Intelligent Short Answer Assessment using Machine Learning
Rosy Salomi Victoria D1, Viola Grace Vinitha P2, Sathya R3
1Dr.Rosy Salomi Victoria D.*, Associate Professor, Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, India.
2. Viola Grace Vinitha P, Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, India.
3Sathya R, Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, India.
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1111-1116 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7889049420/2020©BEIESP | DOI: 10.35940/ijeat.D7889.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Descriptive type answers, Grammatical Checking, Optical Character Recognition, Semantic checking.