
The "Intelligent Web-Based System for Automated Code Assessment and Learning", designed to enhance programming education using Artificial Intelligence and Machine Learning techniques. The system allows users to submit programming code through a web interface, where it is automatically evaluated for syntax, correctness, logic, and efficiency. Unlike traditional manual evaluation methods, this system provides instant and meaningful feedback by analyzing errors, identifying logical mistakes, and suggesting improvements and optimized solutions. This enables learners to better understand their mistakes and improve their coding skills effectively. The web-based platform supports real-time code execution and evaluation, making it scalable and accessible to a large number of users. It also includes features such as performance analysis, scoring mechanisms, and personalized learning recommendations based on user performance. The system can be extended to support multiple programming languages and adaptive learning paths. Overall, this project focuses on developing a smart and efficient solution that bridges the gap between theoretical learning and practical coding skills, offering benefits such as reduced instructor workload, faster evaluation, improved learning outcomes, and enhanced user engagement through intelligent feedback.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
