Blockchain‑based approach to create a model of trust in open and ubiquitous higher education

Vol.:(0123456789)
Journal of Computing in Higher Education
https://doi.org/10.1007/s12528-019-09209-y
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Blockchain‑based approach to create a model of trust
in open and ubiquitous higher education
David Lizcano1
· Juan A. Lara1
· Bebo White2
· Shadi Aljawarneh3
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Currently, the training of the future work force presents challenging problems to
higher education. This training, in the form of practical and theoretical knowledge
can come from multiple platforms, channels and means, both formal and informal.
In addition, it is quite difficult to assess the knowledge skill level that a student has
acquired to optimize their chances for future employability. This, together with
the need to still manage academic curricula on paper, the problems of confidence
when validating these documents and contrasting them with real knowledge, etc.,
means that management in higher education requires revolutionary new tools. This
work evaluates the benefits of the blockchain (or distributed ledger) technology
and advocates a decentralised model of confidence for transactions based on an
academic crypto currency. In this approach blockchain is used to manage transac-
tions of content, teaching and competencies, assessed by consensus by students,
trainers and employers, to eliminate once and for all the “gap” between the aca-
demic world and the working world. This paper aims to address the current chal-
lenges of an increasingly dispersed, open and ubiquitous higher education. The
proposed model can be implemented in any training institution to adapt its teach-
ing to the specific needs of professional profiles validated by employers in the sec-
tor. This model has been validated by means of a prototype with more than accept-
able results.
Keywords Blockchain · Digital certificate · Competencies · Mined by consensus ·
Confidence · P2P
* Juan A. Lara
juanalfonso.lara@udima.es
Extended author information available on the last page of the article

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Introduction, motivation and objectives
The current model of higher education is becoming increasingly decentralised,
heterogeneous and difficult to verify and validate, which results in diverse prob-
lems for each representative of the business model that involves the training of
professionals for their insertion into the working world, i.e. trainers (regulated or
otherwise), students and employers.
It is becoming increasingly common for students not only to receive training
from traditional universities, but also to attend (by means of studies run by these
universities or resulting from a proactive independent study) massive online open
courses (MOOCs), live or remote workshops, video tutorials, talks or video inter-
views, etc. (Bartolomé et al. 2017). All these sources of knowledge, as well as
professional praxis itself, represent vast range of possibilities that enable the stu-
dent to acquire competencies to be used when they enter the working and pro-
fessional world. However, it is very difficult to quantify, value and validate such
knowledge when it does not come from regulated studies dependent on some cen-
tralised administration (Cano and Cabrera 2016). And even when it does come
from regulated studies, the design of each syllabus is so varied that students are
forced to accumulate a huge collection of written documents, as well as sit for
entrance exams and have interviews of all kinds, to demonstrate the competencies
and qualifications they have acquired.
From the point of view of universities, there is increasing criticism regarding
employability, referring to the fact that a university education is often incapable
of adapting quickly and flexibly to the training needs of the labour market with
an adequate “time-to-market”. There has been a gap of more than 5 years since a
bachelor’s degree or engineering curriculum has been designed in such a way that
its first graduates can demonstrate the suitability of their training by carrying out
professional tasks. And the agencies and central bodies that verify the curricula
are constantly embarking on protracted auditing processes that fail to detect train-
ing problems in time, or to improve universities’ ability to adapt in a timely man-
ner to the reality of the labour market.
From the point of view of employers, there are also problems. The boom in the
information society has not made it possible to improve the processes of hiring
personnel or compiling their CVs, so it is very difficult to determine which gradu-
ates are better prepared for a job. In addition, fraud, falsification and confusion is
the norm, so any company that wants to hire a qualified professional often needs
to collect an unmanageable number of heterogeneous and dispersed documenta-
tion from its candidates, check its validity and, finally, determine if the competen-
cies that the student claims to have turn out to be useful competencies to solve
real problems in the professional profile sought.
In Knowledge Works (2016), it is suggested that smart contracts and block-
chains can play a major role in centralising learning around the student, so that
the system would be adapted to each student, rather than each student having to
adapt to the system as is the case today. However, these visionary articles have
not, to date, been transformed into real solutions that can be readily applied.

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Blockchain-based approach to create a model of trust in open…
This paper presents an application of blockchain technology (mainly known for
its use in the consensual, secure, decentralised and unforgeable registration of cryp-
tocurrency transactions, such as Bitcoin) in the field of open and ubiquitous higher
education, recording the acquisition of knowledge and validating it by putting it into
practice in real problems adapted to the business context. By using this prototype,
the following objectives are achieved:
O1 Training institutions can adapt their teaching to the needs of the labour market,
improving its internal quality and knowing promptly whether their students
achieve professional success, as soon as they successfully complete a subject,
workshop or course
O2 An unbiased and objective mechanism is generated to evaluate training institu-
tions on the basis of reputational capital. This reputation depends on the good
performance of the students trained in the actual professional exercise of the
tasks in which they have been instructed, whereby it is possible to identify
quickly which training institutions and resources are better and worse for the
acquisition of certain professional competencies
O3 Students can have a complete, digital, legitimate and updated educational his-
tory/CV easily verifiable by anyone, simplifying the work of documentation
and presentation for access to jobs. In addition, it is provided with a mecha-
nism to ascertain which institutions are best suited to their needs, including not
only regulated universities, but also all types of training resources or sources
of knowledge dispersed through the Internet that verify their teaching with a
signed digital certificate
O4 Employers have a mechanism to direct the training of students for professional
profiles, in a dynamic, proactive and efficient way, without the need to double
check all the abilities, knowledge and competencies of their candidates
O5 Paper-based document management of qualifications, CVs, courses and certifi-
cates is eliminated, as well as the possibility of forging or otherwise compro-
mising any such documents.
The remainder of the work is organised as follows: “Related work and existing
technology” section presents related work and existing technology. The confidence
model proposed for use in education is described in “Confidence model based on
Blockchain, competencies-effort tests and “kudos”” section, while “Prototype and
validation of the proposal” section presents the implementation of the prototype of
this model and its validation. Future lines of research are included in “Future lines”
section. Finally, “Conclusions” section presents the conclusions of the work.

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Related work and existing technology
Blockchain technology
Blockchain refers to a computer technology that enables decentralised and distrib-
uted records of digital transactions to be kept (Tapscott and Tapscott 2016). Its first
implementation took place in 2009, in the context of the first digital currency Bit-
coin and its author(s) hid behind the pseudonym of Satoshi Nakamoto (Nakamoto
2008). It is a complex technology, with enormous potential (Jones 2016; Valenzuela
2016), whose main characteristic and potential is the existence of a world with no,
or virtually no, intermediaries. The problem is that the interpretation of this non-
intermediation fluctuates between two poles with a similar degree of complexity: a
world without intermediaries all depending on just a few centres of power, namely
“Up To Down”, U2D, or a supportive and horizontal world, i.e. “Peer To Peer”, P2P.
As shown in Fig. 1, the Blockchain structure is a linked and ordered list of trans-
action blocks, each identified by a hash function (a fixed-length numerical digital
summary). The hash of each transaction is calculated by means of a multi-step pro-
cess that involves, in turn, the calculation of several hashes until a single final hash,
known as the Merkle root, is generated. Also, each block stores the hash of the pre-
vious block’s header, thus linking the blocks together. This prevents a block from
being altered without also having to modify all subsequent blocks. Transactions are
also linked to each other.
The Blockchain is maintained in collaboration by all members of the network
and it requires proof that a relevant and significant amount of work was invested in
the creation of each block (Proof of Work, POW). This is done to ensure that mali-
cious users who try to modify previous blocks are forced to do an excessively large
amount of work, much more than honest users who just want to add a new block.
Since a block depends on the previous blocks, it is impossible to modify a given
block without having to modify all subsequent blocks. This results in the cost of
modifying a block increasing with each new block added.
The work test algorithm used takes advantage of the apparently random nature of
the hash functions. To prove that the work was done by adding a new block, a hash
must be created from the block header that does not exceed a certain value.
Apart from the so-called Altcoins (forks of the Bitcoin software to define new
“alternative” cryptocurrencies) there are alternative implementations of blockchain
Fig. 1 General structure of Blockchain

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that are not necessarily cryptocurrency alone. These implementations, called
Altchains, consist of a consensus algorithm (Buterin 2015) and a “logbook” distrib-
uted as a platform for contracts, name registration, etc. Altchains use the same basic
blocks and sometimes a coin or token as a means of payment, but their main purpose
is not to act as currency.
In other words, Altchains, as their name indicates, are alternative implementa-
tions of Blockchain, whose main objective is not to be used as currency. Although
many include coins, they use them as “tokens” to store an entity of value or impor-
tance, such as a contract or a resource.
Ethereum is another important example, as it is a contract-processing and execu-
tion system based on a blockchain. Ethereum uses a language that is Turing com-
plete, and has its own currency, called Ether, which is used at the time of execution.
In addition, Ethereum can implement complex systems which are, themselves, an
Altchain. Therefore, Ethereum is a platform for building Altchains.
Altcoins and Altchains can take advantage of the popularity of bitcoin by using
the same PoW mechanism. In this way, a miner (node of the network in charge of
carrying out PoW) can obtain several coins for the price of one. In order to use this
strategy, the Altcoin must be compatible with unified mining. This takes advantage
of the available space of the Bitcoin mint transaction input by storing the Altcoin
information.
Transactions are the leitmotiv and raison d’être of Blockchain-based implementa-
tions. All other parts are built to ensure that transactions are created correctly, prop-
agated on the network (P2P), verified and added to the Blockchain. Transactions are
data structures that store “value” transfers between system participants. Each trans-
action is stored as an input in the Blockchain.
Since this technology has potential uses beyond its original purpose of making
payments, several developers have used Blockchain for other applications such as
notarization, contracts and training, among others.
Work related to Blockchain in education: Blockcert and Edgecoin
Having explained Blockchain technology, it should be noted that it has begun to be
applied in many other domains, including education. However, to date there is no
application of Blockchain technology and competencies mining such as that pro-
posed in this paper, which would enable all the problems described in the introduc-
tion section to be solved.
There are three partial approaches, which address the problem from a less com-
plete perspective, such as the Blockcerts of the MIT and Oxford (MIT 2018), the
Edublock of the IFTF (Learningisearning 2018) and the Edgecoin project (Edge-
Coin Project 2018).
For a few years, the MIT has been producing digital certificates, rather than paper
ones, for certain courses, seminars and workshops held at its institution. These digi-
tal certificates have the advantage of being unequivocally certified by the University
that issues them, so that they are non-transferable and unalterable, being stored in
a Blockchain created for this purpose. In this way, an academic credential is stored

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in an unalterable chain, as in this proposal, but in this case complete qualifications
are stored, which have not been endorsed or validated by the ecosystem where such
qualifications are valid. Therefore, it is possible for students to have a digital ver-
sion of their qualifications that cannot be faked and which is available to employ-
ers, but the applied knowledge resulting from these qualifications is not validated
in any way, nor is it possible to assess in any way the quality of the original teach-
ing, nor will there be any mechanisms to self-assess the degree of employability
or success of the graduates beyond the external and traditional quality management
mechanisms available. This digital certification model, which is included among the
advantages of the proposed model (along with many others) has, nonetheless, had
great institutional support and acceptance, to the extent that other Universities have
now adopted it [such as the University of Texas at Austin, the University of Holber-
ton, the University of Oxford or the University of Nicosia (2018)], as well as private
institutions to certify the qualifications resulting from their own training courses
[SAP, IBM, Fathom or SONY (2016)].
The Institute for the Future (IFTF) and the ACT Foundation presented the idea
called “The Ledger” as a new technology that could link learning with earnings. The
initiative “Learning is Earning” is presented as a game that shows a window to the
future, namely 2026, where Edublock is used, a kind of digital currency to quantify
teaching hours as transactions and be able to store them in Blockchain. In this case,
the approach is the opposite of Blockcert’s, since what is stored are not qualifica-
tions but hours spent in face-to-face or remote classes. However, once again, this
does not serve to record abilities or competencies, or to validate them or use them as
a digital curriculum for employers.
Meanwhile, the Edgecoin project seeks to establish a specific Cryptocurrency,
based on Bitcoin, to regulate the market of goods and services related to the educa-
tional field, such as enrolments in online courses, micro-contracts between training
institutions, and digital transactions of economic assets for the acquisition of books,
support services or regulated studies. This approach simply uses a different aspect of
Bitcoins to regulate the digital market of the sector, without dealing in any way with
the field of education itself, the transfer of competencies and learning outcomes or
the storage, validation and crypto-regulation thereof.
Implementation to choose: Bitcoin, Ethereum or Multichain
Having considered the hypothesis and the model to be developed, it is necessary
to choose a technology and an underlying protocol to put the idea into practice, by
means of a fully functional prototype that allows the benefits of the model to be
ratified. Today there are three main protocols to create a prototype of the model pre-
sented in a fast and efficient way: the protocol used for Bitcoin, the protocol of the
second most widespread Cryptocurrency, Ethereum, and finally the Multichain pro-
tocol that allows you to store different elements that do not have to have the same
structure or typology with respect to each other in the Blockchain.
As this paper does not intend to go into the technical nature of the prototype, but
rather into its usefulness and employability in the field of education, the summary of

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the pros and cons of each one, resulting from the comprehensive technological study
carried out by the authors, is presented schematically in Table 1.
In conclusion, it is not possible to use Bitcoin due to the implicit limitation of
the very technology linked to the maximum number attainable with this Cryptocur-
rency. In addition, besides the mental effort on the part of the student and the miner
on which this proposal is based, the mining system requires a computational effort
that poses serious problems of scalability and energy efficiency for the proposal. Nor
is it possible to use Multichain, because it does not allow the smart contracts that
ultimately arise between the training institutions and their graduates with validated
competencies. Having ruled out the other two proposals, Ethereum appears to be the
best technological alternative: it allows the mining proposed in the model and based
on the reputation of the parties (Schlegel 2018; Clow and Makriyannis 2011) and on
a mental rather than computational effort; it is efficient; it has no overall limits like
Bitcoin; and it allows for smart contracts between the parties, based on SSL certifica-
tions for those signed and summaries of abilities and competencies. In essence, it is
the best alternative to create the Altchain necessary for the recording of acquisition
of competencies that this approach requires. The disadvantages of this technology
affect mainly Cryptocurrency transactions, due to the size of blocks and the diffi-
culty in carrying out computational mining, and therefore they are not limiting for
this proposal. It is necessary to generate a specific currency for transactional account-
ing, which in this case, as discussed later, will be Kudos, a currency of reputation or
nominal prestige for the participating entities (Sharples and Domingue 2016).
Confidence model based on Blockchain, competencies‑effort tests
and “kudos”
The implementation carried out in Ethereum aims to give shape to the proposed
confidence model based on Blockchain to verify the acquisition of competencies
through effort tests consisting of the solution of standard problems in which the
reward is in the form of Kudos.
Figure 2 is included to illustrate the ideas behind the proposed model. It is worth
noting the presence of several parties who benefit from it:
a. Trainer. The institution that instructs students in order for them to acquire a
certain competency. For the trainer’s reputation, it is essential that the students
instructed in a competency demonstrate that they actually possess it. Although in
the current paradigm, training institutions have verified the competencies acquired
by their students, such verification is more formalistic than effective, which is
why a more realistic approach such as the one described here is required.
b. Student. The person who has been instructed by a trainer in a competency. The
student is interested in having this competency recognised by people who are
experts in it (verifiers) so that, in this way, he or she has credibility with regard
to his or her access to the labour market.
c. Verifier. The people, students and professionals (including teachers), trained to
assess the acquisition of a competency by a student. Verifiers, of course, must

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Table 1 Blockchainimplementations
DisadvantagesAdvantages
BitcoinUseofPoWistoohighaneffortasitisexponentialwithrespecttonTransfersmadewithSHA-256
Limit:21millionMostwidespreadmodel
Latency:highlatencyasitgeneratesorsendstransactionsevery10 minPrizeformining:12.5(reducedbyhalfevery210,000blocks)
SoftwareupdatesSimplelanguageused:C++
Scalabilitywhenitisnecessaryfortheblocktoexceed1 MB(7t/s)Consensus:correctchainisthelongest
Difficultyofrecalculationof2016blocks(14 days)Bitcoinitselfisasmartcontract
Onlycounts8decimals.Mostwell-testedchain,beingthefirsttoappear
C++languagewithcommandlimitationrestrictingthetechnology
“Hardfolk”problems
LowlevellanguagebasedonbatteriesthatrequiregreatknowledgeoftheBitcoin
protocol
Samecomputationalcosttosend0.01value,as1,000,000value
EthereumPrizeformining:3EtherBlocktime16 s
ComplexlanguageandproblemstomakemistakesMakesuseofEtHash,fastandsafe
RequiresaCryptocurrencytoworkCanmakeuseof18decimals
Sizeundefined,butlessthan1 MBTuringcompletelanguage:greatercontroloverallnetwork
components(permits,mining,etc.)
Recalculationofdifficultyineachblockevery16 sUnlimitednumberofcurrencies
MultichainDoesnotusesmartcontractsTransactions:1000/s
Maximumtransaction64 MBSupportofmultipleassets
Goodcontrolofpermittingprocess

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continuously demonstrate their aptitude in the competency to be assessed, which
has a positive impact on their reputation vis-à-vis trainers and employers.
d. Employer. They are interested in knowing the competencies that each participant
in the system possesses in order to be able to recruit personnel for their companies
in an appropriate manner. Employers only consult the chain, and do not define
competencies in the current paradigm (other official institutions do); however,
their participation in a hypothetical paradigm shift would seem to be very useful
to better understand the needs of the market in terms of competencies.
As can be seen in Fig. 2, the parties involved in the system interact with the
Blockchain as the central point in which the transactions of the system are stored.
In a case of normal use, everything begins when a trainer instructs a student so
that he or she acquires a certain competency that is transferred to him or her
Fig. 2 Central elements of the proposed model

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(Fig. 2—step 1). Next, the student wants that competency to be recognised. With
the current education paradigm, a qualification or certificate was sufficient which,
in fact, did not serve as definitive proof of the student’s acquisition of the compe-
tency. With the proposed model, the student must demonstrate the acquisition of
the competency by solving a standard problem associated with that competency
(Fig. 2—Step 2). The standard problems, which will be discussed later, are stored
in a repository and are agreed upon by experts in each competency.
Once the student has solved the standard problem, the verifiers, who are
required to have the competency to be assessed (there will be transactions in the
chain that demonstrate this), will also solve, as proof of effort, the same standard
problem that has been provided to the student by the system, and may not have
access to the solution reached by the student until they have solved it themselves
in order to compare the results obtained. In fact, this is the mining process in the
proposed model, which is a conceptual type of mining, consisting of the solution
of a standard problem and the comparison of the results obtained by the student
and the verifier. If the verifiers reach a consensus that the “candidate” has solved
the standard problem correctly, a transaction will be confirmed in the system that
accredits the student’s acquisition of the competency (Fig. 2—step 3). If there is
no consensus, both the student and the training institution would lose the reputa-
tion associated with the assessed competency. Generally speaking, if there is con-
sensus, this gives rise to several consequences:
• The trainer would increase his reputation (Fig. 2—step 4).
• The student, because of the information recorded in the Blockchain, to all
intents and purposes, would be the holder of the assessed competency. This
implies that he or she could now, in turn, assess this competency in future
mining processes and that he or she could be hired by employers requiring
workers with the assessed competency (Fig. 2—step 5).
In general, if the verifiers mine a transaction properly (solve the problem cor-
rectly), they would gain reputation, and if not they would lose it (Fig. 2—step 6).
Among the miners, the first to solve the problem correctly and transmit it to the
network would gain comparatively more reputation than the rest. This incentive is
what encourages verifiers to act quickly when it comes to validating competencies.
Employers, meanwhile, will refer to the chain to find out which subjects in the system
have competencies that are of interest in their recruitment processes (Fig. 2—step 7).
These employers, together with trainers and graduates (holders of a certain com-
petency), will define (off-line) standard problems that allow a student to assess the
acquisition of a competency, always aligning these problems with the labour situa-
tion at any given time (Fig. 2—step 8).
Focusing on the more on-line process of the model (steps 2–4 and step 6 of
Fig. 2), and leaving aside those of a more off-line nature (steps 1, 5, 7 and 8 of
Fig. 2), it would be possible to define the proposed procedure in algorithmic nota-
tion in the following way (for legibility the steps related to hash value calculation
and link between blocks are omitted, focusing on the conceptual mining process):

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Algorithm 1. Verification of competencies
- Input: Chain (BC), Student (S), Trainer (T), Competency (Sk),
Standard Problem (P), Student Result (SR), Kudos involved (K), Number
of people in the network with Competency Sk (NS).
- Output: Consensus Result, Updated Chain (BC'), Updated Kudos in
Miners, Trainers and Student
- Steps:
1. S asks to add F,T,C,SK,*,SR,K to BC (*refers to any
potential miner winner)
2. Supports = 0
3. Rejections = 0
4. For each miner M who mines F,T,C,SK,*,SR,K, provided they have
competency Sk:
4.1 M solves P, giving MR as a result
4.2 M mines the transaction with MR (checks if SR = MR)
4.3 If the mining is positive (SR = MR):
4.3.1 M_Recommendation=Support; Supports++
Otherwise:
4.3.1 (SR ≠ MR): M_ Recommendation=Rejection;
Rejections++
5. Whereas (Supports+Rejections)/NC MINIMUM_THRESHOLD_PERCENTAGE:
go to step 4
6. If Supports = Rejections:
6.1 Result = Support
6.2 Add F,T,C,SK,M,SR,K to BC, giving BC’ as a result (M is
the winning miner, the first to mine correctly)
6.3 F_Kudos = F_Kudos + K
6.4 T_Kudos = T_Kudos + K
6.5 M_Kudos = M_Kudos + K
Otherwise (Supports Rejections):
6.1 Result = Rejection
6.2 S_Kudos = S_Kudos - K
6.3 T_Kudos = T_Kudos - K
7. For each M’ miner who has mined in step 4 (M’ ≠ M, non-winning
miner)
7.1 If M’_Recommendation = Result:
7.1.1 M’_Kudos = M’_Kudos + K/NC.
Otherwise:
7.1.1 M’_Kudos = M’_Kudos - K.

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As can be seen in the algorithm, the student concerned presents him or herself
as having a competency, having solved a particular problem chosen randomly by
the system from among those established for the competency in question (step
1). To do this, the miners solve the same standard problem (assuming a sufficient
number of miners for each type of problem in a permanent operating regime) and
check the student’s performance (step 4). It should be noted that a minimum quo-
rum of miners is required for mining to be effective (step 5). If the student, by
consensus of the miners, possesses the competency, the transaction is confirmed
and kudos are added to him/her, to the trainer and to the “winning” miner. Other-
wise, the reputation of the student and the trainer is subtracted (step 6). For non-
winning miners, if they have mined properly their reputation increases, but not as
much as that of the winning miner. If they do not mine properly, their reputation
decreases (step 7).
Undoubtedly, the culminating step of the above algorithm occurs when it is
proven that a student is effectively in possession of a competency. At that point,
the transaction is confirmed and becomes part of the chain permanently (note that
permanence is one of the key properties of any transaction), and the transaction is
stored in the respective permanent storage device.
To illustrate which particular data are stored when a transaction is confirmed,
Fig. 3 shows the structure of a transaction in the proposed system with an exam-
ple of possible content for each field.
As can be deduced from Fig. 3, the block to be stored in the Blockchain is only
128 bytes, and has the following fields:
Version 02000000
Previous block
hash
17975b97c18ed1f7e255adf297599b55
330edab87803c8170100000000000000 Block Hash
Merkle root 8ª97295a2747b4f1a0b3948df3990344
c0e19fa6b2b92b3a19c8e6badc141787
0000000000000000
e067a478024addfe
cdc93628978aa52d
91fabd4292982a50Timestamp 358b0553
Bits 535f0119
Nonce 48750833
Transaction
counter
63
Id_student 000000B8
Id_trainer 00000071
Id_problem 000003A3
Id_competency 000037E2
Id_miner 0000701D
Result AC34097B
Kudos 00000001
Free_space 00000000000000000000000000000000
00000000000000000000000000000000
00000000
Fig. 3 Structure of a transaction in the proposed system

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a. 5 univocal identifiers: student, training institution, standard problem solved, com-
petency and miner who validated the competency. 8 are used for each one, that
is, 40 bytes in total.
b. The results obtained (by the student and the miner, after comparing that they are
equal) for the standard problem. For this purpose, the IEEE754 double precision
standard is used, using 8 bytes.
c. Amount of kudos involved in the transaction. Up to now, a prototype with indi-
vidual integer values has been used, adding or subtracting a unit of this prestige
coin. If in the future it were necessary to adjust these values, for example to the
complexity of a standard problem, or to the number of graduates with a certain
competency, this field would allow that adjustment. The IEEE754 double preci-
sion standard is used again, using 8 bytes.
d. Free space available for other possible uses or future eventualities, a total of 72
bytes for future eventualities, which would give 128 bytes.
Finally, it is important to note that, in this first version of the proposed model, it
has been decided to use, by way of a test, standard problems that are stored in an
external XML database. XML has been chosen as the base format for the problems
because it simplifies the structuring and organisation of the problems (seeking their
homogenisation), allows a simple exchange via the web and offers great versatility
when it comes to adapting the presentation of the problem to the parties involved
according to their preferences, device, connection characteristics, etc.
Prototype and validation of the proposal
With the algorithm, system and technology described in the previous sections, a
P2P system based on JavaScript, MySQL and HTML5 was developed, very simple,
capable of offering an intuitive interface for both the student role (see Fig. 4) and the
mining role (see Fig. 5).
This prototype, based on Ethereum, has been created for the implementation of
the approach presented in the previous section, which aims to respond to the follow-
ing working hypotheses:
• RQ1: The model makes it possible to compare training institutions involved in
instructing students in the same professional competencies, identifying institu-
tions with training malpractice.
• RQ2: The model makes it possible to endorse the knowledge of students, iden-
tifying students lacking minimum knowledge, as a quick and simple source of
information for employers.
• RQ3: the model makes it possible to recycle regulated and non-regulated teach-
ing in order to adapt it to the business situation.
The fully functional prototype was implemented in accordance with the profes-
sional profile of Systems Administrator, typical of the field of Computer Engineer-
ing according to RD 1393/2007 of 29 October and the white paper prepared for this

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qualification by ANECA. From this source, a subset of officially verified competen-
cies associated with the subjects taken as reference in this experiment was obtained.
These two official competencies were considered in the study:
Fig. 4 Student interface based on Ethereum and adapted smart-contracts
Fig. 5 Miners interface based on transactions

1 3
• Ability to know and apply the functionalities and structure of Computer Net-
works to design and implement systems based on them.
• Ability to manage databases through query languages.
The validation experiment involved 4 training institutions, whose names are omit-
ted to avoid collusion or bias in the study: an Open University with two degree
subjects, a MOOC cycle, studies regulated by a multinational technology acad-
emy and a course on a non-certified online platform with declining prestige. For
each of the institutions, 20 students were registered, who managed to finish their
training satisfactorily, with no bias in terms of age, sex, professional occupa-
tion or previous studies/knowledge, bringing together a total of 80 students who
passed the courses (20 students who passed the two university subjects simul-
taneously, 20 from the MOOC, 20 from the academy and another 20 from the
non-certified platform). Likewise, 2 employing companies were registered, whose
name is also omitted, one focused on the area of Network Administration, and
another on the area of Database Administration. In addition, there were a total
of 100 verifiers, or “miners”, from the academic world (graduates qualified by
Spanish Universities) and the remaining 40% from the professional world of the
sector, also with clearly validated competencies in this professional field. For the
entire sample, a Blockchain based on Ethereum was set up to record the verified
competencies, and an XML relational database for the creation and use of stand-
ard problems. Firstly, the employing companies were urged to provide standard
problems that would make it possible to verify tacit competencies in the field of
the professional profile to be studied.
These standard problems were directly related to the official competencies
described above. A total of 4 types of problems were used: two in relation to the
first competence, and two in relation to the second competence. The two problems
related to the first one were: (1) network administration problems at IP level: alloca-
tion of IP addresses, masks, routing tables and RIP or OSPF configuration in CISCO
systems; and (2) administration problems at TCP level (choice of TCP/UDP proto-
col and parameterization of segments, windows and start/end mechanisms). The two
problems related to the second were: (1) creation, addition and deletion of tables in
relational databases through SQL query; and (2) combined query of multitable data
with multi-condition filters through SQL query. These kinds of problems, based on
the access tests that these companies use in their selection processes, gave rise to a
family of more than 100 different problems, based on the semi-automatic variation
of the parameters that define the problem. These problems were then fed into the
“standard problem” database.
Prior to the validation itself, the 100 miners demonstrated, through a guided
assessment supervised by the authors and a representative of each training institu-
tion, that they were able to solve a problem from among those stored with no dif-
ficulty, thus creating a fully functional system for this initial stage in the experiment.
It was possible to directly engage these miners by motivating them through their
inclusion in the system of confidence in their skills, providing them with Kudos dur-
ing the experiment that would be considered in the final implementation of the pro-
totype in the short to medium term.

D. Lizcano et al.
1 3
After 6 months of operation, the students acquired the competencies outlined by
each trainer. As indicated in the study, 80 students were considered to have passed
all their studies, and were validated by mining, storage in Blockchain, and assess-
ment of the reputation of each institution involved. The results of the experiment are
summarized in the following sections.
• Rq1
Of the total of 80 students who obtained the technical competencies through one
of the training institutions, only 58 managed to validate this such competencies by
solving a standard problem prepared by the employing entities that was positively
mined by consensus of the verifiers. These 58 competencies were included in the
Blockchain, leaving the other students (22) outside the validated record of compe-
tencies (they should possibly have been failed by the training institution in question).
A detailed study carried out by a panel of experts from training and employing
entities confirmed that the training institution in 4th position did not train the stu-
dents adequately, despite expressly indicating that they would acquire competencies
that, on the basis of their methodology, teaching staff and contents, they are unable
to transmit. For the rest of the institutions, the panel more or less agreed that the
institutions were similar in terms of quality of training, possibly highlighting the
institution that ranked first as the most prestigious. Table 2 shows the results.
• Rq2
Direct on-the-job interviews, access tests and analyses were carried out with each of
the 80 students by a panel of experts from the employing companies. In this study,
the results of the mining were ratified at 98.75%, clearly identifying that 22 people
did not have the necessary competencies to deal with the daily tasks inherent to the
professional profile being studied. Of the 58 students who were able to ratify their
competencies, 1 finally turned out to be unfit for the job. This case was studied, by
means of controlled surveys, revealing that the student copied from another student
in the sample during the process of resolving the standard problem. This fact, of
great relevance, is discussed later in future lines. The results are shown in Table 3.
Considering Table 3 as if it were a confusion matrix, we see that the min-
ing procedure has obtained an accuracy of 98.75%, that there have been no false
Table 2 Data relating to the different institutions
Training institution No. students
passed
No. standard prob-
lems answered
No. competen-
cies mined
Relative final reputa-
tion (Kudos)/ranking
position
Open University 20 20 19 + 19 K/2nd
MOOC cycle 20 20 18 + 18 K/3rd
Technological academy 20 20 20 + 20 K/1st
Online tutorial 20 20 1 + 1 K/4th

1 3
negatives (students with real competencies erroneously disqualified by the sys-
tem), and only one false positive from a sample size of 80, due precisely to an act
illicit act performed by the student during the test.
• Rq3
To achieve conclusive results for this working hypothesis would require the
direct and indirect observation of the application of this model in the field of
higher education in a large number of institutions of various kinds, to check its
acceptance and weighting in the designs of future regulated curricula. In order
to achieve inconclusive qualitative results that give an answer to the question, a
case study was proposed, based on interviews conducted using a Delphi method
with a panel made up of the teachers directly involved in the implementation of
the prototype. A total of 12 teachers participated from the training institutions
involved in the experiment, 2 giving classes in the University, 3 giving classes
in the MOOC cycle, 5 in the Technological Academy, and 2 in the online tuto-
rial. To begin with, not all of them were presented with the results achieved in
their students’ mining process, and the weighting of the institutions to which they
belong, until the interviews were concluded.
In the first consultation of the panel, they were asked:
1. Whether they considered this model to be ideal for adapting higher education to
business reality. The result, in a closed yes/no survey, is the one shown in Fig. 6.
There is no representative statistical correlation between the responses and the
training institution to which the respondent belongs.
Only one teacher, from the technological academy under study, did not agree,
considering that, although it is a model of great potential and proven efficiency,
there are certain training aspects that may differ from the business reality because
it is the company itself (in the opinion of this respondent) that should give spe-
cific practical training to its employees in its own human resources training
programmes.
2. If more than 10% of your students do not manage to validate the competencies
they have passed with your teaching, would you consider refocusing your meth-
odology and content? The result, in a 5-value Likert scale, is shown in Fig. 7.
Table 3 Mining results Actual professional
fit
Actual profes-
sional unfit
Predicted professional fit 57 1
Predicted professional unfit 0 22

D. Lizcano et al.
1 3
On this occasion, there do appear to be discrepancies depending on the institution
to which each respondent belongs, and for this reason the figure shows their origin.
After a first consultation, the panel was subjected to a second exploratory consulta-
tion to find out the reasons for each teacher’s answers. In the University, the 2 teach-
ers fully agree with the statement. In the case of the MOOC cycle, 2 teachers were
in full agreement and 1 did not indicate this value because he or she did not consider
it relevant whether or not the teaching methodology should be modified, but only the
contents. As for the technological academy, the teacher who considered it unneces-
sary to adapt contents to the business reality gave a neutral response, another teacher
Fig. 6 Responses of the panel
Fig. 7 Results: “Likert” scale on modification of training

1 3
indicated that he or she agreed to change contents but not the methodology, and
finally 3 teachers (more than 50%) indicated that they fully agreed with the state-
ment. Finally, the Internet tutorial teachers answered differently: one to the effect of
agreeing to change their methodology, but not the contents of their courses because
they considered them to be very appropriate. The other stated that they disagreed
because they considered that their non-formal education should not be subject to
the endorsement of their students’ knowledge and that this adaptation should only
affect official regulated institutions. However, this statement clearly contradicts the
advertising of the tutorial and its cost, which can be criticised if the graduates do not
acquire the promised competencies.
In summary, it should be noted that of the 12 surveyed, 7 (virtually 60%) are
totally in agreement to changing their teaching in the event that the students pre-
sent competencies problems brought to light thanks to the mechanism of confidence
by consensus proposed here, and 10 (more than 83%) would consider, in any case,
adapting the content or methodology used. In addition, more than 90% consider this
model to be ideal for adapting higher education to business reality.
Efficiency and validity of the prototype at computational level
Finally, once the prototype has been validated based on its objectives, it is neces-
sary to validate its viability from a technical and computational point of view. The
scenario used for the validation of the model and its prototype concluded with a
total of 186 participants in the P2P network, and a Blockchain that occupied a total
of 7424 useful bytes and a total of just under 8 KB, including chain references and
i-nodes. Currently, Ethereum manages to effectively handle the total of more than
160 GB occupied by the blockchain for most common implementations today (this
data increases hour by hour) so, making a quick estimate, this prototype would work
with total efficiency for networks with several billion students.
The performance of the P2P network and the transactions that affected the min-
ing, storage, reading, etc. of blocks as well as type standard problems and results,
were also analysed. The results are shown in Fig. 8.
Operations were always maintained between 3 and 818 ms when the network
was fully loaded and the chain had grown to its maximum length. In any event,
operations always took less than a second to complete. On average, the operations
required 85 ms, distributed for each transaction as the graph indicates: an almost
negligible time to load the Blockchain, 74.7 ms to perform the computational min-
ing of adjacent blocks and the target block, 1.1 ms to sign the current target block
Fig. 8 Performance of the P2P
network

D. Lizcano et al.
1 3
and 0.2 s in searches within the chain. There is also a time of about 8.2 ms not asso-
ciated with any operation, but with the latency of the P2P network of the prototype
per transaction.
Once the efficiency of the prototype was verified in the current scenario, a regres-
sion extrapolation study was carried out to check the scalability of the system and its
behaviour in the event of increasing the size of the P2P network and the Blockchain.
The variables to be studied were, on the one hand, the average running time of an
operation in the system when the network of nodes (users) involved grew, and on the
other hand, the transfer of messages in the P2P network, in terms of messages per
second, when the size of the network itself also grew. According to the studies, these
are the two issues that most affect Blockchain-based implementations performance,
and are therefore considered the best indicators for assessing whether the prototype
is feasible for large-scale deployment. Figure 9 summarises the results obtained.
As can be seen, for a small network, such as that of the study scenario (186 par-
ticipants) the average running time of an operation is less than 100 ms (specifically
85 as explained above) and the replication and communication inherent to Block-
chain was maintained at about 140 messages per second, most of them at the level
of the link between each node and the P2P server. It can be seen that by exponen-
tially increasing the size of the network, the running time progresses linearly, always
below reasonable limits (for networks with almost 1 billion users operations are still
below one second of latency), while the network overload remains asymptotically
limited to about 150 messages per second. This is possible thanks to the internal
management of Ethereum and its management of intermediate brokers for polyno-
mial scalability.
These are very promising values that make it possible to ensure that the system is
fully functional and scalable with a view to tackling a complete implementation in
the international domain of higher education, with the network being able to grow
enormously (as mentioned above) without performance being a problem.
The only limiting factor of the prototype is precisely a non-technical one,
dependent on the interest of the community itself in the essential task of mining.
Fig. 9 Regression extrapolation study

1 3
In this model, the task of mining is not supported by a computational effort of
a network device, but by the cognitive and mental effort of the user behind that
connected device, that is, it is a personal effort dependent on the human factor.
There must be sufficient interest in the community for the achievement of kudos
to support the reputation and prestige of each miner in a given competition, and
thus this somewhat “intangible” capital serves as a stimulus and incentive for the
miner. Obviously, this will only be possible if a correct network externality factor
is established where more and more training entities, employers and professionals
adhere to the collaborative environment presented, and in this way, the kudos and
what they represent are sufficient to encourage the work of mining. In the proto-
type, the 100 miners maintained a proactive and fully positive attitude towards
this work, to the extent that, when a new competency had to be mined, the mining
time was always below 20 min in total. The confidence interval for the mining
time was set between 11 and 17 min, and on average each competency took about
15 and a half minutes to mine (Fig. 10—left). These times refer to the first miner
who managed to confirm/validate each competency, but obviously, several mining
attempts accompanied each of the mining processes. Each of the 80 mined blocks
(58 positive, the rest discarded) brought together at least 13 miners and at most a
total of 35 of the 100 available miners. Given the number of miners with differ-
ent competencies in this study, consensus thresholds were set at 12 miners (51%
of total miners with each specific competency). On average each transaction was
mined by 23 miners, establishing as a confidence interval a group of between 19
and 31 miners for each competency to be validated (Fig. 10—right).
These data are valid in this study, but obviously the success of the mining pro-
cess in a full implementation of the system will depend on the number of users
belonging to the community and to this novel system of confidence, in order to
provide sufficient incentives for this mining action in exchange for prestige in the
educational-professional sphere.
Fig. 10 Distribution of mining time and number of miners

D. Lizcano et al.
1 3
Future lines
In this first approach to the solution of the proposed model, an implementation has
been carried out which, although it is fully functional, has been validated in a some-
what limited specific scenario.
For this reason, the following future lines of work are proposed:
a. Ensuring the legitimate process of competencies verification, through supervised
tests in official examination centres.
As discussed in this paper, there are parts of the procedure (Fig. 2) that are carried
out online and are therefore fully controlled by the system. However, there are oth-
ers that are carried out offline, without the control of the system. One of them is the
solution of the standard problem by the student (and also by the assessors). In order
to verify the identity of the authors of the problems, mechanisms such as, for exam-
ple, supervision in official centres should be established. However, we also believe
that fraud, if it occurs, would be self-regulated by the system itself: for example,
if someone solves a problem fraudulently and is then hired by employers, this will
be demonstrated by the fact that they do not actually possess the competency con-
cerned, as has in fact occurred in the case study itself in the solution of RQ2. For
somebody intending to work on this issue, we first recommend to research whether
or not there exist official centres in the particular country of application of this
approach where they can carry out this supervision task. If so, the proposal of a new
official supervision mechanism would be of interest for the community.
b. Applying the prototype to broader professional profiles, including more technical
competencies and greater representativeness of the roles involved in the model.
In this study, a specific scenario with specific profiles has been taken as a reference.
In order to confirm the validity of the proposal and its general character, it would be
advisable to carry out an exhaustive assessment in other areas of knowledge and with a
greater representation of each of the roles. We have proved our approach in the area of
computer science, so it would be interesting for some experts on areas such as social
science to work on a similar approach and compare the results obtained with ours.
c. Analysing the feasibility of verifying non-technical, transversal or general com-
petencies.
The proposed model and its implementation are based on the assumption that it is pos-
sible to obtain a specific result when making a standard problem in order to verify the
acquisition of a competency. This approach is usually valid in areas of primarily tech-
nical knowledge. However, in other, less technical fields or when there are competen-
cies that are difficult to assess, other means of assessment are required. The use of other
non-numerical tests such as the digital portfolio or the collection of video-interviews
conducted by automatic assistants are attractive options. In such cases, the miners may

1 3
not have to have the same competency validated that the student wants to validate, but
a different one associated precisely with the assessment of the competency in question.
This could be applied to more general competencies, transversal or otherwise, linked
to the solution of problems. It is very simple to adapt the proposed model to this pos-
sibility, and it is therefore also applicable in this field. The ideal next step in this line of
research would be to try to define the structure of a digital portfolio to collect documents
and information that can be used for the assessment of these types of competencies, and
then, to propose a similar implementation in order to check its validity in that kind of
field.
d. Competencies management.
In the implementation carried out, the competencies have been taken from an official
agency, i.e. ANECA. The management of competencies (definition, association with
university studies, their progress, etc.) is an arduous and costly task that, in the current
paradigm, is carried out by agencies such as ANECA. If such management continues to
be carried out effectively by this type of institution, the proposed model would continue
to function normally. However, if there is a paradigm shift in which the definition and
management of competencies cease to be the work of a given institution, the implemen-
tation of a parallel Blockchain allowing the collective management of these competen-
cies by the main parties (employers, training bodies and even the experts themselves) is
offered as a future line of work. In that case, this open problem would need a solution
in terms of the selection of the blockchain implementation that is the most convenient
for this case, or the proposal of a new one otherwise. The content of the blocks should
be defined and also the protocol to be used. A comprehensive set of experiments should
be carried out to check the validity of the new blockchain, in particular a qualitative
validation by experts should be welcome.
e. Validation of standard problems.
In this initial implementation, the standard problems have been validated and agreed
upon by the parties involved in the process and present in the experiments, and then
stored in a database. With a view to a global and general implementation, it is proposed
to use another parallel Blockchain to validate these standard problems that would be
recorded in the form of consensual transactions. In this case, somebody interested in
taking this line to next step should follow the same indications as in the previous future
line: define the content of the blocks that will contain the problems, the protocol to be
used and carry out an exhaustive battery of tests to assess that new proposal.
Conclusions
This paper presents a model of confidence in open and ubiquitous higher edu-
cation, based on Blockchain technology, which certifies the acquisition of com-
petencies by students trained in different educational institutions. The proposed

D. Lizcano et al.
1 3
model is based on a consensus protocol of experts who are part of the system
itself.
The approach presented benefits from the advantages of the underlying tech-
nology itself and, moreover, represents per se a great advance in the field of
education, since it allows for reliable verification of the acquisition of compe-
tencies by students, and what is more, guarantees that their training is in accord-
ance with the actual job situation and the current needs of the market. In addi-
tion, it allows the value of the training institutions involved in this process to be
assessed in a fair, automatic and decentralised manner. It enables these entities to
have a quick and effective mechanism for self-assessing their teaching and adapt-
ing to the changing labour market. It also simplifies the processes of hiring and
assessing candidates for employers, who in addition will not have to worry about
document forgery, which will be eradicated. Finally, students will enjoy a com-
plete, unforgeable, easily accessible digital curriculum validated by a competent
community.
The model has materialised in the form of a fully functional implemented pro-
totype, assessed in a real environment, and has obtained positive results that dem-
onstrate the countless advantages of the proposal for students, training institutions,
experts and employers.
One of the main characteristics of the proposed model is its high range of appli-
cability to any scenario in which there are educational institutions that train students
in the acquisition of useful competencies for the labour market.
This paper also opens the possibility of continuing to explore the enormous
potential of Blockchain technology in other aspects of the educational field and
extrapolating the ideas presented in this document to other fields of application as
diverse as medical diagnosis, financial risk assessment or business knowledge man-
agement, among others.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interest.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
David Lizcano holds a Ph. D. with honors in Computer Science (2010) from the Universidad Politéc-
nica de Madrid, and a M.Sc. degree with honors in Research in Complex Software Development (2008)
from the Universidad Politécnica de Madrid. He is Professor at the Universidad a Distancia de Madrid,
UDIMA’s Department of Computer Science. He held a research grant from the European Social Fund
under their Research Personnel Training program and he is working as a Ph.D. at UDIMA, and involved
in several national and European funded projects relating to Web 2.0 and Enterprise 2.0 technologies
and Service Oriented Architectures (SOA), Paradigms of Programming, Software Engineering, Human-
Computer Interaction and End-user Development and Programming.
Juan A. Lara is Associate Professor and Research Scientist at Madrid Open University, UDIMA, Spain.
He is currently Director of the Group of Research in Knowledge Management and Engineering. He is
author of more than five online education books. He holds a Ph.D. in Computer Science and two Post
Graduate Masters in Information Technologies and Emerging Technologies to Develop Complex Soft-
ware Systems from Technical University of Madrid, Spain. He has published some book chapters and
papers on several international conferences, and taken part in national and international research projects.
He is author of more than fifteen papers published in international impact journals. His research interests
in computer science include data mining, knowledge discovery in databases, data fusion, artificial intel-
ligence and e-learning.
Bebo White is a Departmental Associate (Emeritus) at the SLAC National Accelerator Laboratory, the
basic energy science and high-energy physics laboratory operated by Stanford University. Prior to retire-
ment he worked at SLAC as a Computational Physicist and Senior Computing Information Systems
Analyst. He is also a Visiting Professor of Computer Science at the University of Hong Kong. In recent
years Professor White’s work has been dominated by his involvement with World Wide Web technol-
ogy. He first became involved with WWW development while on sabbatical at CERN in 1989 and was
instrumental in establishing the first non-European Web site at SLAC in 1991. Professor White has lec-
tured and spoken internationally to academic and commercial audiences. He is the author of eight books
and over one hundred journal/proceedings articles. In addition, he has been actively involved with the

D. Lizcano et al.
1 3
planning and execution of five major international conference series: The International World Wide Web
Conference, The International Conference on Web Engineering, The IADIS International Conference on
WWW/Internet, The International Conference on ICT in Teaching and Learning, and The Web Science
Conference. His current research interests include The Internet of Things, Cybercurrencies/Bitcoin and
Blockchain, The Future of User Interaction Design, and Social Media in Education.
Shadi Aljawarneh is a full professor, Software Engineering, at the Jordan University of Science and
Technology, Jordan; visiting professor, Concordia University, Montreal, Canada. He holds a BSc degree
in Computer Science from Jordan Yarmouk University, a MSc degree in Information Technology from
Western Sydney University and a PhD in Software Engineering from Northumbria University-England.
He worked as an associate professor in faculty of IT in Isra University, Jordan since 2008. His research is
centered in software engineering, web and network security, e-learning, bioinformatics, Cloud Comput-
ing and ICT fields. Aljawarneh has presented at and been on the organizing committees for a number of
international conferences and is a board member of the International Community for ACM, Jordan ACM
Chapter, ACS, and IEEE. A number of his papers have been selected as “Best Papers” in conferences and
journals.
Affiliations
David Lizcano1
· Juan A. Lara1
· Bebo White2
· Shadi Aljawarneh3
David Lizcano
david.lizcano@udima.es
Bebo White
bebo@slac.stanford.edu
Shadi Aljawarneh
saaljawarneh@just.edu.jo
1
School of Computer Science, Madrid Open University, UDIMA, Carretera de La Coruña, KM.
38,500, Vía de Servicio, nº 15, Collado Villalba, 28400 Madrid, Spain
2
SLAC National Accelerator Laboratory, U.S. Department of Energy Office of Science, Stanford
University, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
3
Faculty of Computer and Information Technology, Software Engineering Department, Jordan
University of Science and Technology, P.O.Box 3030, Irbid 22110, Jordan

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