面向对象编程模块的智能考试评估器

M. Wickramasinghe, H.P Wijethunga, S. Yapa, D. Vishwajith, Udara Srimath S. Samaratunge Arachchillage, N.C Amarasena
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引用次数: 0

摘要

世界范围内的教育工作者认为,基于编程语言的考试的自动化评估是一项更具挑战性的任务,因为它的复杂性和学生实施的解决方案的多样性。本研究探讨了基于java的在线考试评估器的适用性和开发,以解决传统繁重的人工考试评估方法。提出的系统允许学生在Java中进行在线考试,并在实际考试中实现源代码,同时自动向管理员报告结果。因此,本研究考察了现有方法,识别了它们的局限性,并探讨了引入基于面向对象程序的智能考试评估器作为解决方案的意义。这种方法最大限度地减少了所有人为错误,使系统更有效率。自动答题检查器会对答题进行检查和打分,并生成一份报告,其中包含改进答题脚本的可能建议,并生成一份分类报告,以预测学生的期末考试成绩。该软件应用程序采用知识库、抽象语法树(AST)、ANTLR、图像处理和机器学习(ML)作为关键技术。所提出的系统获得了更高的准确率,达到95%,由单独的人类同行执行。这些结果表明,高水平的准确性和自动化标记是节省人力评估工作和最大限度地提高生产力的主要重点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Exam Evaluator for Object-Oriented Programming Modules
Worldwide educators considered that, automate the evaluation of programming language-based exams is a more challenging task due to its complexity and the diversity of solutions implemented by students. This research investigates and provides insight into the applicability and development of a java based online exam evaluator as a solution to traditional onerous manual exam assessment methodology. The proposed system allows students to take online exams in Java for an implemented source code in a practical exam, automatically reporting the results to the administrator simultaneously. Accordingly, this research examines existing methods, identifies their limitations, and explores the significance of introducing a smart object-oriented program-based exam evaluator as a solution. This method minimizes all human errors and makes the system more efficient. An automated answer checker checks and marks are given as human-counterpart and generate a report with possible suggestions for improvement of the answer scripts and generate a classification report to predict the student's final exam marks. This software application uses a Knowledge base, Abstract Syntax tree (AST), ANTLR, Image processing, and Machine Learning (ML) as key technologies. The proposed system gains a higher accuracy of 95% as performed by a separate human-counterpart. These results show a high level of accuracy and automate marking is the major emphasis to save human evaluation effort and maximize productivity.
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