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Academic integrity

Originality checking that keeps learner work inside your institution

Check submissions against your own repository of past work, see exactly where the overlap is, and run a defensible case when something is wrong — with an engine that runs inside your tenant instead of uploading learner assignments to somebody else’s corpus.

Live in production. Native matching engine — no third-party service, no external upload.

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Trusted by leading skills development providers

Divine Favour Progressive College FETElada InstituteThe Great Oasis CollegeEdzani Community DevelopmentHighbury CollegeCollege of AfricaNTCTshwane City CollegeTshimologo FET CollegeRi A Fhata CivilsVictory Education GroupSt Aquinas CollegeHatfield City CollegeTechniconSAIthemba Elihle InstituteThasululo Technical CollegeLin-Glazed Beauty

The POPIA problem with originality checking

Every mainstream originality tool works by adding submissions to a global corpus. That is what makes them effective, and it is also what makes them awkward here: you are transferring identifiable learner work to a foreign processor, retaining it indefinitely, and doing it for learners who signed up for a plumbing qualification. Providers either accept the transfer, get consent they do not really have, or check nothing at all.

  • Cross-border transfer of learner work to a foreign processor, with indefinite retention.
  • Consent that is not meaningfully informed and not meaningfully refusable.
  • Per-submission licensing that makes checking everything unaffordable.
  • Tools built for university essays, used on trade portfolios where they read badly.
  • A similarity percentage with no process behind it, so nothing defensible happens next.

What the integrity module does

Your corpus, your tenant

Submissions are matched against your own institutional repository. Nothing is sent to an external service, and nothing joins a global database you do not control.

Similarity heatmap

See which passages overlap with what, rather than a single percentage that tells you nothing about whether it matters.

Institutional repository

Past submissions build a corpus that gets more useful every intake — and catches the most common real case, which is one learner copying another.

Case workflow

A suspected breach becomes a case with a record: what was found, who reviewed it, what the learner said, what was decided.

Learner self-check

Learners can check their own work before they submit it. Most integrity problems at this level are ignorance rather than intent, and this converts a disciplinary case into a teaching moment.

Integrity reporting

Patterns across cohorts, programmes and facilitators — which is where you find the module whose assignment gets copied every single intake.

How a check runs

  1. 1

    Learner submits

    Work comes in through the portfolio, the same way any other evidence does.

  2. 2

    Check runs locally

    The engine fingerprints the submission and matches it against your repository, inside your tenant.

  3. 3

    Review the overlap

    A heatmap shows what matched what, so a reviewer judges the substance rather than a number.

  4. 4

    Open a case if needed

    Where there is a real problem, it becomes a documented case with a decision at the end of it.

What this does not do yet

  • Matching is against your own institutional repository, not against the open internet or a global publisher corpus. That is the deliberate trade that keeps learner work inside your tenant — it is very good at catching learner-to-learner copying and reused past submissions, and it will not catch a passage lifted from an obscure website.
  • The module surfaces similarity and manages the case. It does not decide whether a breach occurred — a human does, and the workflow exists to record how they reached that decision.
  • Automatic triggering on every submission is not yet wired; checks are run against submissions rather than firing on their own.

Questions providers ask

How is this different from Turnitin?
Turnitin matches against a very large global corpus, and the way it maintains that corpus is by adding your learners’ submissions to it. skillSYMS matches against your own repository, inside your own tenant, and nothing leaves. You give up reach against the open internet and you get a POPIA position you can actually defend, plus no per-submission licence.
Does learner work leave our system?
No. The matching engine is native to skillSYMS and runs against your tenant’s own repository. There is no third-party originality service in the path and no external upload of learner submissions.
What does it actually catch?
Overlap with anything already in your repository — most commonly one learner copying another in the same cohort, and submissions reused from previous intakes. In occupational qualifications that is the overwhelming majority of real cases.
Is a high similarity score proof of plagiarism?
No, and the module is built on that assumption. Shared templates, prescribed formats, standard terminology and quoted regulation all produce legitimate overlap. That is why you get a heatmap showing what matched rather than a verdict, and why the case workflow requires a human decision with reasons recorded.
Can learners check their own work first?
Yes, from the learner portal. It is usually the highest-value part of the module: most integrity problems at this level come from learners who genuinely did not know what counted as their own work, and a self-check before submission fixes that without a disciplinary process.
How does this fit our academic integrity policy?
The case workflow records the finding, the review, the learner’s response and the decision, and the policy itself can be a controlled document in the quality management module with read-and-acknowledge tracking — so you can show the learner was told the rule before they broke it.

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