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Predict Your Turnitin Similarity Score with a Free AI Detector

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Introduction to the Turnitin Score Estimator

The Turnitin Score Estimator is a new tool that can provide students and writers an estimated Turnitin similarity score for their essays and papers before officially submitting them. This can be extremely useful for getting an idea of potential issues in advance and revising content accordingly.

In this blog post, we will provide an overview of how the Turnitin Score Estimator works, details on the process used to create and validate the tool, instructions for using it, and some important limitations and disclaimers to keep in mind.

Overview of the Turnitin Score Estimator

The Turnitin Score Estimator utilizes AI writing analysis to compare an input text against a proprietary database and return an estimated match percentage. For students and writers, this allows determining if an essay's similarity score will trigger plagiarism concerns before formally submitting. Specifically, the tool relies on ContentScale's AI detector to analyze text and provide a "human probability score". This quantifies how likely the writing is to have been generated by a human. There is a high correlation between ContentScale scores and Turnitin similarity percentages, allowing the creation of an estimator.

How the Turnitin Score Estimator Works

To build the Turnitin Score Estimator, samples essays were generated with both AI and human writers across low, medium, and high quality levels. The texts were analyzed by ContentScale to determine AI scores and submitted to Turnitin to get similarity percentages. With matched pairs of ContentScale and Turnitin scores, linear regression modeling determined the correlation and created a formula to translate between the two. Now a single ContentScale score can reliably estimate what the Turnitin match percentage would be.

Generating Sample Essays for Analysis

To properly correlate ContentScale AI scores with Turnitin similarity scores, a robust dataset was needed. Both computer-generated and human-written text samples across quality levels were produced.

30 essay topics were gathered spanning subjects typically assigned in first-year undergraduate university courses. This provided a representative mix of subjects for students and writers.

Using AI Writers for Low, Medium and High Scoring Essays

For artificially intelligence generated text, low, medium, and high quality samples were obtained. The AI writers Claude, SEO Writer, and Jericho Writer created 10 essays each, with configured scoring levels. The texts were automatically generated on the corresponding topics just like a student would receive from a professor. This provided the AI-written portion of the essay dataset with variability in both subject and detected quality. Having the full range covered is important for correlating how ContentScale score translates to Turnitin match percentage.

Human Written Essays for Comparison

In addition to the computer-produced essays, human-written samples were added to the dataset. The purpose was to validate if the Turnitin Score Estimator trained on AI text would transfer effectively. Claude was utilized to produce 10 high quality human-like essays to complement the rest of the training data. Including these along with the 30 other AI-generated samples provided a well rounded collection for stability.

Analyzing Essays with AI Detectors

With 40 total sample essays created, the next step was evaluating the text with AI analysis tools. Both ContentScale and Turnitin processed each submission to quantify quality and similarity levels respectively.

By comparing essay by essay, the relationship between AI detection and plagiarism checker match percentage could start to be understood.

Using ContentScale to Score Essays

The sample set of 40 essays were run through ContentScale one by one to determine the AI score for each. As a reminder, this score rates text on a scale of 0 to 100 in terms of likelihood it was written by a human. The scores ranged considerably based on whether Claude, Jericho Writer, or a human composed the piece. This established the AI matching percentage so plagiarism checker correlations could occur.

Validating with Turnitin

After calculating the ContentScale score for all essays in the dataset, they next had to be processed through Turnitin. One by one the samples were submitted to scan against Turnitin's proprietary database. The returned similarity percentage quantified how much matching text existed. With both an AI score from ContentScale and match percentage from Turnitin, correlation analysis was now possible.

Building the Turnitin Score Estimator

With corresponding data points from ContentScale and Turnitin established across 40 written essays, work could begin on creating a Turnitin Score Estimator model.

Through linear regression, a formula was produced to translate an input ContentScale score into an estimated Turnitin similarity match percentage.

Correlating ContentScale and Turnitin Scores

The first step was quantifying the actual correlation present between the AI analysis and plagiarism checker scores from the sample essays. Calculation confirmed an extremely high correlation of 91%. This meant for a given input ContentScale score, the Turnitin percentage could be reliably predicted.

Creating a Linear Regression Model

With a 91% correlation achieved, work proceeded on generating the regression formula. The matched pairs of data for each essay were input variables, with the output being an equation to convert between them. Code Interpreter processed the calculations, determining slope and intercept terms for the most accurate linear translator. Now a simple web-based tool can take a new ContentScale score and reliably estimate Turnitin match percentage.

Using the Turnitin Score Estimator

With background covered on where the Turnitin Score Estimator comes from and how it was built, instructions can be provided to start utilizing the tool as a student or writer.

The estimator allows getting an AI-predicted Turnitin match percentage from ContentScale before ever submitting to plagiarism checker databases. This assists revising essays in advance if issues seem likely.

How to Get an Estimated Turnitin Score

Using the Turnitin Score Estimator is quite simple: first, copy your written text into the ContentScale analyzer to determine an AI score reflecting document quality. Scores closer to 100 indicate higher human probability. Next, take the score and input it into the estimator. The underlying linear regression formula will output an estimated Turnitin similarity percentage. If it seems excessively high, content adjustments can be made.

Limitations and Disclaimers

While correlated at 91% in testing, the Turnitin Score Estimator cannot guarantee perfectly accurate results. The output is an AI-powered prediction and actual plagiarism checker percentages may differ once formally submitted. Additionally, new essays may cover topics and content outside what the prediction model was trained on. Use the tool wisely with proper context around its limitations.

Conclusion

In closing, estimating Turnitin scores in advance allows students and writers to better revise essays before reaching plagiarism and similarity issues. The Turnitin Score Estimator tool provides this through an engineered relationship between ContentScale AI analysis and Turnitin checker databases.

While not 100% perfect across every text, reliable correlations support the prediction in similar academic essay contexts. Apply disclaimers judiciously, use the tool to aid early drafting, and write responsibly.

FAQ

Q: What is Turnitin?
A: Turnitin is a popular plagiarism detection software used by many educational institutions to check student work for copied content.

Q: How accurate is the Turnitin score estimator?
A: The estimator provides an approximation but may not match real Turnitin scores perfectly. Use discretion when interpreting estimated scores.

Q: Can I use the estimator if I don't have access to Turnitin?
A: Yes, the estimator only requires the free ContentScale tool to function, without needing access to Turnitin.

Q: What is linear regression?
A: Linear regression is a statistical technique used to model the relationship between two variables. It was used here to correlate ContentScale and Turnitin scores.

Q: What AI detector is used?
A: The estimator uses ContentScale, a free AI tool, to detect plagiarism and score originality.

Q: Can I use other plagiarism checkers with this technique?
A: Possibly, but you would need to correlate scores with Turnitin first. ContentScale had the best correlation.

Q: Are AI writers capable of passing plagiarism checks?
A: Sometimes but often not completely. AI writing may still get flagged even if original.

Q: How were the sample essays generated?
A: Using a mix of human writers and AI tools like SEO Writer and Jarvis to create varied originality levels.

Q: What if my actual Turnitin score doesn't match the estimate?
A: The estimator relies on correlation and shouldn't be treated as 100% accurate. Significant deviations can occur.

Q: Can this technique work for non-essay assignments?
A: The estimator was built using essays but may work reasonably for other paper types and assignments.

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