Pyramid: How to share analytics at scale
Ian Holder (Adaptive Learning and Teaching Analyst, Charles Sturt University, Australia)
With more and more academics requesting analytics data, we at Charles Sturt University are trying to determine how best to generate, transform and present detailed analytics across multiple courses. We currently drill-through in Pyramid or use custom Pyramid reports to import/copy the data to Excel, and transform it, combine it with Grade Centre (e.g. child course), and then create charts — which can be time-consuming; though academic feedback is exceptionally positive. Suggestions sought on how we can best streamline this.
The use of Blackboard Predict in UMBC’s Student Success Support Ecosystem
Robert Carpenter (Associate Provost for Analytics and Institutional Assessment, UMBC), John Fritz (Assoc. VP, Instructional Technology, UMBC), & Tom Penniston (Instructional Technology Analyst, UMBC)
Over at least the past decade, changes in the funding environment, population demographics, and public policy has led US universities and colleges to place increased emphasis on student success. Universities are increasingly turning to the enhanced use of student data and analytics to design systems to detect and then support at-risk students to help them achieve their educational goals. UMBC, a mid-sized public university with an emphasis in STEM fields, has made large investments in predictive analytics to support student success. This presentation will detail our in-progress pilot program that makes use of Blackboard Predict to complement existing early detections systems to identify students at risk. The presentation will include both our early validation results of Blackboard Predict estimates against our faculty-generated First Year Intervention (FYI) alerts program before continuing on to discuss our plans to use both systems to increase our detection of students needing additional support and providing this support earlier in the term when there is more time to make adjustments. The presentation will then describe how our new Persistence Committee uses other elements of Blackboard and information from our data warehouse to populate a comprehensive report that identifies combinations of factors that increase students’ academic risk and describes the Persistence Committee’s initial campaigns to improve student success.
Academic Decision Support
Christine Marchand (Institutional Research and Academic Compliance Coordinator, Drake University) & Mitchell Stearns (Data Analyst, Drake University)
Academic Decision Support (ADS) intends to provide University leadership with strategic data to help facilitate decision making. ADS analytic reports offer subjects such as longitudinal major enrollment, student credit hours, course utilization and academic performance. One primary function of ADS is its ability to provide trend based real-time analytical insight. The dynamic nature of these reports also allow users to drilldown on pertinent areas of interest.
ADS meets the needs of recurring analytic University questions but also contains a robust (and growing) repository of information to help serve as a starting point to various initiatives. ADS seeks to provide a holistic picture of student engagement and does not rely on one measure to make or break a decision.
University of Windsor’s Blackboard Customized LMS Administrative Toolkit
Lorie Stolarchuk (Learning Technologies Educational Consultant, University of Windsor)
Have you ever been asked questions that LMS data could answer, but you weren’t sure how to mine it? At the University of Windsor, the LMS Team were being asked questions such as:
- Which instructors are using the Assignments tool this semester, and what are their email addresses (e.g. We need to contact them about the upcoming changes with the Box software integration)?
- For those students who dropped a course, did they enroll in a different section of the same course, take it another year, or not take it at all?
- Can you provide an anonymized report of student activity or performance for research purposes?
These types of questions are commonplace, but finding the answers can be challenging with the relatively limited reporting tools that come out of the box. The University of Windsor has worked to build customizations to respond to these types of questions.
What emerged was a toolkit designed for the system administrators to cull various reports from the system. Further, as the system administrators already have full access to this data, the Pyramid reports for this group were straightforward. The wrinkle in the plan comes when users ask for similar access to some of these reports. This discussion has fueled a data governance conversation on campus with access to new points of data to help decision making.
In this brief presentation, we will share the lessons learned about our customizations and details of the ethical and data governance questions we are still working through.
Curriculum Mapping, A4L Style
Allyson Skene (Learning Specialist, University of Windsor)
Curriculum maps provide effective visualizations for program review and design, as well as for demonstrating quality and rigor in assessment to accreditors. By mapping courses to specific program outcomes, Faculties and Departments can more easily identify gaps or redundancies in their programs, bottle-necks or common trouble spots that students might face, as well as areas of program strength.
Currently neither Blackboard Learn nor A4L come equipped with curriculum mapping tools, but at the University of Windsor, we were able to wrangle existing Learn components (including Goals & Alignments) along with customized dimensions in A4L to build a series of curriculum maps to inform program review and facilitate accreditation reporting.
In this presentation, we will showcase some of the maps we have created: one that visualizes where and how often a particular outcome is being assessed in the program; one that maps where students are meeting, exceeding, or not meeting program expectations across the curriculum; and a third that identifies the relevant assessments that determine student achievement with respect to program outcomes. We will share details about our customizations, as well as suggest ways that these maps may be adapted for a variety of purposes.
As this is just the beginning of our project, we will also share some ideas for moving forward. For example, since Blackboard does not yet have a way to connect distinct sets of outcomes to each other, we are exploring ways to bring complementary data sources into our A4L data model to fill this gap. Ideally, we hope to leverage a combination of customized dimensions in A4L and established alignments in our own database to provide more robust reporting on curriculum.