LEARNING SCENARIOS AND SERVICES FOR AN SME

Collaboration between an SME and a Research Team

Vladan Devedžić, Jelena Jovanović

FON – School of Business Administration, University of Belgrade, Jove Ilića 154, Belgrade, Serbia

Viktor Pocajt, Ksenija Nikoletić

INI d.o.o., Milana Tankosića 27, Belgrade, Serbia

Keywords: Learning organization, SME, Learning scenarios, Learning services, Collaborative learning.

Abstract: This paper proposes learning scenarios and learning services to support collaboration between Small or

Medium Enterprises (SMEs) and research teams. Successful SMEs take special care about constant

improvement of their business processes. To do this, they act as learning organizations – they acquire new

knowledge, facilitate the learning of all their employees, learn collectively, and use various approaches and

tools to support learning processes within an organization. However, competition on the market and

deadline pressures often reduce the time the employees can use to learn in their organizations. To this end,

collaboration with research teams can provide useful guidance to learning SMEs. The paper discusses such

collaboration between a specific SME and a specific research team, but the scenarios proposed can be easily

generalized to other cases of collaboration between SMEs and research teams.

1 INTRODUCTION

Intelligent Learning Extended Organization

(IntelLEO) denotes a learning community emerging

as a temporal integration of two or more different

business and educational communities and

organizational cultures (Stokić et al., 2008). The

integration happens on the grounds of common

interests of the organizations/institutions involved,

in terms of knowledge transfer and harmonization of

interests/objectives of the organizations/institutions

and their members. For example, there may be one

or two companies from industry, a university, and a

training institution. They may want to collaborate

and share business and educational efforts through

performing various vertical and horizontal learning

and knowledge-building (LKB) activities. Vertical

LKB activities are performed within the

organizations involved, whereas horizontal LKB

activities can be performed within and between the

organizations (Tuomi-Gröhn and Engeström, 2003).

The effectiveness of using the IntelLEO concept

in practice in practice is currently under study within

an ongoing international research project

(http://www.intelleo.eu/), being conducted within

the 7th Framework Programme (FP7) of the

European Commission (European Commission,

2006). The project has officially started in February

2009.

Three application cases are designed to conduct

the study, one of them being an IntelLEO involving

an SME and a university research team.

The paper focuses on this specific IntelLEO and

discuses its objectives, learning scenarios, LKB

activities, harmonization between individual and

organizational learning goals, and design issues

related to learning services envisioned to support the

learning scenarios and activities.

2 CASE DESCRIPTION

In this specific IntelLEO, the partner from industry

is INI (http://www.ini-int.com/home.aspx), i.e. its

branch from Belgrade, Serbia. INI is a successful

SME doing its business in the area of e-Engineering

and e-Manufacturing. INI's major product group is

Key to Metals, the metal properties database and

applications, fully operational on Web

(http://www.keytometals.com/). The company has

218

Devedži

c V., Jovanovi

c J., Pocajt V. and Nikoleti

c K. (2009).

LEARNING SCENARIOS AND SERVICES FOR AN SME - Collaboration between an SME and a Research Team.

In Proceedings of the International Conference on Knowledge Management and Information Sharing, pages 218-223

DOI: 10.5220/0002330302180223

 SciTePress

clients around the world. In addition, its sites offer a

knowledge base of 500+ related articles. INI's

employees and experts include metallurgists and

software developers.

The research partner is the GOOD OLD AI Lab

(http://goodoldai.org) from the University of

Belgrade, Serbia (GOOD OLD AI, for short). The

lab members focus on research related to intelligent

systems, semantic technologies, user modelling,

Web engineering, software engineering, and

technology-enhanced learning (TEL).

INI and GOOD OLD AI have already

collaborated on other projects in the past. Figure 1

illustrates INI's typical communication with GOOD

OLD AI and other parties (external communication),

as well as internal communication between INI's

employees. Here 'communication' denotes both

business-related and LKB-related communication.

Figure 1: INI's collaboration with its customers, GOOD

OLD AI, and other partners.

It is obvious from Figure 1 that all of INI's

external communication goes only through some of

the employees. It creates communication

bottlenecks. Note that most of this communication is

currently reduced to email and phone calls. Also, it

increases the intensity of internal communication

between INI's employees. Still, it further reduces the

time needed for organizational learning.

LKB activities in INI are currently performed

through:

• attending relevant seminars and conferences;

• in-house courses and knowledge building;

• collecting technical articles and publishing on

the Key to Metals sites;

• communicating with academic institutions;

• exchanging ideas with users and partners;

• implementing innovative tools and methods.

Recently conducted interviews with INI

employees revealed their common feeling that:

• formal external communication and exchange of

information in terms of LKB are more effective

than informal, ad-hoc internal communication;

• a centralized technical environment (a set of

advanced software tools) for communication

and exchange of information would produce

better and more efficient LKB results;

• subscription to information feed coming from

relevant Web sites is welcome (in addition to

already existing subscription to electronic

resources relevant for INI's work process);

• increase of awareness of available relevant

information on the Internet other than the one

the metallurgists from INI are already aware of,

as well as of awareness of new trends in the

area, is considered highly beneficial in terms of

improving the work efficiency;

• knowledge building should be more formal and

more structured, e.g. adapted seminars and

training courses focused on specific topics (but

not standard and expensive seminars);

• the ratio between guided learning and ad-hoc

learning should change in favour of guided

learning.

As a result, INI and GOOD OLD AI have

decided to team up in an IntelLEO and implement an

IntelLEO platform – a centralized technical

environment encompassing a number of LKB

services to support the above objectives, Figure 2.

Examples of such services include learning resource

provision/discovery, human resource discovery,

learning group composition, collaboration tracing,

and the like.

Figure 2: INI's collaboration with GOOD OLD AI through

the IntelLEO platform.

In addition, the platform should also encompass

some services to support harmonization of

individual and organizational learning goals.

Examples include learning path planning and

generation, organization policy handling (e.g.,

LEARNING SCENARIOS AND SERVICES FOR AN SME - Collaboration between an SME and a Research Team

219

displaying organizational rules when necessary),

selecting and filtering learning content/context

according to the organization policy, and the like.

The IntelLEO platform is supposed to reduce the

communication bottlenecks featuring current

business processes at INI (compare the directions

and the intensity of communication in Figs. 1 and 2).

Note that both LKB services and harmonization

services in this IntelLEO target not only INI

employees, but also GOOD OLD AI members. The

idea is that collaboration between INI and GOOD

OLD AI in this IntelLEO is a two-way LKB process:

• INI obtains guidance from GOOD OLD AI in

finding more easily content, tools, technologies

and human resources relevant for the company's

business (metrics, evaluations, best practices,

and the like);

• GOOD OLD AI gets access to real-world

business cases and situations where they can

apply their research results and ICT tools and

validate them in an industrial setting.

3 LEARNING SCENARIOS

In this specific IntelLEO, several learning scenarios

are envisioned and the IntelLEO platfotm is

designed to support them. Two such scenarios are

described here.

Each learning scenario, in turn, is further

analyzed by one or two more specific usage

scenarios, and each usage scenario is illustrated by a

UML use-case diagram (Fowler & Scott, 1999). In

these diagrams, use cases roughly correspond to

simple learning services that the entire IntelLEO

concept is structured about. Hence usage scenarios

actually represent specific and more complex

application services, composed by orchestrating

more atomic learning services. Application services

are specific to this IntelLEO. However, many of

their parts – the constituting learning services – are

rather general and can be used as components of

application services and learning scenarios in other

SMEs that might want to collaborate with external

research teams.

3.1 IntelLEO Pedagogical Baseline

The pedagogical baseline of all learning scenarios

presented here and of the IntelLEO concept as a

whole is based on the knowledge conversion modes

(Nonaka & Takeuchi, 1995), a handy framework for

characterizing LKB processes in a learning

organization pedagogically. In this framework, one

can speak of:

• socialization of tacit knowledge (knowledge

accumulation by individuals in an

apprenticeship manner);

• externalization of tacit knowledge into explicit

(when individuals articulate tacit concepts or

create new concepts);

• combination of explicit knowledge (primarily

group-based learning activities);

• internalization of explicit knowledge (personal

learning from activities the individuals have

participated at).

3.2 Scenario 1 –

Supporting Guided Learning

At INI, in-house seminars on specific topics are

organized for employees time after time. The

IntelLEO platform can be used to support guided

learning in this context. Occasionally, selected

employees are sent to seminars organized outside the

company. This kind of learning is welcome by INI

employees, which is a good example of

harmonization of the learners' individual interests

with organization's needs.

In-house seminars at INI are given either by an

expert from INI (i.e., a metallurgist giving a talk to

selected other employees on a topic of interest for

the learners' job responsibilities), or by an external

presenter (socialization of tacit knowledge). The

external presenter may be from any area the INI

management decides is of interest for the employees

and for the company (from various areas of

technology to language learning).

The IntelLEO platform can be used to support

this kind of learning in several ways. For example,

when a seminar is organized, IntelLEO Wikis can be

used to upload the learning resources and structure

and workout assignments and practices (learning

path creation/documentation), Figure 3. The

assignments and practices can be conducted

individually (personal learning management) or in

groups (which requires support for team

composition and collaboration, and can also benefit

from enabling social networking activities through

the IntelLEO platform). To do assignments and

practices, the learners may want to use various tools,

which makes a case for ePortfolio management.

The role of the GOOD OLD AI Lab in this

learning scenario is to recommend and possibly

provide tools to support learning activities through

the IntelLEO platform, relevant ontologies and

annotations, and support for seminar planning. For

example, working collaboratively with INI

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employees through the IntelLEO platform in other

learning scenarios, researchers from the GOOD

OLD AI Lab will learn enough about INI business

needs and interests to provide relevant information

feed through the platform. For instance, the

researchers may recommend INI employees to

attend a seminar on a topic of interest given by a

distinguished researcher or practitioner at the

University of Belgrade. Typically, there is a lot of

posts on such events that researchers are aware of,

and employees in industry are not. To some of the

INI employees, this may easily turn into a human

resource discovery case.

Figure 3: Supporting guided learning.

Examples of application services (usage

scenarios) envisioned in this learning scenario

include (but are not limited to):

• planning a seminar

• handling assignments

• specifying learning policies

• collecting and analyzing learners' feedback

• ...

The following subsections illustrate some of

them in more details.

3.2.1 Application Service 1 –

Planning a Seminar

When planning a seminar through the IntelLEO

platform, the INI employee in charge (INI) can

create a list of topics to be presented at the seminar

(Create topic list), Figure 4. On the long run, such a

topic list can be also a "wish list" of topics that INI

employees may want to attend seminars about. A

Researcher from the GOOD OLD AI Lab who

knows enough about INI business needs and

interests can post suggestion to include another topic

in the list (the Post topic use case / learning service),

or information she/he is aware of and related to an

event of interest for INI employees to attend (a

conference, another seminar, a lecture, and the like)

(Post event). Both INI and the Researcher can

browse the posts and the list of topics (View topic

list, View post). INI may decide to Update topic list

with a topic from a post, and/or contact the seminar

instructor about it (Contact instructor).

Figure 4: Usage scenario: Planning a seminar.

3.2.2 Application Service 2 –

Collecting and Analyzing Learners'

Feedback

After a seminar is over, the INI employees who

attended it (INI) may be asked to provide some

feedback about it through the IntelLEO platform

(Post feedback), Figure 5. After a substantial

feedback is collected that way, both INI and

interested researchers from the GOOD OLD AI Lab

(Researcher) can analyze the feedback collected in

order to learn more about the attendees' evaluation

of and feelings about the seminar they attended

(Analyze feedback). Specifically, they may want to

obtain and analyze statistics about the topics

covered, the learning resources used, and the

seminar instructor(s) (View topic statistics, View

resource statistics, View presenter statistics). These

can be useful indicators of the employees' individual

motivation and learning needs and objectives, as

well as indicators of the topics and instructors for

other in-house seminars to possibly run in the future.

Note that in this application/usage scenario the

Researcher is also a very active learner, since the

feedback analysis can provide a number of useful

indicators about the real-world acceptance of certain

topic and resources.

Figure 5: Usage scenario: Collecting and analyzing

learners' feedback.

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221

3.3 Scenario 2 –

Specifying Customer Profiles

It is one of INI's most important business interests to

characterize their customers precisely, in order to

attract more site visitors and registered users to

become subscribers. On the other hand, user

modelling is a topic of high interest to GOOD OLD

AI researchers. They can collaborate with INI to

devise and apply suitable user modelling approaches

to characterize INI's customers.

All registered users of INI's products have filled

the registration form. This is how their basic profile

is extracted. However, not all registered users are

subscribers to INI's products. Attracting a registered

user to become a subscriber (if this user is not a

subscriber already) implies:

• studying business interests, behavior, and

typical interactions with INI products of both

subscribers and other registered users;

• discovering differences between the two groups

of users;

• undertaking appropriate business decisions

(such as marketing campaigns) related to

registered users who did not subscribe to INI

products yet.

Currently, an INI employee can extract very few

relevant facts about INI customers from their basic

profiles and log files that track the customers'

interactions with INI products. Standard tools used

for tracking visits to INI's Web-based products, such

as Google Analytics (www.google.com/analytics/),

generate statistics about the site usage and can be

relevant for site re-design. INI also uses a

proprietary log file analysis tool, but it can discover

only statistics about specific page visits and what

data the visitors have searched for.

The IntelLEO platform enables INI employees to

learn more about user modelling, user profiling

tools, and how to apply them to their products. For

example, an INI employee in charge of taking care

of customers' profiles can consult the IntelLEO as a

"hub" for learning about user modelling (available

Web-based literature, tools, reviews, events, and the

like), Figure 6. Continuous contacts with the GOOD

OLD AI researchers will enable socialization of her

tacit knowledge. Likewise, using the experience she

acquires this way in her efforts to characterize INI

customers better, she will externalize her tacit

knowledge into explicit. It is up to the GOOD OLD

AI researchers to take care of a continuous relevant

information feed to the "hub" and structuring of this

information, thus providing a kind of guided

learning support. These researchers are especially

interested in trying out the user modelling tools they

have developed (or other tools they are familiar

with) at INI (harmonization with organization's

needs). Note that through the use of the IntelLEO

platform they can try out such tools in two ways:

• to profile (model) INI's customers, working

collaboratively with selected INI employees

(this may include selection of suitable profiling

tools, extraction of customer profiles, more

precise definition of stereotypical customers,

and the like);

• to model the INI employees involved as the

learners (of user modelling) in this BC instance.

Figure 6: Specifying customer profiles.

Examples of application services (usage

scenarios) envisioned in this learning scenario

include (but are not limited to):

• selecting profiling tools

• learning to use a profiling tool

• extracting profiles

• defining stereotypical customers

• ...

The following subsections illustrate some of

them in more details.

3.3.1 Application Service 1 –

Learning to Use a Profiling Tool

In this usage scenario, an employee from INI is

supposed to learn how to use a profiling tool

suggested by GOOD OLD AI Lab researchers,

Figure 7. Typically, such a tool will enable INI to

create a stereotype of a customer profile (Create

stereotype profile) that characterizes a new

customer. Such a tool may also support using data

mining and Web mining techniques to extract more

interesting, dynamic details about visitors of INI

Web sites (INI customers). A Researcher from the

GOOD OLD AI Lab and INI can collaboratively use

such tools to Extract customer profile, i.e. to infer

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about the customer's needs and intentions from

her/his interactions with INI Web site and products.

This requires a lot of collaborative effort; it is

necessary to View customer profile often and for

different customers, in order to Extract customer

profile in a meaningful way. To characterize a

customer better, Researcher and INI may need to

learn more about the category (group) of a customer

(a special case of View group) and about the subset

of INI tools and products the customer typically

interacts with (a special case of View portfolio).

Note that these two characteristics are of dynamic

nature and should be updated regularly.

Figure 7: Usage scenario: Learning to use a profiling tool.

4 CONCLUSIONS

The specific application services presented in this

paper are based on use cases that can be easily

interpreted as not-so-specific learning services.

When we rewrite these learning services in

sequences, it is easy to notice that they can be easily

applied to other learning scenarios and other

learning application cases as well:

• Create topic list, View topic list, Post topic,

Post event, View post, Update topic list, Contact

instructor;

• Post feedback, Analyze feedback, View topic

statistics, View resource statistics, View

presenter statistics;

• Create stereotype profile, Extract customer

profile, View customer profile, View group,

View portfolio.

Moreover, even a quick overview of these

learning services suggest means of implementing

some of them (e.g., using Social Web, user

modelling, and Web portal technologies).

ACKNOWLEDGEMENTS

This document is the property of the IntelLEO

Consortium. This document may not be copied,

reproduced, or modified in the whole or in the part

for any purpose without written permission from the

IntelLEO coordinator with acceptance of the Project

Consortium.

This publication was completed with the support of

the European Commission under the 7th Framework

Programme. The contents of this publication do not

necessarily reflect the Commission's own position.

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