Opinion | Research

Turning ‘Google Maps for Education’ From Metaphor to Reality

By Jim Goodell     Jul 14, 2018

Turning ‘Google Maps for Education’ From Metaphor to Reality

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 MedBiquitous standards 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*NET career 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.

Opinion | Research

Turning ‘Google Maps for Education’ From Metaphor to Reality

By Jim Goodell     Jul 14, 2018

Turning ‘Google Maps for Education’ From Metaphor to Reality

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 MedBiquitous standards 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*NET career 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.

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