Intelligent Short Answer Assessment using Machine Learning

Abstract

Education is fundamental for human progress. A student is evaluated by the mark he/she scores. The evaluation of student’s work is a central aspect of the teaching profession that can affect students in significant ways. Though teachers use multiple criteria for assessing student work, it is not known if emotions are a factor in their grading decisions. Also, there are several mistakes that occur on the department's side like totaling error, marking mistakes. So, we are developing software to automate the evaluation of answers using Natural Language Processing and Machine Learning. There are two modules, in the first module, we use Optical Character Recognition to extract a handwritten font from the uploaded file and the second module evaluates the answer based on various factors and the mark is awarded. For every answer being entered, evaluation is done based on the usage of word, their importance and grammatical meaning of the sentence. With this approach we can save the cost of checking the answers manually and reduce the workload of the teachers by automating the manual checking process. The evaluation time is also reduced by using this software.