A Neural Based Approach to Evaluate an Answer Script

TLDR: This paper gives cue of the possibility to cut down the teacher’s workload on open questions by a component managing a neural network-based model of the students’ decisions, involved in a peer-assessment task.

Abstract: Assessment of answers with particular questions is a heavy task as in assumption that all the students answers have to be awarded. In this paper we give cue of the possibility to cut down the teacher’s workload on open questions by a component managing a neural network-based model of the students’ decisions, involved in a peer-assessment task. The answer is recognized by OCR and it converted by machine readable format. The network of constraints and relations established among the answers through the students’ knowledge, allows us to compare and relate with set of possible keyword of the database. Convolution neural network plays a vital role in comparison of answer database and student database. The Receiver Operating Characteristic curve are constructed based on the accuracy of students marks. Based on the comparison result obtained, the accuracy of student answer is measured and awarded. Our computer system suggests that the subset of the answers is evaluated with database answer in which the performance of evaluation is measured. This is used to reduce workload of humans and it automatically evaluate the answer. It is mainly used in schools, colleges, university etc.