CO Attainment: Assessment of Course Outcomes for Engineering Colleges Affiliated to Anna University
Arunkumar S, Beni Joe J, Raihaan G, Ramsundar A, Nareshprabu P, Ms. Anu Prabhakar
Department of CSE, Coimbatore Institute of Technology
Coimbatore – 641 014
Abstract
The CO Attainment project addresses the challenges associated with the manual assessment of student answer scripts in colleges, which is often labor-intensive, prone to errors, and inefficient. The project leverages a deep learning approach to automate the recognition of marks from scanned answer scripts and facilitates the systematic tracking of curriculum outcomes (COs). The core of the project is a Convolutional Neural Network (CNN) trained on the MNIST dataset, which is adept at recognizing handwritten digits with high accuracy. This model is integrated into a user-friendly Tkinter-based GUI, allowing college staff to input subject details and curriculum outcome question numbers, and upload scanned answer scripts for automatic mark extraction.
The project follows a multi-step methodology: it begins with the collection and preprocessing of scanned answer scripts to enhance image quality and standardize formatting. The CNN model, trained on the MNIST dataset, is fine-tuned to accurately recognize marks on these scripts. A Tkinter GUI facilitates interaction with the system, enabling staff to seamlessly upload scripts and input necessary metadata. The extracted marks are processed using Pandas to compute aggregated scores for each curriculum outcome and are then stored in a structured CSV format.
The CSV data is subsequently integrated into a database management system (DBMS) with robust access control mechanisms to ensure data security and integrity. The database allows for efficient storage, retrieval, and analysis of the assessment data, which is accessible to authorized personnel across the college campus. Comprehensive testing and validation are conducted to ensure the accuracy and reliability of the system. Furthermore, college staff are provided with thorough training to effectively use the GUI and interpret the results.
Through the automation of mark recognition and the systematic tracking of curriculum outcomes, the CO Attainment project aims to enhance the efficiency, accuracy, and transparency of the academic evaluation process. This project not only reduces the workload of college staff but also facilitates timely feedback and data-driven decision-making in educational institutions.
KEYWORDS: Deep Learning, Database Management System, Graphical User Interface, Comma Separated Values, Toolkit Interface, Course Outcome, Artificial Intelligence, Convolutional Neural Network, Machine Learning, Database, Sum of Points, College Staff, Subject Assessment, Data Management System
