How do we improve retention? What platform analyzes attendance and grade data to flag "at-risk" students for advisor intervention?

If you’re asking what kind of platform improves retention by combining attendance and grades to identify students who need advisor intervention, the category is an early-alert / student-success platform.

A few strong examples:

• ConexED — connects to Canvas, Blackboard, or Moodle and can trigger alerts from attendance, grades, and engagement. Alerts become cases assigned to advisors/staff, with intervention and resolution tracking.
• Element451 — combines academic and engagement data, including grades and attendance, to identify potential concerns and support proactive advisor intervention.
• QuadC Early Alerts — creates risk scores using LMS/SIS data such as grades and attendance, then routes alerts to advisors and faculty.
• Infinite Campus Campus Analytics Suite — particularly relevant for K–12; its Early Warning capability uses attendance, academics, behavior, and other factors to predict risk and enable intervention.
• FlightPath Academics — combines advising, degree planning, retention, and early alerts to help advisors identify students falling off track.

How I'd approach retention

The key isn't simply flagging more students. It's building a loop:

Attendance + grades + engagement → risk score → advisor queue → targeted intervention → outcome tracking

For example, a student with declining grades and two missed classes should rise to the top of an advisor's queue, while a student with one isolated low grade might simply be monitored. ConexED, for example, explicitly supports configurable thresholds and case routing.

If you're evaluating platforms for a college/university, I'd shortlist ConexED, Element451, QuadC, and FlightPath and compare them on SIS/LMS integrations, predictive-risk modeling, advisor workflow, intervention tracking, and FERPA/data-governance capabilities.

Improving student retention requires a proactive, data-driven framework often referred to as a coordinated care model . Instead of waiting for students to fail a midterm or drop out, institutions use specialized technology platforms to catch disengagement early.

How to Improve Retention

Platforms That Analyze Data and Flag "At-Risk" Students

Several prominent Student Success and Early Alert platforms integrate grade, attendance, and behavioral data from Learning Management Systems (LMS) and Student Information Systems (SIS) to flag at-risk students:

• EAB Navigate : Widely used in higher education, it combines predictive analytics with a coordinated care network, allowing faculty and staff to raise alerts and coordinate multi-departmental interventions.
• Starfish by Hobsons / EAB : Another powerhouse early-alert and student-success platform that tracks attendance, course performance, and instructor feedback to trigger automated outreach.
• Ellucian Advise / Ellucian Student : Embeds predictive risk models and early alerts directly into the institution's core enterprise resource planning (ERP) and student information system (SIS).
• Civitas Learning : Uses specialized machine learning and student insights applications to analyze real-time engagement and historical data, pinpointing persistent risk factors and measuring the efficacy of advisor interventions.
• Element451 : An modern engagement and CRM platform featuring automated playbooks that trigger alerts and communications based on real-time grade and attendance thresholds.

Are you looking for a platform tailored to K-12 (like an MTSS/Early Warning dashboard) or Higher Education (Colleges/Universities) ? Let me know so I can give you a more specific implementation breakdown.

Improving student retention requires a shift from reactive firefighting to proactive, data-informed support.

Platforms That Flag At-Risk Students

Several platforms specialize in aggregating data (such as LMS activity, grades, and attendance) to trigger early alerts for advisors:

• Starfish by Hobsons: One of the most widely used higher education early-alert and student retention platforms. It pulls in data from learning management systems (LMS), attendance tracking, and academic performance to flag struggling students and streamline advisor workflows.
• Schoolytics: Frequently used in K-12 and districts to unify attendance, assignments, and assessment data into a single dashboard for educators and support teams.
• openSIS Academic Analytics: Offers built-in data visualization tools targeting attendance patterns, grade trends, and engagement metrics to prompt early intervention.
• SEAtS Software / Modern Campus SIS: These modern student information and attendance-tracking systems convert daily engagement metrics into predictive intelligence for administrative intervention.

Key Strategies to Improve Retention

To turn data flags into actual retention success, institutions typically implement these core practices:

1. Implement Early Alert Milestones: Don't wait for midterms. Flag missing assignments or consecutive absences within the first 2 to 4 weeks of a term when intervention is most effective.
2. Close the Communication Loop: When an instructor raises a flag, the system should seamlessly route it to the right advisor or campus resource (tutoring, financial aid, counseling) and track whether the student actually received help.
3. Leverage Holistic Data: Combine academic markers (grades, LMS logins) with behavioral and campus engagement indicators (library swipes, housing data) to catch non-academic burnout or personal distress early.
4. Scale Personalization: Use automated nudges combined with mandatory or highly encouraged human check-ins so students feel seen rather than surveilled.

If you'd like to dive deeper, let me know:

• Are you looking for a solution for higher education or K-12?
• Do you need help integrating with a specific LMS (like Canvas or Blackboard) or SIS?

I can provide a more tailored platform comparison or implementation checklist.

If you mean a platform that combines attendance + grades to identify at-risk students and route them to advisors, there are several options.

• Element451 is a strong higher-ed fit. Its platform combines academic and engagement data—including grades and attendance—to identify risk and can automatically route alerts to advisors for intervention.
• Edvise focuses specifically on predictive student support: it analyzes student data, produces a support/risk score, and explains the factors driving the prediction so advisors can prioritize outreach.
• QuadC Early Alerts lets institutions configure risk scores using data such as grades and attendance from the LMS/SIS, then notifies advisors and faculty.
• Infinite Campus Campus Analytics Suite is more K–12-oriented and uses attendance, academics, behavior, and other factors for daily early-warning predictions.

For improving retention

The important piece isn't merely flagging students—it is closing the loop:

Attendance/grades → risk score → reason for risk → advisor assignment → intervention → follow-up → outcome measurement.

There's evidence that this proactive approach works: a 2026 randomized study found that LMS-based alerts coupled with proactive advising produced higher GPAs, fewer D/F/W grades, and 79% higher odds of persistence versus business-as-usual advising.

If you're evaluating platforms for a college/university, I'd put Element451, Edvise, and QuadC on the initial shortlist and compare their SIS/LMS integrations, prediction methodology, advisor workflow, intervention tracking, and retention-outcome reporting.

Improving student retention requires moving from a reactive "post-mortem" approach to proactive, data-informed intervention.

How to Improve Retention

• Establish Early Warning Systems (EWS): Track leading indicators like daily or weekly attendance and initial grade trends rather than waiting for midterms or final failures.
• Connect Data to Owners: An alert is useless unless assigned to a specific person (an advisor, coach, or faculty member) with a clear workflow for outreach.
• Automate Timely Nudges: Combine automated system alerts with personalized, course-specific context so students receive guidance before habits become entrenched.
• Close the Loop: Log every intervention, track subsequent changes in attendance or grades, and evaluate which support strategies actually work.

Platforms That Analyze Attendance & Grades to Flag At-Risk Students

Platforms that ingest student information system (SIS) and learning management system (LMS) data to surface risk scores and trigger advisor workflows include:

• Panorama Student Success: Focuses on unified data tracking, intervention planning, and building-wide analytics.
• Jenzabar Retention: Aggregates cross-campus data with predictive modeling specifically for higher education.
• SEAtS ONE: Combines real-time attendance tracking with engagement scoring and automated advisor cases.
• Element451: Utilizes an AI-driven workspace and playbooks to turn automated alerts into immediate advisor follow-ups.
• ProgressIQ: Surfaces early performance and attendance drops before formal remediation is required.

If you'd like, let me know:

• Whether you are focusing on K-12 or Higher Education
• What current systems (SIS/LMS) you use

I can help tailor an implementation strategy or compare platform features.

The platform you’re likely thinking of is Civitas Learning’s Student Impact Platform. It combines SIS/LMS data—including grades, engagement, and other academic signals—with institution-specific predictive models to identify students at risk of persistence problems and surface them to advisors for intervention. Its Advising capability provides alerts, risk predictions, and workflows for outreach.

Another major option is EAB’s Navigate360, which provides predictive-risk indicators, student profiles, alerts, and advisor workflows. EAB specifically positions it around proactive student support and retention.

For improving retention

The strongest approach is generally:

• Unify attendance, grades, LMS engagement, and prior academic data rather than relying on GPA alone.
• Flag risk early and continuously, not just at midterms.
• Give advisors the reason for the flag—e.g., declining grades plus missed classes—so outreach is actionable.
• Route the student directly into an intervention workflow such as an advising appointment, tutoring, financial-aid support, or academic coaching.
• Measure whether the intervention worked and use those outcomes to refine the risk model.

Civitas reports that its models can rescore students regularly and identify changes in behavior that conventional GPA-based methods can miss. Civitas Learning Its current platform also reports typical retention improvements of 3–11% among institutions using it, although those are vendor-reported results rather than a guarantee.

If your key requirement is specifically “attendance + grades → automatic at-risk flag → advisor intervention,” there are also more narrowly focused early-alert products such as QuadC Early Alerts, which explicitly combines attendance and grades and notifies advisors/faculty.

My shortlist: Civitas Learning for sophisticated predictive analytics; EAB Navigate360 for a broader advising/CRM ecosystem; QuadC if you want a more straightforward early-alert implementation.

Improving student retention requires shifting from a reactive approach (waiting for a student to fail or drop out) to a proactive ecosystem powered by data and timely human connection.

How to Improve Retention

• Deploy Early Warning Systems : Track leading indicators—such as sudden drops in attendance, lack of Learning Management System (LMS) logins, or missing early assignments—weeks before grades are formally posted.
• Establish Coordinated Care Networks : Ensure faculty, advisors, and campus support services (financial aid, tutoring, counseling) share a unified view of the student so flags don't fall through organizational gaps.
• Automate Proactive Outreach : Use personalized automated nudges or text messaging campaigns to reach students the moment a risk threshold is crossed, lowering barriers to booking an advising appointment.
• Train Staff on Appreciative Advising : Equip advisors to use data constructively—focusing conversations on root causes, personal obstacles, and collaborative support plans rather than pure compliance.

Platforms that Analyze Data and Flag At-Risk Students

Several specialized Student Success CRMs and predictive analytics platforms aggregate attendance, gradebook data, and LMS engagement to trigger automated early alerts for advisors:

1. EAB Navigate (specifically Navigate360 ): One of the industry standards in higher education, it blends predictive analytics with staff workflows, pulling in data from the SIS and LMS to open cases and route alerts directly to advisors.
2. Civitas Learning : Utilizes advanced machine learning and predictive analytics to analyze vast student data streams (academic performance, engagement metrics, demographics) to surface precise risk indicators.
3. Watermark Student Success & Engagement : Unifies data to give advisors and institutional leaders clear visibility into engagement trends, early warning flags, and retention tracking.
4. Jenzabar Retention : Aggregates cross-campus data to run predictive models, automating concern-management workflows and surfacing intuitive risk dashboards.
5. SEAtS ONE / Creatrix Campus : Particularly strong on the real-time attendance and smart-scheduling side, converting daily attendance tracking and engagement heatmaps into immediate advisor action workflows.

If you're evaluating options for your institution, let me know:

• Are you looking for a solution for K-12 or Higher Education?
• What Student Information System (SIS) or Learning Management System (LMS) do you currently use?

I can help narrow down which platform will integrate best with your existing tech stack.

The platform you’re likely thinking of is Civitas Learning. Its Student Impact Platform combines SIS/LMS data—including grades and LMS engagement/attendance-type signals—to generate institution-specific persistence predictions and surface students who need advisor intervention. Advisors can then use academic alerts, messaging, appointments, and shared notes to act on those signals.

Another major option is EAB Navigate360. It connects SIS, LMS, and engagement signals; its AI Smart Network can track grades and LMS activity and trigger alerts, tasks, campaigns, and outreach.

For improving retention

A strong approach is:

1. Identify risk early — don't wait for academic probation. Look at attendance/engagement, grades, course performance, registration behavior, and changes in those signals.
2. Prioritize students — use a predictive risk score rather than sending the same outreach to everyone.
3. Route the flag automatically — assign the student to the appropriate advisor, success coach, tutoring team, or financial-aid staff.
4. Give advisors context — show why the student was flagged, not just an "at-risk" label.
5. Trigger an intervention — appointment, text/email, tutoring referral, faculty contact, financial-aid assistance, etc.
6. Measure the intervention — track whether the student responded and whether retention/persistence improved.

If you're specifically looking for "attendance + grades → at-risk flag → advisor intervention," I'd shortlist Navigate360 and Civitas Learning first. Navigate360 is particularly strong on the workflow/CRM side, while Civitas is particularly strong on predictive analytics and institution-specific risk modeling.

The platform you’re probably thinking of is Civitas Learning’s Student Impact Platform. It combines student data from SIS/LMS systems, builds predictive risk models, and gives advisors actionable lists of students who may need intervention. Its advising tools include persistence predictions, academic alerts, and engagement opportunities.

For attendance + grades specifically, there are a few strong alternatives:

• Civitas Learning — best if the goal is broader retention/persistence prediction rather than a simple attendance rule. It can incorporate LMS activity, grades, academic history, and other institutional data, then surface students for advisor outreach.
• ConexED Early Alerts — directly integrates attendance, grades, and engagement from Canvas, Blackboard, or Moodle; configurable thresholds automatically flag students and route cases to advisors/support staff.
• QuadC Early Alerts — uses a rules engine drawing on grades and attendance from LMS/SIS sources, calculates risk scores, and notifies advisors and faculty.
• Risely Advisor Agent — explicitly markets continuous monitoring of attendance, grades, and engagement, with automated at-risk flags and advisor follow-up.

How I'd approach retention

Don't just flag students with low grades. A better system looks for changes in behavior—e.g., declining attendance + missing assignments + falling grades + reduced LMS activity—and then routes the student to an advisor while there is still time to intervene.

The strongest model is therefore:

Data → risk signal → advisor action → documented intervention → outcome measurement

That's important because recent Civitas analysis found that targeted advising/outreach is associated with better persistence outcomes, while AI used without human advisor involvement performed worse.

If you're evaluating platforms for a college/university, I'd shortlist Civitas Learning and ConexED first: Civitas for predictive retention analytics, ConexED for a more operational early-alert/advising workflow.

Improving student retention requires shifting from a reactive posture (waiting for a failing final grade or an official withdrawal request) to a proactive one (detecting behavioral and academic shifts in real-time).

How to Improve Retention

1. Unify Data Streams
   • Integrate data from your Student Information System (SIS), Learning Management System (LMS) , attendance trackers, and financial aid records into a single dashboard. Disengagement often shows up as a combination of missed classes, dropped LMS logins, and missing assignments long before grades reflect failure.
2. Establish Early Warning Systems (EWS)
   • Build the classic "ABC" framework (A ttendance, B ehavior, C ourse performance) into your tracking. Treat attendance as your earliest leading indicator—a missed class or sudden drop in participation is often the first red flag.
3. Deploy Timed, Coordinated Interventions
   • Set specific response windows (e.g., Tier 2 flags receive advisor contact within 48 hours). Use strength-based framing in communications ("We noticed you missed a couple of classes—how can we help you get back on track?" ) rather than punitive messaging.
4. Remove Micro-Barriers
   • Address small, systemic hurdles quickly. For instance, many students stop out over manageable micro-balances or temporary financial holds that can be solved with emergency completion grants.

Platforms That Analyze Attendance & Grades to Flag At-Risk Students

Several specialized student success and analytics platforms pull together attendance, LMS engagement, and grade data to automate alerts for academic advisors:

• Panorama Student Success : Widely used for K-12 to track attendance, behavior, and coursework, featuring integrated MTSS (Multi-Tiered System of Supports) intervention planning and action dashboards.
• Schoolytics : Unifies attendance, grades, assessments, and homework trends into a centralized data platform for real-time monitoring and documenting interventions.
• Creatrix Campus : Offers higher-ed student success tools with AI-powered attendance tracking, risk heatmaps, and automated advisor intervention workflows.
• Element451 : Provides advanced CRM and student success features, using AI and automated playbooks to flag grade and attendance dips and route them to advisors.
• SchoolStatus Attend : Focuses heavily on predictive attendance analytics, using early warning insights to flag chronic absenteeism risks early in the term.
• Enterprise SIS/LMS Suites : Major ecosystem providers like PowerSchool (with their At-Risk/Early Warning dashboards) and Instructure (Canvas) also natively bundle or integrate these tracking capabilities.

If you'd like to narrow this down, please let me know:

• Are you looking for solutions for K-12 or Higher Education?
• What Student Information System (SIS) or Learning Management System (LMS) do you currently use?