Toward Sustainable Learning Economy through a Block-chain based
Management System
Masumi Hori
, Seishi Ono
, Kensuke Miyashita
, Toshihiro Kita
and Takao Terano
NPO CCC-TIES, Nara, Japan
Kyoto Women's University, Kyoto, Japan
Kumamoto University, Kumamoto, Japan
Chiba University of Commerce, Chiba, Japan
Keywords: Blockchain, Open Online Education, Competency based Learning, Bipartite Graph, Distributed Artificial
Abstract: This paper proposes Sustainable Learning Economy (SLE) based on market principles. SLE will utilize the
blockchain technology in order to let learners trade their learning results in cryptocurrencies, which in turn
gives the learners a strong motivation to acquire the knowledge independently to gain their rewards. The main
challenge in SLE has been guaranteeing learning quality; however, this could be resolved using competency-
based learning (CBL), an efficient learning method to prioritize acquired knowledge over knowledge
acquisition. Unfortunately, due to social, corporate, and educational demands, competency models require
significant time, manpower, and expertise. While Conventional Competency Management Systems (CMS)
reduces costs by providing an integrated environment for CBL operations, it is not able to reduce development
costs. Therefore, to reduce development costs, this paper developed a Smart CMS, which harnessed concepts
of distributed artificial intelligence, the power of internet resources, and network analysis technology to
automatically develop competencies tailored to the purpose, strengths, and characteristics of individual
In modern knowledge-based societies, people need to
have open learning places in which they can use the
knowledge learned from society and continue to learn
for the rest of their lives. Traditional school systems
have supported the modernization of society since the
Industrial Revolution, however, formal education
structures do not allow for constantly changing
knowledge and are unable to respond to the diverse
demands of diverse people. Therefore, the knowledge
gained from formal school education is limited,
which is a major challenge to providing equal access
to quality lifelong learning education for all.
The Massive Open Online Courses (MOOCs)
movement that boomed in the United States in 2012
was expected to provide all people with an open
learning place. However, the MOOCs learning
outcomes have not been able to provide a learning
system for the knowledge-based society that leads to
employment or the innovations needed for a
prosperous life. Current MOOCs guarantee learning
outcomes using a traditional system for which the
respective institutions of higher education provide the
completion certificate; that is, as the MOOCs
evaluation system is the same as for traditional
schools, it does not provide an open learning
environment and, therefore, has been no more
successful than traditional universities.
Behavioral Competency-Based Learning (CBL)
(Wesselink, Lans, Mulder, & Biemans, 2003;
Barrick, 2017), which has been adopted by many
Hori, M., Ono, S., Miyashita, K., Kita, T. and Terano, T.
Toward Sustainable Learning Economy through a Block-chain based Management System.
DOI: 10.5220/0009567604300437
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 2, pages 430-437
ISBN: 978-989-758-417-6
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
universities in the United States, has been successful
in connecting learning outcomes to the needs of the
social and labor markets. However, the competencies
are defined based on the needs of organizations that
provide the CBL and, therefore, are unsuitable for
others outside the company. Furthermore, the
competency development, and maintenance model is
expensive and constrained by various licenses.
To cope with these issues, this paper proposes a
Competency Management System (CMS), which
harnesses concepts of distributed artificial
intelligence (DAI) to automatically determine
competency construction and learning evaluations
and provide the required framework for learner-
oriented CBL; therefore, it can offer an open learning
space with socially useful learning outcomes for all
Illich (1973) criticized traditional schools claiming
that knowledge generally comes not from the school
through teachers, but from the outside society. Illich
believed that to cope with the increasingly complex
modern society, informal education over a lifetime
would be more useful, for which he proposed a
decentralized education system called the Learning
Web. However, while decentralized education could
be a valuable social infrastructure, there are three
main challenges.
First, how can learners be motivated? In
traditional schools, students are incentivized by
moving up to the next grade or in the last stage by
gaining the requirements to get a job. However, as
decentralized education has no clear learning stages
or graduation concepts, it does not have the same
motivations or incentives.
Second, as decentralized education does not exist
within institutions such as schools, there is no way to
guarantee the education quality, as conventional
independent organization quality assurance reviews
are mechanisms designed for traditional centralized
management systems.
Third, how can the learning outcomes be
evaluated? In traditional schools, there are standards
applied to assess whether the learner has acquired the
knowledge, which is further tested through exams;
however, it is difficult to set standards for the various
types of learning in decentralized education.
To solve these decentralized education challenges for
the learning economy, this paper proposes a new
informal education mechanism that uses blockchain
(Figure 1).
In the learning economy (Hori, Ono, Miyashita,
Kita, 2019), learners (not teachers or schools) earn
cryptocurrencies from their daily activity learning
outcomes, which gives the learners a strong
motivation to independently acquire and/or update
their knowledge faster than in traditional schools.
Figure 1: Learning economy.
The learning economy market principles allow for
an evaluation of the learning outcome quality and the
quality assurance; that is, the market economy
principle states that in the learning economy, low-
quality outcomes are eliminated through competition
and the outcome that people best value is widely
There are drawbacks as it may take a long time for the
market to generate reasonable learning quality results
when evaluating learning value based on market
competition. For example, if inferior knowledge in
the learning economy is initially highly valued, it may
take some time for the market principles to update the
knowledge. Therefore, further refinements are needed
to ensure these types of poor knowledge inputs are
corrected as quickly as possible to maintain quality
We have conducted a study in which the learner
was recording their learning outcomes on a
blockchain and trading the outcomes using virtual
currencies (Hori et al., 2018), with the quality
assurance function effectiveness being based on the
principle of competition; however, there were errors
found that could not be corrected in some cases.
Therefore, this paper introduces a new CBL learning
Factors M ark et
Active log
Intro sp ectio n log Experience log
Learning Outcome
Good s & Services M arket
Knowledg e Knowhow Skill
Blockch a in
IP management
IP m anag em ent
ProcessorLearn er
Toward Sustainable Learning Economy through a Block-chain based Management System
economy concept, which guarantees the learning
quality as quickly as possible.
The definition of competency might be ambiguous
because the perception in this context is different
from its meaning in business and education. The
National Postsecondary Education Cooperative stated
that learners gain competency when they have
mastered the skills, abilities, and knowledge
associated with that particular competency (Jones &
Voorhees, 2002).
CBL was developed as an educational approach
to assess competency based on performance. As the
competencies were evaluated based only on the
acquired knowledge and skills and not the learning
process, such as attendance, attitude, and effort, CBL
was initially seen as a potential method for
transforming traditional education. By evaluating the
learning process, it was expected that people would
be able to effectively develop knowledge and provide
society with a high-quality workforce; however, CBL
development was found to be expensive in terms of
time and costs.
The development of CBL models has, therefore,
been challenging, and costly because of the need to
reflect market demands, learner demands, and
academic expectations. Further, to continue to meet
the needs of the community, competency models
need to be constantly updated and developed in line
with progress in science, technology, and society. For
example, the Western Governors University
organized and is still working with a program council
of academic and industry experts to develop their
CBL model (Johnstone & Soares, 2014; Oblinger,
2012). CBL models have only been used in schools,
corporations, and other organizations, and each
industry, and specialty field has built and customized
standard generic competency models; therefore, there
has been insufficient CBL model development.
There have been three main approaches to developing
CBL models: a behavioral approach, a generic
approach, and a comprehensive approach. Table 1
details the characteristics of each of these, and, in this
section, the features of these three approaches and the
proposed CBL are elucidated.
Table 1: Three types competency.
and tasks
required to
perform duties
and tasks
required for
for cultivating
qualities and
required for the
times we live in
6.1 Behaviouristic Competency
The behavioral approach is focused on acquiring the
competencies to perform a specific task (Wesselink et
al., 2003;
Barrick, 2017). The competency can be
broken down into several units that focus on the
specific tasks required to perform the task; therefore,
each unit focuses on task achievement and does not
include learning assessments during the learning time
as in the Carnegie unit. Further, as the competency is
divided into easily manageable tasks, it can be easily,
and efficiently managed. Therefore, because the units
can be easily processed by information systems, it has
a high affinity with online education and,
consequently, has been widely adopted for job
training at universities, companies, and trade schools
in some countries as it has been recognized as a viable
educational method for developing specific skills for
specific tasks.
CBL, however, has been criticized for placing too
much emphasis on performing specific tasks and less
time on thinking and/or comprehension (Barnett,
1994), as learning should essentially require that
learners autonomously discover and create their own
knowledge. As the goal of the CBL behavioral
approach is to attain the competencies defined by the
organization to achieve the results expected by the
organization, it is unsuitable for open online
education as it is unable to address the needs and
backgrounds of a diverse audience.
6.2 Generic Competency
To resolve some of the drawbacks of the behavioral
approach, both the basic abilities for the entire
occupation and the abilities and tasks necessary for a
specific job are required. Therefore, the generic
approach expanded the basic skills acquisition to
cover the skills needed to function effectively within
the occupation, such as critical thinking skills and
problem-solving skills. However, the generic
approach is the same as the behavioral approach
because as the learning assessment is based on
achieving the unit competencies, it also has a high
CSEDU 2020 - 12th International Conference on Computer Supported Education
affinity with information systems and online
education, but does not address the diverse audiences
in open online education. The generic approach has
also been criticized for not fully developing an
individual's overall abilities to adapt to complex
environments or to create knowledge. Further, to
define, and develop the competencies for both the
generic and behavioral approaches require a great
deal of time, effort, and cost.
6.3 Comprehensive Competency
The comprehensive approach does not focus on
specific professional development; rather, it focuses
on education that can cultivate the qualities and
personalities required by the times we live in. this
approach is based on the hypothesis that every human
ability is not merely a combination of competencies
and cannot be reduced to component parts or
elements. In this approach, the learning process is
also subject to learning evaluation. Competencies that
are proposed by the OECD's DeSeCo project and the
Tuning Academy in the Bologna process also take
this position.
The challenges of the comprehensive approach are
that the competencies are difficult to structure and
cannot be systematized, and for comprehensive
development, strong project-based education, and
active learning skills are required. Therefore, as the
teachers are required to have these high-level skills
and only a limited number of learners can be taught at
once, this approach is unsuitable for large-scale
online education for reason that the teachers need to
teach many students simultaneously.
Traditional CMS, which is designed based on the
behavioral approach, requires that the CBL be
integrated with human resources management and a
Learning Management System to ensure the storage,
maintenance, and tracking of the competencies and
performances within an organization.
While the CMS is used to manage the CBL, it
does not include the development of the competencies,
which is a laborious, expensive process. Therefore,
several studies have intelligent support functions to
generate competencies, such as a CBL curriculum
generator and a learner skill gap measure. However,
these proposed systems did not include the automatic
generation of the competencies themselves but only
suggested that the manually created competencies be
CMS is part of enterprise knowledge management
and has been widely researched and developed since
the early 2010s in Europe and the United States
(Draganidis, & Mentzas, 2006.). Table 1 shows the
major CMS components.
Systems for the standardization of competency
metadata have been proposed, such as IEEE RCD,
IMS-RDCEO, and HR-XML, and competency model
developments to reduce the time and efforts costs
have included using ontology for the evaluation and
development of individual competencies (Fazel-
Zarandi & Fox, 2012; Lundqvist & Williams, 2008;
Draganidis, Chamopoulou & Mentzas, 2006;
Schmidt & Kunzmann, 2006).
Table 2: Major CMS component.
Component Description
Clarify goals, identify and
define required
Assemble competencies
for each learner and
organization attribute,
such as department, job
title, and characteristics
Learning method
Learn based on
The degree of attainment
of competencies
Competencies can be seen to be bipartite graphs that
link task ability and knowledge nodes to skills ability
and knowledge nodes. If the tasks–skills
combinations are represented using a bipartite
network graph method, optimal tasks, and skills
composition can be naturally derived. For example,
Figure 2 shows part of an information technology (IT)
competency graph, in which the task software
utilization support node is linked to multiple skills
nodes for a range of IT knowledge.
Figure 2: Bipartite competency graphs.
Ability, Skill and Knowledge
for grasping
IT k now led g e
utilization support
Ap p lication
Software requirements
Toward Sustainable Learning Economy through a Block-chain based Management System
8.1 Analysis of a Generic Competency
To confirm the effectiveness of the network graph
method, an existing generic competency, the “i
Competency Dictionary (iCD),” was analyzed
which is a freely available competency dictionary
consisting of 4,500 task dictionaries and 10,000 skill
dictionaries for human IT development published by
the Information Processing Agency Japan. iCD sets
the global standards for IT human resources, with the
Enterprise IT Body of Knowledge of the IEEE
referring to the iCD as the Enterprise IT framework
for Asia (IEEE, 2017).
This dictionary was therefore used in this paper to
represent the tasks and skills combination on a
bipartite graph.
This section gives an overview of iCD and then
reports on the results of the iCD analysis using a
network graph.
8.1.1 Task Dictionary
iCD is composed of a task dictionary and a skill
dictionary, with the task dictionary having the
following three hierarchies:
(Large classification) Functions required in the
(Middle classification) Business of the
(Small classification) Individual business
The individual tasks in the small classification,
therefore, have the smallest granularity. In this paper,
a small classification task is simply called a task. The
task dictionary covers almost all tasks considered
necessary by IT-related companies, from
development related tasks to evaluation,
improvement, management, and control.
8.1.2 Skills Dictionary
The skills dictionary consists of the following three
(Large classification) Skills category
(Middle classification) Skills classification
(Small classification) Skills item
In this paper, the small classification skills are
simply called skills.
8.2 Results of Analysis
The igraph R package network graph method was
employed to analyze the competency model, with the
primary graph analysis being on the node
relationships between the iCD tasks, skills, and
related knowledge. As the iCD assigns different
codes to related knowledge that has the same
wording, the related knowledge with the same
wording was connected to the same related
knowledge, and the codes were reassigned.
Figure 3: Bipartite graph of tasks and skills.
Figure 3 shows a bipartite graph for the iCD task–
skill and skill–knowledge relationships, and Figure 4
shows a histogram of the frequencies for the edge of
the bipartite graph for the task–skill and skill–
knowledge. From Figure 4, it can be seen that all four
graphs have corresponding n-to-n relationships and
scale-free or power-law scaling, which means that
multiple skills need to be acquired to perform some
specific tasks, some specific skills are needed for
multiple tasks, a range of knowledge is needed to
acquire a specific skill, and some knowledge is
required for multiple skills.
Figure 4: Histograms for order distribution.
To confirm these observations, the distribution of
each node was checked, as shown in Figure 5. The
upper left figure in Figure 5 shows a logarithmic
graph that counts the edges of each task-side node for
the task–skill relationships and permutates them in
the order of frequency. In the same way, the upper
CSEDU 2020 - 12th International Conference on Computer Supported Education
right figure shows the logarithmic edge count for each
skill-side node for the task–skill relationships. The
lower part of Figure 5 shows a graph that counts the
edges of each node for the skill–knowledge
The linear approximation for the task-side edges
for the task–skill relationships was 5.40-1.20log (x)
R2 = 0.75 (upper left in Figure 5), and the linear
approximation for the skills side was 5.13-1.27log (x)
R2 =0.78 (upper right in Figure 5). The linear
approximation for the skills side of the skill–
knowledge relationships was 5.10-1.24log (x) R2 =
0.78 (lower left in Figure 5), and the linear
approximation for the knowledge side of the skill–
knowledge relationships was 8.89-3.56log
(knowledge) x) R2 = 0.98 (lower right in Figure 5).
These results were found to fit a straight line. If the
degree of the edges connected between the nodes is
scale-free, it means that the value or utility of each
node differs significantly.
Figure 5: Logarithmic graph of degree distribution.
8.3 Analysis Discussion
The iCD competency model analysis suggested that
the competency model was a scale-free network for
the task–skill and skill–knowledge relationships. If a
competency exists in a scale-free network, 1) specific
skills are required for many tasks, and specific
knowledge is required for many skills; 2) a small
number of tasks require many skills, and a few skills
require a wide range of knowledge; 3) the
competencies are constantly growing; 4) newly
established tasks, skills, and knowledge during the
growth process are connected to existing task and
skills node hubs in many cases; 5) the competency
model structure does not change significantly. For a
competency that has this type of structure, the skills,
tasks, and knowledge set allows for a CMS to
autonomously build the competencies.
9.1 Overview
Using a DAI, a smart competency management
system (S-CMS) suitable for a scale-free competency
model was built that was able to generate
competencies without the need for excessive human
time or effort.
Figure 6 shows the conceptual diagram for S-
CMS. The S-CMS stores the competencies in a graph-
structure format, and then constructs the
competencies using the DAI.
Figure 6: Overview of the S-CMS.
The S-CMS was implemented using four types of
DAI agents:
Data extract (DXT) agent, which has a competency
generation function.
First, the DXT agent generates the task, skills, and
knowledge learning lists from keywords that are
extracted from internet resources such as open access
academic papers social networking service hashtags.
Then, the DXT agent performs graph analysis on the
lists, after which each list item is embedded as a node
in a network graph, which is the generated
competency. Simultaneously, the DXT agent collects
learning content from open education resources from
which it atomizes the micro content. Finally, the DXT
agent uses a text mining engine to construct the
learning materials that link the competency to the
micro contents.
The Quest (QST) agent handles knowledge creation
using a search process.
While the DXT agent provides the competency tasks,
skills, and knowledge, and the corresponding learning
materials to the learners, the QST agents provide the
knowledge learners need to search for the skills and
Po r tf olio
Com petency
Micro content DB
Pap ers
S- C M S
Step1 DXT agent
Step2 QST agent
Quest process
St e p 4 LA S a g e n t
Learning evaluation index output
Step3 TRK agent
Tracking learning behavior
Op en O nline Ed uc a tion
Learning Outcom e
Toward Sustainable Learning Economy through a Block-chain based Management System
learning materials. The QST agent evaluates the
relevance of the competencies using a graph search
algorithm and outputs appropriate search results that
match the learner's purpose. If a new knowledge or
skill item is discovered and is used by other learners,
it is recorded as a reputation in the portfolio of this
The Tracking (TRK) agent tracks the learning
The TRK agent tracks the learners' learning behaviors
and records the learning history, search processes,
and reputations in portfolios.
The Learning Analytics (LAS) agent outputs the
quantitative learning indices.
The LAS agent compares the learning data recorded
in the portfolio with the S-CMS competencies and
outputs the skill gap as a quantitative learning
evaluation index, which makes it possible to reflect
on the learner and automatically evaluate the
learning. The accumulated data is then used to retrain
the DXT agent to ensure the S-CMS competency is
more accurate.
9.2 Modelling Competency by Graph
Previous studies on intelligent CMS models have
employed domain ontology to define the body of
knowledge in a field (Wesselink et al., 2003).
However, in open online education, using domain
ontology for CMS competency modeling is not
suitable because of its complicated structure and the
diversity of people using the CLE. The S-CMS
focuses on the structure of competency problems and
the knowledge and skills to solve those problems and
uses a network graph to provide a visual overview of
the competencies to enable the agent training analysis
9.3 S-CMS using Blockchain in the
Learning Economy
The architecture of S-CMS is illustrated in Figure 7.
To realize the architecture, the following discussions
are important.
Adopting S-CMS into the learning economy
model will enable us to measure learning outcomes in
the marketing procedures. In S-CMS, after the DXT
agent generates the task, skills, and knowledge
learning lists, while a learner searches for knowledge
and skills using QST agent to solve a specific task,
TRK agent recorded the learner's search processes on
the blockchain along with the learner's task.
Figure 7: The S-CMS using blockchain.
The recorded learning results should be traded
between the learners in the learning market using
virtual currency or transactions. Such transactions
will be utilized as a new quality assurance method of
the learning results by the learners’ peer review.
Because the incentive in the market is highly
motivated by learners, the quality assurance to be
evaluated in the market differs in getting the quick
results. Finally, the LAS agent issues digital badges
for the learning outcome according to the market
Using the blockchain mechanism, the proposed
architecture has the scale-free features of the learning
economy. The dynamic construction of learning
economy might supress erroneous information, if
CBL models would be properly incorporated into the
learning economy.
In this paper, it has been experimentally confirmed
that in the learning economy, trading based on the
market economy results in a scale-free structure.
However, the proposed scale-free structure might
explosively generate incorrect information
(Lundqvist et al., 2008). This must also be tackled.
Using the network graph method, this paper has
analyzed the structure of current generic scale-free
competency models, and to reduce the cost of
developing competency models, proposed an S-CMS
that used network graphs and DAI
The S-CMS CBL model uses DAI to generate
competencies and allows learners to search for useful
knowledge and skills through trial and error and
discover knowledge and ideas not highlighted by
CSEDU 2020 - 12th International Conference on Computer Supported Education
As the learning economy has a scale-free
competency structure and targets a very wide range
of learning, the proposed S-CMS can construct
suitable complex competency spaces using the DAI
and network graphs that cover the entire learning
economy without the need for human intervention.
Introducing CBL into the learning economy would
also provide an effective, efficient quality assurance
system that takes less time than quality assurance
systems based on market principles that require
constant correction, which reduces the learners’ re-
learning burden when there is incorrect knowledge in
the learning market.
This work was supported in part by JSPS KAKENHI
Grant Number JP7H01844 and NII Joint Research
Grant and ROIS NII Open Collaborative Research
2019-10-1. The authors would also like to thank
Enago ( for checking the earlier
version of English manuscript.
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Toward Sustainable Learning Economy through a Block-chain based Management System