Adaptive Testing vs Laureate’s Approach: Why We Chose a Better Model for Real-World English Assessment

In the world of digital assessment, adaptive testing has become a buzzword. Many testing platforms promote it as the most “advanced” way to measure language proficiency — but adaptive tests also come with significant limitations, fairness issues, and practical challenges for education providers.

At Laureate Online Testing, we deliberately chose not to use adaptive testing.Instead, our system uses a calibrated, multi-level, randomised model that gives every candidate a fair, balanced experience while providing institutions with highly reliable data.

Here’s why our approach is different — and why it’s better for the sectors we serve.

What Is Adaptive Testing?

Adaptive testing changes the difficulty of the next question based on whether the candidate got the previous one correct.

In theory, it sounds efficient:

• Get a question right → next question gets harder

• Get it wrong → next question gets easier

The algorithm keeps adjusting until it predicts the student’s level.

Where Adaptive Testing Works Well

Adaptive testing performs best in:

• Large-scale, high-stakes exams

• Very controlled testing environments

• Highly trained test-takers

• Situations where the goal is a quick estimate, not a broad skill diagnostic

But these conditions rarely match VET, ELICOS, CRICOS, pathway, or language-support contexts.

The Problems With Adaptive Testing

Despite the marketing, adaptive tests come with real drawbacks:

1. They can be unfair for nervous or unfamiliar candidates

   If a learner answers the first few questions incorrectly (due to nerves, misunderstanding, or test anxiety), the algorithm drops them to an artificially low difficulty band — and often never fully recovers.

2. They reduce skill coverage

   Because the test rapidly narrows the difficulty range, many core skills may not be sampled at all.

   A learner might finish the entire test without:

   • Reading a medium-length text

   • Encountering certain grammar structures

   • Being tested on writing coherence

   • Completing a higher-order listening task

   This creates gaps that affect placement accuracy.

3. They are opaque and hard to audit

   Regulators, teachers, and institutions often cannot see:

   • How the difficulty jumps occurred

   • How many items were delivered at each level

   • Whether the algorithm behaved consistently

   For compliance-driven sectors (like RTOs and CRICOS providers), this lack of transparency is a genuine problem.

4. They are easier to “game”

   Adaptive tests can be tricked by:

   • Pattern guessing

   • Memorising question templates

   • Purposely answering early items wrong to avoid hard ones

   This leads to unreliable scores.

5. They provide limited diagnostic information

   Adaptive testing can tell you roughly “what level” someone is — but not why they’re at that level, or where they need improvement.

Why Laureate Does NOT Use Adaptive Testing

Laureate Online Testing was built for real education environments:

• VET

• CRICOS

• ELICOS

• University pathways

• Corporate training

• Foundation English

• Skills-based placement and progression

These sectors need accuracy, fairness, transparency, and repeatability — not a black-box algorithm.

Our model provides exactly that.

The Laureate Model: Calibrated, Multi-Level, Randomised Testing

Instead of adapting mid-test, Laureate uses a smarter structure:

• A large database of calibrated questions

  Questions are written and graded across five difficulty levels (A1–C1 style, aligned to CEFR/IELTS benchmarks).

• Each test automatically pulls a balanced sample

  When a test is generated, the platform randomly selects an equal number of questions from each level.

• Every test version is unique

  Randomisation prevents memorisation and increases integrity.

• Every learner is tested broadly, not narrowly

  All macro skills — and all difficulty ranges — are sampled.

• Scores reflect demonstrated ability, not algorithmic assumptions

  This produces a clear, balanced picture of a learner’s strengths and weaknesses.

• Easy to audit, easy to interpret

  Institutions can see exactly:

  • Which question bank items were used

  • What difficulty bands were included

  • How marks were distributed

• Consistent, repeatable, and fair for all candidates

  No learner is punished or advantaged by how they answer early questions.

Why Our Approach Is Better for Real-World Teaching

1. Full coverage across all levels

   You never miss important skills because the algorithm narrowed too fast.

2. Transparent for teachers, regulators, and auditors

   Perfect for RTOs, CRICOS providers, NEAS review, and government audits.

3. More diagnostic information

   You can see not only the level but also:

   • Grammar performance

   • Reading comprehension range

   • Listening accuracy across difficulty bands

   • Writing/speaking rubric performance

4. More stable and reliable

   Adaptive testing can swing dramatically.Laureate’s method gives stable results across all cohorts.

5. Perfect for placement AND progression

   Initial placement + ongoing monitoring both make sense with this structure.

So Which Method Is Actually Better?

It depends on the context.

Adaptive Testing

Good for:

• Very large, high-stakes, single-score exams

Not ideal for:

• Diagnostics

• Language learning

• VET

• CRICOS

• Placement

• Low-stakes entry screening

• Vulnerable cohorts

• Fairness-driven environments

Laureate’s Multi-Level Randomised Testing

Good for:

• Accurate diagnostics

• Placement

• Progress monitoring

• Language learners of all levels

• Fairness across cohorts

• Compliance-heavy sectors

• Transparent scoring

• Repeatability and integrity

In short: Adaptive testing estimates a score. Laureate testing demonstrates a score.

We built Laureate Online Testing this way because it serves real classrooms, real trainers, and real learners — not abstract psychometric models.

Our approach is:

• Fair

• Transparent

• Reliable

• Auditable

• Educationally meaningful

And most importantly:It gives institutions the data they actually need to support learners.