Academic institutions invest in Learning Management Systems (LMS) like Moodle, Canvas, D2L Brightspace and Blackboard to manage courses and support teaching and learning. These platforms offer essential built-in reports, particularly for instructors who need to monitor student activity within their individual courses. However, as institutional demands for data-driven decision-making grow—especially around compliance, funding, and student support—LMS-native reporting often falls short.

IntelliBoard bridges that gap by extending LMS data into strategic, actionable insights that serve the entire institution—not just the classroom.

What Native LMS Reporting Does Well

LMS-native reporting is designed primarily for instructors and course-level tasks. These reports allow faculty to:

• Track individual student activity within a course
• Review assignment and quiz submissions
• Monitor participation through logs or dashboards

Some platforms also offer simple student-facing dashboards. These features are valuable for classroom management and teaching support.

At the same time, while LMS-native tools are effective for instructors working within a single course, they are not designed to serve institutional leaders, compliance officers, or program managers who need to see across courses, terms, or departments — or even instructors teaching multiple courses.

How IntelliBoard Goes Further

1. Institution-Level Aggregation
   Where native LMS reports focus on one course at a time, IntelliBoard aggregates data across the entire system. This enables administrators to:

• View a student’s engagement across all enrolled courses
• Analyze instructor performance across terms or programs
• Compare metrics across departments or delivery modalities (online vs. face-to-face)

With this broader scope, institutions can monitor learning trends, identify at-risk students earlier, and make decisions that support institutional goals—something not possible with course-bound LMS reports.

2. Automation for Compliance and Funding
   LMS-native reporting typically requires administrators to access individual course gradebooks or logs to determine things like the date of first academic activity—a common requirement for attendance verification and financial aid eligibility. This manual process is time-consuming and error-prone.

IntelliBoard automates this work. It calculates the first and last dates of attendance across all courses and students, consolidates the data, and presents it in a format ready for financial aid audits or census reporting. This means institutions can reliably validate participation without digging through hundreds—or thousands—of course records.

3. SIS Integration and Contextualized Reporting
   LMS tools are typically siloed, with limited or no integration to student information systems (SIS). As a result, they lack access to demographic data, enrollment types, and academic programs.

IntelliBoard integrates with SIS platforms to provide contextualized reporting. Institutions can:

• Filter and group reports by student subpopulations, such as workforce development participants, dual enrollment students, or student-athletes
• Evaluate performance by scholarship status or financial aid eligibility
• Align data views with specific institutional missions and compliance needs

This deeper level of insight transforms LMS data into institutional intelligence.

4. Precision at Scale
   Instructors often change assignment names, course structures vary, and students may engage in ways not captured cleanly by LMS logs. LMS-native tools struggle to reconcile this variation across a large system.

IntelliBoard standardizes these inconsistencies by tracking meaningful academic activity across the institution—regardless of naming conventions or course formats. It delivers consistent, repeatable metrics that administrators can trust.

5. Intentional, Scalable Use Cases
   IntelliBoard’s strength lies not just in its ability to access LMS data, but in its focus on purposeful, repeatable use cases:

• Supporting census and financial aid reporting with verifiable participation metrics
• Tracking learner success across programs and terms
• Monitoring student groups that require special support or intervention

Clients value IntelliBoard not because it replicates what their LMS already does, but because it brings together LMS and SIS data to address strategic questions—and makes that data easy to act on.

Why Institutions Choose IntelliBoard

Institutions exploring new analytics tools or reconsidering their current solutions often find that LMS-native options are limited to a single course. IntelliBoard offers a different path: one focused on institution-wide insight, compliance-ready data, and strategic reporting that adapts to unique campus needs.

For colleges and universities tasked with doing more with data—from funding justification to student success—IntelliBoard delivers what the LMS was never designed to provide.

Final Thoughts

The LMS will always play a central role in supporting teaching and learning. At the same time, when it comes to institution-wide reporting, policy compliance, and data-informed decision-making, IntelliBoard adds critical value.

If your institution is ready to move beyond course-level dashboards and build a foundation of intelligent, integrated reporting, IntelliBoard is the partner that can help you get there.

1.  Learner Risk Points

One of the key reasons institutions and organizations choose the IntelliBoard Learning Analytics Platform to gain insight into their data is its ability to identify learners at risk and intervene to support learning outcomes. In the past, we’ve offered many default reports to help in this challenge based on two mechanisms: rule-based reporting and machine learning-based predictive analytics (PLA). For example, clients had access to dashboards that focused on learners with low estimated course grades (Projected DFW Risk) or low activity (Inactive Learners in Courses Dashboard). These tools are based on rules and thresholds that can be customized by each client. Also available is an integrated Machine Learning Predictive Model system that analyzes a client’s historical data and automatically sets thresholds of key metrics that predict poor learner outcomes, generating predictions on a schedule defined by the client.

The Learner Risk Overview Dashboard shows both summary and details of each learners’ risk in a single course over time.

Learner Risk Overview Dashboard

Now, IntelliBoard is able to offer our clients a third way to identify learners at risk: Learner Risk Points. This method automatically compares each learner to their peers in the same course daily, based on key metrics of engagement, attendance, and progress toward course goals.

Risk points are based on the concept of the bell curve. Most students will cluster around the average value of any metric; some will be outliers. Learners who are significantly different from their peers are assigned Risk Points; more Risk Points indicates more likely risk. These values are captured in “snapshots” each day (if enabled).  A new series of reports, charts, and dashboards organize this data for key participants such as instructors, advisors, and department heads. Because Risk Points are assigned based on a statistical comparison to peers, no custom thresholds need to be set. The calculations are fully transparent and can be summarized, charted over time to observe trends, and used to drive notifications to support better learning outcomes.

The line chart shows each learner’s total risk over time. The table shows data for each day and each learner.

Course Learner Risk Summary Dashboard

Learner Risk Points are valuable to all clients, from academic learning to professional development. They automatically focus attention on areas where learner behavior differs the most, taking into account different course designs and learning populations. They can also be combined with rule-based risk calculations, allowing clients to manually weight different types of risk, and with predictive models as well, automatically taking peer comparisons into account in finding the best formula to predict learner risk. Learner Risk Points can also be used to identify specific areas of learner risk when a Predictive Model makes a risk prediction.

The Current Prediction and Risk Points table combines the most recent prediction from our integrated Machine Learning system with details.

Find out how you can use Learner Risk Points at your institution! Current IntelliBoard clients can enable Data Snapshots by contacting helpdesk@intelliboard.net. To learn more, download our Risk Points whitepaper!