data

Education and Career Pathways: Maps for Learning and Job Success

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).
Figure        SEQ Figure \* ARABIC     1      . A pathways map has milestones (which are like points of interest on a street map) connected by paths (which are like road segments on a street map).

Figure 1. A pathways map has milestones (which are like points of interest on a street map) connected by paths (which are like road segments on a street map).

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.

Figure 2. A pathways map can have multiple routes (which are also called routes on a street map). The route in blue represents one of many education/career possibilities in nursing.

Figure 2. A pathways map can have multiple routes (which are also called routes on a street map). The route in blue represents one of many education/career possibilities in nursing.

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.

More information about education and career pathways

QIP team members are working with teams from edtech initiatives (such as those mentioned in my recent EdSurge article on initiatives working on learner navigation) to help define standards for pathways data that will serve all levels of education, training, and careers. I will be facilitating a session on this topic at the upcoming National Defense Industry Association (NDIA) iFEST conference in Alexandria, Virginia, on August 27–29. See also my video Demystifying Pathways Data on YouTube for another look at education and career pathways.

Jim Goodell (@jgoodell2is 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.

Big Data in Education: Researchers’ Responsibilities

bigdataimage.png

While big data’s growing influence has impacted our lives across a spectrum of issues, it also has created many questions and concerns, particularly among education researchers.

Big data allows researchers to uncover patterns in data that might be otherwise invisible. This has led to several powerful advances, such as better treatments for disease, improvements in agriculture, and more timely and effective responses to natural disasters. The benefits of big data have even been highlighted in popular media, such as in the movie Moneyball, which dramatizes how the pioneering use of large datasets helped a general manager assemble a winning baseball team.

But the rise of big data has also prompted many to note its potential negative consequences. Within education, researchers have identified not only benefits to using big data, but also legitimate concerns. As they do with all data, education researchers have a responsibility to focus on both the integrity of their research using big data and on clear communications about this research to the public. Further, their communications with the public should focus not just on the research itself and its useful possibilities, but also on the precautions they are taking to ensure that the rise of big data does not negatively affect the education community.

Big data, defined

The term “big data” refers to very large and complex datasets—those datasets that have been described as “defying traditional data-processing applications” (National Academy of Education, 2017). Modern technologies allow us to capture information in previously unforeseen ways and transform it into digital data. This has resulted in datasets that are much larger and more complicated than anything seen before. From a research standpoint, big data changes data collection from an often lengthy and painstaking process to one that can happen nearly automatically, given the right connections to sources.

Big data in education: improving teaching and learning

Big data in education tends to fall into two major categories: administrative data and learning process data. Combining digital data from these two areas in innovative ways can allow researchers to identify patterns or correlations that may otherwise go unnoticed.

  • Administrative data can be demographic, behavioral, and achievement data and may include items such as attendance records, transcripts, and test scores.
  • Learning process data are continuous records of students’ behaviors and may include online assessments, keystrokes, or time latencies (e.g., the time it takes a student to respond to a question).

Innovative data analyses can lead to useful solutions to problems in schools and classrooms, uncover potential inequities in learning opportunities, and zero in on students’ needs in ways that reveal how to personalize learning more effectively. The overarching goal of this data collection and analysis is to expand possibilities for teaching and learning—including how to meet individual students’ needs.

Big data in education: legitimate concerns

Education researchers have raised some legitimate concerns about big data. While they recognize that big data has many exciting possibilities, researchers have also identified some potential problems with its use—or misuse. These concerns tend to fall into three main categories: misinterpretation, inappropriate use, and data privacy and security.

  • Misinterpretation concerns center on the possibility that studies using big data may be misunderstood by readers—especially if the studies are distilled or simplified before reaching the public—and that these misinterpretations could lead to inaccurate decisionmaking.
  • Inappropriate use concerns suggest that the public nature and accessibility of some big data may lead to people using the data in ways that were not intended and that defy accepted research standards.
  • Data privacy and security concerns are based on concerns that individuals’ personal information may not be properly protected, which could lead to data breaches or other inadvertent disclosures of private information.

As the education field continues to move toward greater use of big data, each of these issues should be specifically and consistently addressed. This can be accomplished through strong data governance, research standards, and other precautionary measures.

Researchers’ responsibilities: communication with the public

Education researchers must think not just about the research on big data, but also about how the public is receiving and reacting to this research. Public discussion of big data is frequently negative and inaccurate. Unlike the measured considerations of big data presented in academic articles, much of the communication about education-related big data to the public has encouraged skepticism and fear. It is not surprising that many parents and other stakeholders have developed negative views, given the frequent headlines that tout the “big dangers” of big data. The public less frequently encounters news that describes the potentially positive aspects of this education information or the clear standards that are in place to protect the privacy of personal information.

At the same time, researchers should work to ensure that members of the education community understand the legitimate concerns about big data and what we can all do to avoid or mitigate problems that may arise from misinterpretation, inappropriate use, and data privacy and security issues. Walking the fine line between explaining the intricacies of this difficult topic and communicating concisely and clearly is something education researchers must strive to master.

Big data is indeed a problem if it is used ineffectively, inappropriately, or by individuals without a requisite level of comprehension of the complexities of the subject. But that is true of all research data. Data, in various forms, can reveal that something has happened, that a phenomenon exists, or that variables appear to have a relationship, but data cannot on their own reveal why. It is the responsibility of researchers—especially those in the public sphere—to provide the lenses that make research relevant and comprehensible to varied audiences, from parents and teachers to administrators and elected officials.

It is important for education researchers to make clear that they are using the same stringent research standards for big data analysis that they have adhered to with previous types of data. Additionally, they must communicate to the public that they are regularly discussing the potential hazards of big data and routinely updating methodologies and security protocols as projects and analyses become increasingly complex. The clearest path to public trust in the research process is via straightforward and detailed communication.

Bridget Thomas (@DrBridgeQIP) is Senior Education Researcher at QIP and Adjunct Professor at George Mason University. Her work focuses on early childhood policy and translating research for multiple audiences.