Abstract
Most automatic scoring systems use pattern based that requires a lot of hard and tedious work. These systems work in a supervised manner where predefined patterns and scoring rules are generated. This paper presents a different unsupervised approach which deals with students’ answers holistically using text to text similarity. Different String-based and Corpus-based similarity measures were tested separately and then combined to achieve a maximum correlation value of 0.504. The achieved correlation is the best value achieved for unsupervised approach Bag of Words (BOW) when compared to previous work.
Highlights
Educational community is growing endlessly with a growing number of students, curriculums and exams
Writing assessment comes in two forms Automatic Essay Scoring (AES) and Short Answer Grading
Latent Semantic Analysis (LSA) [23] is the most popular technique of Corpus-Based Similarity, LSA assumes that words that are close in meaning will occur in similar pieces of text
Summary
Educational community is growing endlessly with a growing number of students, curriculums and exams. Automatic Scoring (AS) systems evaluate student’s answer by comparing it to model answer(s). Writing assessment comes in two forms Automatic Essay Scoring (AES) and Short Answer Grading. AS systems designed for scoring essay questions is a more difficult and complicated task as student’s answers require text understanding and analysis. This research presents an unsupervised approach that deals with student's answers holistically and uses text to text similarity measures [1, 2]. The proposed model calculates the automatic score by measuring the text similarity between each word in model answer to all words in the student’s answer which saves the time spent by experts to generate predefined patterns and scoring rules. This paper is organized as follows: Section II presents related work of the main automatic short answer grading systems.
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