This piece originally appeared in two parts on the Quality Information Partners site:
Recent statistics show a mismatch between the skills secondary and postsecondary students are acquiring and the rapidly changing needs of industry. In June 2018, the Bureau of Labor Statistics reported that U.S. job openings had increased to 6.6 million, while the number of unemployed people was down to 6.3 million. According to the 2017 ExcelinEd white paper Putting Career and Technical Education to Work for Students, “Many of these open positions offer middle- and higher-wage salaries, as well as opportunities for continued training and advancement by employers, but they go unfilled due to a lack of appropriately skilled workers who have completed aligned programs of study.” Pathways data—data that help students navigate through different points in their education and career trajectories—can help solve this problem. These data define not just the routes to success (i.e., to the desired destination), but also the milestones along the way.
It is clear from these reports that current students and education providers could use better alignments to the most promising opportunities in higher education and the workforce. At the macro level, we see gaps between what students are learning and what they need to learn to transition into the college programs of study and work positions that are available. At the micro level, a student’s skill gap in any area (e.g., proportional reasoning) becomes a roadblock for learning further skills that depend on that prerequisite understanding or ability (e.g., operations with fractions, word problems, and physical science applications). The lack of well-defined education pathways data—and the failure to use the information that is currently available—is limiting opportunities for students, employees, and employers.
Four kinds of education and career pathways
There are four kinds of pathways that serve different purposes:
- Competency pathways define recommended sequences of learning. They show prerequisite and post-requisite relationships between competencies. Competencies can include skills, knowledge, dispositions, or practices.
- Content pathways define sequences of learning resources or learning experiences.
- Credential pathways define sequences of credentials that build an individual's qualifications. These pathways often include “stackable” credentials that can help a person qualify for a different and potentially higher-paying job, by adding qualifications to those he/she already has. (See also this explanation of stackable credentials from the U.S. Department of Labor.)
- Career pathways define a series of structured and connected education programs and support services that enable students, often while working, to advance over time to better jobs with higher levels of education and training. (See also this explanation of career pathways from the Career Ladders Project and this definition from ExelinEd.)
Visualizing pathways as a map
Although the four kinds of pathways have different purposes, their structure looks the same. In each case, the information can be visualized as a map. Points of interest on the map, called milestones, can represent
- a competency (e.g., a skill, piece of knowledge, disposition, or practice);
- content (e.g., a learning resource or program);
- a credential (e.g., a qualification or degree); or
- a career opportunity (e.g., an internship or job).
While these different types of milestones can all be points in a pathways map, the metadata for each will be different, depending on type. For instance, a credential milestone will have different metadata properties than a competency milestone.
A path is a connector between two milestones. Paths, similar to road segments on a street map, represent recommended ways someone can navigate from point A to point B. On a pathways map, a path shows how to get to a slightly more advanced milestone via its prerequisite milestone. Figure 1 shows the relationship between two milestones and a path.
A pathways map can be formed by connecting many milestones and paths. People can then select routes based on interests and needs. A career pathways map in nursing, for instance, may have several possible routes. There could be an entry-point milestone of a high school diploma, with two paths leading from there, one to a Licensed Practical Nurse (LPN) qualification and another to an Associate Degree in Nursing (ADN) to qualify as a Registered Nurse (RN). Another path could lead from the LPN to the RN. The LPN and RN could each have a path to a Bachelor of Science in Nursing (BSN). All of this creates many possible routes and destinations (illustrated in figure 2). Additional routes could be created, thus expanding the map, by adding paths from the BSN to graduate degree qualifications for other positions in health care.
Note that, unlike a street map, a pathways map is unidirectional. While people commonly travel from point A to point B and then back to point A, they do not travel from a more advanced milestone to its prerequisite. Of course, people may need to relearn a prerequisite they either missed or forgot in order to advance; they may also decide to double back and change routes. But they will never begin at a master-level job and move from there to a basic internship in the same field, or start by learning differential equations before moving on to addition and subtraction.
Data Standards for Pathways
Education and career pathways are maps. Students, educators, employees, and employers can use them to navigate through the various stages of attending school and participating in the workforce. As I explained in my previous blog post on education and career pathways , just as people use regular maps to travel from point A to point B, they can use education and career pathways to advance from one milestone to another in their education and careers.
In order to create education and career pathways maps, we need data and metadata. We also need standards to make the data interoperable. These data collection and standards efforts must be open and created with input from various stakeholders.
Moving toward a Google Maps model
Google Maps is a good metaphor for education and career pathways maps. In both types of maps, people can choose among possible routes based on needs and interests.
Data attached to each milestone (like a credential or job) help people determine where they are and what their goal or destination is. Data allow the technology to show different ways to reach each destination and to suggest the fastest or best route, given internal and external circumstances.
On Google Maps, the internal circumstances may be that a person is riding a bike, or a driver can’t take toll roads. The external circumstances may be construction or traffic congestion on some roads. In education and career pathways, the internal circumstances may be that a person has a job, is a single parent, and lives 50 miles from the nearest college. The external circumstances may be that a state law passed that will change certification requirements in three years’ time. Like Google maps, a data-driven pathways navigator would suggest personalized routes based on the circumstances. It would recommend different career pathways to people in different circumstances, even if both share the same goal.
We have not yet gathered the large amount of data and metadata needed to create education and career pathways maps. We also don’t have a complete set of standards that can make data operable between systems. Although several promising initiatives aim to address these problems, we are still in the beginning stages of creating rich and open pathways maps that have the power and utility that Google Maps brings to street navigation.
Data needed for the four kinds of pathways
Education and career pathways come in four varieties. Each kind of map serves different purposes and requires different kinds of data and metadata.
In a competency pathways map, routes are defined based on expert recommendations for sequencing learning. Each milestone contains data defining a competency (a skill, piece of knowledge, disposition, or practice). For example, mathematics teachers recognize that proportional reasoning skills are prerequisite to success in algebra (see this Doing What Works presentation on developing proportional reasoning). A competency pathways map may indicate that students must reach a defined level of mastery in proportional reasoning before learning about linear equations.
A content pathways map serves the needs of curriculum developers who are building coherent sequences of learning activities. Each milestone contains data defining a learning resource (for example, a video or discussion guide). Digital resources are alternatives to static resources such as printed textbooks. Data linking specific lessons and activities may define prerequisite and post-requisite relationships to maintain a coherent sequence while allowing for personalized learning. The data of each content milestone may also link to competency definitions (milestones in a competency pathways map) that define what the learning resource is intended to teach or assess.
In a credential pathways map, routes indicate means of achieving each credential. This kind of map shows how “stacking” credentials in different ways could lead to the same outcome. A credential pathways map could show, for example, that a series of micro-credentials add up to the same qualifications as a certificate program.
A career pathways map may include milestones for career options as well as for job qualifications. Many professions require education credentials, licensure tests, entry-level experience (for example, working as an apprentice), and/or achieving full certification. Additional conditions might be required before becoming a master of the trade or profession. Data on a career pathways map must be attached to the destination milestone (the job itself, linked to the competencies required for the job and other metadata), as well as to milestones that indicate how one can qualify for the job.
The future of education and career data systems
Pathways maps can help bridge traditional institutional boundaries—such as between K-12 and higher education and between education and employers. When education and training programs are better aligned to what lies ahead, they can prepare students for long-term opportunities. Moreover, students are able to make more informed choices when they understand the full range of options available to them.
Furthermore, as new careers are invented, learners will be able to see how to train for emerging, high-demand, higher paying jobs. If learners have trouble acquiring new competencies, they can explore other modalities of learning and practicing to achieve the same milestone.
Learning pathways data, combined with experience data, can be improved using artificial intelligence (AI) technology to optimize route recommendations. The full potential of this kind of optimization will depend on pathways data being open on the web and fully interoperable, and with comprehensive coverage connecting competencies, credentials, and careers.
Making education and career pathways a reality
Without access to robust learner navigation systems, students are not fully informed about routes to prosperous and fulfilling careers. Educators and students often make guesses about which routes are best, or make random choices due to uncertainty. Education institutions assume they are helping students acquire the competencies they need for their futures, but data show a mismatch between workforce needs and job seekers' skills.
I invite you to join in the effort to work toward robust education and career navigation systems, and to create the data standards needed to make systems interoperable. With dedication and collaboration among a variety of experts, organizations, and agencies, we can make standardized, open-data pathways maps a reality.