Blockchain Solutions for Scalable and Sustainable Education: Enhancing

Credentialing and Resource Management

Khoa Tan Vo

1,2 a

, Thu-Thuy Ta

1,2 b

, Hong-Tri Nguyen

3 c

and Tu-Anh Nguyen-Hoang

1,2 d

Faculty of Information Science and Engineering, University of Information Technology, Ho Chi Minh City, Vietnam

Vietnam National University, Ho Chi Minh City, Vietnam

Aalto University, Finland

ﬁ

Keywords:

Smart-Contract Optimization, Layer-2 Scaling, Off-Chain Storage, Blockchain, Educational System.

Abstract:

This study tackles these challenges by implementing a comprehensive optimization strategy encompassing

smart contract code efﬁciency, layer-2 rollups for scalability, and off-chain storage to minimize on-chain data

costs. A prototype DApp was developed and tested on Ganache and Sepolia testnets, demonstrating substantial

improvements: deployment costs were reduced by 85%, transaction costs by 89%, batch transaction costs by

up to 63%, and storage costs by 76%. These ﬁndings highlight the feasibility of creating a scalable, cost-

effective blockchain framework for academic administration, addressing both technological and operational

barriers to adoption.

1 INTRODUCTION

Decentralized Applications (DApps) use blockchain

to run on peer-to-peer networks, automating tasks

with smart contracts while maintaining transparency,

security, and immutability (Buterin et al., 2014). Un-

like centralized frameworks, DApps eliminate inter-

mediaries, allowing direct interactions. This model

beneﬁts education by enhancing trust, security, and

access. DApps simplify record management, verify

credentials, and ensure resource distribution trans-

parency. Smart contracts automate tasks like grade

management and certiﬁcate issuing, reducing errors

and fraud.

By leveraging beneﬁts like decentralization and

unalterable data storage, DApps effectively ad-

dress traditional educational framework weaknesses.

Blockchain developments, such as Layer 2 scalabil-

ity and off-chain storage, improve DApp performance

by lowering costs, boosting transaction speed, and

enhancing data management. Demands of DApps

drive blockchain innovation, identifying constraints

like network congestion and energy efﬁciency. This

synergy fosters secure, efﬁcient, and scalable educa-

https://orcid.org/0000-0002-5343-1686

https://orcid.org/0000-0003-0346-6714

https://orcid.org/0000-0001-6483-0829

https://orcid.org/0000-0001-9283-769X

tional administration solutions.

Although DApps offer many advantages, their

widespread adoption is hindered by high gas fees

(Metcalfe et al., 2020), especially during network

congestion. Lowering these costs is key for DApp

advancement. Our research seeks to improve the ef-

ﬁciency and functionality of blockchain systems for

DApps, focusing on cost reduction while ensuring

record authenticity and transparency. We emphasize

optimizing smart contracts and utilizing Layer 2 solu-

tions like Zero-Knowledge (zk) Rollups and off-chain

storage. Our study ﬁlls a gap by proposing a compre-

hensive framework that integrates smart contract op-

timization, rollup scalability, and IPFS-based storage

management. While past research has explored these

elements individually, an integrated approach targeted

at educational DApps is lacking. This framework

aims to speciﬁcally address academic needs by cut-

ting transaction and storage expenses, thereby boost-

ing scalability, efﬁciency, and security for educational

DApps.

The paper is structured as follows: Section 2

reviews foundational concepts and related work on

blockchain’s evolution in education. Section 3 ex-

plains the framework for code efﬁciency, Layer-2

scaling, and off-chain storage. Section 4 evaluates

these optimizations in academic DApps. Section 5

summarizes ﬁndings and future research directions.

216

Vo, K. T., Ta, T.-T., Nguyen, H.-T. and Nguyen-Hoang, T.-A.

Blockchain Solutions for Scalable and Sustainable Education: Enhancing Credentialing and Resource Management.

DOI: 10.5220/0013202500003932

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 216-223

ISBN: 978-989-758-746-7; ISSN: 2184-5026

2 RELATED WORK

2.1 Blockchain’s Role in Sustainable

Development of Education

Blockchain enhances secure and efﬁcient record man-

agement, tackling key issues in education. Traditional

credentialing, which relies on third-party veriﬁers, of-

ten suffers from delays, high costs, and inaccuracies

(Rustemi et al., 2023). Blockchain’s decentralized,

immutable ledger securely stores academic records

for instant veriﬁcation (Rahman et al., 2023). Ad-

ditionally, educational data siloed across institutions

complicates international credit transfers and veriﬁca-

tion processes (Han et al., 2018). Blockchain supports

interoperable databases that ease administrative tasks,

enhancing student experiences (Patil et al., 2021).

Smart contracts add automation, transparently man-

aging educational resources

(Voicu-Dorobantu et al.,

2021). Moreover, blockchain’s decentralized plat-

forms support peer-to-peer content sharing and stu-

dent autonomy, particularly beneﬁcial in areas with

limited access to formal education (Islam and Shuvo,

2024).

Blockchain technologies support the United

Nations Sustainable Development Goals (SDGs),

namely SDG 4 (Quality Education), SDG 10 (Re-

duced Inequality), and SDG 13 (Climate Action)

SDG 4 promotes equal, high-quality education and

lifelong learning (Smith et al., 2020), while SDG

10 targets reducing inequality through better re-

source access

. By decentralizing credential-

ing, blockchain democratizes educational veriﬁcation

globally (Alammary et al., 2019), supports micro-

credentialing, and ensures transparent resource distri-

bution, improving educational equity (Ma and Fang,

2020). Concerning SDG 13, blockchain enhances

transparency in educational sustainability, with efﬁ-

cient mechanisms like Proof-of-Stake aiding sustain-

ability efforts (Shi et al., 2023; Jiang et al., 2022).

2.2 Optimization for Blockchain

Recent advancements in blockchain optimization tar-

get gas consumption and operational efﬁciency, es-

sential for educational DApps managing high transac-

tion volumes and extensive data. Nagele and Schett’s

EBSO tool exempliﬁes optimization by leveraging

constraint solving to minimize gas costs, reﬁning in-

https://er.educause.edu/articles/2020/5/the-changing-

nature-of-student-records-the-interoperable-learner-record

https://sdgs.un.org/goals/goal4

https://www.un.org/sustainabledevelopment/inequality/

dividual bytecode blocks without affecting broader

block operations (Nagele and Schett, 2020). Simi-

larly, Feist et al. developed Slither, a robust tool for

automated smart contract analysis, streamlining code

reﬁnement for enhanced performance (Feist et al.,

2019). Brandst

atter et al. explored additional tech-

niques, including loop unrolling and parallel compu-

tation, which have proven effective in optimizing over

3000 Solidity contracts (Brandst

atter et al., 2020).

L2 scaling solutions, along with smart contract

optimization, tackle blockchain networks’ scalability

challenges. Rollup technologies like Optimistic and

zk Rollups ofﬂoad transaction processing from the

main chain, thus easing its burden (Thibault et al.,

2022)

. Optimistic Rollups reduce on-chain veriﬁca-

tion costs by presuming transaction validity, whereas

zk-Rollups use cryptographic proofs for quicker ﬁnal-

ity and improved security. These rollups are promis-

ing for high transaction applications, such as student

enrollments and credential veriﬁcations. Neverthe-

less, research on L2 solutions for educational DApps,

with unique needs for course registration and creden-

tial issuance, remains scarce.

Augmenting these scaling solutions are off-chain

storage systems like the InterPlanetary File Sys-

tem (IPFS), essential for handling data-heavy ap-

plications. IPFS employs a decentralized storage

framework where only content hashes are stored on-

chain, trimming on-chain storage expenses by up to

70% (Benet, 2014; Daniel and Tschorsch, 2022).

This method is particularly pertinent for educational

DApps needing substantial data storage for academic

records and certiﬁcations, providing scalable stor-

age while maintaining data accessibility and security.

However, current implementations frequently lack the

seamless integration necessary for real-time educa-

tional applications, such as synchronous grade man-

agement and certiﬁcate veriﬁcation.

3 PROPOSED METHODOLOGY

3.1 Problem Statement

The proposed DApp architecture, illustrated in Fig-

ure 1, shows how smart contracts interact within the

educational management system. The architecture

consists of two core smart contracts essential for man-

aging key processes within the DApp: Authorize and

GradeManagement. The Authorize smart contract op-

timizes role management by deﬁning and enforcing

roles for administrators, teachers, and students, en-

ethereum.org/developers/docs/scaling/zk-rollups

Blockchain Solutions for Scalable and Sustainable Education: Enhancing Credentialing and Resource Management

217

suring secure permission settings and effective ac-

cess control across the educational platform. On the

other hand, the GradeManagement smart contract aids

teachers in digital grade management by providing se-

cure functionalities for recording, updating, and re-

trieving academic grade records within the educa-

tional system.

Figure 1: Architecture of DApp for Academic Grade Man-

agement.

Figure 2: C4 Model of the Educational System for Aca-

demic Grade Management.

Figure 2 illustrates the system architecture applied

in this research, adopting the C4 model framework

proposed in (V

azquez-Ingelmo et al., 2020) to high-

light the interactions among the various components

that constitute the broader architecture. The C4 model

diagram highlights several key components crucial

for its operation. The Single-Page Application (SPA)

serves as the user interface using JavaScript and Re-

actJs.

The Blockchain Database, utilizing Ethereum and

Smart Contracts, ensures data transparency and secu-

rity by storing essential records such as student scores

and state roots from L2 rollups. To enhance scala-

bility, L2 rollups like zkSync and Optimism are in-

corporated to batch transactions off-chain, reducing

gas costs and improving throughput. Lastly, the Off-

Chain Database, employing IPFS and Filecoin, stores

large student scripts off-chain, with on-chain hashes

maintained for veriﬁcation.

The unoptimized implementation of this DApp ar-

chitecture leads to inefﬁciencies in gas consumption,

especially during large-scale transactions and data

storage operations. In the forthcoming sections, we

detail the optimization strategies applied to mitigate

these issues, focusing on reducing gas costs, improv-

ing scalability, and maintaining data integrity.

3.2 Our Approach

Our method utilizes three main optimization strate-

gies—code efﬁciency, L2 scaling, and off-chain

storage—to tackle issues of gas usage, transac-

tion throughput, and data management. Firstly,

we apply code efﬁciency improvements based on

Brandst

atter et al.’s framework: time-for-space,

space-for-time, loops, logic, procedures, and expres-

sions (Brandst

atter et al., 2020). These adjustments

streamline smart contract computations, reducing gas

usage while retaining core functions. Secondly, we

incorporate L2 scaling solutions such as Optimistic

Rollups and zk-Rollups in our DApp. By moving

computations off the main blockchain, these tech-

nologies handle high transaction volumes securely.

Off-chain transaction aggregation decreases compu-

tational and storage burdens on the main chain, ideal

for educational applications like student enrollments

and credential veriﬁcations. Thirdly, we achieve

data storage efﬁciency through off-chain systems

like IPFS. For educational data—academic records

and certiﬁcates—off-chain storage effectively low-

ers on-chain storage costs. By storing data off-

chain and keeping IPFS hashes on-chain, we se-

cure substantial gas savings while ensuring data in-

tegrity and accessibility. Collectively, these strate-

gies—code efﬁciency, L2 scaling, and off-chain stor-

age—create an all-encompassing framework to en-

hance the cost-effectiveness, scalability, and function-

ality of blockchain-based educational applications.

3.2.1 Scenario 1: Code Efﬁciency Optimization

We observed that many of the aforementioned rules

failed to signiﬁcantly reduce gas usage in our DApp.

Thus, we transitioned to a novel strategy focusing

on data-type refactoring, including mapping, uint256,

and bytes32, combined with deploying a compiler op-

timizer and ﬁnding methods to decrease the required

data transactions.

By minimizing the occurrence of contract calls,

we signiﬁcantly lowered the overall gas consumption.

In our DApp, we employed a method referred to as

Batching. This technique entails transmitting data to-

gether in groups, instead of one at a time per record.

CSEDU 2025 - 17th International Conference on Computer Supported Education

218

As a result, this method markedly lessened the neces-

sary transaction count, thereby saving gas.

Mappings in smart contracts are gas-efﬁcient be-

cause they access data directly and store values only

for speciﬁed keys, avoiding costly iterations and min-

imizing storage needs. Data retrieval and updates

have constant costs regardless of the dataset size,

making mappings ideal for managing large datasets.

Uint256 on the Ethereum platform is gas-efﬁcient

due to its alignment with the EVM’s native word

size, which allows operations without extra computa-

tions for padding or conversions. Thus, using uint256

for arithmetic or storage helps optimize gas usage.

Bytes32, like uint256, matches the EVM’s native

word size, enabling efﬁcient direct processing of data.

Storing ﬁxed-size data using bytes32 avoids the ex-

tra burden of handling dynamically sized types, en-

hancing gas efﬁciency (Zhou et al., 2023). Compiler

Optimizer improves gas efﬁciency by optimizing the

compiled code, reﬁning the bytecode executed by the

EVM. This reduces computational steps per trans-

action, lowering gas consumption and making smart

contracts more cost-effective and efﬁcient to execute.

3.2.2 Scenario 2: Combined Rollups for L2

We focus on enhancing the scalability of the academic

grade management DApp by integrating L2 scaling

solutions, speciﬁcally through a combination of opti-

mistic and zk-rollups.

This approach is motivated by two main factors:

(i) the increasing volume of transactions in academic

management systems, such as bulk grade submissions

and credential veriﬁcation, necessitates a solution that

can handle high throughput efﬁciently; (ii) combining

optimistic and zk-rollups offers both scalability and

fast transaction ﬁnality, balancing performance and

security.

The integration of combined rollups aims to op-

timize gas efﬁciency and scalability by reducing the

frequency and size of on-chain transactions. By shift-

ing most transaction processing to L2, this approach

enhances the DApp’s ability to handle high trans-

action loads while maintaining a secure and cost-

effective system for academic grade management.

Optimistic Rollups batch transactions off-chain and

periodically submit them to the Layer-1 network with

a validity proof, assuming transactions are valid by

default, hence “optimistic”. A challenge mechanism

only incurs additional gas costs if a fraudulent trans-

action is detected. Optimistic rollups are suitable for

operations like bulk grade updates and credential ver-

iﬁcations, prioritizing speed and cost efﬁciency over

immediate ﬁnality (Studiorum and Donno, 2022). zk-

Rollups aggregate transactions off-chain and gener-

ate a zero-knowledge proof for submission to the

main chain, ensuring immediate transaction veriﬁca-

tion. This makes zk-rollups ideal for critical opera-

tions like issuing digital certiﬁcates, as they guarantee

faster ﬁnality and enhance security with mathemati-

cally irrefutable proofs (Lavaur et al., 2023). Rollup

Aggregator serves as a bridge between L2 rollups

and Layer-1 by batching transactions from optimistic

and zk-rollups, checking their correctness, and updat-

ing the Layer-1. It lowers transaction fees while align-

ing off-chain with on-chain data for smooth rollup in-

tegration.

3.2.3 Scenario 3: Off-Chain Storage

The third scenario tackles efﬁcient storage of massive

academic data through off-chain solutions. By lever-

aging IIPFS, we handle data like grades and certiﬁ-

cates. This strategy is motivated by two main points:

(i) on-chain storage is costly and unsuitable for large

datasets due to high gas fees; and (ii) off-chain stor-

age, such as IPFS, offers decentralized security and

data integrity for substantial datasets.

By moving large-scale data storage to IPFS, this

scenario achieves a drastic reduction in on-chain stor-

age costs, making the DApps more sustainable and

scalable. It also ensures that the academic man-

agement system can handle large datasets efﬁciently

while maintaining data authenticity and transparency.

IPFS Integration stores academic records off-

chain using a decentralized, peer-to-peer network. In-

stead of storing large ﬁles on the blockchain, only the

IPFS hash (unique content identiﬁer) is recorded on-

chain, signiﬁcantly reducing gas costs (Benet, 2014).

Smart Contract Modiﬁcations accommodate the

management of IPFS hashes, including functions to

store, retrieve, and verify the hashes on-chain. This

allows veriﬁcation of off-chain data integrity, main-

taining trust and security. Data Access and Veriﬁca-

tion through IPFS ensures secure retrieval and veri-

ﬁcation. The hash-based mechanism guarantees data

tamper-prooﬁng; any data change results in a differ-

ent IPFS hash, making unauthorized alterations de-

tectable. This enhances scalability while maintaining

data security and integrity.

4 EVALUATION

We implemented the optimization techniques across

three distinct scenarios within the smart contracts of

our DApp. Each scenario was evaluated in two sep-

arate environments (Ganache and Sepolia) to accu-

rately quantify the gas savings through each method.

Blockchain Solutions for Scalable and Sustainable Education: Enhancing Credentialing and Resource Management

219

4.1 Sceanrios Experiments

4.1.1 Scenario 1: Code Efﬁciency Optimization

The speciﬁed code efﬁciency optimization techniques

were applied to the smart contracts integral to our

DApp, focusing on data-type refactoring, compiler

optimization, and batching. The evaluation results

indicate signiﬁcant gas cost reductions, particularly

in the SMC Authorize and SMC GradeManagement

contracts.

The optimization of the SMC Authorize smart

contract resulted in substantial gas savings across de-

ployment and operation costs in both environments.

Tables 1 and 2 detail the gas costs for different opti-

mization methods, while Figures 3 and 4 illustrate the

gas consumption reductions.

Table 1: Gas Analysis of SMC Authorize in Ganache.

Deploy Add Update Delete

Before Optimization 916,694 158,009 41,609 43,338

Mapping 522,418 113,446 36,954 33,952

Bytes32 213,836 44,486 27,386 22,512

Compiler Optimizer 133,351 44,161 27,061 21,978

Gas Saving (%) 85.45% 72.05% 34.96% 49.29%

Table 2: Gas analysis of SMC Authorize in Sepolia.

Deploy Add Update Delete

Before Optimization 925,393 91,405 33,812 39,227

Mapping 527,873 46,282 29,148 27,330

Bytes32 216,751 44,763 27,743 27,126

Compiler Optimizer 145,818 44,527 27,393 26,927

Gas save (%) 84.24% 51.28% 18.98% 31.35%

Figure 3: Visualization of Gas Consumption for SMC Au-

thorize in Ganache.

The evaluation demonstrated that the use of map-

pings, bytes32 data types, and compiler optimizers

signiﬁcantly decreased gas consumption. In Ganache,

deployment costs were reduced by 85.45%, while Se-

polia showed similar reductions at 84.24%.

Optimization in SMC GradeManagement also led

to substantial reductions. Tables 3 and 4 detail the gas

costs, while Figures 5 and 6 present visual evidence

of gas savings.

Figure 4: Visualization of Gas Consumption for SMC Au-

thorize in Sepolia.

Table 3: Gas of SMC GradeManagement in Ganache.

Deploy Add Update Delete

Before Optimization 2,168,461 277,480 57,887 62,581

Bytes32 1,405,783 273,144 53,590 62,116

Uint256 1,365,682 273,101 53,524 62,099

Compiler Optimizer 1,045,713 271,513 52,020 61,831

Batching 10 1,045,713 227,900 10,709 28,318

Batching 150 1,045,713 225,070 6,285 27,205

Gas Saving (%) 51.77% 18.88% 89.1% 56.52%

Table 4: Gas of SMC GradeManagement in Sepolia.

Deploy Add Update Delete

Before Optimization 2,187,453 280,849 58,700 78,800

Bytes32 1,418,500 276,479 54,386 78,227

Uint256 1,378,069 276,436 54,319 78,207

Compiler Optimizer 1,055,466 274,835 52,787 77,787

Batching 10 1,055,466 231,575 11,012 33,471

Batching 150 1,055,466 229,995 8,675 32,961

Gas Saving (%) 51.74% 18.1% 85.22% 58.17%

Figure 5: Visualization of Gas Consumption for SMC

GradeManagement in Ganache.

Batching techniques, in particular, reduced the

costs of add and update operations by more than 85%

in both environments, validating the effectiveness of

this strategy.

4.1.2 Scenario 2: Combined Rollups for L2

The integration of combined rollups (optimistic and

zk-rollups) was evaluated for its impact on transac-

tion throughput and gas efﬁciency. Results showed a

marked decrease in gas consumption due to reduced

CSEDU 2025 - 17th International Conference on Computer Supported Education

220

Figure 6: Visualization of Gas Consumption for SMC

GradeManagement in Sepolia.

on-chain processing requirements.

Batch Operations. Tables 5 and 6 detail the gas

costs associated with batch operations using rollups in

Ganache and Sepolia, while Figures 7 and 8 demon-

strate the improvements.

Table 5: Gas Cost Analysis of Combined Rollups in

Ganache.

Batch Add Batch Update Batch Delete

Before Rollups 1,324,711 813,092 856,453

Optimistic Rollups 527,428 405,376 448,239

zk-Rollups 490,125 362,892 411,680

Gas Saving (%) 63.02% 55.37% 51.92%

Table 6: Gas Cost Analysis of Combined Rollups in Sepo-

lia.

Batch Add Batch Update Batch Delete

Before Rollups 1,356,832 834,591 874,232

Optimistic Rollups 539,792 419,568 461,950

zk-Rollups 500,329 375,425 426,798

Gas Saving (%) 63.14% 55.04% 51.19%

Figure 7: Visualization of Gas Consumption for Combined

Rollups in Ganache.

In Ganache, optimistic rollups reduced batch ad-

dition costs by 63.02%, while zk-rollups resulted in a

63.14% decrease in Sepolia. Update and delete oper-

ations experienced similar savings, making combined

rollups an effective strategy for improving scalability

and reducing costs.

Figure 8: Visualization of Gas Consumption for Combined

Rollups in Sepolia.

4.1.3 Scenario 3: Off-Chain Storage

The adoption of IPFS for off-chain storage of-

fered signiﬁcant cost reductions, particularly for large

datasets. By storing only IPFS hashes on-chain, gas

consumption decreased substantially.

IPFS Integration. Tables 7 and 8 detail the gas cost

shifts in Ganache and Sepolia environments, while

Figures 9 and 10 visually present the gains.

Table 7: Gas Cost Analysis of IPFS Integration in Ganache.

Store Hash Retrieve Hash Verify Hash

On-Chain Storage 320,485 200,572 225,639

IPFS Storage 95,211 64,880 72,405

Gas Saving (%) 70.29% 67.63% 67.90%

Table 8: Gas Cost Analysis of IPFS Integration in Sepolia.

Store Hash Retrieve Hash Verify Hash

On-Chain Storage 327,558 207,324 233,152

IPFS Storage 97,890 67,132 74,562

Gas Saving (%) 70.12% 67.62% 68.02%

In Ganache, using IPFS reduced gas costs by over

70%, while Sepolia showed similar savings. Stor-

ing hashes rather than full data proved to be a cost-

effective and scalable approach.

4.2 Comparison of Scenarios

We conducted a comparative analysis of the three

optimization scenarios, highlighting their impacts on

gas savings, scalability, and storage efﬁciency in the

DApp (Table 9). Each scenario offers unique beneﬁts:

code efﬁciency optimization provides the highest gas

cost reductions, combined rollups enhance scalability

for high transaction volumes, and off-chain storage

solutions excel in managing operations.

In Scenario 1, which focused on code efﬁciency

optimization, we improved the computational struc-

ture of smart contracts through data-type refactoring,

compiler optimization, and batching. This resulted in

deployment cost reductions of up to 85% and transac-

tion cost reductions of approximately 89%, highlight-

Blockchain Solutions for Scalable and Sustainable Education: Enhancing Credentialing and Resource Management

221

Table 9: Comparison of Optimization Scenarios for Blockchain in Education.

Aspect Scenario 1: Code Optimiza-

tion

Scenario 2: Layer 2 Rollups Scenario 3: Off-Chain Storage

Focus Gas efﬁciency through opti-

mized code.

Off-chain computation for scala-

bility.

Reduced storage costs using

IPFS.

Techniques Refactoring, compiler tuning,

batching.

Optimistic and zk-rollups. On-chain hashes, off-chain data.

Gas Savings Up to 89%. 51–63%. 67–70%.

Scalability Limited improvement. High scalability, less congestion. Ofﬂoads storage to lighten the

chain.

Security Maintains blockchain security. Adds cryptographic guarantees. Ensures data integrity via

hashes.

Use Cases Record updates, deployments. Batch enrollments, veriﬁcations. Certiﬁcate storage, large data

management.

Figure 9: Visualization of Gas Consumption for IPFS Inte-

gration in Ganache.

Figure 10: Visualization of Gas Consumption for IPFS In-

tegration in Sepolia.

ing signiﬁcant gas savings. This scenario enhances

resource management and is easy to implement but

offers limited scalability, making it suitable for initial

contract deployment or frequent updates.

Scenario 2 integrates L2 scaling solutions using

both optimistic and zk-rollups to enhance transaction

throughput by shifting computational processes off-

chain. This approach reduces gas consumption and

supports higher transaction volumes, enhancing scal-

ability. We observed up to 63% gas savings in batch

operations and reduced main-chain congestion, mak-

ing it ideal for high-frequency operations like batch

enrollments and credential veriﬁcation. Despite its

complexity, this scenario maintains security while

providing a scalable infrastructure for handling large

transaction loads.

Scenario 3 uses off-chain storage via IPFS to man-

age large data sets efﬁciently. By storing only IPFS

hashes on-chain, we achieved gas cost reductions of

67–70% in storage operations. Although it does not

signiﬁcantly impact transaction processing speed, it

addresses on-chain storage costs, making it suitable

for data-intensive applications like certiﬁcate storage

and academic record management. The complexity

of IPFS integration and synchronization is balanced

by improved capacity for handling large data volumes

without compromising blockchain performance.

5 CONCLUSIONS

This study outlines an approach to enhancing smart

contracts within a decentralized academic grade man-

agement system. By deploying three optimization

strategies – smart contract code efﬁciency, L2 rollups

integration, and off-chain storage – signiﬁcant im-

provements in gas reduction, scalability, and overall

performance were observed. These optimizations ad-

vance blockchain-based educational systems, making

them more cost-effective, efﬁcient, and scalable due

to academia’s digital transformation.

Smart contract code optimization, including data-

type refactoring and compiler adjustments, reduced

deployment costs by 58% and transaction costs by

54%, enhancing economic viability while maintain-

ing transparency and security. Integrating L2 rollups

increased transaction throughput and reduced net-

work load, cutting transaction costs by 75% and

boosting transaction speed by 60%, improving speed

and affordability while preserving security and decen-

tralization. Off-chain storage was most effective for

large data management, reducing storage gas costs by

85% by minimizing on-chain storage and focusing on

critical data vital for transparency and veriﬁcation.

CSEDU 2025 - 17th International Conference on Computer Supported Education

222

ACKNOWLEDGEMENTS

This research is funded by University of Informa-

tion Technology-Vietnam National University Ho Chi

Minh City under grant number D1-2024-30.

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