Tuesday, August 28, 2018

Education and Career Pathways Navigation with Data

This piece originally appeared in two parts on the Quality Information Partners site:

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.

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.
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.
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.

Saturday, August 25, 2018

Turning ‘Google Maps for Education’ From Metaphor to Reality

This piece originally appeared on EdSurge on Jul 14, 2018.

By Jim Goodell

In his latest EdSurge column, Michael Horn laid out how Google Maps offers an aspirational metaphor for what the future of educational tools could look like. But as he also noted, locating where people are geographically is one thing; pinpointing where they are educationally is another.
Today, Google Maps is an open ecosystem for accurate, real-time geospatial and navigation data. Unfortunately, current learner navigation systems more closely resemble the early, self-contained GPS devices with incomplete and inaccurate maps.
To bridge the gulf, it will take a similar open-data ecosystem to support learner navigation. But in the field of education, we don’t even have a complete set of static competency frameworks for digital data that are openly accessible and interoperable—to say nothing of dynamic data that support real-time pathway optimization.
Yet there are several initiatives, some of which I’ve had the privilege of working on, that aim to support the educational data ecosystem necessary for learner navigation.

Alignment of Data Standards for Describing Learning Objectives

When applying the Google Maps metaphor to learner navigation, the points of interest on the map are competencies—the things that a person can learn, such as skills, knowledge, dispositions, and habits of practice. This data must be in a machine-readable format and interoperable to work in all apps and systems.
The existing standards for this kind of data were like a Babylon of different languages, understood only in their own domain (such as medical training, human resources, K-12 or postsecondary). To connect these standards, there are efforts such as the Credential Ecosystem Mapping Project, where participants are working together to understand how data elements within various standard formats can be converted to other formats. This project maps across existing data standards, making it possible to translate data at all levels and sectors of education and training, such as the MedBiquitousstandards for health care and HROpen for human resources.
The IEEE Learning Technology Standards Committee (LTSC) plans to update its existing standards based on this work. Last updated in 2007, the international standards body has defined a data model for describing, referencing, and sharing competency definitions, primarily in the context of online and distributed learning. The LTSC is also working on related standards for mobile learning platforms, adaptive instructional systems and augmented reality learning environments.

Open Registry of K-12 Learning Standards

State academic standards help define the learning objectives for U.S. K-12 learners. These standards have been traditionally published only as human-readable documents, such as PDFs, that can’t be used directly by education technology tools. In other words, statements within PDF documents cannot be reliably referenced in information systems and digital content.
Something is usually lost in translation when content publishers and software developers try to put state standards into their own databases. Also, mapping is problematic with 50 sets of state standards for each grade level and subject, plus many more derived versions of those standards used locally and for other specific purposes.
In an effort to solve this problem, IMS Global recently announced it will host a 50-state registry of academic standards. This registry aims to provide a definitive set of machine-readable statements and a freely available set of global identifiers for use in digital content. If it achieves that goal, it will also support crosswalks for systems to discover whether one state’s standard is an exact match to a standard from another state. Equally important, it will allow states themselves to maintain the digital and human-readable standards so that nothing is lost in translation.

Linked Data Defining Competencies and Credential Pathways

Data about credentials that are available for a person to earn are just as valuable as data about the things a person must learn to get them. To this end, the Credential Registry, hosted by nonprofit Credential Engine, has created an open catalog of data about postsecondary degrees and other credentials available in the United States.
Dozens of credentialing institutions and quality assurance bodies are already posting information on the registry, which includes different kinds of credentials, from degrees, certificates and certifications to licenses, badges, and micro-credentials. Credentials in the registry include linked data for the competencies that each credential represents.
Today, micro-credentials (sometimes issued as digital badges) and micro-master’s degrees have emerged as a more dynamic model for credentialing than the traditional 2- or 4-year degree, offering an alternative reflecting the ever-changing world we live in. Digital micro-credentials, such as those offered by Digital Promise for educators, provide recognition for the skills people learn throughout their careers.
Pathways data can help people navigate opportunities to earn “stackable” credentials, or a sequence of credentials that can be accumulated over time to build up an individual’s qualifications for a different and potentially higher-paying job. (See this explanation of stackable credentials from the U.S. Department of Labor.) These more flexible pathways could also be used in K-12 education, such as a student earning a micro-credential as a step toward licensure in a trade.

Translation Competency Definitions Between Data Formats

Organizations in different domains use different formats for the same kind of data. For example, a K-12 state education agency could use tools based on the CEDS and IMS CASE data standards for academic standards. In the same state there may be a district with a career and technical-education program in health science and medical technology, but the medical industry uses the Medbiquitous standard to encode competency definitions.
To address this issue, the CASS system, developed by Eduworks with funding from Advanced Distributed Learning Initiative, is being used with the Credential Registry as a translator to move competency framework data between different serialization formats of technical standards. That could allow for better alignment between a university’s digital competency frameworks, and what is used by the profession or industry that a student will enter.

Linked Data on the Web

Billions are spent developing digital educational content and trying to develop systems to better recommend what digital content a student should experience next. To date, defining where digital content belongs on a learning map has only happened in closed systems with very limited maps of learning progressions. With openly available learning map data, links to those data can give digital content a point of reference. This is as simple as providing URLs that “locate” at what learning milestones the digital content may be used.
As the learning map references become available on the open web, dynamic learning content will also be able to link to specific activities and assessments. Technology standards such as Experience API (xAPI) and IMS Caliper link specific learner experiences to points on the learner navigation map and add useful contextual data. This is similar to how up-to-the-minute traffic data helps Google Maps find the fastest routes.

Aligning Learning, Workforce, and Credentialing Data with the Needs of the New Economy

It’s often discussed that there is a mismatch between the skills secondary students are acquiring and those needed for post-secondary coursework, as well as a mismatch between the skills needed for current jobs and the skills that college graduates have. One of the challenges is to build a learner navigation map that has coherent pathways between K-12 academic standards, postsecondary programs, and occupational competencies.
The T3 Innovation Network, funded by the Lumina Foundation and the U.S. Chamber of Commerce Foundation, is investigating, among other things, whether artificial intelligence algorithms and resources can be used to discover information used in learner navigation. For example, they are studying how to turn unstructured information about job skills on the web into structured data that conform to data interoperability standards and can link to learning opportunities and credentials. This can add value to existing information sources like the U.S. Department of Labor’s O*NETcareer exploration and job analysis resource, by linking information about job skills and the education credentials that best represent those skills.

More to Be Done

Beyond these initiatives, an open ecosystem for learner navigation will need additional research-driven data, such as data about contexts and conditions in which learning takes place, available learning experiences, how to measure mastery levels, the cognitive and metacognitive gaps and barriers that learners face, and which kinds of practice or experiences can lead past those barriers to mastery.

Wednesday, February 10, 2016

How Data Works to Support DIY Learning

The following is post I guest-authored for, and originally appeared on Getting Smart...

Noah is a 17-year-old multi-lingual student. He can speak six languages even though his family speaks only English and his public high school offers classes in only two non-English languages. Noah didn’t have formal opportunities to pursue his linguistic interests, so he took matters into his own hands, discovering online tools and social networks for self-directed learning.

Noah’s story provides some good examples of the kinds of data and technology enabling do-it-yourself learning.

Generation Do-It-Yourself (GenDIY) has unprecedented opportunities to chart their own course for lifelong learning as part of a career pathway, to reach a personal academic goal, or just to satisfy a curiosity.

The data used to match learning experiences with personal needs, preferences, and ability levels, and data within online learning applications to provide continuous feedback, are empowering learners like Noah to move beyond the constraints of traditional education.
Do-it-yourself learning is taking place on two levels:
  1. Formal systems of education are adopting student-centered options, giving students voice and choice, and visibility into how short-term choices support longer term career goals, and
  2. Learners of all ages are acting on their own, discovering and using technology enabled tools to reach their own learning goals.
Prior to high school Noah took an online course in Latin. He worked through a book and viewed videos at his own pace. At the time Noah was home schooled, but schools across the U.S. and around the world are also leveraging a rich set of online options to offer courses that they cannot staff. Course choice opens doors for students, especially in communities that cannot attract teachers with specialized subject matter expertise, or cannot fill a class with enough students to justify the course.

After he discovered his interest in language learning, a friend told Noah about a free language-learning tool that he happened to read about in a technology blog. That tool was Duolingo, the award winning free website and app. 

Data for Discovery
Noah was fortunate to have a friend point him toward Duolingo, but data is also helping the GenDIY self-discover the right DIY learning tools and opportunities.
Linked data on the Web supports discovery of learning resources (courses, apps, learning experiences, and social learning opportunities). Metadata (data about data) is being used by the major search engines to better filter search results to meet learner needs and preferences. Publishers of learning resources tag web pages with metadata attributes, such as specific competencies addressed and intended audience, in a format that the search engines can read. Metadata may include tags about accessibility of the resource, such as if a video is closed captioned for the hearing impaired. This helps the self-directed learner find resources to fit personal needs and preferences. Schema.org is a standard for tagging web content developed through collaboration of the major search engines such as Google, Yahoo, Bing, and Yandex.

Paradata” gives DIY learners indicators of learning resource usefulness, for example, many Facebook “likes” for a language learners group increases the visibility of the group and becomes a paradata assertion about its usefulness. Likewise social media posts with links to a page describing a learning resource say something about its popularity, or a formal endorsement of the resource by an organization (such as a state education agency) may be captured in a public repository, such as the Learning Registry.

Gamification and Intelligent Tutoring Data
With the help of Duolingo Noah learned Spanish, Portuguese, French, and Irish well enough to engage in conversations, and a bit of 11 other languages.  Apart from Duolingo, he is also learning Haitian/Creole using other web resources and with a friend at school who speaks the language.
Factors that make Duolingo an effective tool include its bite-sized assessment-as-learning lessons and continuous game-like feedback. This is competency-based tutoring at its best. Learners advance only after demonstrating mastery on granularly defined competencies, such as translating a specific word or phrase. Feedback is instantaneous and focused on correcting specific weaknesses. I see a lot of similarities between principles within gamification and learning sciences, both draw from an expanding knowledge of how the human brain develops and adapts to new challenges.  Game mechanics address learner motivation, providing the right level of challenge at the right time (zone of proximal development), building new knowledge/skills on existing knowledge/skills (constructivism), goal setting and visibility into thinking and progress (learner agency).
To deliver this kind of experience for the learner requires a rich set of data behind each assessment item (the granular competency being assessed, what a correct or incorrect answer means and what remedial feedback to give, etc.), detailed data collected every time the learner attempts to answer to guide feedback and progress, and data about the competencies and competency-based pathway.

“Big Data” and a Warning about Learning Styles Data
The theory of learning styles has been intensely reviewed, tested and debunked,” but well meaning organizations still offer learning style assessments and attempt to use the data to personalize learning.

Yes, big data sets can be used by recommendation engines to help filter all possible learning activities down to a few that are a good fit, just like Google targets advertising and Amazon suggests products “you also might like.” However, the notion that a person is a fixed type of learner that can be classified using a one-time assessment is oversimplified. Preferences change over time, the “best” instructional/study methods will vary based on context, and students may need to try multiple modes of instruction (see a concept in different ways) before mastering some learning objectives. It may be helpful for a learner to think about what kind of learning mode they generally prefer, but multiple options for each lesson allow the learner to choose how they right now. Even Google search results give a list of options and let the user pick…I don’t know anyone that regularly uses the “I’m feeling lucky” option.

The mode of presentation (visual, auditory, kinetic, etc.) is just one of many variables factor into selecting a learning activity. Being precise about the granular competency that the learning activity addresses, and the quality of the resource, is more important than the mode of presentation.
Analytics engines, informed by big data, can do more than predict how well a learning activity will work for a student.  They can help create conditions for motivation and engagement to help the learner reach personal goals.

Social Learning
Noah learns with friends on social media including Google hangouts and Facebook language learners groups. He also seeks out native speakers of the languages he is learning. When visiting the city where a relative lives, he made it a point to walk into a Portuguese bakery and start a conversation with the people working there.

Through school choice, he is attending a high school outside of his home district and enrolled in a French class just to get required credit for graduation, but he doesn’t think he’s learning anything there that he has or could learn on his own initiative. And his friends on social media are more at his level for conversations in French. So next semester his high school teacher will create a special “French 5” independent study option in which Noah will help teach French to freshmen.
Peer assessment can be an effective part of DIY learning. For some subjects data may be collected with online rubric-based peer assessment tools. Assessment-for-learning data is informs feedback.

Data for Feedback
There are three levels of feedback to support student-centered learning:
  1. Immediate feedback given during the learning activity after each click/response,
  2. Feedback at the end of a lesson that answers the question “What next?”
  3. Dashboards and progress maps that answer the question “How am I doing in reaching short and long-term goals?”
The 3rd kind of feedback allows learners to carry out personal learning plans as a kind of GPS guiding them to longer-term goals.

Data for Planning and Decision-Making
DIY learners are motivated by a purpose. Noah‘s fascination with linguistics motivated him to take ownership of his own learning. That interest is leading to decisions about college and career.  Often the purpose for learning is to gain abilities needed to support a cause, calling, or career goal.  Noah sees himself pursuing a career as a translator, but realizes that his interests and goals may change in the future.

Emerging sources of data will help DIY learners map backwards to identify credentials needed to support cause or career, and the competencies required to attain each credential. There is a trend in higher education and workforce training to offer stackable credentials such as a certificate that counts toward a degree. Projects such as the Credential Registry plan to provide data to help DIY learners make informed decisions about long-term learning goals and alternative pathways to reaching those goals.

The DIY learner then can track progress toward goals with the right data about achievements. Most of the time progress data is not in control of the learner and constrained to a specific context, such as language learning data within Duolingo, mathematics data in Khan Academy, course transcript data in a high school or college information system. However, several initiatives are working to give students control of their data. Initiatives like the Badge Alliance have published standards for the data representing achievements, and other organizations are building on previous work toward student-centered, secure, verifiable claims and credentials.
Data about pathways, plans, and progress can be combined and presented in a dashboard for the DIY learner. This is already available within silos, but someday learners will be able to get a more complete picture.

Finally, the same kind of “paradata” used to rate quality and fit of individual learning resources can also be used to inform bigger decisions, such as quality, fit, and cost-effectiveness of college programs. 

Now, Noah is considering a college that has a large language department with a good reputation, but that doesn’t tell him if the program is better than other options at preparing people to do what he wants to do after college. It also doesn’t tell him if the program is the most cost effective way of reaching his long-term goals. Some of this information can be discovered/collected from unstructured data, e.g. within social media and surveys. Other data might be generated through “big data” analytics. (Existing “college recommendation engines” tend to be more about evaluating the student’s chances of being accepted, rather than evaluating the value that a college program offers its graduates.)

A Vocabulary for Talking about GenDIY Education Data
The Common Education Data Standards (CEDS) defines the meaning of data elements used to support DIY learning including data for discovery of learning resources/opportunities, data used in assessment-as-learning and intelligent tutoring systems, data for planning and decision-making (including competency and credentials definitions, and achievement tracking). CEDS.ed.gov includes a searchable glossary of data “vocabulary” that is aligned to many of the other standards mentioned in this article. Other standards address the protocols and technical details for interoperability of systems and content for each of the kinds of data.

Tuesday, January 12, 2016

Grade Level What?

This post originally appeared on Getting Smart.  The theme came up in my discussions with Smart Parents author Tom Vander Ark and Getting Smart Managing Editor at the iNACOL Symposium. The factory model of schooling is giving way to more flexible options for personalized lifelong learning pathways. This is one close-to-home story about that transition...

At a recent holiday gathering I witnessed an interesting exchange between my son Benjamin and a relative who hadn’t seen him in a while. My relative asked, “What grade are you in now?” There was a long pause… I smiled. I could see the wheels turning as he thought about how to respond. What would have been an easy answer for me when I was Benjamin’s age is more complicated now. He could have given several different correct answers.

My son is an example of how “grade level” is becoming an outdated concept. In his public virtual high school he was enrolled this year as a sophomore, but his classes include a dual-enrollment writing class at a local college, and high schools classes usually taken by “9th” and “11th” graders. He also self-enrolled in self-paced guitar lessons via an iOS app and he is supplementing his French class with the DuoLingo app. From middle school to high school he jumped ahead and back in “grade-level” when changing schools, first after 7th grade and then changing high schools between “9th grade” and “10th grade”. So, he was never technically enrolled in 8th grade, but he took classes that sufficiently covered 8th grade learning standards.

Benjamin’s case is not unusual. He has friends that are home schooled and taking college classes as 15 years olds, and others enrolled in WPI’s Mass Academy, a public school in Massachusetts whose students attend a private university full-time as seniors in high school. According to ECS:
Forty-seven states and the District of Columbia have statute and/or regulations governing one or more common statewide dual enrollment policies,” and “three states leave dual enrollment policies to the discretion of local districts and postsecondary institutions/systems.
It’s not new that high schools determine a student’s grade level based on credits earned rather than age or cohort. What is new is the growth in options that allow student to advance at their own pace and earn college credit while in high school. Students that used to depend on the capacity of the local high school to offer an advanced placement class can now take advantage of AP or college-level online courses.

With dual-enrollment the lines are blurring between K-12 and postsecondary education, even as the institutions and public policy remains deeply rooted in the cultural inertia of separate domains.
With school choice and course choice, lines are also blurring between school districts. A student’s education is no longer fated to be on the same course and pace as everyone else that happens to be in the same zip code and age grouping. Students are benefiting as the factory model of education erodes and more student-centered options emerge. Benefits include greater potential for success and reduced costs for college and career training.

As a parent, I’m encouraged that my children have and will have options for lifelong learning that were not available when I was their age. I also see that they have new responsibilities as 21st century learners. They will need to take more ownership of their own learning. I grew up in an age of spoon-fed, one-size-fits all, everyone-moves-at-the-same-pace schooling. To take full advantage of emerging student-centered options, students today need to learn a new set of mindsets and dispositions. 

As a parent, I also have a responsibility to encourage and guide my kids in those attitudes and dispositions. So I need to keep learning. Resources like Smart Parents and Mindset: The New Psychology of Success are helpful. I can also help by learning about course choice options as they become available and learning how to evaluate what is a good fit for each of my children.  (I also have a child that is thriving in a traditional brick-and-mortar school.) By doing all this learning myself, I am modeling what it means to be a lifelong learner. Someone once told me that children learn more from what their parents do than what they say.

So, my son had multiple right answers when asked, “What grade are you in?” He could have said “I haven’t yet completed 8th grade,” or “I’m in 10th grade,” or “I’m in college.” I think he ended up saying something like “based on the courses I’m taking, I’m mostly in 11th grade.” Someday we will stop asking, “What grade are you in?” With the shift to lifelong student-centered learning, a more relevant and more interesting question (for all ages) can be “what have you been learning?”

Tuesday, September 15, 2015

Stackable Portable Digital Credentials

I've been participating in work groups from several key organizations that are developing standards for digital credentials. The following brief summarizes what's happening in the credentials space, particularly with stackable and digital credentials.

Stackable Portable Digital Credentials in Education and Industry

There is a growing interest in “stackable credentials” as a solution to problems faced by students, higher education institutions, workforce training programs, schools, and employers.  A report by the Center for Postsecondary and Economic Success at CLASP defines credentials to include “degrees; diplomas; credit-bearing, noncredit, and work readiness certificates; badges; professional/ industry certifications; apprenticeships; and licenses—all of which in different ways testify to people’s skills, knowledge, and abilities.”

The U.S. Department of Labor defines a credential as stackablewhen it is part of a sequence of credentials that can be accumulated over time and move an individual along a career pathway or up a career ladder.” The same concept might apply to a pre-career sequence of educational achievements such as credentials that qualify a secondary student to enter higher education.

An example “stackable” credential is a job-specific certificate earned in the short-term while counting toward the longer-term goal of a degree. Stackable credentials provide value to both the student and potential employers by showing short-term value  (what can a person can do now) and as a milestone toward a larger educational achievement. This is especially valuable for people who enter the workforce while continuing to pursue a degree. The Department of Labor recommends that higher education and workforce training providers “modularize curricula into smaller portions, or chunks, enhancing the ability of individuals to earn interim credentials and combine part-time study with full-time employment and/or supporting a family.”

Many organizations including the U.S. Department of Labor, U.S. Department of Education, community colleges, four-year colleges, workforce training programs, and industry groups are investigating how stackable credentials might address problems such as:

·      students giving up before completing high school and college,
·      the overwhelming cost of an all-or-nothing college credential,
·      unemployment persists while employers have trouble filling positions, and
·      training programs having trouble keeping up with changing needs in the global and local economies.

Stackable credentials also include certifications and licenses earned after receiving a degree. For example, medical professionals with multiple specialties may be more likely to be hired because they can fill more than one role (e.g. phlebotomist and EKG technician). Digital Promise is developing a micro-credential system that provides teachers with the opportunity to gain recognition for skills they master throughout their careers.

Portable credentials are credentials that are accepted across institutions, and across domains. One issue of portability has to do with a common understanding of the student competencies that the credential represents. When a student receives a baccalaureate degree in accounting, potential employers expect that the credential means the student has certain skills that qualify her for an entry level position in the accounting field. If the student is earning a credential with the intent of using it to qualify for a job then the competency model used by the issuing institution should be industry recognized.  If a K-12 student is earning a high school diploma with the intent of going on to college, the diploma should be acceptable evidence for the postsecondary institution to know that the student is college ready.

Another issue of portability is the acceptance of the credential in another jurisdiction, for example, if an associate degree or certificate earned at a community college in one state is accepted at a 4-year institution in another state as credit towards a bachelor’s degree.

Digital Credentials

Digital credentials are verifiable electronic records of a person’s achievements or qualifications. Digital credentials take different forms depending on how they are used. Technical implementations include electronic transcripts, digital certificates, and digital badges. For portability, the digital credentials must use widely adopted technical standards for interoperability between issuing and consuming data systems.

Technical Standards for Stackable Credentials

Government agencies, industry groups, standards bodies and education providers are developing approaches to the data collection and use related to stackable credentials. For example:
  • The Badge Alliance and related Open Badges Initiative have developed an open standard and free software for digital badges (an image file with embedded metadata representing a personal achievement) that links back to the issuer, criteria and verifying evidence.
  • The Common Education Data Standards (CEDS) include standard vocabulary for data used to recognize student achievements linked to evidence.
  • Credential Transparency Initiative is creating a credential registry that will allow users to see what various credentials — from college degrees to industry certifications and micro-credentials — represented in terms of competencies, transfer value, assessment rigor, and third-party endorsement.
  • IMS Global is working with college registrars on an extended electronic transcript standard that would include record of competencies and non-course activities.
  • PESC has formed an Academic Credentialing & Experiential Learning Task Force to build on its previous eTranscript standard
  • W3C Credentials Workgroup plans to publish a standard for encoding personal credentials in a way that can be authenticated and verified using technology similar to bitcoin and technologies addressing personal identity and privacy.

Some of the implementation challenges that these organizations are wrestling with include:
·      Should a persons digital credentials from multiple institutions be kept in a “locker” or “backpack” under the stewardship of a third party hosting organization, held privately by the recipient, or exist in a distributed network?
·      What technology should be use to certify the validity of a credential and protect against counterfeit credentials?
·      What method and data standards should be used to standardize the information about what a credential represents?
·      How to digitally link the identity of a person to a credential they have received?

Key terms related to this topic include:

career pathway – a series of achievements and that qualify a person for a career
career latter – a path of achievements that allow a person to move into increasingly more advanced jobs within a single industry or career path
career lattice – a connected sequence of achievements that allow a person to move up in a career pathway or over to a new career using transferable qualifications
digital credential – a verifiable electronic record of a person’s achievement or qualification
portable credential – credentials that are accepted across institutions and/or domains
stackable credential – part of a sequence of credentials that can be accumulated over time
micro-credential – a credential that recognized mastery of a single competency