Using Machine Learning Technology in Online Language Testing
Introduction
There are several ways to use machine learning in language testing. One common way in using machine learning algorithms is to grade written or spoken responses in a language test. This can be done by training a model on a large dataset of language test responses that have been manually graded by experts. The model can then predict the grades of new responses with high accuracy.
Machine learning definitely improves the efficiency and accuracy of language testing. Besides, it supports the development and administration of language tests in different ways.
Mysoly approach to E-Learning and Testing Development
Mysoly’s nt2oefening.nl solution is completely designed for the first option. NT2 Oefening has developed Machine Learning algorithms not only for reading and listening exams but also for writing and speaking exams, unlike its competitors. This is its unique feature and the main difference from the other NT2 market apps.
Machine learning reduces the necessary time and effort to grade language test responses. It also provides more consistent and objective grading. Because the model doesn’t have the same biases and inconsistencies that can sometimes affect human graders.
Yet, it is important to note that using machine learning for language test grading is not easy. When it is done, the model will learn more with natural language feedback which will develop the system accuracy.
Stay tuned with us and wait for the first launch!
