In the current state, the ability of higher education institutions to provide holistic assessments of student learning, development, and success and to provide comprehensive advising (using curricular and co-curricular data) and other student services using disparate systems is virtually impossible. This is because the interoperability between systems may be limited, or they require IT, staff/vendors, to develop interfaces so that data can be moved between the systems through some form of files, including text, XML files, or other means. In addition to the limited interoperability, the lack of data liquidity (the ability to move data from one system to another) I shared in this post is an even bigger constraint. That there is not a single common structured data model in higher education is one of the big impediments to an environment where disparate systems within the institution can have a set of systems working together as one. Even a bigger goal is for multiple higher education institutions to have the ability to exchange information between their systems in cases where students may be attending both institutions or if they transfer from one to the other.

I wrote this blog about a proposal for a Common Learning Portfolio Markup Language in 2013 based on my observation working with several information systems at our university and the inability of these systems to easily exchange data among them. These systems include electronic medical records, student information systems, residential management systems, judicial conduct, and other systems. I observed that these systems could not interface with each other because they were either created by different vendors or our developers developed them. These different systems also did not share a common data model or infrastructure, making it easier for our developers to readily build programs to exchange data without developing additional programs to extract, transform, and load (ETL) the data.

Recently, I noticed different vendors’ efforts to develop/implement their versions of structured higher education data models and infrastructures. I haven’t delved into the details of each model/infrastructure to discuss how they are implemented. Still, given my limited access and understanding of the data models, it seems these efforts by the vendors are specific to their set of products (and their partners, however, that’s defined). In addition, these data models do not seem to include co-curricular information such as involvement with student organizations, career internships, and volunteer activities. The links below provide information about these different efforts:

Oracle Higher Education Constituent Hub (HECH)

“Constituent data is distributed across the enterprise among various systems (e.g., HR, Student Information, CRM, and Learning Management) across the Campus and all University locations. It is typically fragmented and duplicated across operational silos, resulting in an inability to provide a single, trusted Constituent profile to business consumers. It is often impossible to determine which version of the Constituent profile (in which system) is the most accurate and complete. The HigherEducation Constituent Hub (HECH) solves this problem by delivering a rich set of capabilities, interfaces, standards-compliant services, and processes necessary to consolidate Constituent information across the institution. This enables the deploying institution to implement a single consolidation point that spans multiple languages, data formats, integration modes, technologies, and standards.”

Salesforce Higher Education Data Architecture (HEDA)

“Leverage a newly established data standard and managed package to meet the needs of any institution. Institutions can continue to deliver value across campus by building on core objects, fields, and automation and integrating with a growing number of Higher Education AppExchange apps that are standardized on HEDA.”

Ellucian Higher Education Data Model

In many industries standards already exist, albeit with only partial adoption. In the HE sector, however, Ellucian had a unique opportunity to start with a “clean slate” and to create something new…and so we created the Higher Education Data Modal (HeDM). HeDM is a defined standard to illustrate a uniform view of “the world”, so that users can view data and interact with each other. The data model itself creates a defined data object or entity, reaching all corners of an institution, covering Recruitment, Students, Finance, Advancement and beyond.”

The US government has also started its effort to standardize education data through a Common Education Data Standards (CEDS) project. This project seems to be more abstract in that the data model is not designed specifically for any set of vendor products. Still, rather more of a definition of a structured data model, and the adoption is voluntary.

While education institutions across the P-20W (early learning through postsecondary and workforce) environment use many different data standards to meet information needs, there are certain data we all need to be able to understand, compare, and exchange in an accurate, timely, and consistent manner. For these, we need a shared vocabulary for education data—that is, common education data standards. The Common Education Data Standards (CEDS) project is a national collaborative effort to develop voluntary, common data standards for a key set of education data elements to streamline the exchange, comparison, and understanding of data within and across P-20W institutions and sectors.

We are moving in the right direction with the efforts I mentioned above, though we are still years away from having a set of common data models that all higher education institutions can use.

As I noted in my introduction above, it seems to me that until a common structured data higher education data model that can be used as a standard exists, higher education institutions will not be able to develop a holistic assessment of student success and provide services such as advising that use curricular and co-curricular information.