performs this function automatically.
In addition, because position titles, roles, and responsibilities vary across organizations and industry, it would be challenging for humans to note similarities between these jobs in a way that would organize and summarize a targeted workforce at scale. Moreover, given how dynamic many professions are, there’s no way to determine how long results from manual processes would be valid. NLP offers a way to leverage unstructured text and incorporate continual monitoring and updating of information on jobs and job functions.
For example, rather than classifying a workforce by the positions held, it can be classified according to the major aspects of the work performed. NLP can efficiently summarize and organize task-level information within job postings to create robust job function profiles for a given industry. Based on these job function profiles, HumRRO can help credentialing organizations better understand their potential certificant base and its needs, audit coverage of their existing certification test blueprint against industry trends, and identify avenues to explore for potential program expansion.