Identifying at-risk learners is essential to improve student success. The earlier an instructor or institution recognizes that a learner is disengaging, the greater the opportunity to intervene before poor performance leads to withdrawal or failure.
An at-risk learner is a student whose learning behaviors indicate they may struggle academically, disengage from a course, or withdraw before successfully completing it. While grades are one indicator, early warning signs often appear first in engagement data, such as declining attendance, reduced participation, limited collaboration, poor quiz performance, or decreased interaction with class activities. Identifying these patterns early allows instructors to provide timely support before performance declines.
In a physical classroom, instructors naturally notice when learners stop participating. Most video conferencing platforms don't capture these behaviors in a meaningful way. Common challenges include :
Limited visibility into engagement
Participation is difficult to measure
Breakout rooms are a black box
Once learners enter breakout rooms, instructors have limited awareness of:
Scattered engagement data
Instructors often need to combine information from multiple systems:
By the time these signals are combined, it may already be too late to help the learner. Instead of relying on a single metric like attendance, instructors can identify patterns across many indicators to formulate early intervention strategies.
For example:
| Learner | Attendance | Participation | Collaboration | Risk |
| Jonny | High | High | High | Low |
| Patty | High | Very Low | None | Moderate |
| Ana | Declining | Declining | Declining | High |
| Brian | Low | None | None | Critical |
Notice that Patty appears to be "present" but may still be at risk because they are not actively engaged.
When engagement data is collected continuously, predictive analytics can identify learners whose behavior resembles learners who previously struggled. Examples of early warning indicators include:
These indicators allow instructors and advisors to intervene with personalized support before the student falls too far behind.
Every interaction tells a story. Predictive learning analytics transform attendance, participation, collaboration, and informal assessments into early warning indicators, helping educators identify at-risk students before they disengage and enabling timely, personalized interventions that improve retention and academic success.
Here are eight powerful ways institutions can use predictive learning analytics to improve learner success and overall retention:
Because Engageli captures continuous engagement data during learning, predictive analytics can go far beyond grades and attendance. You can build models that identify learners who are likely to struggle, disengage, or excel based on their actual learning behaviors.
Here are examples of predictive learning analytics that Engageli's engagement data supports:
| Predictive Insight | Engagement Signals Used | Potential Action |
| 1. At-Risk LearnerPrediction | Attendance trends, participation frequency, quiz scores, speaking time, note-taking, inactivity | Alert instructor or advisor for early intervention |
| 2. Dropout Risk | Declining attendance, missed activities, reduced collaboration, lower engagement week-over-week | Trigger success coach outreach |
| 3. Course Completion Probability | Assignment completion, live participation, module completion, assessment performance | Recommend additional support or tutoring |
| 4. Knowledge Mastery Prediction | Quiz trends, sprint accuracy | Personalize review materials or practice exercises |
| 5. Engagement Score Forecast | Chat activity, reactions, table collaboration, speaking time, whiteboard contributions | Identify disengaging learners before grades decline |
| 6. Collaboration Effectiveness | Table participation, peer interactions, document edits, speaking balance | Suggest different team compositions or instructor facilitation at a private table |
| 7. Instructor Intervention Recommendation | Low participation combined with poor assessments | Recommend office hours or one-on-one meetings |
| 8. Institution Retention Prediction | Multi-course engagement patterns, attendance consistency, LMS activity, course satisfaction | Help student success teams prioritize outreach |
Traditional learning analytics typically report what has already happened, for example, test scores, assignment completion, or course grades. Predictive learning analytics goes a step further by continuously analyzing engagement patterns to forecast which learners may be showing knowledge gaps, By combining behavioral signals such as participation, collaboration, speaking time, note-taking, and assessment performance, predictive models can identify learners who would benefit from early intervention, enabling instructors to act before learners fall behind.
Attendance only indicates that a learner logged into a class, it does not reveal whether they were actively engaged. A learner may attend every session yet rarely participate, collaborate, ask questions, or complete learning activities. Rich engagement data provides a more complete picture by measuring behaviors such as speaking participation, collaboration at tables, quiz and print performance, note-taking, reactions, and interaction with recorded lessons. These insights help educators distinguish between learners who are merely present and those who are actively learning, making interventions more timely and effective.
At-risk learners can have a significant financial impact on schools, colleges, and universities. When students withdraw, fail courses, or stop attending, institutions may lose tuition revenue, state funding, or performance-based funding tied to retention, completion, or attendance. Recruiting a new learner is also far more expensive than retaining an existing one, making student success a critical financial priority. By transforming engagement data into early warning indicators, institutions can improve both learner outcomes and long-term financial health, creating a stronger return on investment in student success initiatives.
Traditional video conferencing systems primarily answer the question: "Was the student present?"
A learning platform with rich engagement analytics can answer a much more valuable question: "Is the student actively learning, participating, collaborating, and showing signs of success? Or are they beginning to disengage?"
That distinction is critical for identifying at-risk learners early, enabling timely interventions, improving retention, and ultimately helping more learners achieve successful outcomes.