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

Intelligence.

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

learners.

1 INTRODUCTION

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

https://orcid.org/0000-0003-3797-9008

https://orcid.org/0000-0003-0843-8325

https://orcid.org/0000-0002-1877-3517

https://orcid.org/0000-0001-8950-144X

https://orcid.org/0000-0002-2364-6950

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

430

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

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

people.

2 DECENTRALIZED

EDUCATION

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.

3 LEARNING ECONOMY

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

circulated.

4 LEARNING ECONOMY

CHALLENGES

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

assurance.

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

431

economy concept, which guarantees the learning

quality as quickly as possible.

5 COMPETENCY

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.

6 COMPETENCY BASED

LEARNING

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.

Behavioristic

approach

Generic

approach

Comprehensive

approach

Abilities

target

Competencies

and tasks

required to

perform duties

Competencies

and tasks

required for

all

occupations

Competencies

for cultivating

qualities and

personalities

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

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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.

7 CMS

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

automated.

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

Definition

Clarify goals, identify and

define required

competencies

Model

Assemble competencies

for each learner and

organization attribute,

such as department, job

title, and characteristics

Learning method

Learn based on

competency.

Evaluation

The degree of attainment

of competencies

Component

Description

8 NETWORK GRAPH

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.

Task

Ability, Skill and Knowledge

Technique

for grasping

existing

software

Acceptance

support

Educational

methods

Testing

Software

quality

Training

IT k now led g e

Software

utilization support

System

development

approach

Ap p lication

design

Software requirements

definition

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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

(https://www.ipa.go.jp/english/humandev/icd.html),

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

organization

(Middle classification) Business of the

organization

(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

hierarchies:

(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

TASK

KILL

Tas

Skill

ill

Knowledge

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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

relationships.

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 SMART CMS

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

Tas

Skill

ill

Knowledge

Po r tf olio

Com petency

Learning

Micro content DB

Open

Access

Data

Academic

Pap ers

Web

Learner

Learning

Evaluate

S- C M S

Task

Knowledge

Task

Learner

Step1 DXT agent

Competency

generation

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

CBE

Op en O nline Ed uc a tion

Learning Outcom e

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435

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

learner.

The Tracking (TRK) agent tracks the learning

behaviors.

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

Structure

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

technology.

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

evaluation.

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.

10 CONCLUDING REMARKS

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

experts.

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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.

ACKNOWLEDGMENT

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 (www.enago.jp) for checking the earlier

version of English manuscript.

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