Publication: Smart Exam Evaluator for Object-Oriented Programming Modules
| dc.contributor.author | Wickramasinghe, M.L. | |
| dc.contributor.author | Wijethunga, H.P. | |
| dc.contributor.author | Yapa, S.R. | |
| dc.contributor.author | Vishwajith, D.M.D. | |
| dc.contributor.author | Samaratunge Arachchillage, U.S.S. | |
| dc.contributor.author | Amarasena, N. | |
| dc.date.accessioned | 2022-03-04T04:01:51Z | |
| dc.date.available | 2022-03-04T04:01:51Z | |
| dc.date.issued | 2020-12-10 | |
| dc.description.abstract | 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. | en_US |
| dc.identifier.doi | 10.1109/ICAC51239.2020.9357320 | en_US |
| dc.identifier.isbn | 978-1-7281-8412-8 | |
| dc.identifier.uri | https://rda.sliit.lk/handle/123456789/1485 | |
| dc.language.iso | en | en_US |
| dc.publisher | 2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT | en_US |
| dc.relation.ispartofseries | Vol.1; | |
| dc.subject | Knowledge base | en_US |
| dc.subject | Abstract Syntax tree | en_US |
| dc.subject | Image Processing | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | ANTLR | en_US |
| dc.title | Smart Exam Evaluator for Object-Oriented Programming Modules | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication |
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