Blockchain in HE: Smart Contracts, Smart Data

Gazali et al (2017) inform us that in Malaysia, only 0.05% of HE loans are repaid. Their paper offers a blockchain- and smart contract-based solution. In the design proposed, borrowers are granted full access to their accounts and total ledger visibility; the authorities receive automatic notification of loan account activity. The design exploits the Ethereum protocol. Following basic blockchain principles, smart contracts are distributed across nodes, but, usefully, Ethereum supports three types of contract activity: creation of new, transfer to other parties, and function invocation. These give borrowers flexibility in repayment options and schedules, and provide lenders audit trail visibility.

To prevent false billing, the smart contract commences at the moment of enrolment. The financial contract commences when the learner formally agrees to receive a government loan. The smart contract allows learners to follow a transparent schedule and to track the punctuality and amounts of their payments. This reduces management costs incurred by handling queries. If the learner takes additional loans, these too can be governed by separate or consolidated smart contracts.

This design borrows heavily from the application of smart contracts in health service management.

Ocheja et al (2018) propose a blockchain-based method of connecting learning data across institution systems. Blockchain’s capabilities can ensure data consistency, accessibility and availability, immutability, stability, security, privacy, and general controllability of student records. Learners move between institutions so need portable proof of their learning achievements. Such proof is typically a digital/or paper certificate that demonstrates proof of completion. Certificates possess little (if any) analysable data, so admissions processes remain manual. University learning records are usually stored in isolated, stand-alone systems – a constraint that slows the onboarding process further.

The ideal learning data would include more than certificates. Data showing performance in quizzes and tests, assignment briefs and submissions, exam questions and scripts could be mined for analysis and present a much richer picture of the learner. Employers may weight different areas of performance according to job requirements. Moreover, this would be blockchain data stored using protocols that allow trans-institutional interoperability. Such affordances would benefit the nomadic, eclectic learner particularly. Institutions could accept or refuse credentials and credits, and speedily ascertain in very high resolution the suitability of any applicant. Blockchain-based records would also enable education administrators and researchers to mine for relationships between data points and variables, with implications for curriculum design, learning combinations, and employability tracking.

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