Privacy and fairness

Measuring contribution responsibly means drawing a hard line: enough visibility for fairness, no more. Dwixel’s privacy model is a feature, not a footnote, and it is enforced in the product.

Privacy and access, by design

Instructors can review the group’s work as part of oversight, and see who contributed what. What Dwixel does not do is turn that into covert profiling: the contribution measurement is deterministic and explainable, students always see their own data, peer review is confidential, and access is enforced by row-level security so work is never exposed to other groups or the public. That directly answers the central ethical concerns with educational trace data, which a review of 77 studies groups around privacy, consent, and the validity of what is inferred. ¹

Confidential peer review, done carefully

Peer assessment is a useful second signal, and Dwixel collects it confidentially: students cannot read their own ratings. The design follows what the research recommends, because peer scores inflate when graded and carry friendship bias even with a rubric. ^{2 3} Some feared biases are smaller than assumed, though: with multiple raters, reciprocity accounts for only about one percent of score variance, ⁴ and peer marks track teacher marks reasonably well under good conditions. ^{5 6}

Access control and recovery

• ·Access is enforced by row-level security in the database, not by interface convention.
• ·A student’s work is theirs; contribution data is shown back to them in full.
• ·Documents and presentations are backed up automatically, with attributed version history, so accidental loss is recoverable.