Parent Research Center | PRC Investigation 001
Investigation 001 Evidence Board & Primary Source Archive
Companion Research Record for “What Does Your Child’s School Know About Them?”
This page functions as the canonical research companion to Investigation 001. The article explains the findings; this page lets the reader inspect the paper trail.
About This Evidence Board
Parent Research Center uses several kinds of evidence because different sources answer different questions. A government rule is strongest for establishing what the law permits or requires. A contract is strongest for showing what a vendor or agency was authorized to do. A technical document can establish how a system is designed. An evaluation can show how a program operates. A regulator can document failures. Independent journalism can uncover implementation, relationships, consequences, or contradictions not foregrounded in institutional materials. Historical documents can establish what earlier educators, researchers, and policymakers actually proposed.
EVIDENCE QUESTION
For each major finding, PRC asks: What exactly does this source establish?
Evidence Classification Key
- CLASSIFICATION
DOCUMENTED - MEANING
Direct official, contractual, technical, legal, or historical evidence establishes the claim. - CLASSIFICATION
INDEPENDENTLY CORROBORATED - MEANING
Non-institutional research, journalism, audit, or outside reporting confirms or materially supports the finding. - CLASSIFICATION
REGULATORY FINDING - MEANING
A regulator, court, inspector, or enforcement body documents a material practice, allegation, settlement, or failure. - CLASSIFICATION
HISTORICAL DOCUMENT - MEANING
Contemporaneous source material establishes the earlier educational concept, practice, or institutional development. - CLASSIFICATION
INVESTIGATIVE LEAD - MEANING
Independent reporting identifies a relationship or architecture that PRC follows back to source records.
Evidence Board
EB-001 — Modern education formally addresses nonacademic student competencies
DOCUMENTED
Modern SEL frameworks explicitly concern knowledge, skills, attitudes, identity, emotion management, relationships, empathy, goals, and decision-making—not merely academic content.
Evidence
CASEL describes SEL as developing knowledge, skills, and attitudes related to healthy identities, emotion management, personal and collective goals, empathy, relationships, and responsible decisions. It identifies five competency areas: self-awareness, self-management, social awareness, relationship skills, and responsible decision-making.
Source: CASEL — What Is the CASEL Framework?
Why it matters
It establishes that the modern educational framework formally extends into characteristics of the learner beyond conventional subject mastery.
EB-002 — Nonacademic and affective educational measurement has deep historical roots
HISTORICAL DOCUMENT
Educational interest in attitudes, values, emotional responses, and related affective outcomes substantially predates contemporary SEL.
Evidence
Historical material in DDDoA reproduces affective-domain work discussing evaluation instruments, desired affective objectives, longitudinal assessment, and changes in attitudes and beliefs.
Source: Iserbyt, Appendix XIX / affective-domain source material
Why it matters
It places contemporary nonacademic measurement within a longer history of educational interest in the learner’s attitudes, dispositions, behavior, and affective development.
EB-003 — CASEL promotes systemic SEL beyond a stand-alone classroom lesson
DOCUMENTED
CASEL’s modern SEL model is designed for coordinated implementation across schools, classrooms, homes, communities, and broader school systems.
Evidence
CASEL describes schoolwide SEL as a systemic approach that infuses SEL throughout students’ educational experience and coordinates efforts across classrooms, schools, families, and communities.
Source: CASEL — Schoolwide SEL Essentials
Why it matters
SEL is not limited to a single curriculum unit; its leading institutional framework explicitly describes systemwide implementation.
EB-004 — Education data standardization is designed to support information exchange across systems
DOCUMENTED
Federal education-data standards were developed to make information more consistently defined and interoperable across education systems.
Evidence
CEDS says its purpose is to streamline understanding of data within and across P–20W institutions and sectors. Its Assessment Interoperability Framework supports exchange between assessment systems and SIS, SLDS, LMS, and broader education technology.
Source: CEDS — History / Assessment Interoperability Framework
Why it matters
Modern linkage does not require every record to be kept in one database. Standardized systems can exchange compatible information across separate platforms.
EB-005 — Utah uses personally identifiable student information in state-supported early-warning infrastructure
DOCUMENTED
Utah’s Panorama Student Success agreement involved personally identifiable student information, including student names and statewide student identifiers.
Evidence
Utah’s official data-agreement repository identifies the Panorama contract as using personally identifiable student information and lists student name, statewide student identifier, grade, school, district, gender, and date of birth among shared fields.
Source: USBE Data Agreement USBE210049MA
Why it matters
The system is structured around identifiable individual students, not only anonymous aggregate statistics.
EB-006 — Utah’s Panorama contract integrated academic, attendance, behavior, and SEL information
DOCUMENTED
The state described Panorama Student Success as an early-warning system integrating academic, attendance, behavior, and social-emotional learning information in real time.
Evidence
Utah’s data-agreement repository describes the platform as integrating these categories to help educators support individual students and student groups.
Source: USBE Data Agreement USBE210049MA
Why it matters
This provides a concrete example of information categories being brought into one student-support environment.
EB-007 — Utah now requires student-level digital early-warning systems with automatic flags
DOCUMENTED
Utah currently requires LEAs to use digital early-warning programs containing specified student-level information and automatically flag students meeting established thresholds.
Evidence
USBE’s current EWS guidance requires student-level academics, attendance, behavior, discipline, safety, and course-failure information; automatic flagging; and intervention-plan tracking.
Source: Utah State Board of Education — Early Warning Systems
Why it matters
This demonstrates the transition from storing information to using information to produce institutional decisions about individual students.
EB-008 — Early-warning algorithms can perform similarly to simpler student-risk indicators
DOCUMENTED FEDERAL RESEARCH
More complex algorithmic student-risk models do not automatically produce materially better predictions than simpler prior-performance systems.
Evidence
An IES study comparing a machine-learning algorithm with a prior-performance early-warning system found similar accuracy for several outcomes when comparable proportions of students were flagged and identified subgroup accuracy differences.
Why it matters
Technical complexity should not itself be treated as evidence of superior decision quality.
EB-009 — Adding human-services data did not substantially improve risk identification in a federal study
DOCUMENTED FEDERAL RESEARCH
Adding child-welfare, justice, homelessness, and other human-services information did not substantially improve identification of near-term academic risk in the Pittsburgh/Allegheny County study.
Evidence
IES reports that models using ordinary school data already achieved moderate-to-high predictive accuracy and that adding human-services events did not substantially change success in identifying near-term attendance, behavior, and course-performance risks.
Source: IES — Do Data from Child Welfare and Human Services Improve Early Warning Systems?
Why it matters
It provides a direct empirical example in which expanding the amount and sensitivity of information did not yield a comparable increase in predictive benefit.
EB-010 — Automated student monitoring can assign named predictive categories
DOCUMENTED TECHNICAL ARCHITECTURE
School monitoring software can automatically interpret student activity and assign named predictive classifications before a human reviewer acts.
Evidence
GoGuardian’s Beacon 24/7 terms define Predictive Phase Categories automatically assigned by algorithms, including Active Planning, Self-Harm, Suicide Research, and Suicidal Ideation.
Source: GoGuardian — Beacon 24/7 Terms
Why it matters
The platform is not simply storing digital activity. Software is participating in the interpretation and triage of that activity.
EB-011 — Student activity monitoring has resulted in law-enforcement contact
INDEPENDENTLY CORROBORATED
Independent research documents school monitoring systems leading to law-enforcement contact in actual practice.
Evidence
The Center for Democracy & Technology reported teacher accounts of students being contacted by law enforcement because of behavior flagged by student activity monitoring. RAND has also documented school-stakeholder concerns about law-enforcement involvement in AI-based suicide-risk monitoring.
Why it matters
It documents a real pathway from digital student activity to consequences outside ordinary classroom instruction.
EB-012 — School threat-assessment teams can include outside mental-health and law-enforcement personnel
DOCUMENTED
Federal guidance recognizes multidisciplinary threat-assessment teams that can include external mental-health professionals and local law enforcement.
Evidence
The Department of Education says threat-assessment teams may include principals, counselors, school law-enforcement officials, outside medical and mental-health professionals, and local law-enforcement officers.
Source: U.S. Department of Education — Threat Assessment Team FAQ
Why it matters
Student information can be considered inside a multidisciplinary institutional process extending beyond ordinary educational staff.
EB-013 — A single incident can create records governed under different rules
DOCUMENTED LEGAL ARCHITECTURE
A student event can generate both education records and separately created law-enforcement-unit records.
Evidence
Federal FERPA guidance distinguishes qualifying law-enforcement-unit records from education records while preserving FERPA status for education records merely shared with law enforcement.
Source: U.S. Department of Education — FERPA FAQs
Why it matters
The same underlying event can produce more than one institutional record, with different access, disclosure, and retention rules.
EB-014 — Parents have federal rights to inspect education records and seek amendment
DOCUMENTED LEGAL RIGHT
FERPA gives parents important baseline rights over qualifying education records.
Evidence
The Department of Education states that FERPA provides parents the right to inspect and review education records within 45 days, seek amendment of records believed inaccurate, and consent to disclosure except where federal law authorizes an exception.
Source: U.S. Department of Education — FERPA Legal Basics
Why it matters
Parent access depends first on understanding which maintained records qualify as education records and which other institutional record categories may exist.
EB-015 — PPRA gives parents rights concerning third-party surveys and protected areas
DOCUMENTED LEGAL RIGHT
Federal law provides parent inspection and privacy rights for specified surveys and information categories.
Evidence
Department guidance states that LEAs must develop policies allowing parents to inspect third-party surveys before administration and must establish privacy protections for surveys involving PPRA’s protected areas.
Source: U.S. Department of Education — PPRA Local Policy Requirements
Why it matters
Some parent rights operate before information is collected, rather than only after a record exists.
EB-016 — AI-assisted school security can generate consequential false alerts
REGULATORY FINDING
Automated security systems can produce false positives that result in real-world screening and intervention.
Evidence
The FTC alleged that Evolv’s AI-powered weapons-screening system missed weapons while flagging ordinary items and described a school deployment in which increased sensitivity produced a reported 50 percent false-alarm rate.
Source: FTC — Evolv Technologies action
Why it matters
An algorithmic error in a school setting can become a physical search, delay, investigation, or other student consequence.
EB-017 — Large education-data systems have experienced breaches involving highly sensitive student information
REGULATORY FINDING
The risk created by large student-information environments is operational, not merely hypothetical.
Evidence
In June 2026, the FTC finalized an order against Illuminate Education following allegations that security failures led to unauthorized access to information concerning 10.1 million students, including student records and health-related information.
Source: FTC — Illuminate Education final order announcement
Why it matters
As the informational richness of student systems increases, a security failure can expose substantially more than traditional academic records.
EB-018 — PBIS has become national-scale behavioral infrastructure
DOCUMENTED INSTITUTIONAL DIFFUSION
Positive Behavioral Interventions and Supports is supported through a long-running federally funded technical-assistance infrastructure operating at national scale.
Evidence
The National Center on PBIS says it was initially federally funded in 1998 and currently supports implementation in more than 25,000 schools across all 50 states; it received new federal funding extending its work through 2028.
Source: National Center on PBIS — About
Why it matters
Modern behavioral systems spread not only through individual school choices but through national technical-assistance and implementation infrastructures.
EB-019 — Iserbyt explicitly linked automated longitudinal records with behavioral research
HISTORICAL DOCUMENT / ISERBYT FRAMEWORK
Iserbyt’s historical concern extended beyond data storage to what detailed longitudinal information could enable researchers and institutions to analyze about the learner.
Evidence
Her Appendix XVI discussion of automated education records and longitudinal data connects detailed student information with behavioral research and analysis.
Source: Iserbyt, The Deliberate Dumbing Down of America, Appendix XVI
Why it matters
It establishes the historical lens through which PRC approaches the modern infrastructure: not simply what data exist, but what institutions can infer and do once those data accumulate.
EB-020 — Klyczek identified the cross-system architecture as an investigative problem
INVESTIGATIVE LEAD
John Klyczek’s independent work placed SEL, psychological profiling, technology, mental health, behavioral systems, and policing on the same investigative map.
Evidence
His 2020 investigation raises concerns about psychological profiles and predictive uses of student information and examines school/community/mental-health and policing-related institutional connections.
Source: John Klyczek — Socioemotional “Thought Crimes” in American Schools
Why it matters
Klyczek’s reporting supplied questions that could then be tested against contracts, federal studies, privacy law, vendor architecture, and contemporary implementations.
PRIMARY SOURCE ARCHIVE
Archive A — Historical & DDDoA Source Trail
A-01 — The Deliberate Dumbing Down of America — Charlotte Thomson Iserbyt
Use for affective-domain history, automated educational records, longitudinal-information concerns, behavioral objectives, outcome-based systems, and historical formation questions.
A-02 — Historical affective-domain material reproduced within DDDoA
Use for attitudes, values, feelings, desired affective objectives, evaluation, and longitudinal measurement.
A-03 — Outcomes Accreditation material reproduced within DDDoA
Use for measuring student behavior and self-perception, and comparing present performance with desired outcomes.
Archive B — Modern SEL & Behavioral Frameworks
Establishes CASEL’s 1994 founding history and the development of the term social and emotional learning.
B-02 — CASEL — What Is the CASEL Framework?
Establishes the five modern SEL competencies and system-level framework.
B-03 — CASEL — Schoolwide SEL Essentials
Establishes systemic implementation across classroom, school, family, and community contexts.
B-04 — National Center on PBIS — About
Establishes federal PBIS technical-assistance infrastructure and national implementation footprint.
B-05 — PBIS Center — Funding Through 2028
Establishes continuing federal support and implementation scale.
Archive C — Data Standards, Linkage & Interoperability
C-01 — Common Education Data Standards — History
Establishes federal data-standardization history, SLDS relationship, and P–20W information architecture.
C-02 — CEDS — Assessment Interoperability Framework
Establishes technical compatibility across assessment systems, SIS, SLDS, LMS, and broader education technology.
Archive D — Utah Early-Warning System Case File
D-01 — USBE Data Agreement USBE18028MA — Panorama Education
Establishes the 2018 early-intervention pilot and identifiable student-data environment.
D-02 — USBE Data Agreement USBE210049MA — Panorama Education
Establishes identifiable students, statewide identifiers, and integrated early-warning architecture.
D-03 — USBE — Current Early Warning Systems Page
Establishes current state requirements, student-level indicators, automatic flagging, intervention tracking, and Panorama designation.
Archive E — Predictive Analytics & Student Risk
Establishes machine-generated individual risk scores, threshold tradeoffs, model comparison, and subgroup accuracy differences.
E-02 — IES — Do Data from Child Welfare and Human Services Improve Early Warning Systems?
Establishes cross-agency prediction research and limited incremental predictive benefit from additional external data.
Archive F — Student Monitoring & Algorithmic Alerts
F-01 — GoGuardian — Beacon 24/7 Terms
Establishes automatic Predictive Phase Categories and human-review pathway.
F-02 — Center for Democracy & Technology — Hidden Harms
Establishes reported consequences including law-enforcement contact and public-safety alerting.
F-03 — RAND — Artificial Intelligence–Based Student Activity Monitoring for Suicide Risk
Establishes contemporary AI monitoring practices and stakeholder concerns.
Archive G — FERPA, PPRA & Record Boundaries
G-01 — U.S. Department of Education — FERPA Legal Basics
Establishes 45-day inspection right, amendment rights, and general consent rule with exceptions.
G-02 — U.S. Department of Education — What Is a Threat Assessment Team?
Establishes multidisciplinary team membership including outside mental-health and law-enforcement professionals.
G-03 — FERPA Sharing With Outside Threat-Team Members
Establishes qualifying nonconsensual disclosure to outside team members and redisclosure restrictions.
G-04 — U.S. Department of Education — FERPA Frequently Asked Questions
Establishes law-enforcement-unit record distinctions and related FERPA rules.
G-05 — PPRA — Local Policy Requirements
Establishes parent inspection rights for third-party surveys and protected-area policies.
Archive H — Regulatory & Operational Failure
H-01 — FTC v. Evolv Technologies
Establishes regulator allegations concerning AI-performance representations, missed weapons, harmless-item alerts, and false-positive tradeoffs.
H-02 — FTC v. Illuminate Education
Establishes breach affecting information associated with 10.1 million students and mandatory security, minimization, retention, and deletion remedies.
Archive I — Independent Investigative Research
I-01 — John Klyczek — SEL / behavioral-data investigation
Research contribution: psychological profiling; SEL-data architecture; behavioral systems; mental-health and community integration; policing/predictive-system questions.
I-02 — Whitney Webb — Gabriel / surveillance infrastructure investigation
Research contribution: networked school/community security, automated detection, sensors, first-responder integration, platform expansion, and surveillance-infrastructure questions.
How PRC Uses Sources
Parent Research Center distinguishes between what an institution says, what a contract authorizes, what a system is technically capable of doing, and what independent evidence shows occurred in practice. Government and institutional records are indispensable for establishing official policy and architecture. Contracts reveal obligations and access. Technical documents reveal system capability. Academic research tests outcomes. Regulators and audits document failures. Independent investigative journalism helps locate relationships, implementation details, consequences, and source trails that institutions may not foreground. PRC follows each form of evidence for the question it is best equipped to answer.
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