Beyond ABC: The Next Generation of Leading Indicators for Student Success

The ABC Framework—Attendance, Behavior, and Course performance—has been the backbone of early warning systems for over a decade. And for good reason: these indicators are readily available, research-validated, and genuinely predictive. They've helped countless schools identify and support struggling students.

But they have a limitation: ABC indicators are lagging indicators of deeper processes. By the time a student starts missing school, failing classes, or getting behavioral referrals, something has already gone wrong. The attendance decline is a symptom, not the disease. The course failure is a consequence, not a cause.

A new generation of early warning indicators aims to move further upstream—detecting the mindset shifts, engagement changes, and relationship disruptions that precede observable behavioral changes. These next-generation indicators don't replace ABC; they supplement it, potentially buying schools weeks or months of additional intervention time.

Social-Emotional Learning Data

As social-emotional learning (SEL) programs have expanded, so has the systematic measurement of student SEL competencies. Surveys measuring self-awareness, self-management, social awareness, relationship skills, and responsible decision-making are now administered in thousands of schools. This data, initially collected for program evaluation, is increasingly being used for early warning purposes.

The logic is straightforward: students who are struggling emotionally often struggle academically soon after. A student whose self-reported sense of self-efficacy declines significantly, or who reports increasing difficulty managing emotions, may be heading toward the kind of disengagement that eventually shows up as attendance or academic problems.

Research from CASEL and partner organizations has validated this connection. In multiple studies, changes in SEL survey responses predicted subsequent changes in academic engagement and achievement. Students who reported declining sense of belonging, for instance, were significantly more likely to show attendance problems within the following quarter.

Schools using SEL data for early warning typically administer surveys 2-4 times per year and track changes over time. A student whose belonging score drops significantly between administrations would be flagged for check-in, even if their attendance and grades remain unchanged—addressing the underlying issue before it manifests behaviorally.

Student Voice and Pulse Surveys

Beyond formal SEL assessments, schools are increasingly using brief, frequent "pulse surveys" to monitor student wellbeing in real-time. These might be as simple as a daily emoji check-in ("How are you feeling today?") or weekly questions about current challenges and needs.

The power of pulse surveys lies in their frequency. Traditional SEL assessments occur 2-4 times per year, creating significant gaps between measurements. Pulse surveys can capture changes as they're happening, enabling much faster response.

"We ask three questions every Friday," explains counselor Maria Garcia. "How was your week? What's one thing you're worried about? Do you need to talk to someone? It takes students two minutes. But when a kid who usually reports 'good' starts consistently saying 'struggling,' we know something has changed—often before their teachers would notice anything."

The qualitative data from open-ended questions can be particularly valuable. A student writing "I'm worried about my mom" or "I don't have any friends in this class" is providing specific, actionable information that no ABC indicator could capture.

Digital Engagement Metrics

As learning management systems (LMS) and digital learning tools have become ubiquitous, they've generated a wealth of data about student engagement that was previously invisible. Login patterns, assignment submission behaviors, time-on-task, and participation in online discussions all leave digital traces that can reveal changing engagement.

Research in learning analytics has identified several digital behaviors that predict academic risk:

The challenge with digital engagement metrics is their variability across contexts. A student who rarely uses the LMS might be struggling—or might simply prefer paper materials. Effective use of these indicators requires establishing individual baselines and tracking changes from that baseline, rather than comparing to universal thresholds.

Relationship Mapping

One of the strongest protective factors for student success is connection to caring adults in the school building. Research consistently shows that students who can identify at least one adult at school who knows them and cares about their success are significantly more likely to persist through challenges.

This insight has led some schools to implement systematic "relationship mapping"—surveys that ask students to identify adults they feel connected to, combined with staff surveys about which students they know well. The resulting maps reveal students who appear disconnected—not yet struggling academically or behaviorally, but lacking the protective relationships that support resilience.

"We discovered that 14% of our students couldn't identify a single adult at school they felt connected to," recalls Principal James Thompson. "That wasn't showing up in any of our traditional data. But those students were dramatically more likely to disengage when they faced challenges. Now we use the map to intentionally build connections before problems arise."

Life Events and Context Indicators

Sometimes the earliest warning signs aren't found in school data at all—they're found in what's happening in students' lives outside school. Experienced educators know that certain life events—parental divorce, family illness, moves, economic stress—often precede academic struggles. But this knowledge has rarely been systematized into early warning systems.

Some districts are experimenting with more proactive collection of life context information:

Family liaison check-ins: Regular contact with families can surface emerging challenges—a parent losing a job, a grandparent becoming ill—that the school can respond to proactively.

Student self-reporting: Creating safe channels for students to share life challenges, with clear commitments about how that information will be used supportively.

Community partner integration: Connecting with social services and community organizations that may be aware of family challenges before schools are.

This approach requires extreme sensitivity to privacy and must be implemented with clear guardrails around how information is collected, stored, and used. But when done well, it can identify students who need support before school-based indicators would ever trigger.

Integrating Next-Generation Indicators

The most sophisticated early warning systems combine ABC indicators with these next-generation data sources, creating layered detection that catches risk at multiple levels:

This layered approach means that different students might be identified at different layers depending on how their struggles manifest. A student whose sense of belonging is declining but whose attendance remains strong would be caught at Layer 1. A student whose digital engagement is dropping but whose grades haven't yet suffered would be caught at Layer 2. The goal is catching everyone at the earliest possible layer.

Challenges and Considerations

Next-generation indicators offer real promise, but they also present challenges that must be navigated thoughtfully:

Privacy Concerns

Collecting data about students' emotional states, relationships, and life circumstances raises significant privacy considerations. Schools must be transparent with students and families about what data is collected and how it's used, establish clear policies about data access and retention, and ensure that expanded data collection genuinely serves student welfare rather than enabling surveillance.

Validation Requirements

While ABC indicators have decades of validation behind them, many next-generation indicators are newer and less thoroughly researched. Schools should be appropriately cautious about relying on indicators whose predictive validity hasn't been established in rigorous studies.

Response Capacity

More sensitive indicators will identify more students earlier, which only helps if schools have the capacity to respond. A school that can barely respond to ABC flags may not benefit from additional indicators that increase the volume of flagged students.

Appropriate Intervention Matching

Different indicators require different interventions. A student flagged for declining sense of belonging needs connection and community, not tutoring. Schools implementing next-generation indicators must develop correspondingly sophisticated intervention menus.

The Future of Early Warning

The evolution of early warning systems mirrors a broader evolution in education: from reactive to proactive, from behavior-focused to whole-child, from standardized to personalized. As our understanding of what drives student success deepens, our ability to detect and address emerging challenges earlier will continue to improve.

But technology and data are only tools. The goal remains unchanged: ensuring that every student receives the support they need to succeed. Whether that support is triggered by an ABC alert or a pulse survey response, the outcome that matters is a student who felt seen, who received help, and whose trajectory was changed for the better.

The next generation of early warning systems won't replace human judgment and caring relationships. They'll enhance them—providing educators with better information, earlier, about which students need attention. What educators do with that information will always determine whether early warning systems fulfill their promise.