Of That

Brandt Redd on Education, Technology, Energy, and Trust

29 March 2013

A Taxonomy of Education Standards

I've previously posted and updated A Four-Layer Framework for Data Standards. When working with education standards I've also used the following taxonomy that categorizes standards according to their purpose. For convenience, this taxonomy is also available in PDF form under a CC0 dedication.

Types of StandardsThere are three types of standards that are involved educational efforts: Academic Standards, Data Standards and Technology Standards.

Academic Standards include achievement standards like the Common Core State Standards (CCSS) plus curriculum and testing standards. Contemporary practice in the U.S. is to describe academic standards in the form of learning objectives – descriptions of skills that students can acquire or demonstrate. Historically it was more common to describe standards in syllabus form – as a list of subjects to be studied.

Encouraged by the No Child Left Behind Act, the 50 states have each defined core curriculum standards. More recently, the CCSS standards for Mathematics and ELA-Literacy have been adopted by 45 states. Using a similar process, the Next Generation Science Standards have been proposed for multi-state adoption. In higher education there is no such consistency. Some institutions have developed their own sets of standards but most leave the objectives up to the professor. A few industry organizations publish standard sets. These include the AAAS Benchmarks for Science Literacy[3] and the National Center for History in the Schools standards for History.

Data Standards define the data elements and structures used to store and exchange educational information. In the Four-Layer Framework data standards may include layers 1-3 (Data Dictionary, Data Model and Serialization).

For education, the three major domains of data standards are Student Data, Educator Data and Content Data. Important metrics like graduation rate, student financial aid repayment or college-going rate are derived from data sets but aren’t data in and of themselves.

Student Data includes traditional demographic information as well as a student record which includes academic achievements, assessment results, learning activities, attendance and so forth. Educator Data includes information about teachers and staff. It includes qualifying information like academic credentials, a portfolio of creative works and publications and data about teaching performance. Content Data, often called metadata, is information about learning materials including textbooks, assessments, multimedia and digital resources. Content data often indicates the alignment between learning resources and academic standards like the CCSS.

Technical Standards define how systems interoperate. Accordingly, they usually include the protocol layer of the Four-Layer Framework. A wide variety of standards may fit into this category but the majority of education-related technical standards involve Content Packaging Formats, Interoperability Protocols and Data Exchange Protocols.

Content Packaging Formats support the transport of learning content (e.g. text, video, graphics, etc.) and assessments between systems. Examples include IMS Common Cartridge and SCORM.

Interoperability Protocols support interoperability among learning systems. The most common use case is integration of learning tools (like simulations, games or assessments) into learning environments (like a learning management system). Key functions are to identify the user to the learning tool, ensure that they are authorized to access the content, transfer control to the tool, and collect data back. Common examples include OpenID, SAML, OAuth and IMS QTI. Data Exchange Protocols represent layer 4 in the Four Layer Framework for Data Standards. Thus, data exchange protocols are usually paired with a corresponding data standard. Frameworks for setting up data exchange protocols include ESB, SOAP and REST.


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  2. I would add a fourth category of data standards, i.e. Process Data. Process data is not about tangible things (nouns) such as students, educators or content; it is about the interactions between people and content.

    We are seeing a shift away from the focus on education data as "counts", e.g. the number of students, by ethnicity, enrolled in a school or who pass a state test. An emerging emphasis is on process metrics that inform personalized, competency-based learning. Process metrics capture data about interactions between students, educators, and content -- including details about actions and reactions during lessons/activities and embedded assessments.

    Data about how learners interact with content and about student progress and/or remaining misconceptions after interacting with the content support formative feedback loops to optimize learning for the student.