Author(s) :

Chetan Bapu Sonawane.

Volume/Issue :

Abstract :

The process of evaluating descriptive answers to gauge student performance poses significant challenges. Typically, this assessment is conducted manually, which can introduce subjective biases. The outcome may be influenced by factors such as the authority's nature, mood, and the student-teacher relationship. Furthermore, manual analysis is time-consuming and demanding. To address these issues, this proposed system aims to automate the analysis process by leveraging technologies such as Machine Learning, Natural Language Processing (NLP), and Deep Linguistic Analysis (DLA). By employing these techniques, we can extract pertinent information from textual answers and measure the similarities between the extracted summaries and the correct answers.

No. of Downloads :

12