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.
To learn more, attend the National Defense Industry Association (NDIA) iFEST conference, in Alexandria, Virginia, on August 27–29, at which I will be facilitating a session on this topic. I also invite you to view my video Demystifying Pathways Data.
Jim Goodell (@jgoodell2) is Senior Analyst at QIP. He works on connections between education sciences, policy, practice, and personalized/optimized learning. He wrote Turning ‘Google Maps for Education’ From Metaphor to Reality for EdSurge. Learn more about Jim here.