Every Journey Begins with a Single Step

On the road to information management, a great deal of time is spent doing “data archaeology” and “data wrangling”. Locating all the data created by and consumed within an organization can be a daunting task. Couple that with data that now resides off-site in cloud-based or third-party applications, you have a lot of discovery to do.

This was the case in public education, lots of systems collecting data with no central understanding of how these data can be categorized and defined to be analyzed and actionable. The National Education Data Model (NEDM) was an attempt made by the National Center of Educational Statistics to create a catalog of all of the recommended data a school should be collecting. In a lot of ways, NEDM started the discussion about an educational data standard; however, it did not consider the state’s laws regarding the collection and use of learner data. Soon states were once again looking for a standard for data governance. Further, NEDM defined elements for collection classified Personal Identifying Information (PII), and special rules were imposed by the Family Educational Rights and Privacy Act (FERPA).

In September 2010, the Common Education Data Standards (1.0) was released to the public. It defined the collection and values of 161 data elements. Fast forward to today, and version 7.0 contains over 5,000! CEDS works to streamline and assemble data models, tools, and metadata used within P-20W institutions and sectors. This is more than just an educational asset, but a living initiative that evolves with new standards put forth every year. CEDS initiative impacts personas from IT Developers and data stewards to policymakers and practitioners, ensuring the community of education stakeholders have a common understanding of the data management initiatives.

So why do educational agencies choose to align to CEDS? First, it allows for an intelligent and informed collection of data based on a specific intended use. Schools do not have a large IT staff to extract data and provide it for research, instead, they rely on a Student Longitudinal Data Store (SLDS) to store and integrate data from Student Information Systems (SIS), Learning Management Systems (LMS) and digital assessments. Educational agencies can collaborate, using a standard like CEDS in concert with open standard data movement tools, such as Ed-Fi and the Access for Learning (A4L) to collect, integrate and align. Successful implementations include Early Learning to Kindergarten feedback and actions as well as college and career readiness reports for feeder high schools.

Next time you get ready to tackle and educational information project, check out the CEDS 7.0 standard for a catalog of defined elements and relationships. A great place to start the journey!

For more insight into data initiatives in the Education space, contact us.

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(sigh) Me, too…

There’s something about having the option of self-service that makes our lives easier and more accessible. Whether it’s the efficiency of buying a train ticket from a Billetterie in Paris, ordering dinner through our favorite food delivery app, or answering critical questions in seconds from a self-service BI report, we feel empowered without the need for advanced tech training.

From my experience, this is true for Higher Education consumers, too.

When I was a student, I loved being able to access my degree audit and run what-if scenarios:
– “What if I changed my major from Computer Science to International Studies?”
– “What if I pursued a double degree or a double major?”
– “What if I stopped going to Biology this semester?” (Not an example I’d suggest living out.)

Similarly, as a staff member and instructor, I loved the ability to:
– Understand student progress
– Know when critical systems were down
– See how many of my colleagues rallied their departments to contribute to the annual fund

…all without having to bother someone (or dial the university switchboard because I didn’t know whom to bother.)

Power BI has taken data democratization to another level.

Data democratization means making digital information accessible to everyone who should have access, without gatekeepers, request forms, or – in my case – university switchboard dialing. It’s a big part of the self-service paradigm that has gained significant strength in recent years thanks to demand and the pace of tech advancement in Power BI, regardless of industry.

In Higher Ed, we can specifically use these advances to prioritize students and promote an inclusive culture. We certainly saw the impact and needs during the last year of remote learning, with virtual and hybrid education taking place throughout the world.

And the capabilities continue to expand, including the democratization of artificial intelligence and data science and the integration of Power BI for Microsoft Teams.

So, what’s missing?

A need that has come up repeatedly with folks I have worked with is to earmark records for a specific purpose. As an example, when I worked at a small liberal arts college – a business user asked if there was an easy way to select a couple of records in a report and mark them with an X so she could follow up with them later about an upper-level course offering. At a larger university, the controller wanted ways to create an exception report for records that should be flagged to reconcile later.

In other words, sometimes end-users want to take action with their data quickly, and now they can with the integration of the Power Automate visual in Power BI.

Power Automate empowers us to go from insights to action.

Power Automate (formerly known as Microsoft Flow) enables Robotic Process Automation (RPA) and is part of the Microsoft Power Platform collective:These four low-code tools can be used separately or together to support and improve productivity.

With the April 2021 Power BI update, we can now take action on Power BI reports interactively through Power Automate (preview).

Power BI + Power Automate in action.

The following demo is an example of Power BI + Power Automate in action, built on top of Microsoft’s Open Analytics for Education dataset:

As you can see, consumers now have a way to invoke actions with their selected data. In addition to updating an excel table from Power BI, some other helpful templates currently available to try include:

Learn More

If you want to learn more about Power BI and Power Automate together, including current limits and limitations, read more about it in the Microsoft documentation.

If you’re looking to learn more about all Power BI capabilities, 3Cloud has several resources to explore.

Contact us to learn how we can help you envision, build, and deploy a modern analytics platform.

The post Power BI + Power Automate for Higher Education appeared first on 3Cloud.

One common theme I’ve learned, regardless of industry, is that there is a lack of data interoperability among disparate systems.

In education, that translates to something like this:

The Registrar’s office can analyze student registration and grades because they understand the student information system (SIS). Professors can analyze online student engagement with their courses because they have access to the learning management system (LMS). Assessment professionals can track annual reports and related activities because they have an assessment software system. And so on…

Independent, siloed systems usually exist to make things more focused for practitioners and those served. To that end, they provide great value. It is the lack of data interoperability that becomes a significant burden.

Accidental Architecture

In Higher Ed, Institutional Research is a place where data silos often converge. Perhaps the President’s Cabinet wants to understand the influence of online course engagement on student success. Or a reputation survey might ask about the carbon footprint of commuting students and employees. One time, I was asked to append bread preference (white/wheat/multigrain) by class and location for a campus picnic!It is excellent to piece together data elements to help people with sense-making. But still, the impact on the greater system is that of an accidental architecture.

Ad hoc data convergence is not always efficient. It isn’t easy to share or replicate, and sometimes – it costs more than the value it produces.

Better Together

Like close friends, a needle and thread, or your favorite mixed beverage – data signals are better together. Shared data models with rich semantics, cloud resources, and data science techniques are being used to:

– Optimize manufacturing,
– Improve genomics research, and
– Help job seekers with accessibility needs find careers.

In education, that translates to:

The results being that the greater academic community, acting in students’ educational interest, are able to access and analyze student needs, preferences, and risks. Practitioners would continue to optimize their operational area, and university leaders and constituents are better equipped to understand the big picture and support students holistically.

Open Education Analytics

To see how this works in action, Microsoft partnered to develop an open-source project that lowers entry barriers to the Modern Analytics ecosystem for education.

Openeduanalytics.org is a repository that contains assets for setting up a reference implementation and architecture in Azure that will take you from raw data to a Power BI report in just a couple of hours. The implementation guide contains all the steps needed to introduce the solution.

I encourage you to try it out if you’d like to bring your data together under one (virtual) roof and see immediate results. And if you are anything like me – it will also be a great tool to help others understand and contribute to some of the capabilities of modern analytics!

Here are some questions to explore after deployment:

• What value and insights did you find in the visualizations provided in the solution?
• How would you explain those insights to decision makers?
• Are there elements that you would improve?
• If this use case doesn’t apply to your school, university, or district, what makes more sense?
• Who on your campus would be interested in seeing this deployment?
• What questions do you have that are still unanswered?

We would be glad to discuss these questions with you or keep reading if you prefer to learn more on your own!

Learn More

If you would like to learn more about scaling and deploying Modern Analytics in education beyond the Open Education Analytics guide, 3Cloud can help you build a foundation for analytics at scale. Please contact us directly to learn more.

The post Data Lakes and Modern Analytics for Education appeared first on 3Cloud.

Analytics can save higher education.

It was the first time these three national associations, which collectively serve
2,500 institutions and 22 million students, have publicly converged to make a call to action.

So why would they do this?

I asked myself that, too, listening to the announcement of the initiative last September.

Working in education, in general, is often described as a labor of love—the “why” matters a lot. And the power of serving a strong purpose propels many who choose a profession in education.

The collaboration for this initiative turned out to be no different: The faculty, staff, and affiliates involved are committed to helping their institutions use analytics to gain insight and act on complex issues.

To accompany the call to action, the associations outlined six principles to accelerate the meaningful use of analytics in higher education decision support:

1. Make it an institutional commitment.
2. Establish a team approach.
3. Prepare for setbacks.
4. Invest time, talent, and money.
5. Understand it has real impact on real people.
6. Take action now.

From my experience working in higher education decision support, I support the commitment and its principles. And I understand it is not as easy as it sounds.

This three-part series will elaborate on why and how to drive organizational change in higher education with analytics. The three key improvement areas I will focus on are components of institutional identity, including purpose, culture, and brand:

• Purpose: Put students first by providing responsive and timely support.
• Culture: Promote inclusivity in the decision-making process.
• Brand: Be strategic in an ever-changing landscape.

Before I get into those, I want to acknowledge some past and existing barriers to progress. Specifically, the intentional and unintentional misuse of analytics is consequential. Responsible use of data should always be a top priority, coupled with training, transparency, and a shared understanding of any underlying methods used. These barriers should not prevent institutions from using analytics to drive organizational change. Instead, they should be used to lead conversations about how to design and improve processes, protocols, and skills throughout the institution.

Now, let us discuss how analytics can help institutions fulfill their purpose:

Put students first by providing responsive and timely support.

Most university mission statements include aspirations emphasizing student learning, such as: “We nurture lifelong learners” or “We are a learner-centered research university.” It is, therefore, imperative to prioritize student learning as a top reason for higher ed leaders to commit to using analytics to improve higher education.

And we should do it now.

While the current climate might not seem ideal to launch an analytics initiative, faculty, staff, and students agree student learning is critical in this new environment. Institutional leaders know that they must make adjustments to support student learning and will need help determining how to best do that.

The following example in practice is a sample illustration involving responsive and timely support for student learning through the COVID-19 pandemic. This university, like many, transitioned to a remote learning environment in the middle of the Spring 2020 semester. To determine and prioritize student needs, they paired student data with real-time survey data administered during the transition. Microsoft Power BI was used to transform and analyze the data to make actionable content available to student support service providers, faculty, and senior leaders.

The high-level conceptual illustration of this deployment is as follows:

Immediate Needs Solution

At the beginning of the transition to remote learning, data mining the survey results revealed that the institution had to address some immediate physiological needs to support students:

Using drill-through capabilities, student support service providers were able to contact each student the same day to provide or arrange for resources. Technology needs, such as access to laptops and the internet, were also addressed early.

Remote Learning Insights

After remote learning began, timely check-ins with students revealed that regardless of how far along they were in the student lifecycle, most students were increasingly worried about their GPA. To supplement that, faculty and student feedback indicated the most and least effective instructional methods across the institution:

Using this data, synchronous courses were identified as the most effective instructional method for faculty and students. To facilitate prioritization of this instructional method, additional funding was secured to fund faculty-led mentor workshops for synchronous courses, while a technology committee is assessing learning management systems to improve assignment and course-material delivery.

Retention Modeling with Artificial Intelligence (AI)

AI methods were used to determine the key influencers involved in whether students plan to return to the institution in the fall. Expectedly, students were almost 10 times more likely to be unsure about their return if they had financial concerns. Nearly as important, if students reported feeling disconnected or dissatisfied with the transition to remote learning, they might be less likely to return.

On the flip side, for students who are likely to return, top influencers included: feeling connected to the institution and preferring synchronous course lectures as an instructional methodology. In other words, AI intelligence takes text into account as well.

These results helped senior leaders prioritize applying for grants to help students with financial needs and increase virtual town halls, appointments, and live hosted events to keep students connected.

Summary

The primary reason to drive organizational change in higher education with analytics is to put students first by providing responsive and timely support. In the example provided, the university was able to meet students’ needs because they had the information they needed at their fingertips, enabling them to act quickly.

Next time, we will discuss why and how to use analytics to promote inclusivity in the decision-making process.

Until then, we would love to help you!

Contact us to learn how we can help you envision, build, and deploy a modern analytics platform.

The post Driving Organizational Change in Higher Education with Analytics appeared first on 3Cloud.

To help set the stage a bit with some technical specificity – this project is about building a data warehouse in SQL Server from the ground up, incorporating three primary domains of data from within a fairly large school district with approximately 30,000 students:  student data, financial data, and HR data.   With the data structured and consolidated in one place, Microsoft’s Power BI is then used to provide insights to staff throughout the district using sophisticated analytical logic, powerful aggregation capabilities, and centralized web-service hosting and deployment for ease of collaboration and consumption.

Holistic Process, Structured Data Are Key

Something I’ve written about in the past (if I can be permitted to go ahead and climb atop my soap box right out of the gate) is that properly structuring data is absolutely paramount for an analytics project – and education is no exception;  if anything, it only underscores the importance.  This isn’t to say that the data we worked with started off unstructured (though it can in some cases, as with sentiment data drawn from survey feedback), or that insights can’t be drawn from raw data, just that the state and structure of our source data is seldom ideal to an analytics practice – both in terms of what is most performant in the analytics tools being used, but also with regard to what is most conducive to imparting meaning and insight to our data consumers.

So how is such an important thing attained?  Aside from the technical particulars of what constitutes a “proper” data structure for analytics – which represents a body of nuanced information far greater than what can be placed into a blog format – I’d like to focus more on the holistic process that needs to be established as a prerequisite to building an analytics solution.  In summary, that process is about garnering understanding, which itself is contingent upon something far more interpersonal and organic than technical:  the establishment of a dialogue between those who work within the day-to-day processes which generate our intended source data, those who are tasked with collecting and structuring that data, and those who will become consumers of the analytical insights which are the essential output of the project.   This represents no small feat of coordination, and often defies some manner of technological “silver bullet” for remediation.

In education, a lot of data is generated by human beings entering data about other human beings; there is margin for error, bias, and assumption.  There are contextual qualifications, such as assessment scoring,  that do not fit neatly into universal schemas.  So it is absolutely critical that the processes which generate source data are understood fully. This is the foundation to any analytics solution, and like anything, the soundness of the foundation represents the stability of everything built on top of it.  As an analytics practice matures, the necessity becomes more apparent than ever.  Advanced analytics, like those on offer in Microsoft’s Azure AI suite, absolutely require a high degree of confidence in the data inputs that they draw upon. The process by which data is structured for analytics is also one in which such confidence is built and standardized across an organization.

Breaking Down Siloes Boosts Shared Goals

If those tasked with building and maintaining an analytics practice draw the foundation of their understanding from those that oversee the generation and curation of source data, then we can say that the building plans are drawn from the perspectives of those that will be consumers of the data.  This is the source of our big picture, and the sophisticated logic that crystalizes it.  This is the perspective that provides the basis for breaking down the silos in which our source data is often stored, and the impetus for finding value in the exercise itself.  In education, every facet of a school district’s operation bends ultimately to a single common goal:  improving student outcomes.  This means that a truly holistic view involves not just the data about students and education directly, but also about the district’s function across the board. This includes everything from how budget is allocated to program management efficacy – from staffing of open positions to credentialing and qualifications of those staff.  In terms of structuring data for analytics, this particular process is often referred to as “conforming”, or tying disparate sources of data to a single common form for the sake of deeper, more complete insight.

As a technologist, I’ve always been drawn to the way in which new tools help facilitate old tasks.  Or, more specific to Business Intelligence, how new technologies can ease the turning of the gears of discovery and implementation which drive an analytics practice along its path to greater maturity and, by extension, greater insights.  However, as a consultant, I am also fascinated by the more organic nature of problem solving, and the elemental facets of such which seem to defy the passage of time – largely I think because they stem from our strengths and weaknesses as human beings.  There’s a certain resonance to that when working in the education space, which is fundamentally about advancing human understanding – an endeavor which requires a certain measure of due diligence,  an open dialogue among those with something at stake, and a spirit of breaking down barriers if it is to succeed.

We Build Solid Foundations

BlueGranite can help your organization implement a holistic, thriving analytics practice, too. Whether you’re considering an Azure-based modern data warehouse, or how to put AI and machine learning to work for your enterprise, we can help. Contact us today to learn how we can implement a strategic analytics framework tailored to your needs.

The post Building an Analytics Practice for Primary and Secondary Education: Sharing Some Thoughts and Experiences appeared first on 3Cloud.