Evaluation of Descriptive Exam Answer Scripts using Word Mover’s Distance
The knowledge and competency assessment have paramount significance in the education system. Recent scenario of COVID-19 witnessed the need of migrating from traditional education system to a modern online learning environment. Currently in the online assessment process, descriptive exam answer scripts evaluation is one of the tedious tasks to the teachers. The knowledge assessment may sometimes lead to biasing based on the mood of the evaluator and other circumstancing parameters. In general, though the evaluation process is well defined, still when two evaluators evaluate the same scripts, there are very less chances to award the same marks. The proposed model aims to address such real time issues and outer performs of the evaluation of descriptive answer scripts by using text semantic similarity measure. The proposed model works based on the word mover’s distance, whose purpose is to measure the semantic similarity among the actual answer and the answer given by the students. In this work, the data set is generated from the descriptive on-line examination platform. The data set contains student’s answers, which can pre-process initially and measure the semantic similarity among key answer and student’s answers. The given automatic evaluation procedure, could guarantee the impartiality and concealment of the evaluation.
Machine learning, Semantic similarity, Skip gram model, Text mining
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