Improving the Effectiveness of a Stock Simulation Trading Course via Blockchain and Social Networking: A Taiwanese Study

Abstract

Online courses in higher education became prevalent during the COVID-19 pandemic; however, their application requires technology to be fully integrated into the curriculum. This study explores the integration of a blockchain-based platform in a private online stock simulation trading course during the COVID-19 pandemic. Using a pre–post experimental design with 142 college students, it assessed learning behaviors and outcomes. Students collaborated with teaching assistants via LINE groups, fostering discussion and engagement. They received cryptocurrency rewards, which enhanced motivation and connected the course to their career goals. The findings suggest that combining blockchain and social networking is an effective approach to improving online education. This contributes to the literature on educational technology and online learning by exploring the integration of blockchain and social networking in higher education, specifically within the context of stock simulation trading courses, and demonstrates its impact on student motivation and learning outcomes.

1. Introduction

Massive open (MOOCs) and small private online courses (SPOCs) have increasingly become popular learning tools in higher education. During the COVID-19 pandemic, university students were attending online courses to avoid physical contact in classrooms. Although many online courses aimed to improve students’ learning motivation [1] and educators’ teaching skills [2], not many have managed to do both simultaneously. Accordingly, this study addresses this gap using a blockchain platform with a social networking system (SNS) to simulate an online course.

This study combines blockchain technology with an SNS, i.e., LINE—a communication app that connects people, services, and information. The objective of this course is to reconstruct stock simulation trading (SST) for building a market-oriented mechanism representing actual events. A simulation application is proposed to facilitate students’ learning evaluation and track the sharing frequency of related themes, duration of group discussions, and profits or losses from preowned cryptocurrency as a starting asset. The proposed SST application with added experimental mechanisms is posited to increase learning motivation and enhance students’ stock trading skills under practical conditions. The significance of this phenomenon lies in its potential to foster a more engaging and interactive online learning experience. By blending blockchain technology with social networking, this approach not only promotes collaborative learning but also equips students with essential skills in stock trading under conditions that reflect real-world scenarios. This innovative framework is poised to transform online education by enhancing motivation and practical skill acquisition, ultimately preparing students for future career opportunities in finance and investment.

2. Literature Review

2.1. Blockchain in a Classroom

Various applications of blockchain technology have drawn increasing attention from scholars in financial business and academia. A blockchain, which serves as a shared ledger (database), can facilitate timely events through its distributed network that maintains transparent records of critical transactions among event stakeholders [3]. Blockchain is also the underlying technology of Bitcoin [4], with its key contribution of establishing mutual trust between users. Without a centralized authority control, blockchain cryptography resolves problems related to Internet trust. Asymmetric encryption technology stores data in each block to execute connected transactions, and a miner uses an algorithm to decrypt and verify the correct transaction. A blockchain is a consecutive chain of blocks, storing transaction records. Through its data storage and consensus algorithm, data authenticity and verification can be maintained by participating nodes and a distributed network [3,5]. This system enables shared duplicates against malicious tampering and results in a trustless operational environment without centralized trusted third parties [6]. The blockchain promotes transparent and auditable transaction records with chronological time stamps, allowing participants to trace related transactions and information flow [7].

Blockchain can enhance the transparency of transactions and traceability of supply chains [8,9,10]. Moreover, its affiliated technology, i.e., smart contracts, may be deployed in the blockchain environment to execute event-based contract terms or agreements [11]. A smart contract is an encoded computer protocol to digitally facilitate, verify, or enforce terms or agreements. Moreover, if the preset conditions are met, smart contracts can transform contract terms into programmable logic with automatic execution [12]. Therefore, smart contracts may replace some human operations and facilitate automatic process executions [13]. Thus, this study provides a case of the potential use of blockchain to improve the liability of academic records and traceability of student learning by analyzing the outcomes and feedback of students at the end of a semester course in SST.

The decentralized and shared distributed ledger of blockchain, characterized by authenticity, immutability, and consensus, as well as records of a transaction history without tampering, ensures a good fit for certain educational courses. Oganda et al. [14] proposed blockchain technology with an MOOC-based platform in a business class and critiqued the technical features and basic applications of blockchain, indicating that using the proposed framework enabled students and instructors to record learning tracks in the reconstruction of university SPOC studies. The key drivers of blockchain technology in higher education are interconnected. It not only enhances the speed and confidentiality of administrative tasks but also opens up new opportunities for students to explore knowledge in emerging fields [15,16]. Researchers used the LINE app because its penetration rate for the third quarter of 2022 in Taiwan was approximately 95.7% [17]—the highest rate for an SNS. LINE surpassed Facebook, Instagram, Facebook Messenger, TikTok, X (then Twitter), and WeChat, among others. The real-time correspondence structure of the proposed system potentially enhanced students’ timely self-motivation to engage in current trends in online education.

This study analyzes the distinct requirements of online education to determine how a blockchain-based architecture may provide additional value to higher education. In addition, demonstrates that the architecture aims to deliver a blockchain application framework for developing various practical applications that utilize blockchain technology to enhance student learning or provide a tool for effective teaching.

2.2. Current Scenario

Most educators argue that the traditional method of attending lectures in the classroom fails to incorporate adequate real-time events for effective learning [18,19]. Concerning our stock trading class, we hypothesized that if instructors acted as dealers of online game card sales, they could effectively demonstrate how to target customers to purchase their online game merchandise. Considerably, sales should demonstrate the features and promote the entertainment value of the game. When these sales techniques are adopted for teaching, instructors can better connect with students [20]. This game-style motivation may develop into a virtuous circle that can adapt to students’ various needs and reactions. Such reactions would promote the students’ interest in class materials, response behaviors, participation, and focused persistence, which would be reflected in the teacher feedback provided by students at the end of each semester. Ratinho and Martins [21] systematized and identified the impact of gamification strategies on student motivation. To demonstrate the effectiveness of games, Ghergulescu and Muntean [22] and Garris et al. [23] combined instructional content with appropriate games in education, enabling the game cycle to induce repetitive and spontaneous learning. Xu et al. [24] implied that students learn effectively through group learning via social networks and online peer collaboration. Sousa-Vieira et al. [25] applied the social network analysis and the machine learning/deep learning (ML/DL) domains to confirm the influence of the learning path on learning results. Related research proves that students need tools to focus their attention on innovative, practical methods.

To achieve the research objective, this study conducted SST in a college during a credited semester course to elucidate the key reasons for improving online courses and provide insights into probable negative factors. The research stemmed from class feedback from past semesters, demonstrating students’ dissatisfaction with the course structure and conduct.

3. Method

3.1. Research Design and Course Information

This pre–post experimental research design was the result of a routine semester class in SST. Student participants voluntarily enrolled in elective 3-credit courses from different colleges. During the course, instructors lectured on stock trading recognition and provided a blockchain-based stock trading simulation program for students to practice in a real-time environment. The simulation trading program matched the on-time pricing of Taiwan stock market trading for bids and sales.

All class materials were prepared and recorded using short videos for installation on the school platform for the long-distance learning program, restricted and limited to class-registered students. Materials represented a typical SPOC without public broadcasting platforms like YouTube. Because of the COVID-19 pandemic, a new hybrid class format was scheduled, which included a few weeks of classroom attendance and online video learning for the remaining weeks. Teaching assistants (TAs) read current stock market news and posted relevant topics for weekly discussions.

The process was as follows:

• Step 1. Students were informed that in addition to individual trading operations, any five students could spontaneously form a team to operate a joint account.
• Step 2. SST software is open to all students, and the first questionnaire will be distributed in the third learning week.
• Step 3. TAs participate in SPOCs and join LINE groups as members to provide immediate assistance.
• Step 4. Stock trading balances are announced weekly, and select board posts are discussed in class.
• Step 5. The final questionnaire is issued in the last week of the semester.

Analysis of 142 student records of 2020 for course no. 0456 (titled “Stock Investment Simulation and Practice”) is shown in

Table 1. Number of students who attended course no. 0456.

Colleges/Schools	Male				Female				Pop. (%)
	Fr.	Soph.	Junior	Senior	Fr.	Soph.	Junior	Senior
General School, Liberal Arts	2	0	0	0	0	1	0	0	2.11
Engineering, Math, Science, Technology	4	16	17	34	0	3	1	10	59.86
Law and Business School	0	7	1	5	0	1	4	2	14.09
Agriculture and Related School	0	7	10	8	0	2	0	7	23.94
Population (%)	4.23	21.12	19.72	33.1	0	4.93	3.52	13.38

.

3.2. Blockchain-Based Platform Design

A blockchain-based SST platform was developed for virtual trading in the Taiwanese common stock market. Figure 1 illustrates that the blockchain platform uses a P2P mode to structure the data storage architecture. Students (game players) purchased a virtual currency (or first basic funding from an instructor), deposited it into a personal wallet to activate their accounts, and stored trading records in an offline database (Figure 2).

The system is decentralized because the network is entirely operated by its members without relying on a central authority or infrastructure.

Table 2. Value-added items of SST between the traditional database and the blockchain-based platform.

Stock Simulation Trading System (SST)	Traditional Database	Blockchain-Based Platform
Language	DDL, DCL, TCL	Remix (Solidity), Python
Structure	Centralized	Decentralized, shared public ledger
Processing time	Manually fixed	On time, auto-trigger (every 5 min match-up from data grasping)
Data integrity	Easy for hackers	Harder to break the chain; shared public ledge
Transparency	Controllable	Shareable
Data correction	Possible for deletion	No deletion, only adding and reading
Buy and sell (trading activities)	Faster	Slow down by incomplete data grasping from TWSE *

* TWSE: Taiwan Stock Exchange Corporation.

compares the transaction between a traditional database and a blockchain-based architecture.

Herein, the Taiwan common SST system, established on a web server, is a real-time trading platform on the Internet. The browser and server communicate through HTTP. Student users could easily link to the course website to use the system during the opening hours of the stock market. Supplementary Materials contains screenshots and steps of SST system operation to provide a better understanding from a student’s perspective. This research set up a BSTS platform on macOS. We started by installing Ganache to create a development environment for an Ethereum private chain. Using Solidity, we wrote the applied smart contracts and then compiled them with the Remix online IDE. Finally, we deployed and tested the contracts in Remix’s simulation environment. We set up Node.js for the server side of the system, developed the front-end user interface using React, and connected with the smart contract via web3.js to enable the use of each contract function.

We programmed two smart contracts (Order and Match) to extract the trigger function that allowed users to link their blockchain wallets and execute orders when the market price reached preset bids or sales prices. In addition, the trade multiplier enabled users to increase the purchase shares and expand their investment. Students used the issued eligible contract addresses through the smart contract to match the setting conditions. If the bid price reached the trading condition through the contract address in the match smart contract, the match smart contract triggered calls for limited price transactions. Researchers established a plug-in Oracle to read the current stock market prices from the website for the Institute of Taiwan Stock Exchange price bulletin to match student orders. The on-time stock trading was determined through the CheckValue function of the match smart contract to determine whether a limit transaction was established or abandoned. Figure 3 depicts the architecture of the web application.

3.3. Smart Contract to Trigger

Smart contracts can establish a nontraditional, new platform for students who are noncomputer savvy to enable easy operation of the simulation trading system. In other words, students can set up a simple trading request without any prior knowledge of Ethereum programming or coding skills. We generate smart contracts at the back end by setting simple logic trading conditions. Using a trigger mechanism, students link their blockchain wallets to use a prepaid amount of cryptocurrency. Through nontampering, identity verification, nonretrospective, and decentralized blockchain features, it can be used in this SST system to achieve instant delivery, thereby reducing settlement costs and shortening the time for stock delivery and cash flow completion. Through the Taiwan Depository and Clearing Corporation’s (TDCC) Clearing and Settlement process demonstration [26], the use of smart contracts in a blockchain-based platform to improve the current stock market trading process can be innovated as Figure 4.

Smart contracts are subordinate to the application of the Remix program compilation, using Oracle to fetch the external data. First, the order function of the smart contract receives a trading transaction set up by users, including the account number, stock code, number of shares, and bid price of users, all of which are assigned to a private chain environment. We set a time sequence (every 5 min) to grasp online stock price information and execute the transactions of students waiting in the trading request list every 5 min. Second, the match function of the smart contract reads the obtained data information to execute the pricing function and decides whether to complete or reject each trading through Oracle. Immediate download of new prices from the public bulletin of the Institute of Taiwan Stock Exchange Center by an Oracle intercepts each common stock market price. Third, if the requested trading price reaches the bid price (buy or sale), satisfying the conditions of the CheckValue function within the match function of the smart contract, the completion of the requested transaction will be recorded in the private blockchain, and it will be indicated as a successful deal.

3.4. Learning Blockchain Route Design

The records in the blockchain-based architecture of the SST system are traceable, including a series of trading times, shares, and stock varieties. Oracle captures the current price in the Taiwanese common stock market through public information posted on its official website. All student accounts and passwords are generated using algorithms to avoid intrusion from logical calculus. Ferro et al. [27] used Learning to Rank to analyze the source of online point-and-click learning kinetic energy. Furthermore, Chen et al. [28] proposed the application of a blockchain to track effectiveness in business education. Thus, this study applies the smart contracts of the blockchain in the learning management system framework to track the learning trajectory of students based on the framework proposed by Ocheja et al. [29].

The registrar learning provider contract registered manager contract (RLPC) is the authorized learning provider on the learning blockchain. Learner learning provider contract (LLPC) represents the proof of existence of a learner’s learning data on a learning provider’s platform. The index contract contains all LLPCs established between learners and learning providers, which includes all learning activities on the blockchain, and data index contract was the three smart contracts allocated and structured (Figure 5).

RLPC first sets an account called S-ID (ETH address) for each student in the blockchain and grants authorization via public and private keys (password). Through individual authorization, learners can share the restricted learning record with other authorized learners, e.g., S1 (ID-1), S2 (ID-2), and S3 (ID-3). Using the permissioned blockchain, S1 can read the authorized data (LLPC-2 and LLPC-3) from S2 and S3. This data-sharing mechanism is used within a closed group, and the group can be extended through cross-sharing. This mechanism is similar to the Facebook friend platform for organizing your friend circle.

Student S can select any URL in the set learning web project to enter learning browsing. A Registrar is authorized by student users to share their browsing records via the learning blockchain platform. The users’ records include persistent times, data readings, and time stamps for login and logout under the RLPC smart contracts. The learning blockchain application programming interface (LB-API) uses the Ethereum client to program smart contracts with the trigger functions to allow an authorized student (S1–Sn) to access permissioned data, including references, websites, or course files uploaded by the Registrar (Teacher/TA). As illustrated in Figure 5, students S1, S2, and S3 are grouped as a learning team. Through the LLPC smart contract in the LB-APIs, team members share their learning records with authorized people on the same team. The individual learning data generates information when the student first logs in to the platform.

Blockchain smart contracts are programmed to collect information on students’ learning records, including the number and frequency of online visits to a specific group of provided research websites. The results of personal SST performance (cryptocurrency balance) and data regression comparison of the student questionnaires applied at the beginning and end of the semester were analyzed to determine the various influences between pre- and post-learning outcomes.

3.5. Case Design with SNS Grouping

Applying the SST program followed eight steps: implementing the first student questionnaire; setting up student accounts and passwords in the SST software on the blockchain-based platform; initializing trading practice by providing 2 million cryptocurrencies as the basic funding for each student; classifying students into learning groups (i.e., with the SNS [LINE group] or without SNS); checking trading data daily (conducted by TAs), including stock variety, number of shares, and buy and sale prices; TAs providing stock market news to students in the LINE groups for discussion (by participating in the LINE groups, TAs observe and record the content and duration of student discussions); implementing the final student questionnaire and course feedback; and using SPSS to analyze student outcomes. Regression analysis was performed on data collected from the pre- and post-questionnaire surveys.

4. Results

Data were collected for over 18 weeks from 142 students (Pretest n = 141 + Post-test n = 140) with a 99% valid return ratio, which was distributed twice in the third and final weeks of the semester. There were 111 males and 31 females; the distribution from four different colleges (or Schools) was 2.11% of the students from the General and Liberal Arts College, 59.86% from the EMST colleges, 14.09% from the Law and Business Schools, and 23.94% from the College of Agriculture and other related colleges. Finally, the distribution of students was 4.23% for freshers, 26.05% for sophomores, 23.24% for juniors, and 46.48% for senior students attending the course. The following reviews the aspects most relevant to students, beginning with the score associated with their final currency balance. The second section addresses the analysis of student comments and questionnaire data. The third section presents a comparison of differences between group studies and individual studies. Finally, the discussion encompasses three distinct models: traditional, moderate, and objectively designed.

4.1. Stock Simulation Trading Cryptocurrency Balance

After around an 18-week practice on SST, students gained (or lost) their prepaid amount of NTD 2,000,000.

Table 3. Cryptocurrency balance report using the stock simulation trading program.

SST Program	N	Mean	SE	Median	SD	Var.	Max.	Min.	%
									25%	50%	75%
Balance Amt.	142	1,953,310	23,671	1,952,098	282,069	79,562,761,575	3,290,931	1,152,033	1,813,944	1,952,098	2,038,739
Transaction No.	142	49	4	29	48	2296	252	1	15	29	69

presents the participants’ average balance report.

Account balances (from highest to lowest) ranged from 3,290,391 (+64.5%) to 1,152,033 (−42.4%). Notably, the number of trades and total lot size or variety of stocks traded were not proportional to gains or losses. The trading transactions of the students indicated an average of 49 times; however, the spreadsheet denoted that trading numbers were 6–20, exhibiting high levels of performance (Figure 6). The blockchain-based SST program counted daily trades and provided a table and graph that informed traders about completed and uncompleted records.

The students’ trading records in the SST program revealed the number of stock trades (buys and sales) from 252 to 1. TAs provided additional point suggestions to the instructor according to students’ group performance. A game cryptocurrency was provided by the number of individual opinions given within the LINE groups or an online public discussion board (

Table 4. Student SST balance and account access index.

0456	Total Asset Balance	Currency Balance	Share Values	Board Lots (1 Lot = 1000 Shares)	Trading No.	LINE Discuss.	Group Discuss Board	TA Adding Points	Cryptocurrency	* ROI (%)
Highest	3,290,931	143,931	3,147,000	25	252	31	66	4	60	64.546
Lowest	1,152,033	575,933	576,100	0	1	13	0	0	0	−42.398
Average	1,953,310	1,036,785	754,194	3	47	20	13	0.19	1
Middle	1,952,097
SD	282,068.718

* ROI: Return on investment.

). Additionally, we made a final manual adjustment (elimination) for students who provided only short phrases such as “Good” or “Yes”.

4.2. Questions for Measuring Reliability and Validity

A total of 281 valid responses were received from both surveys through Google Forms. Furthermore, SPSS was used for analyzing reliability and validity estimates with a certain variability to confirm confidence. We first reviewed evaluation forms from students in different universities, including those in the US, undergraduate study centers, and several domestic universities [30,31,32,33]. In addition, the questionnaires were itemized into several categories to demonstrate the core elements of information that may be obtained from students. The results can guide and assess the implementation of the curriculum, teacher competencies, and student learning activities to improve the effectiveness of the current hybrid SPOCs, including the designed group learning and automatically recorded learning records to include in the final grading. The questionnaire comprised six categories: student cognition (5 items), student performance (6 items), class preference (5 items), group learning (3 questions), instructor ability (4 items), and flipper and online game style (3 items). Each item was rated using a five-point Likert scale to measure priorities [34]. Moreover, the analysis revealed that the Cronbach’s alpha values [35] of the questions reached α = 0.928. If any single omitted item does not improve the overall correlation, it implies that the solid Cronbach’s alpha values were 0.922–0.933. The results demonstrated that the items presented a reliable correlation (

Table 5. Measurement of the post-test student questionnaire.

Categories	Item	No.	Mean	SD
Student cognition	Weekly learning hrs	140	2.56	0.954
	Preview materials	140	3.54	1.028
	Interaction with teacher	140	3.36	1.005
	Time and location conv.	140	4.01	0.848
	More study time	140	3.96	0.974
Student performance	Self-evaluation	140	4.06	1.037
	More understanding	140	3.61	0.903
	Discussion motivation	140	3.51	0.933
	Innovative creativity	140	3.74	0.916
	Independent thinking	140	3.59	0.847
	Not showing my learning (-)	140	3.00	1.093
Class preferences	Hybrid learning style (Onsite class + Online SPOC)	140	3.34	1.098
	Online SST program	140	3.76	0.959
	Internet self-learning	140	4.00	0.921
	Flipper class creativity	140	3.70	0.811
	Design and materials	140	3.76	0.830
Group learning	More interaction	140	3.36	1.011
	Peer cooperation	140	3.41	0.913
	More engagement	140	3.36	0.931
Instructor ability	Course knowledge	140	3.71	0.773
	Lecturing clarity	140	3.87	0.855
	Engagement	140	3.63	0.970
	Problem solving	140	3.26	0.955
Flipper and online game style	Cryptocurrency rewarding	140	3.61	1.023
	Higher self-motivation	140	3.43	1.100
	Do not like online (-)	140	3.39	1.233

).

Standard deviation (SD) is a main factor that contributes to the reliability of the population mean [35]. Six sample questions were drawn from the questionnaire, revealing the scale distribution of the mean and median, as well as the measure of SD (Figure 7). Responses to questions on student’s discussion motivation, more study time, independent thinking, group engagement, peer cooperation, and instructor course knowledge were highly reliable (SD < 1).

Levene’s test was used to determine normal distributions regarding relative variation [36]. The test was briefly introduced by Gastwirth and Kusumah [37,38].

Table 6. Levene’s test of variances and t-test of means according to gender.

Categories	Items	Levene’s Test for Equality of Variances			t-Test for Equality of Means
		F	Ρ (Sig.)	t	DF	ρ. (Two-Tailed)	MD	SE	Difference of 95% Reliability
									Lower	Upper
Student cognition	Weekly learning hours	2.001	0.159	−2.087	138	0.039	−0.360	0.173	−0.702	−0.019
Student performance	Not show my learning (−)	3.722	0.56	2.927	138	0.004	0.571	0.195	0.185	0.956
Course preferences	Do not like online course (−)	1.049	0.308	2.244	138	0.026	0.500	0.223	0.059	0.940

presents 3 of 26 selected questions under six categories, with a significant t-test p-value < 0.05 distinguishing the variance between males and females. The study reported that female students exhibited higher mean values than male students in terms of weekly learning hours, which is significant. Two negative error-detecting questions also pointed to a significant variance between genders.

4.3. SNS (LINE) Grouping and Grade

Of the 142 students, 25 volunteered to be random independent samples and team up with the experimental groups. Six LINE groups were set up, in which each group was composed of 3–5 students with a designated TA. Weekly stock news and chapter-related topics were shared and discussed between TAs and students.

Table 7. Student LINE group performance in group discussion and final grade.

LINE Groups	LG1	LG2	LG3	LG4	LG5	LG6
* DISC. Times	23	21	17	31	13	16
** DISC. Rate	100%	100%	100%	100%	100%	100%
Average Grade	87.25	84	87	88.5	88.33	85.67

* DISC Times: Discussion times were accumulated through class participation and public board records. ** DISC. Rate: Number of students that participated in discussions/group of student numbers.

indicates that students in LINE groups (LG1–LG6) exhibited higher average rates of discussion (100%) than those in general groups. The results demonstrate that the students gained high learning motivation and cognitive opportunity to obtain higher grades during the initial stage of voluntary participation. This tendency was observed in high percentages of answering, problem-solving, and motivational intention, thereby indicating competence. The study also found that one or two students particularly and deliberately increased the discussion time without substantive content, which is considerably a data bias.

4.4. Using the Technology Acceptance Model to Compare Three Learning Modes

The result referred to the use of two domains of the technology acceptance model perceived usefulness and perceived ease of use—to explain the acceptance of information technology by comparing three categories: traditional classroom, SPOC class and online SST program, and hybrid class of LINE group discussion and a game-based SSD program.

The results showed that the students who undertook the course believed that the SPOC class and online SST had significantly higher confidence than the traditional classroom lecturing method regarding convenience pertaining to time and field freeness. Further, the hybrid classroom style—which uses an application for a game-based SST program presented in the blockchain-based learning record tracking environment and is conducted in a physical class and off-campus online video class—obtained higher ratings in the evaluation than those of the other methods because of the cognitive acceptance of the study in terms of learning efficiency, motivation, and interest (

Table 8. Feedback on questionnaire and comparison among the three learning modes.

Questionnaire (Likert-Type Scale)	Traditional Class	SPOC Online Class	Blockchain-Based Game-Style Class
	1. Classroom lecturing	1. Off-campus video learning	1. Hybrid class attendance
	2. SST and class quiz	2. Stock simulation trading (SST) program	2. LINE group discussion with SST
	Ave. (SD)	Ave. (SD)	Ave. (SD)
Personal adaption
Meets my expectation **	3.779 (0.637)	3.504 (0.857)	4.080 (0.734)
Convenient in terms of time and space **	3.690 (0.974)	4.071 (0.863)	4.159 (0.751)
Increases my motivation and interest **	3.628 (0.734)	3.513 (0.937)	3.956 (0.772)
Class and program conducting
Simple and easy (text)	3.761 (0.782)	3.637 (0.856)	3.903 (0.876)
Clear and understandable (Instructor) *	3.735 (0.779)	3.540 (0.877)	3.959 (0.795)
SST Program is adequate **	4.053 (0.730)	3.779 (0.874)	4.053 (0.754)
Overall learning performance
Attendance willingness ***	3.655 (0.753)	3.593 (0.831)	4.035 (0.801)
Satisfaction **	3.726 (0.735)	3.655 (0.843)	4.062 (0.794)
Acceptance class style ***	3.717 (0.796)	3.628 (0.878)	4.142 (0.822)

Ps: Five points: (1) strongly disagree; (2) disagree; (3) neither agree nor disagree; (4) agree; (5) strongly agree; * p < 0.05, ** p < 0.01, *** p < 0.001.

).

5. Discussion

This study provides an innovative course design for implementing an SPOC in SST using blockchain technology and social networking. The results indicate the possibility of real-time tracking of stock trading status and traceability of student learning records. Moreover, the hybrid class design using SPOC online materials and cross-teaching using a flipped classroom grouping method resulted in frequent information exchange among students, which was higher than initially expected. Students seemed highly motivated, such that the ending balance of cryptocurrency displayed no correlation with students’ cheerfulness and enthusiasm. Integrating a blockchain-based framework into the current curriculum for higher education appeared to elevate the quality of education based on the outcome. Reconstructing the class provided business courses with a better model for student evaluation from the smart contract perspective and better efficiency from tracing learning records.

Using the proposed blockchain-based framework with smart contracts, instructors can track the progress of students learning online and the daily time frame of stock trading to develop corresponding strategies that promote game-style learning. Students tended to make their learning records look positive in the proposed learning structure design because of real-time transparent tracking and group discussion. The study demonstrated that students preferred an environment that promotes self-motivated learning with functional tools and real-time supervision. This reflects that previous research has paid less attention to response timeliness during weekly waiting periods.

In addition, the interactive pedagogy in the SPOC class and the flipped classroom through the LINE group discussions increased student motivation toward off-campus online learning. The ability to incorporate blockchain technology into various educational processes to improve students’ academic performance and instructors’ skills should be prioritized in higher education courses

6. Conclusions

Implementing efficient strategies in educational courses can be a beneficial prerogative for institutions. Using the proposed blockchain-based process to track student learning records and enhance the automation of the online learning process is a good starting point for future research concerned with improving the academic performance of students in higher education. Analyzing feedback from students through the blockchain-based framework used for this hybrid course may help in understanding changes in learning. This is perhaps a positive outcome of the COVID-19 pandemic as it forced innovative practices in many educational services. This study demonstrated that the efficient use of SNS and game-style blockchain-based SST course design for creative teaching is a promising practice for long-distance learning. Even though new AI software and technological tools offer students a more convenient learning environment in the classroom, classroom size limitations and peer influences remain intangible factors. Future work should continue to explore the effectiveness of this approach in designing transparent, supportive learning environments that facilitate the development of hybrid courses and instructional skills.