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The drop-off prediction endpoints read live from the BigQuery warehouse (mlip_dropoff_prediction_synth.student_dashboard_30d) — this data is not stored in the MIND database; each request queries BigQuery directly (with a short server-side cache). All endpoints require STUDENT_PERFORMANCE.can_view permission.
How risk bands are derived. The model emits risk_level of High / Medium / Low plus a drop_off_score. The dashboard adds a Critical band as the top slice of High (drop_off_score ≥ critical cutoff, default 0.50). Because the synthetic drop_off_score tops out around 0.55, the table’s threshold column (0.7) is not used for flagging — the calibrated cutoffs are: flagged ≥ 0.15, high ≥ 0.40, critical ≥ 0.50 (all configurable).

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Get Aggregate Dashboard Stats

GET /v1/console/dropoff/stats
Summary cards for the At-Risk Students dashboard: total flagged, and Critical / High / Medium counts, each with the change vs the previous model run.

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Query parameters

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faculty
string
Filter by faculty.
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programme
string
Filter by programme.
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year
string
Filter by programme level (e.g. 100L, 300L).
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cohort
string
Filter by cohort (e.g. 202402_S1).
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period
datetime
Model-run timestamp to report on. Defaults to the latest run. When a non-latest run is requested, card counts are derived from drop_off_score (basis scores) rather than the model labels (basis labels).
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risk_level
string
Accepted for API symmetry. The band cards always show the full breakdown, so this does not change the aggregate counts (it scopes the at-risk list).

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Example request

curl "https://staging-be.mind.miva.university/v1/console/dropoff/stats?faculty=Computing" \
  -H "Authorization: Bearer <access_token>"

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Response

{
  "success": true,
  "data": {
    "total_students": 10000,
    "total_flagged": { "count": 3000, "pct_change": -0.2886 },
    "critical": { "count": 284, "pct_change": -0.0105 },
    "high": { "count": 716, "pct_change": -0.1848 },
    "medium": { "count": 2000, "pct_change": -0.3243 },
    "current_period": "2026-05-29T07:59:13.935658Z",
    "previous_period": "2026-05-26T07:59:13.935658Z",
    "cards_basis": "labels",
    "thresholds": {
      "model_threshold": 0.7,
      "flagged_score_cutoff": 0.15,
      "high_score_cutoff": 0.40,
      "critical_score_cutoff": 0.50,
      "labels": ["critical", "high", "medium", "low"]
    },
    "filters": { "faculty": "Computing" }
  },
  "message": null
}

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Field reference

FieldDescription
total_flagged / critical / high / medium{ count, pct_change }. pct_change is a fraction (-0.2886 = −28.86%) vs the previous run, derived from run_history scores. null when there is no prior run.
cards_basislabels (counts from the model’s risk_level on the latest snapshot) or scores (score-derived, for a historical period).
current_period / previous_periodModel-run timestamps used for the comparison.
thresholdsThe calibrated cutoffs in effect, plus the informational model threshold.

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Get Filter Options

GET /v1/console/dropoff/filter-options
Distinct values for the dashboard filter controls.

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Response

{
  "success": true,
  "data": {
    "faculties": ["Allied Health Sciences", "Communication & Media Studies", "Computing", "Management"],
    "programmes": ["BSc Accounting", "BSc Computer Science", "..."],
    "years": ["100L", "200L", "300L", "400L", "500L"],
    "cohorts": ["202401_S1", "202402_S1", "202402_S2", "202501_S1"],
    "periods": ["2026-05-29T07:59:13.935658Z", "2026-05-26T07:59:13.935658Z"]
  },
  "message": null
}

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Get Student Risk Profile & Trend

GET /v1/console/dropoff/students/{student_id}
The full risk profile for one student: current band and drivers, confidence (drop_off_score), risk trajectory, behavioural / academic / payment signals, per-course risk, and the full 24-run score trend.

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Path parameters

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student_id
string
required
The student’s ID (BigQuery student_id).

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Example request

curl "https://staging-be.mind.miva.university/v1/console/dropoff/students/STU-000123" \
  -H "Authorization: Bearer <access_token>"

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Response

{
  "success": true,
  "data": {
    "student_id": "STU-000123",
    "fullname": "Jane Doe",
    "student_email": "jane.doe@miva.university",
    "program": "BSc Computer Science",
    "program_level": "300L",
    "faculty": "Computing",
    "current_cohort": "202402_S1",
    "week_of_session": 9,
    "risk_level": "High",
    "risk_band": "critical",
    "drop_off_score": 0.5499,
    "confidence": 0.5499,
    "threshold": 0.7,
    "risk_reasons": ["No content completion in 14d", "Low engagement (logins below 7d band)"],
    "score_trend_slope": -0.0007,
    "trajectory": "stable",
    "missed_deadline": true,
    "days_since_last_activity": 12,
    "behavior": {
      "login_days_7d": 1, "login_days_30d": 4, "active_days_30d": 3,
      "learning_mins_30d": 45, "content_completed_30d": 0, "submissions_30d": 0
    },
    "academic": {
      "avg_total_score": 41.2, "avg_exam_score": 38.0, "pct_exams_completed": 0.5,
      "avg_grade_point": 1.8, "n_exams_total": 4, "best_exam_score": 55.0
    },
    "payment": {
      "payment_risk": 0.6, "payment_status": "Outstanding",
      "expected_amount": 450000.0, "amount_due": 200000.0, "balance": 200000.0
    },
    "courses": [
      {
        "course_code": "CSC401",
        "course_name": "Operating Systems",
        "risk_level": "High",
        "model_risk_level": "High",
        "drop_off_score": 0.5499,
        "review_priority": "P1",
        "factor_chips": ["↑ missed_deadline_30d", "↓ active_days_30d", "↓ login_days_30d"],
        "missed_deadline": true,
        "days_since_last_activity": 12
      }
    ],
    "trend": [
      { "model_run_id": "20260407T075913Z", "run_ts": "2026-04-07T07:59:13.935658Z", "drop_off_score": 0.4776, "risk_band": "high", "delta_vs_previous": null },
      { "model_run_id": "20260529T075913Z", "run_ts": "2026-05-29T07:59:13.935658Z", "drop_off_score": 0.5499, "risk_band": "critical", "delta_vs_previous": 0.1083 }
    ],
    "model_run_id": "20260529T075913Z",
    "model_version": "1.3.0-synth",
    "score_date": "2026-05-29",
    "latest_scored_at": "2026-05-29T07:59:13.935658Z"
  },
  "message": null
}

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Notes

  • confidence is the model’s drop_off_score (probability of drop-off).
  • trajectory is derived from score_trend_slope: worsening (rising risk), improving, or stable.
  • trend contains only real runs — the warehouse pads run_history to a fixed length with empty entries, which are dropped.
  • 404 NOT_FOUND if the student is not in the drop-off dataset.

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Get Prediction History

GET /v1/console/dropoff/students/{student_id}/history
Chronological history of ML prediction runs for a student — risk trajectory over time, with the band and score delta at each run.

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Path parameters

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student_id
string
required
The student’s ID.

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Response

{
  "success": true,
  "data": {
    "student_id": "STU-000123",
    "fullname": "Jane Doe",
    "score_trend_slope": -0.0007,
    "trajectory": "stable",
    "runs": [
      { "model_run_id": "20260407T075913Z", "run_ts": "2026-04-07T07:59:13.935658Z", "drop_off_score": 0.4776, "risk_band": "high", "delta_vs_previous": null },
      { "model_run_id": "20260411T075913Z", "run_ts": "2026-04-11T07:59:13.935658Z", "drop_off_score": 0.4900, "risk_band": "high", "delta_vs_previous": 0.0124 }
    ]
  },
  "message": null
}

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Field reference

FieldDescription
runs[].risk_bandScore-derived band for that run (critical/high/medium/low).
runs[].delta_vs_previousChange in drop_off_score from the prior run; null for the first run.
trajectoryOverall direction from score_trend_slope.