Introduction to PTE Mock Test
It has been along debated among the PTE Aspirants regarding the scoring of PTE Mock Tests and whether AI is used by online portals or Pearson to get the desired scoring.
In this blog, we will be discussing what goes into the scoring of PTE Mock Test and what role AI plays in scoring.
An intelligent machine learning algorithm
In the Pearson Test of English or PTE tests, the scoring needs to be done for all the four sections that are speaking, writing, reading & listening.
While the speaking section involves evaluation of the audios (speaker’s response), the writing section involves the evaluation of written contextual texts.
The complexity of the answers led to the integration of machine learning algorithms in the initial stages so as to attain the desired results which were later integrated into AI.
The Initial Stage
When the initial development started, the discussion was about converting the audios into text using the speech to text method and then evaluate the text based on the markings of the machine.
The machine was initially fed with compiled data (correct answers) that were used to check if the text obtained after conversion was matching with the compiled data upon which any result was obtained.
However, the process has its own limitation as it was not possible to interpret any newly added answers as they had no suitable records.
Introduction of AI
It was very evident that PTE Mock test is one of the mock test extensive mock test scenario which involved evaluation of audios as well as sample answers that were both in written and spoken form. It was also evident that cross-matching answers with user answers had their own limitation and didn’t achieve the desired result.
Machine learning was getting the results for PTE Mock Test, but it was not thoroughly accurate and didn’t attain the necessary results.
A basic algorithm was then developed to work on understanding the complexity of the answers and understanding the varied answers.
Feeding data to AI
Once the algorithm was developed, it was allowed to go through the answers of users who have given the PTE Mock Test. The idea was to make it understand the vast difference among user answers and how it can grade them accordingly.
The audio answers of PTE Mock Test takers were directly fed to the AI without changing the orientation or converting the speech to text.
Once the algorithms went through a set of data, a max point and low point was set up to understand the marking of the answers.
Self-evaluation criterion
The introduction of data to the AI along with setting up the marking matrix created a self-evaluation system with which the AI was able to self-score the students based on their answers.
The attempted PTE Mock tests scored could be generated within a fraction of second marking all the four sections that are speaking, writing, reading & listening.
Accuracy of score
While the scoring was completely based on AI evaluation, the PTE Mock Test’s initial scoring was formulated on the guidelines of Pearson which didn’t come with a generic set of tried and tested guidelines.
In order to achieve the desired score accuracy up to 96%, the speech ace technology was used to grade the PTE mock tests. The introduction of the speech ace lead to accurate scoring factors along with the change in data output and was helpful in marking enabling skills like pronunciation, oral fluency, and written discourse.
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Difference between native & non-native speakers
PTE Mock Tests are basically given by both native speakers from countries like the USA and non-native speakers like Indians who have a certain accent.
The algorithm was then fed with both types of audio responses so as to improve the scoring criterion and get the desired scoring output which is not based on the accent but works on getting the results based on the answers provided.
The introduction of AI-led to an effective and quick scoring that helped test takers obtain quick and effective scores that helped them understand their skills level and areas of improvement. The same also helped them get used to the concept of AI scores which is cheap, efficient, and accurate.
