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Authored from the Learning Engineering Toolkit — pending expert review

This page was authored on 2026-07-16 directly from the source text of the Learning Engineering Toolkit (Jim Goodell & Janet Kolodner (Eds.), 2023). Every factual claim carries an inline <cite> citation to the specific chapter it draws on; the full references are listed at the foot of the page. The prose is grounded in the primary source but has not yet been validated by a subject-matter expert. Use the Edit button to validate, correct, or expand.

Chapters: LET-07 (Chapter 7), LET-17 (Chapter 17)

11.2.1 Data Privacy and Learner Rights

Learning engineering systems collect intimate behavioral data about learners: what they attempt, how long they spend, where they struggle, when they disengage. These data carry significant privacy implications that practitioners must address through both technical design and institutional governance.

Regulatory Framework

The primary U.S. regulatory framework is FERPA (Family Educational Rights and Privacy Act), which governs student education records at institutions receiving federal funding — restricting disclosure and granting students rights of access and correction. For learners under 13, COPPA (Children's Online Privacy Protection Act) imposes additional consent requirements. The EU's GDPR applies to systems with European users and sets stricter requirements around consent, data minimization, and the right to erasure. [LE-LS-GL-007]

Technical Privacy Principles

Data minimization requires collecting only the data necessary for the stated purpose — not storing rich behavioral telemetry indefinitely when aggregate metrics suffice for the evaluation question. [LE-LS-GL-004] IEEE 9274 (xAPI) provides technical infrastructure for consent-aware data collection: statement vocabularies and LRS access control policies can encode which data types require explicit consent. [LE-LS-SG-002]

Pseudonymization and anonymization reduce re-identification risk in research contexts, though the Journal of Learning Analytics has documented that educational telemetry data is often re-identifiable even after standard anonymization due to behavioral uniqueness. [LE-LS-JO-004]

Learner Rights

Learners have rights that LE practitioners must protect: the right to know what data is collected and why; the right to access their own records; the right to correction of inaccurate data; and — increasingly under GDPR — the right to explanation when algorithmic systems make consequential decisions about them. [LE-LS-GL-004] IEEE ICICLE's professional standards require that practitioners can articulate the data governance practices of their systems to learners, parents, and institutional administrators on request. [LE-LS-CO-001]

From the Learning Engineering Toolkit

The Toolkit situates data privacy within information ethics, a set of concerns the team needs to weigh during implementation — particularly privacy, consent, and user autonomy. [LET-07] Jordan Richard Schoenherr emphasizes that what privacy demands is shaped not only by law but by a community's sociocultural traditions, so grasping a community, its relationships, and its values matters. [LET-07]

Building on the APA principle of respect for people's rights and dignity, the book holds that learning engineering should keep learner data confidential, anonymous, and secure; learners ought to be told how their data are used, informed of any retention rules, and offered ways to have their data deleted. [LET-07] Under beneficence and nonmaleficence, learner data should serve only the purpose intended, with learners aware of that purpose and giving valid consent. [LET-07]

The book highlights the GDPR as perhaps the most significant standard governing lawful and ethical use of personal data, pointing to its provision that lets individuals have their personal data corrected or erased — an expectation mirrored in learning engineering standards for integrated, comprehensive learner records. [LET-07] It notes that California enacted a comparable Consumer Privacy Act, and it references the IEEE principle of data agency, under which people retain control over their identity and can share their data securely. [LET-07]

Chapter 17's SEEM-ED tool converts these commitments into evaluative prompts under respect for people's rights and dignity — for instance, whether the product or process safeguards users' rights to privacy and confidentiality, and how anonymity or confidentiality was maintained in the data. [LET-17] Additional prompts ask whether any feature might undermine a user's self-determination, and how users were informed or educated about the effects of using it. [LET-17]

Sources from the Learning Engineering Toolkit

  1. [LET-07]Jordan Richard Schoenherr (2023). Chapter 7: Learning Engineering is Ethical. In Jim Goodell & Janet Kolodner, Learning Engineering Toolkit (pp. 201–228). Routledge / Taylor & Francis. doi:10.4324/9781003276579
  2. [LET-17]Jordan Richard Schoenherr & Jodi Lis (2023). Chapter 17: Ethical Decision-Making Tools. In Jim Goodell & Janet Kolodner, Learning Engineering Toolkit (pp. 361–363). Routledge / Taylor & Francis. doi:10.4324/9781003276579