2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 15 , Issue 2 , PP: 245-260, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Asma Khazaal Abdulsahib 1 * , Ruwaida Mohammed 2 , Ahmed Luay Ahmed 3 , Mustafa Musa Jaber 4
Doi: https://doi.org/10.54216/FPA.150221
The pedagogical of computer programming education is being enriched and improved through the interactive learning material. Visualization, modeling, and internet platforms for developing interactive visual skills are only a few examples of the types of specialized learning material currently accessible for use in a wide range of computing classes. There are some specific challenges related to the implementation of active learning, such as insufficient time for class, an increase in preparation, implementing students' engagement in extensive courses, and a lack of necessary materials, technology, or supplies. Computer vision is a subfield of AI that allows machines to learn from visual data (such as photos, movies, and other digital media) and then act on or offer solutions to problems. To enhance the efficiency of intelligent interactive learning and practice, this article incorporates a visual machine vision analytical framework under the guidance of Artificial intelligence to create a Machine-Vision-based Smart Education Assistance System (MV-SEAS). Visualization speeds up and simplifies regular communication by consolidating several forms of information into a single visual representation. This study discusses how visualizing information is crucial for students' initial knowledge acquisition and continued education and development. The seamless amalgamation of automated innovative education analyses and interactive visualizations is emphasized. The paper aimed to identify and characterize the technical challenges mentioned above must be surmounted to make it simpler for computer educators to discover, adopt, and tailor intelligent learning materials. The study concludes by proposing an MV-SEAS for storing, integrating, and disseminating smart educational data. It investigates whether it can be done using existing standards and guidelines. In the end, this essay combines trials to prove the effectiveness of the proposed smart education method. The findings demonstrate that interactive visualization of AI-assisted smart education may effectively combine subject experts' information with educators' experience to produce more powerful and easily intelligible machine intelligence.
Smart education , visualization , Artificial Intelligence , Interactive learning , computer vision analysis.
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