A Survey on Automated Assessment Questions Generation System using Supervised Algorithms

Abstract

Assessment is the sole way to determine whether a teacher's lessons are effective and whether pupils are learning what they are supposed to. When test questions are manually generated, they are first created and then assigned at random to the exam. Manually producing test papers that adhere to the standards of a good test paper requires a lot of time and work. Assessments that are created expressly to evaluate students' technical abilities require more time to develop, and trainers are constantly looking for methods to learn more about their candidates by asking them questions on technical concepts. With the help of our technology, questions regarding numerous technological topics and domains may be automatically generated from a database. We use classification and supervised algorithms like the Naive Bayes Classifier, Random Forest Classifier, and Decision Tree to generate test papers depending on the predicted skill of the applicant. The supervised algorithms Naive Bayes, Random forest, and Decision tree are all examples. Instead of assessing the candidate's knowledge in a completely unrelated field, test questions based on the candidate's talents will efficiently evaluate the candidate's knowledge in the skills that they know. The opportunity for the recruiters to fully comprehend a candidate's capabilities will also allow them to make greater use of their talents and assign tasks to applicants based on how well they do in a specific skill.