Unizin Data Marts Special Issue: November 2022
This report is to be shared by design to communicate the full scope of Unizin impact, work products, and status at your institution.
Big Data, Bigger Challenges, Even Bigger Solutions
The Unizin Data Platform (UDP) was made available to all Unizin members in late 2017, to bring together and unify data from two large systems: the Student Information System (SIS) and the Learning Management System (LMS). Using a standards-based approach, Unizin can combine contextual data, such as student demographic information, with data describing things like assignments in the LMS, as well as event data that streams into the UDP as students navigate their digital learning ecosystems. The UDP continually grows as our list of vendor partners grows to incorporate additional context and event data from tools such as Canvas, Top Hat, and Kaltura. Today, the average size of a UDP tenant is approximately 11TB, and it’s not uncommon to see upwards of 5 million events stream into a UDP tenant on a given day early in the fall semester.
As the data in the UDP grows, so do the complexities of working with the data to extract valuable insights. Turning 11TB of data into information requires professionals with significant SQL expertise to parse the data and additional staff with different, complementary skill sets to deliver the right information, to the right person, at the right time to inform important decisions.
Information can be presented in data visualizations or dashboards using tools such as Microsoft Power BI or Tableau. At the same time, other information may be surfaced in custom web applications that require multi-talented teams to create and maintain. While significant personnel resources are required to move from data to information, then deliver that information to various individuals or stakeholder groups, investing in these efforts can help answer some of the most important questions in higher education:
- What is it worth, from a resource perspective, to increase graduation rates by 5%?
- To decrease the rate at which students enter academic probation by 3%?
- To increase the number of students who successfully complete large STEM gateway courses?
- What resources are required to ensure a viable journey to student success?
With resources, particularly technical resources, in high demand, lowering the barrier to entry to the UDP can help accelerate the application of data and information to directly support these types of student success initiatives.
There is a data mart for that
One way to make the UDP more accessible and impactful to a larger number of individuals is by creating bespoke data marts, a subset of a data warehouse or database, based on structuring or modeling data, around a set of proposed outcomes or inquiry. Data marts bring aggregated and calculated data fields together in flat, easily accessible tables or sets of tables.
Unizin data marts are designed and developed collaboratively with our members and can significantly accelerate a member institution’s ability to derive meaningful information and knowledge from the UDP. The goal is to deliver a triple-bottom-line benefit to our members:
- Data marts include calculated data fields that represent new information that can be used to support decision-making around student success.
- Data marts are accessible to a larger number of data consumers, who may not have the technical or SQL expertise required to generate the information otherwise.
- Data marts reduce the time needed for local, technical resources at our member institutions to draw value from the data.
For example, to understand the potential learning impact of time spent in the LMS – namely, does time spent in Canvas positively or negatively correlate with student outcomes – we would need to hone in on a specific subset of data within the UDP. The resulting data mart would include context data describing students and courses, as well as event data generated by students, such as navigating to a page, submitting an assignment, or clicking on a reading. Combining these data sets could further our understanding of the relationship between time spent in learning tools and student outcomes.
Unizin Data Services has created just such a data mart that does the work of specific modeling against the UDP, employing SQL that selects the appropriate tables and fields, and executes computations tailored to a data consumer’s preferences around how long to gauge whether or not a student is still active in a course. Our data mart draws from the Course Offering, Course Section, Academic Term, and events tables. It calculates duration based on 10, 20, and 30-minute cutoffs (reflecting various thresholds for what researchers define as “inactivity” within the LMS). By including all of these data in an easily accessible data mart, data consumers can much more easily and quickly integrate calculated data fields that describe a student’s overall activity into new or existing applications, reports, or dashboards.
Unizin created an initial set of core data marts to help members understand trends around LTI tool launches, Canvas tool utilization, student-content interactions, and student activity patterns. In 2021 Unizin launched two Task Forces to expand our suite of data marts. These Task Forces brought together a diverse set of personnel from our member institutions, charged with identifying important data points to support student success initiatives and serve various stakeholder groups, including faculty, students, advisers, and other student success staff. Through these efforts, Unizin is piloting a series of new Unizin Student Success Data Marts, to support instructor, student, and adviser decision-making with data that describes weekly student activity patterns in a course, student activity, and performance on various types of assignments and student time spent within the LMS.
These new data marts, and those that will follow, are key to transforming the UDP from a data aggregation tool to a knowledge and insight engine for our members and expanding the impact, reach, and value of Unizin to constituents across the institutions they represent.
If you want access to these data marts and associated training resources, please contact firstname.lastname@example.org.
Taskforce Data Marts - History and Current State
Beginning in the spring of 2021, the task forces for Student Success (for advisors/student Success teams and faculty/students) began the journey of identifying analytics use cases that would inform the creation of targeted data marts. This journey moved from defining three overarching use cases (impact of course activities on learner outcomes, measures of engagement/disengagement, and course profiles) through three levels of analysis: student, course, and program. Working with a small working group (Gwen Gorzelsky, Colorado State, Lauren Marsh, University of Minnesota, and Cid Freitag, University of Wisconsin), the DSS team has focused on realizing 9 core data marts for level 1 analytics (at the student level) for all three use cases (impact of course activities, measures of activity/engagement, student profiles). Course and curriculum aggregations (levels 2 and 3) will naturally follow from the grain of the data exposed through level one data marts.
Beginning in September, Unizin began making the Task Force data marts available for testing in parallel with the Data Services and Solutions team’s self-paced training site, first with University of Colorado, and then opening up the marts to a number of additional institutions interested in testing and providing feedback.
Documentation on the TF level 1 data mart can be found on Unizin’s Resources site.
Already we are receiving excellent feedback around data mart modeling as well as broader questions focusing on accessing UDP data through Big Query. We look forward to making available to members all feedback and incorporating suggested changes to the marts over the course of the fall semester.
To request access to the data marts for member institutions, members are asked to submit a support request through our service desk portal.
Stepping Stones Foundational Curriculum for Faculty on Learner Data
Understanding learning analytics data and how it can inform decision-making is nuanced. With a growing number of Unizin member institutions putting learning analytics data in front of different stakeholder groups, our community recognized very early that training and support are critical for learning analytics initiatives to succeed. Stepping Stones is a faculty development curriculum designed to support faculty in their use of learning analytics data in courses that is ethical, effective, and equitable.
We’ve all seen how powerful data use can be in a variety of contexts: enhancing social media, impacting politics, informing financial decisions and enabling more effective communications… and we’ve also seen a lot of the ways those data uses can go wrong. “With great power comes great responsibility,” as they say. Higher education has an opportunity to learn from those who jumped into this field before us. We can use their practical experiences and the research on their efforts that our institutions have engaged in to take advantage of the many ways data can help grow our teaching and all of our students’ success. “Stepping Stones” aims to provide some pathways our faculty can use to get there.
Emily Oakes, Principal Unizin IT Consultant, Indiana University
Co-chair, Unizin Faculty Development Subcommittee
The Stepping Stones curriculum can help our instructors promote the success of all of our students by connecting best practices and research out of the learning analytics field with more familiar evidence-based teaching methods. Stepping Stones situates learning analytics squarely in the classroom context – our instructors’ area of expertise – providing a more familiar avenue for implementing a powerful tool that can provide novel insights into our students’ learning.
In addition to focused materials that cover how to leverage existing features in Canvas that provide analytics, Stepping Stones also covers core questions that are important to explore before using data to support student success, such as:
- How do I use data in a way that is ethical and respects student privacy?
- How do I design my course to obtain useful data?
- How can I use data to support engagement, assessment, and reflection?
We hope that our Unzin colleagues will use and adapt these materials to meet their needs…and then share with us their feedback, insights and the improvements they made. In this way the community can develop a robust library of resources in support of teaching with learning analytics.
Lauren Marsh, Academic Technologist, University of Minnesota
Co-chair, Unizin Faculty Development Subcommittee; Chair, Unizin Teaching & Learning Advisory Group
The curriculum was developed by Unizin’s Faculty Development subcommittee – consisting of faculty development professionals, instructional designers, and others who support classroom practitioners – from across the consortium. Designed to be used by facilitators working with faculty on the adoption of learning analytics, the Stepping Stones materials can be found in Canvas Commons and are intentionally designed to be customized to align with the local culture and context of each Unizin member institution. The Unizin Teaching & Learning Faculty Development subcommittee seeks facilitators that are interested in piloting the Stepping Stones curriculum in the upcoming Spring 2023 semester.
Additional Data Mart Availability
Throughout 2021, our Data Services and Solutions teams worked with several member institutions on what we call our foundational dashboards, meant to shed light on specific aspects of course design and student activity. The five foundational dashboards include:
LTI Tool Launches
Student Last Activity
Canvas Tool Usage
The data that powers these dashboards can now be accessed directly via data marts in the UDP, allowing you to include specific data points in your own research, internal dashboards, web applications, or to support systems integrations.
As we socialized the dashboards with various Unizin members, several interesting use cases were identified that support various data. For example, the LTI Tool Launch data can provide a detailed view of what tools are being launched, across different time periods, and can be scaled to examine the entire University tool ecosystem, a specific College or Campus, a program, or a specific course. Financial administrators indicated these data can be of value as they engage vendors around pricing during contract renegotiations, as well as exploring cost-sharing opportunities if, for example, the vast majority of a tool’s usage occurs within a single College or Campus.