Attendance as Signal: Rebuilding Early Alerts with Compassion

Episode 5: Attendance As Signal Rebuilding Early Alerts With Compassion with blow horn

By: Dr. Teranda Knight, DBH, LSSGB

Edited with Human-led AI

Friday, January 16, 2026

Treating attendance as a compassionate signal rather than a punitive verdict best aligns with what the evidence and field practice jointly suggest, because facts show that simply running an Early Alert (EA) program did not improve short‑run course performance in one rigorous causal study, while other peer‑reviewed work finds EA systems can still meaningfully identify students at heightened discontinuation risk when designed and evaluated with appropriate temporal models (Oliveira, 2024; Harrison et al., 2021). In my view, the reasonable reconciliation is that EA detection is often necessary but insufficient: alerts flag the who and when, and compassionate, rapid human follow‑up does the work of changing the what next, a design pivot that converts data into care rather than compliance (Oliveira, 2024; Harrison et al., 2021).

Early Alerts with Student Voice on Language and Actionability

If the main idea is to encourage students toward persistence, the fact that students report positive, specific, actionable messages as most helpful should anchor message design, because those messages correlate with self‑reported gains in attendance and study behaviors even when initial emotions are mixed (Imundo et al., 2025). While I believe institutions often over‑index on risk labels, the better reading of the evidence is to embed affirmation plus clear steps for example, “We noticed a dip; we believe in your capacity; here are two supports this week,” and to track whether such scripts increase within‑term engagement (Imundo et al., 2025; Tippetts et al., 2022).

Early Alerts Through Nudges, Timing, and Follow‑Through

Because timeliness often separates noise from help, my professional judgment is to target ≤72 hours from trigger to human contact, and while no single study dictates that threshold, facts from randomized and quasi‑experimental work indicate that motivational SMS nudges and advisor texting can improve exam outcomes and increase within‑college persistence effects that plausibly decay when outreach is delayed (Brandt et al., 2024; Tippetts et al., 2022). The analysis here is pragmatic: nudges are not panaceas, but when they are motivational, targeted, and coupled to real people, they appear to move proximal behaviors (responses, bookings, preparation) that sit upstream of grades and retention (Brandt et al., 2024; Tippetts et al., 2022).

Building Smarter Triggers with Course‑Level Evidence

A multi‑signal trigger stack for instance, two missed classes in ten days, seven days of LMS inactivity, and a low first quiz balances sensitivity with specificity, and this opinion is grounded in facts that course‑embedded EA designs have improved approval rates in a large health‑majors sample when iterative, teacher‑informed strategies followed quiz‑level analytics (Cabezas et al., 2024). An additional evidence‑based justification is that microeconometric and survival‑analysis approaches better capture time‑varying hazard, so stacking signals early in the term makes both statistical and service sense by prioritizing limited advising time toward students with rising risk profiles (Harrison et al., 2021; Cabezas et al., 2024).

Measuring the Right Engagement

Because not all “engagement” is equal, my analysis favors signals with demonstrated predictive value, and the fact that video watch‑time outperforms page views as a performance correlate in virtual learning environments supports refining dashboards toward higher‑signal behaviors (Jones, 2022). In practice, I recommend instrumenting the LMS to capture quality interactions (e.g., assessment rehearsal, content mastery behaviors) and pairing them with attendance events, since combining richer digital traces with physical presence should reduce false positives and increase the helpfulness of outreach (Jones, 2022; Brandt et al., 2024).

Governance and Ethics: Design for Compassion, Not Surveillance

While EA tools can scale detection, my position is that governance must codify compassion, and the fact that a responsible‑innovation framework improved the inclusiveness and reflexivity of an institutional EA build shows that design processes who is at the table and how decisions are made shape student trust and ultimate impact (Patterson et al., 2023). Aligning this with the evidence that temporal models identify risk but do not, by themselves, change outcomes, I argue for cross‑functional oversight (faculty, advising, counseling, student voice) that sets language standards, response SLAs, and data‑use boundaries to minimize unintended harms and maximize support (Patterson et al., 2023; Harrison et al., 2021).

From Detection to Action: A Minimal Viable Playbook

A practical translation of the literature is to move from alerts to appointments: send one supportive nudge that uses the Notice→Affirm→Action→Bridge pattern and routes the student to a 15‑minute human check‑in; this opinion leans on facts that motivating messages and low‑friction access to humans improve proximal engagement and sometimes performance (Brandt et al., 2024; Imundo et al., 2025). To sustain gains, I advise weekly data refreshes, bi‑weekly team huddles to tweak thresholds, and continuous outcome monitoring (conversion to appointment, attendance recovery, course completion, and retention), because studies show course‑embedded analytics plus iterative instructional response can lift achievement and help normalize supportive contact (Cabezas et al., 2024; Tippetts et al., 2022).

Synthesis: What Works, What’s Next

In sum, the main idea is simple: EA systems are more effective as care coordination tools than as grading interventions, and the facts support skepticism about grade impacts absent humanized follow‑through (Oliveira, 2024). My analysis informed by survival‑analysis case studies, student voice, and nudge experiments is that institutions should stack early signals, speak with affirmation and specificity, and guarantee fast human contact, because that is the most defensible way to transform attendance from a compliance metric into a wellness‑oriented prompt that students can welcome (Harrison et al., 2021; Imundo et al., 2025).

References

Brandt, A., Oskorouchi, H. R., & Sousa‑Poza, A. (2024). The effect of SMS nudges on higher education performance. Empirical Economics, 66, 2311–2334. https://doi.org/10.1007/s00181-023-02516-5 [link.springer.com]

Dawson, S., Jovanovic, J., Gašević, D., & Pardo, A. (2017). From prediction to impact: Evaluation of a learning analytics retention program. In Proceedings of the International Conference on Learning Analytics & Knowledge (LAK ’17). (Preprint). https://www.researchgate.net/... [researchgate.net]

Feygin, A., Miller, T., Bettinger, E., & Dell, M. (2022). Advising for college success: A systematic review of the evidence. American Institutes for Research (College Completion Network). https://files.eric.ed.gov/fulltext/ED626933.pdf [files.eric.ed.gov]

Harrison, S., Villano, R., Lynch, G., & Chen, G. (2021). Microeconometric approaches in exploring the relationships between early alert systems and student retention: A case study of a regionally based university in Australia. Journal of Learning Analytics, 8(3), 170–186. https://doi.org/10.18608/jla.2021.7087 [files.eric.ed.gov], [learning-a...ytics.info]

Imundo, M. N., Goldshtein, M., Watanabe, M., Gong, J., Crosby, D. N., Roscoe, R. D., Arner, T., & McNamara, D. S. (2025). Awareness to action: Student knowledge of and responses to an early alert system. Applied Sciences, 15(11), 6316. https://doi.org/10.3390/app15116316 [mdpi.com]

Jones, T. J. (2022). Relationships between undergraduate student performance, engagement, and attendance in an online environment. Frontiers in Education, 7, 906601. https://doi.org/10.3389/feduc.2022.906601 [frontiersin.org]

Oliveira, A. R. de. (2024). Evaluating the short‑term causal effect of early alert on student performance. Research in Higher Education, 65, 1395–1419. https://doi.org/10.1007/s11162-024-09795-6 [link.springer.com]

Tippetts, M. M., Davis, B., Nalbone, S., & Zick, C. D. (2022). Thx 4 the msg: Assessing the impact of texting on student engagement and persistence. Research in Higher Education, 63, 1073–1093. https://doi.org/10.1007/s11162-022-09678-8 [link.springer.com], [eric.ed.gov]

What Works Clearinghouse. (2022). Effective advising for postsecondary students: Practice guide (WWC 2022003). U.S. Department of Education, Institute of Education Sciences. https://ies.ed.gov/ncee/wwc/Docs/practiceguide/WWC-practice-guide-summary-effective-advising.pdf