Implementation of Microprocessor Based Automated Assessment Systems Using IoT

With the rapid growth of IoT applications, the number of deployed embedded devices has significantly increased, many of which operate in safety- and security-critical environments. Consequently, the need for well-trained embedded software engineers has become more crucial than ever. To support educa...

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Bibliographic Details
Published in:2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI) pp. 1425 - 1430
Main Authors: B, Madhumohana Raju A, P, Parthasarathi, A, Dhanalakshmi, L, Balaji, Kaliappan, S, K, Vinoth Kumar
Format: Conference Proceeding
Language:English
Published: IEEE 09.07.2025
Subjects:
Automated assessment
Codes
Education
Embedded software
Embedded systems
embedded systems education
Internet of Things
microcontroller programming
Microcontrollers
Programming profession
secure software
Software reliability
student feedback
Surveys
Systems support
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Summary:With the rapid growth of IoT applications, the number of deployed embedded devices has significantly increased, many of which operate in safety- and security-critical environments. Consequently, the need for well-trained embedded software engineers has become more crucial than ever. To support educators in providing consistent and timely evaluation, automated assessment tools have been developed over the past decades. A vital component of embedded systems education is cultivating a strong understanding of secure and reliable software development practices. This paper introduces an automated evaluation system designed to deliver prompt and precise feedback on student code submissions, particularly in embedded software courses. The system serves as an initial feedback mechanism, enabling students to identify and resolve issues in their code independently. This iterative feedback approach fosters deeper learning and promotes early error correction. We detail the system's architecture and evaluate its deployment within a microcontroller programming course. The evaluation incorporates both qualitative data from surveys conducted among students and instructors, and quantitative data derived from student performance metrics. Results indicate a positive reception and suggest that students value such immediate feedback, expressing interest in its integration across additional programming courses
DOI:10.1109/ICDICI66477.2025.11134878