14 Important Initiatives for Education Big Data and More
Just like Sheryl Abshire(Chief Technology Officer of Calcasieu Parish Public Schools, LA) pointed out :
Districts already have more data than they can manage, and the new assessments and teacher evaluations for 2014 promise to add more to the overflowing pile of numbers.
District leaders are overwhelmed right now because they face many changes in 2014, and they’re all data-related. The first thing to do is figure out three issues: (1) What sort of assessments you are giving, (2) how the data are being analyzed, and (3) who is accessing and using the data, and how.
For all the data available to us through technology, school leaders and educators still lack the ability to easily transform that data to information to help guide decisions about instruction, school administration, and operations. Simply put, education data and information systems need to be in service of learning. SETDA had developed a report, “Transforming Data to Information in Service of Learning,” to raise awareness about the major K-12 data standards and interoperability initiatives underway to address this gap and to offer recommendations for how K-12 education can become more responsive to educators and better targeted toward individual student success.
Citation: Fox, C., Schaffhauser, D., Fletcher, G., & Levin, D. (2013). Transforming Data to Information in Service of Learning. Washington, DC: State Educational Technology Directors Association (SETDA). (licensed under the Creative Commons Attribution 3.0 Unported License)
This is a scenario of the vision demonstrated in the report, from this example the usefulness of each initiative (bold text) is shown:
Jack, a new student, is struggling to understand the concept of variables, which surfaced in a state math standard aligned to the Common Core State Standards (CCSS) for high school algebra. His math teacher knows from previous quiz results that Jack tends not to do well with word problems. She also knows that his quiz results tend to be better when he’s heard explanations rather than just reading them.
The teacher gained this information through a combination of sources. First, because Jack’s new and old high schools support the PESC standard, the new school was able to accept delivery of a high school transcript that was immediately added to his latest records. Second, because the schools are in separate states that support the Digital Passport, they’re also able to share student data maintained in their respective longitudinal data systems. And third, Jack’s teacher downloaded assessment data from a website the school was granted access to by Jack’s parents. Before moving from one state to another, they had used MyData functionality on the parent portal maintained by his previous school in order to capture a snapshot of Jack’s school records—including assessment results—and then saved it to a secure online data repository.
Both the district that Jack’s family has moved from and the one they’ve moved to participate in the Common Education Data Standards effort, which defines how data should be formatted for optimal integration. That means his new district could absorb the data made available to the school and add it without human intervention to the data systems and applications already in use by Jack’s new teachers.
Because the district has adopted the inBloom user identity directory, which provides a single-sign-on capability, the teacher only has to log in once to access multiple instructional programs.
The teacher searches for a video for Jack on “variables.” Along with the key words in the search, she specifies grade level, preferred mode of learning (video), and time requirement (10 minutes or less). This filtering capability is available because the content creators have used the tagging scheme laid out in the Learning Resource Metadata Initiative. She does a quick comparison of the various videos, whose paradata has been exposed through the Learning Registry, and chooses one with a strong rating as accorded by other educators.
Jack watches the four-minute video that explains variables and works through a similar type of problem as the one he’s trying to solve. The instructor who created and posted the video is somebody who lives and teaches in another state. Fifteen minutes into the class, he has finished the first of two story problems and has settled into his work. The assessment results, couched in the common language provided by IMS and SIF’s Assessment Interoperability Framework, are automatically made available to a number of the applications in use by the school, one of which is an Ed-Fi Solution dashboard, which the teacher will use to review the results of that morning’s learning efforts.
Meanwhile, elsewhere in the class, the teacher consults her tablet and identifies the next math standard she’d like her more advanced students to work on. She pulls up a formative assessment application from the Smarter Balanced Assessment Consortium that has mapped its assessment content to the CCSS using specifications defined through the Granular Identifiers and Metadata (GIM-CCSS) project. She guides those students to work through specific practice problems for which she’s already provided instruction in order to see how much they remember from the lesson.
The instructor is experimenting with the Open Badges program, which allowed one student to prove her expertise in algebra by earning a digital badge through a free online university course; this student is moving immediately to self-study in a different area suggested through xAPI, guidance formulated by an artificial intelligence analysis of her previous learning activities.
Since Jack’s schools and districts have implemented crucial data and interoperability initiatives, he has been able to seamlessly transfer schools and re-enter the learning experience with minimal disruption. Likewise, administrators and teachers have been able to customize Jack’s instruction quickly and accurately with very little time or costs diverted to re-assessing him or ensuring his paperwork was in order.
In this report, SETDA profiles 14 distinct initiatives, and provides a detailed view into each of these initiatives in three broad categories, which help define the primary purpose for each:
- Ensuring consistent data definitions
- Enabling the sharing of information across systems
- Facilitating the search and discovery of education resources
Also, Brandt Redd, Chief Technology Officer at the Smarter Balanced Assessment Consortium, had proposed “A Four-Layer Framework for Data Standards“, and use the following taxonomy to categorize standards according to their purpose. (A Taxonomy of Education Standards)
Types of Standards: There 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 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.
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.