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Using AI to Decrease Demand and Supply Mismatch in ITC Labour Market

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In information technology and communication (ITC) industry the technology advances are unexpected and moving mysterious ways causing significant mismatch between demand and supply in labour market. To some extent the mismatch is due to emergence of new technologies replacing the old ones. On the other hand, it is also due to lack of capability and capacity of educational system to provide up to date and spot on graduates. There are several attempts to bridge the gap between supply and demand, yet both public and private sector had at least partially failed in the task. This paper presents a novel AI driven modus operandi that provides students or persons already in labour market a way to match one’s competencies against existing or future competency requirements, and thus being valid employee or applicant. On the other hand, the service discussed in the paper is also tool for giving input for forecasting future labour needs. And the third, the service also serves as mid- and long-range planning apparatus for education provider when making decisions on what academic modules best serve the needs of society and individuals. The paper presents the concept of technology and functionalities as well as roadmap for developing and implementing the service. The presented concept is heuristically proofed in expert evaluation. Moreover, the heart of the service has been technically and functionally tested in several cases. At the conclusion the paper presents model for matching system for labour competency development. The issues related to implementation and the next steps are also discussed.
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Using AI to Decrease Demand and Supply Mismatch in
ITC Labour Market
Jussi Okkonen
1
,*, Harri Ketamo2, Hanna Lindsten1, Teemu Rauhala1,
Jarmo Viteli1
1 Tampere University, Faculty of Information technologies and Communications Sciences,
FIN-33014 Tampere University, Finland
{jussi.okkonen, hanna.lindsten, teemu.rauhala, jarmo.viteli }@tuni.fi
2 HeadAI, Rautatienpuistokatu 7, FI-28130 PORI, Finland
{harri.ketamo }@headai.com
Abstract. In information technology and communication (ITC) industry the
technology advances are unexpected and moving mysterious ways causing sig-
nificant mismatch between demand and supply in labour market. To some ex-
tent the mismatch is due to emergence of new technologies replacing the old
ones. On the other hand, it is also due to lack of capability and capacity of edu-
cational system to provide up to date and spot on graduates. There are several
attempts to bridge the gap between supply and demand, yet both public and pri-
vate sector had at least partially failed in the task.
This paper presents a novel AI driven modus operandi that provides students
or persons already in labour market a way to match one’s competencies against
existing or future competency requirements, and thus being valid employee or
applicant. On the other hand, the service discussed in the paper is also tool for
giving input for forecasting future labour needs. And the third, the service also
serves as mid- and long-range planning apparatus for education provider when
making decisions on what academic modules best serve the needs of society and
individuals. The paper presents the concept of technology and functionalities as
well as roadmap for developing and implementing the service. The presented
concept is heuristically proofed in expert evaluation. Moreover, the heart of the
service has been technically and functionally tested in several cases. At the con-
clusion the paper presents model for matching system for labour competency
development. The issues related to implementation and the next steps are also
discussed.
Keywords:: Labour market mismatch · Natural language· Digital twins ·
Competency development
1 Motivation for developing and researching the service
Extensive use of educational technology, especially digital learning environments,
digital curricula, and digital managerial systems have brought about need for analytics
to monitor the use of the learning environments, the learning itself, and most im-
portant the individual competency development. Technology that is more sophisticat-
ed is provided by education technology industry to better serve teachers, individual,
end users, as industries and primary customer. There are four main trends in enhanc-
ing teaching and learning practices. On micro level (learning event), analytics is for
assessment of achieving certain goals. On meso level (subject level), i.e. implement-
ing the curricula analytics are for achievements, adaptivity and general assessment.
Macro level analytics is for promoting management by knowledge, risk assessment
and measuring KPI’s on different levels. The fourth trend is compliance, privacy, and
security issues on the level mentioned and can be considered as the primary prerequi-
site for utilising learning analytics.[1]
In information technology and communication (ITC) industry the technology
advances are unexpected and moving mysterious ways causing significant mismatch
between demand and supply in labour market. To some extent the mismatch is due to
emergence of new technologies replacing the old ones. On the other hand, it is also
due to lack of capability and capacity of educational system to provide up to date and
spot on graduates. There are several attempts to bridge the gap between supply and
demand, yet both public and private sector had at least partially failed in the task.
Universities are on the edge of education paradigm shift as labour market is not inter-
ested graduates with 3-year bachelor or 5-year master degrees, but they are actively
seeking competencies to meet changing needs. Moreover, the shift from studies and
labour is made more flexible and therefore also employees are actively seeking up-
dates for their skillsets.
This paper presents a novel AI driven modus operandi that provides students or
persons already in labour market a way to match one’s competencies against existing
or future competency requirements, and thus being valid employee or applicant. On
the other hand, the service discussed in the paper is also tool for giving input for fore-
casting future labour needs. And the third, the service also serves as mid- and long-
range planning apparatus for education provider when making decisions on what aca-
demic modules best serve the needs of society and individuals. The paper presents the
concept of technology and functionalities as well as roadmap for developing and im-
plementing the service. The presented concept is heuristically proofed in expert eval-
uation. Moreover, the heart of the service has been technically and functionally tested
in several cases. At the conclusion the paper presents model for matching system for
labour competency development. The service contributes practical work in education
and competency development micro-level and macro-level.
2 Key functionalities and technology
The Service consist of three layers. The first user interface layer is for user manage-
ment and access. The second layer is actual user interface for utilising service's ana-
lytical capacity, seeking for personal development scheme and putting input for anal-
ysis. The third layer is the data processing layer and the heart of the service. Figure 1
summarises the key functionalities and relations.
Figure 1: Outline of the service
Most of the natural language processing is currently done with machine
learning algorithms focusing on multidimensional classification and/or grouping. That
makes such applications narrow, dependent on training sets, and so makes cognitive
reasoning, predictive analytics and explainable decision making very challenging.
Furthermore, the privacy/security concerns related to black boxes are enormous.
Digital Twins are digital replicas on e.g. objects, entities or knowledge. Idea of Digi-
tal Twins raises from industry, but they have been applied in multiple contexts outside
industry. Digital Twins serves as prepared and cleaned data layer that enables cogni-
tive, predictive and explainable operations for next layer algorithms.
In this study, the strengths of unsupervised learning and reinforcement learning is
brought together in order to build language based digital twins on knowledge do-
mains. Digital Twins serves as prepared and cleaned data layer that enables cognitive,
predictive and explainable operations for next layer algorithms.
Digital Twins on i) job demand, ii) person’s current skills and iii) course offering are
demonstrated as real world examples. Training data consist of +10000000 job open-
ings, relevant news and curriculums/syllabuses. When also individual’s competences
are modelled as Digital Twin, algorithms can set up learning goals relevant for career.
Because the course offering (online, onsite, P2P) is modelled AI and construct a
learning pathway relevant for career. [2,3]
From company point of view Digital Twins can be used to strengthening
both career planning and recruiting pipeline. In national level Digital Twins help in-
dustries to get optimal talent (minimum training time) and training companies / uni-
versities to build optimal curriculums (for companies and individuals). Figure 2 pre-
sents general outline of AI powered core technology.
Figure 2: Outline of the core technology
In terms of anonymising data [4], digital twins can be used to ensure only
meaningful words will be included into anonymised data. In other words, we con-
struct Digital Twin on medical record by using e.g. general ontology and medical
ontology in selected language. Such Digital Twin will be based on only meaningful
set of verbs and substantives (including their derivatives and compound forms if
meaningful) and so basically every sentence is ‘subject-predicative-object’ type of
sentence. No identities will be passed to Digital Twin, because general ontology and
medical ontology do not have general identities inside the model. However, some
identities, like ‘Alfred Nobel’, are there, but such identities’ meaning is based on
compound form of the word and no ‘Alfred’ nor no ‘Nobel’ will be passed to model.
In general, if identity is not in encyclopedia, it is safe.
There is naturally trade off. In this case trade off is the details. After Digital Twin on
medical record is built, lot of details that raw information had, is cleaned out. Finally,
if we need to make maximum safe anonymised data, at least two different methods
should be used. By combining Headai Digital Twin -model and traditional supervised
machine learning anonymization we can be sure we have more secure data that with
only one method.
In general, even though in national and regional level we should consider
skills as economical data, in individual level it is confidential information like health
records. Not necessarily the skills person has, but what the person is missing com-
pared to group averages. That makes anonymization crucial in this kind of approach-
es. To enable economical analysis without risking individuals’ privacy.
As stated above, the service utilises novel way to approach competency development
on individual and company level. At this point the feasibility of the technology is
already known and the actual implementation will start. The following section pre-
sents the roadmap for implementing the service.
3 Roadmap for building the service
The service is implemented to Tampere University web environment and the user
interface follows the graphical appearance of any other service provided by the uni-
versity. However, the actual user interface is adaptation of Microcompetencies by
HeadAI and it is individual setup to university context. This step also consists of read-
ing supply of academic modules from the university databases.
The second phase of the implementation is to build individual profiles for
any user. This requires separate software component and it is for reading data of
course completition from the university database as well as other relevant data on
completed degrees or professional development courses. In addition to that the con-
sent by individual for utilising data possessd by university is also needed at this point.
This considers especially alumni, since there is already existing body of data based on
their prior activities.
The third phase is the mapping, recognition and definition of skills needed by
the companies. These are also translated to educational needs and operated into aca-
demic modules. It starts with mapping the demand for labour and acknowledging
competency gaps. Demand information brings about the critical competencies and
shed light on the rivalry on competent employees. On the other hand, there should be
also mapping on the supply. Especially what is delivered by the educational system,
i.e. what academic and professional development modules are available. The mis-
match is visualised in the user interface for supporting decision making.
The fourth phase is to enable company specific competency models for creat-
ing development schemes. This is done by refining the data provided by the compa-
nies. Also, the stakeholder data is utilised in this phase. The refinition is based on text
form data entered to system. This data will be also augmented by data gathers from
national sources consisting of for example trend analyses, macro level news, and in-
vestment data. The augmentation enables forecasting development trends and foresee
the change in needed academic modules.
The fifth phase is screening and picking academic modules to meet company
requirements as well as individual needs. It depends on the user what modules, pro-
grams or professional development schemes are highlighted. Individuals wil get more
precise recommendations to match their competency gaps, yet companies receive
general recommendations according to their own development avenue. The compe-
tency development is based on the topics university already provides.
The sixth phase is composing development scheme for complementing aca-
demic modules. This phase is data driven and it is extension of the labour market
demand analysis executed before. This requires also dialogue with existing supply of
academic modules. This functionality is aimed for the developing education and can
be considered as a positive externality of running the service.
The seventh phase in implementing the service is to provide individuals personalised
analysis on their labour market fit. This applies to all individual using the service,
despite if they are students, alumni, or external users. By the personalised analysis
one’s labour market position is recognised and personal competency development
scheme is outlined. By aggregating this data, a holistic view on labour supply and
demand is made.
The service provides for individual, company, industry and university. The
motivation for individual to participate and surrender personal data is based on accu-
rate professional development or even early stage guidance to specialise in the stud-
ies. Companies benefit of complementing their company specific competency devel-
opment by having reflected their position to general development of the industry as
well as seeing the feasibility of new technologies labourwise. The industry benefits
for bridging the competency mismatch gap and proving educational system input on
future needs for academic modules. For university this provides critical market infor-
mation and helps in development activities of education.
4 Discussion
The presented service is based on design and heuristic testing of concept. It will be
implemented during spring 2019 and it is fully operational in spring 2020. Since the
technology utilised in the implementation is existing and requires only little tailoring
to meet the requirements the further work is about validation of the concept, operation
model and arranging the setup for meeting external requirements. There are at least
two major issue to be contemplated. The first issue is the ethical considerations about
using the service, and the second one is the validation of the service.
The ethical considerations are issue on the perspective of individuals, and on the
other hand on the perspective of the labour policy. The individual perspective is about
privacy and security as well as maintaining control on individual. The privacy is quite
easily tackled if there is responsible service provider and sufficient guidelines to use
the services. The security is issue as it is with any data repository containing personal
information. The control issue is the most complex one, since using the service indi-
vidual surrenders control, at least to some extent. The control on self-regulation and
decision power on own career is in flux. Moreover, the revealed competency gaps are
efficiently recognized and communicated also to employer.
The labour policy perspective is not straight forward either. By using service to an-
alyse the trends and setting development agendas it makes decision making easier, yet
it might make long range planning more difficult as it might attract to offer academic
modules that serve short-term needs. However, this is the issue of sound judgement
and therefore beyond the scope of the development and research endeavour.
The validation of the service is issue of several open questions. The first one is that
is a (simple) market test sufficient way to validate the concept? As brought about
previously, the technical issues are solved and now the implementation is more about
integration and setting the operational limits. This tends to suggest that concept is
already validated by a market test and assessment should go further. The next step is
user experience research in wild with various user groups. Most evident are compa-
nies, students, and administrative users. The second tier of users are the key stake-
holders, yet their insights are needed too. What is needed along the user experience
research is the proof for validity of actions, say assessment of the effect on labour
market, effect on individual’s labour market position, assesment of competency de-
velopment in industry, and the effect on development of industry locally. This is de-
rived straight forward the presumed cause and effect justification, yet there are several
sub-studies to be conducted. In near future the next steps will consist of implementing
and testing the service as well as recruiting users.
References
[1] Okkonen, J., Helle, T., Lindsten, H. (2020) Expectation differences between students and staff of using learning analyt-
ics in Finnish universities. In Proceedings of ICITS2020
[2] Ketamo, H., Passi-Rauste, A., Vesterbacka, P. & Vahtivuori-Hänninen, S. (2018). Accelerating the Nation: Applying
AI to scout individual and organisational human capital. In proceedings of ICIE2018 International Conference on In-
novation and Entrepreneurship, March 5th-6th 2018, Washington DC.
[3] Ketamo, H., Moisio, A., Passi-Rauste, A. & Alamäki, A. (2019). Mapping the Future Curriculum: Adopting Artificial
Intelligence and Analytics in Forecasting Competence Needs. In Sargiacom, M. (Ed.) Proceedings of the 10th European
Conference on Intangib les and Intellectual Capital ECIIC 2019, Italy, 23-24 May, pp. 144-153. ISBN: 978-1-912 764-19-8
ISSN: 2049-0941
[4] Alamäki, A., Aunimo, L., Ketamo, H. & Parvinen, L. (2019). Interactive Machine Learning: Managing Infor-
mation Richness in Highly Anonymized Conversation Data. In L.M. Camarinha-Mat os, H. Afsarmanesh & D. Antonelli
(Eds.), Collaborative Networks and Digital Transformation. The Proceeding of 20th I FIP WG 5.5 Working Confere nce on
Virtual Enterprises, PRO-VE 2019, pp. 173-183
ResearchGate has not been able to resolve any citations for this publication.
Chapter
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The aim of this paper is to examine and discuss the dissonance between expectations and hopes towards utilising learning analytics in Finnish universities. The analysis is based on data collected among Finnish university students and staff in spring 2019. As a key result we present, that the university staff found it important that all data and information should and could be used for various planning, management and counselling purposes. At the same time student found it unnecessary or even harmful to allow university staff examine their personal data. We therefore propose that universities should develop and implement specific policy for using of analytical data.
Accelerating the Nation: Applying AI to scout individual and organisational human capital
  • H Ketamo
  • A Passi-Rauste
  • P Vesterbacka
  • S Vahtivuori-Hänninen
Ketamo, H., Passi-Rauste, A., Vesterbacka, P. & Vahtivuori-Hänninen, S. (2018). Accelerating the Nation: Applying AI to scout individual and organisational human capital. In proceedings of ICIE2018 International Conference on Innovation and Entrepreneurship, March 5th-6th 2018, Washington DC.
Mapping the Future Curriculum: Adopting Artificial Intelligence and Analytics in Forecasting Competence Needs
  • H Ketamo
  • A Moisio
  • A Passi-Rauste
  • A Alamäki
Ketamo, H., Moisio, A., Passi-Rauste, A. & Alamäki, A. (2019). Mapping the Future Curriculum: Adopting Artificial Intelligence and Analytics in Forecasting Competence Needs. In Sargiacom, M. (Ed.) Proceedings of the 10th European Conference on Intangibles and Intellectual Capital ECIIC 2019, Italy, 23-24 May, pp. 144-153. ISBN: 978-1-912764-19-8 ISSN: 2049-0941