Guide to Risk Points

Risk Assessment Types:

IntelliBoard previously offered two primary methods to assess learner risk: Rule-Based and Machine Learning models.

1. Rule-Based Models: Identify at-risk learner by applying If-Then rules. This method requires no historical data and offers flexibility for customization by individuals. With Rule-Based models, results are generated each time a report is run.

2. Machine Learning Models: Use AI-generated rules that analyze historical data to mark learners “At Risk” based on patterns from past successful and unsuccessful learners. Machine Learning models require substantial historical data and are usually managed by a central office to ensure consistency. Results from this model are stored and compared over time, making it useful for long-term risk monitoring.

Now, IntelliBoard introduces a third, Risk Points. Risk Points provide an easy, statistical way to gauge learner risk by comparing a learner's engagement and progress to their peers in the same course.

3. Risk Points: Mark learners as “At Risk” by comparing their performance to that of their current peers in the same course, eliminating the need for historical data or configuration. With Risk Points, results are calculated and stored nightly in snapshots, along with underlying data, keeping data current and actionable. Risk Points are fully transparent, with all metrics viewable and documented, which builds trust and meets compliance standards.

Risk Points Metrics:

Risk Points calculate learner risk using three main categories: Engagement, Attendance, and Progress. These categories encompass a variety of metrics designed to give educators a comprehensive view of learner activity and performance. Risk Points use Z-scores, which show how much a learner’s activity or performance differs from the course average. By comparing each learner’s score to the group average, Z-scores reveal whether a learner’s performance is typical or stands out, helping educators easily identify learners who may need additional support.

For Engagement, Risk Points track actions like:

• Visits, which capture page views and link clicks, representing passive engagement.

• Time spent, which estimates online engagement and automatically times out if the learner is inactive.

• Participations, such as quiz attempts, assignment submissions, and discussion posts, which indicate active engagement.

• Submissions, which are participations in graded items only.

Attendance metrics measure both passive and active participation.

• “Days Since Last Visit” shows how long it’s been since a learner last accessed course content. If the learner has never accessed course content, this is the amount of time since learner expected start.

• “% Days Visited” calculates the percentage of days the learner has engaged passively.

• “Days Since Last Participation” tracks the days since the learner’s last active engagement. If the learner has never participated, this is the amount of time since learner expected start.

• “% Days Participated” reflects the proportion of days with active participation since expected start.

• “Time to First Submission” measures the time to the first submission to a graded activity. If the learner has never submitted to an activity, this is the amount of time since learner expected start.

• “Time Since Last Submission” measures the time since the last submission to a graded activity. If the learner has never submitted to an activity, this is the amount of time since learner expected start.

Progress metrics provide insights into a learner’s academic standing.

• “Current Course Grade” offers a snapshot of the learner’s grade in graded items.

• “% Points Earned” reflects progress based on the total possible points in the course, with items weighted according to the configuration in the course grade book.

• “% Activities Completed” tracks completion rates for both graded and ungraded activities with completion criteria configured.

• “Activities Completed per Day” monitors if learners are keeping pace with expected course activity completion.

• “Current Overdue Submissions” reports the number of graded activities with past due dates that the learner has never submitted to.

• “Total Overdue Submissions” reports the number of graded activities with past due dates where the learner did not submit or submitted after the due date.

Benefits of Risk Points:

Risk Points simplify risk assessment by requiring no setup. This model allows users to begin utilizing it without any complex configuration. It offers real-time peer comparison, updating nightly to reflect where a learner stands relative to others in the course. Because Risk Points are highly transparent, all metrics are well-documented, accessible, and easy to interpret, helping to build trust among learners and educators alike. Risk Points will also included in IntelliBoard’s default reports and dashboards, so institutions can start tracking student risk from the moment they implement IntelliBoard.

1. Automated Data Capture and Reporting:

   • Nightly Snapshots: Data is captured nightly, so reports aren’t delayed by real-time calculations. This also allows for easy trend tracking over time.

   • Included in Default Dashboards: Risk Points are built into dashboards from day one, providing immediate insights for all users.

2. Proactive Alerts and Notifications:

   • Automated Notifications: Risk Points can trigger alerts for instructors, advisors, or learners, helping intervene before issues escalate.

3. Data Privacy and Compliance:

   • Compliance Features: Fully compliant with privacy regulations, with options to set retention limits and exclude specific learners if needed.

   • Granularity and Audits: Every metric can be reviewed over time, with transparent, documented data for auditing.

4. Utilizes Z-score:

   • Peer Comparison: Z-scores allow learners to be compared directly with their peers, showing how each learner’s engagement and performance stacks up against the course average.

   • Identifying At-Risk Students: By identifying learners whose scores fall significantly below the average (usually indicated by low or negative Z-scores), educators can pinpoint learners who may need extra support.

   • Standardized Assessment: Z-scores provide a consistent way to measure risk across different courses, ensuring that each learner's risk is measured relative to their unique course environment.

   • Transparency: Because Z-scores are based on standard statistical calculations, the process is clear and easy to explain, helping educators and learners understand why a learner is considered at risk.

Datasets Utilizing Risk Points:

Dashboard: Course Learner Risk SummaryPreview

Dashboard: Learner Risk OverviewPreview

Report: Course Learner Risk Points SummaryPreview

Report: Learner Risk Points DetailsPreview

Chart: Learner Daily Risk PointsPreview

Chart: Weekly Learners At RiskPreview

Data Snapshots must be enabled by IntelliBoard. Contact your IntelliBoard account manager or send a request to helpdesk@intelliboard.net for more information.

Frequently Asked Questions: