Novel Approach Towards Academic Module Based on Blockchain
Keywords:
Blockchain, IPFS, SHA256, Academic Module, Smart-Contracts, Cryptography, Proof Of Stake, Remix IDE, Node JS, Ethereum Blockchain, Ethereum Wallet MetaMaskAbstract
This research paper presents a smart contract-based system for managing academic courses and student results on the Ethereum blockchain. The system allows the creation of courses, subjects, and faculty members, as well as the registration of students and their enrollment in courses. Students can also view their subjects, and faculty members can add marks to a student’s subject. The system uses various Solidity data structures such as mappings, arrays, and structs to store and manage the data. The paper provides a detailed description of the smart contract functions and their usage, along with code snippets. The system’s reliability and security are enhanced by the use of the Ethereum blockchain and its decentralized nature. Additionally, the use of smart contracts ensures that all transactions are transparent, immutable, and tamper-proof, which reduces the risk of fraud or errors. The system’s scalability is also improved by leveraging the Ethereum blockchain’s ability to handle a large number of transactions simultaneously. This paper highlights the potential of De-centralized systems in the academic module and provides a room for future development in this area. Overall, this research presents a novel solution for academic management on the blockchain.
Downloads
References
Paper published by S. Nakamoto, on “Bitcoin : A Peer-to-Peer Electronic Cash System | Satashi Nakamoto Institute.”
Refer “A next-generation smart contract and decentralized application platform,” V. Buterin Etherum, 2014.
Refer to Remix Documentation- “Welcome to Remix documentation!.”
https://www.researchgate.net/publication/352389531_Smart_Contract_enabled_Online_Examination_System_Based_in_Blockchain_Network[2021] “Welcome to Solidity documentation!- https://docs.soliditylang.org/en/v0.8.19/index.html
Dhiman, O. ., & Sharma, D. A. . (2020). Detection of Gliomas in Spinal Cord Using U-Net++ Segmentation with Xg Boost Classification. Research Journal of Computer Systems and Engineering, 1(1), 17–22. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/20
Rose, J. D. ., R, V. R. ., Lakshmi, D., Saranya, S. ., & Mohanaprakash, T. A. . (2023). Privacy Preserving and Time Series Analysis of Medical Dataset using Deep Feature Selection. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 51–57. https://doi.org/10.17762/ijritcc.v11i3.6201
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.