Bridge the Gap of Codification and Personalization

Strategies: Gain and Lost of Knowledge Management of

Postgraduate Student Project Meetings

X. Dai

, M. Oberhagemann

, K. P. Truong

1,3

and F. van der Velde

1,2

Centre for Telematics and Information Technology, University of Twente, Enschede, The Netherlands

Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, The Netherlands

Human Media Interaction, University of Twente, Enschede, The Netherlands

Keywords: Knowledge Capturing, Collaborative Decision Making, Student Project, Human Factors.

Abstract: It is acknowledged that knowledge management (KM) brings several benefits for an organization, and two

types of knowledge management strategies exist: codification and personalization, and a gap exisits between

both strategies. In this paper, the context of KM is placed on the campus of higher education. A knowledge

capturing tool, in the form of a tablet based APP, is introduced to capture problem solving knowledge

during a student project meeting. This tool intends to propose a new way of technology support to codify

knowledge during socialization. A series of comparative experiments were undertaken to investigate the

APP’s influence on users’ meeting experiences. This paper will present results based on interviews with the

participants, which showed both positive and negative effects of the APP usage on their meeting experience.

1 INTRODUCTION

The concept of knowledge management has existed

for almost 40 years, originated from the

consultancy business in the late 1980s (Koenig and

Neveroski, 2008), theorized by Nonaka and

Takeuchi in the 1990s (Nonaka and Takeuchi,

1995). It is no doubt that a good knowledge

management strategy can foster innovation (López-

Nicolás and Meroño-Cerdán, 2011), increase an

organization’s competitiveness (Carneiro, 2000),

and promote organizational learning (Vera and

Crossan, 2003). In the last two decades, with the

fast development and vast implementation of

computer systems, IT begins and continues to

shape the knowledge management strategies. A

knowledge management strategy should to be

tailored to an organization’s specific context to

achieve its goals (Bierly and Daly, 2002). In

general, two knowledge management strategies can

be identified based on the use of IT: codification

and personalization (Hansen et al., 1999). In the

codification strategy, knowledge is extracted,

codified and stored, as a resource independent of

its creator. This process is usually done with the

help of a knowledge engineer, rather than the

knowledge creator him or herself. In the

personalization strategy, knowledge stays in the

memory of people, and it can be shared through

socialization among individuals. However, these

two strategies are not mutually exclusive

(McMahon et al., 2004), both strategies are

necessary for an organization, codification can

nurture personalization, and vice versa.

Several efforts have been made to bridge the

personalization and codification strategy together.

The ubiquitous use of computer systems in our

everyday life and work environment has drastically

changed the way we share knowledge. Many

collaborative work platforms and groupware have

been developed to support project management,

task realization and online communication, while

knowledge can be extracted from the voluminous

data generated through these platforms (Ackerman

et al., 2013) (Eseryel et al., 2002). However, face

to face meetings still play a crucial role in project

decision making (Kirkman et al., 2004), computer

mediated communication still can’t replace

physical conversation between individuals.

Recently, social computing has gained more and

more attention, it is an area of computer science

that focuses on the intersection of social behaviour

Dai X., Oberhagemann M., Truong K. and Velde F.

Bridge the Gap of Codiﬁcation and Personalization Strategies: Gain and Lost of Knowledge Management of Postgraduate Student Project Meetings.

DOI: 10.5220/0006584602470254

In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KMIS 2017), pages 247-254

ISBN: 978-989-758-273-8

and computational systems (Wang et al., 2007).

New ICT technologies result in new technology

augmented human interactions, which brings new

opportunities for knowledge codification during the

process of personalization.

In this paper, the organizational context of

knowledge management is placed into a university

campus, the target group being graduate students in

higher education. J. Duderstadt envisioned in the

late 90s that higher education would evolve into a

global knowledge industry, and future universities

should offer new opportunities of learning through

the use of information technology in the “age of

knowledge” (Duderstadt, 1997). Nearly two

decades have passed since then, and the form of

higher education nowadays is hugely shaped by IT.

Academic knowledge is being captured, structured,

represented, codified, and shared through all kinds

of computer systems: e-learning platforms, campus

wikis, academic online forums and social networks

just to name a few. However, numerous research

universities are still unable to grasp the importance

of KM (Tan, 2016). On one hand, collaboration is

regarded as the breeding base for new knowledge

in research universities, and a strategic knowledge

management approach is needed to encourage

knowledge sharing. On the other hand, the

educational domain is often engaged in a massive

and senseless duplication of effort due to the lack

of educational material sharing (Robson et al.,

2003). The few attempts that we found on

university campus knowledge management fall

exclusively into the division of codification

strategy, by implementing IT based infrastructure

within certain academic activities to facilitate

computer mediated knowledge sharing (Bender and

Longmuss, 2003) (Cain et al., 2008), or

personalization strategy, by proposing a

management policy to foster socialization among

individuals (Petrides and Nodine, 2003)

(Rasmussen et al., 2006) (Raymond et al., 2010)

(Marques et al., 2006).

This research is focused on a specific academic

activity: graduate student projects. With the trend

of project-based learning in higher education,

especially in engineering science and management

science, post graduate students are usually required

to realize several group projects with real industrial

partners. These student projects not only train

student’s working skills, but also contribute “real

world” value to their industrial partners (Hertel,

2002) (Gorman, 2010). Some of the student

projects may even lead to a real business. During

student projects, group members are involved in

complex problem solving, in which knowledge is

shared, used and learned. Therefore, these graduate

students should be considered as knowledge

workers (Nonaka et al., 1998), and their project

experience harbours valuable knowledge capital.

Student projects are usually documented in their

final project reports, which are usually a

combination of each group member’s task.

Although collaborative tools are generally used in

student projects for the main purpose of

communication and coordination, e.g. google

groups, FB messengers and what’sapp, face to face

meetings still play an important role in

collaborative decision-making (Joo and Mark,

2008), and undocumented face to face project

meetings remain a black box of knowledge.

In this paper we are going to introduce a tablet-

based APP to capture the problem-solving

knowledge produced during project meetings, in

order to codify knowledge produced through

personalization. However, this APP will change the

naturalistic communication among users, and it

may bring negative effects to the meeting

experience. A series of comparative experiments

were undertaken to study the influence of this APP

usage on participants’ perception of their meeting

experience, and this paper will present the results

based on the interviews with the users.

The paper is structured in five sections. First,

the concept argumentation-based design rationale is

introduced, and the APP interface will be

demonstrated. Second, methodology of the

experiment is explained. Third, results of this

experiment are shown. Next, the gain and cost of

this APP use for the purpose of knowledge

management are discussed. Finally, conclusions,

limitations and future research are presented.

2 ARGUMENTATION-BASED

DESIGN RATIONALE

One way to extract problem-solving knowledge

from a meeting is by analysing the meeting

transcription. The raw meeting transcription data is

a chaotic pool of dialogue among people, it is

extremely difficult for other people to make sense

of it. In order to transform the meeting data into

comprehensible knowledge, the data should be

further classified into concepts, and relationships

among these concepts need to be drawn. By doing

this, the dispersed meeting data will be structured

into knowledge networks (DAI et al., 2014).

Design rationale is originally defined as the reason

behind design decisions. From this perspective, it

can be used as a knowledge capturing structure for

the decision making process.

According to the source and goal, design

rationale can come in various forms. The ISAL

model aims to extract design rationales from design

documents (Liu et al., 2010), which consist of three

layers, namely issue, solution and artefact. The

decision rationale language (DRL) model is a

descriptive language that represents the elements

related to design decisions (Moran and Carroll,

1996). The argumentation-based design rationale

model adapts argumentation as the knowledge

representation of the design reasoning, and

argumentation is considered as the most common

form of reasoning (Toulmin, 2003), hence closest

to natural communication.

Several knowledge representation models were

developed to capture the design rationale, most of

these models are extensions of two fundamental

models, namely IBIS (Conklin and Yakemovic,

1991) and QOC (MacLean et al., 1991). These

models generally involve three major concepts:

issue, position, and argument. They are represented

in graphs, consisting of nodes as concepts and links

as relationships. The QOC model has proved to be

useful for each individual designer to clarify their

design intentions, but is unable to represent the

collaborative decision making (Lewkowicz and

Zacklad, 2000). Therefore, we choose to use the

issue based structure IBIS, since it is a flexible

structure that describes communication of the

design deliberation (Regli et al., 2000). In order to

better represent the dynamic negotiation process,

the IBIS model is further elaborated into a semantic

network as illustrated in Figure 1.

The semantic network of decision making

includes the classic IBIS model concepts: issue,

proposition (position), and argument. In order to

represent the evolving nature of issue, the

relationship “reform” is introduced between

argument and issue to indicate that issue may be

modified according to the arguments, and a new

issue may be established if the group decides to

accept the modification. Compared to the IBIS

model, the concept “decision” is added to indicate

the outcome of problem-solving. This semantic

network will be used as the knowledge structure for

meeting data.

Figure 1: The semantic network of decision making

process.

The IBIS model has been adapted by many

computer systems to capture the decision rationale,

but most of which support only computer mediated

communication. Since we want to codify directly

face to face meeting, a tablet-based APP called

MMrecord/MMreport is used in this research. It was

developed by the University of Technology of

Troyes, and can be downloaded from the APP store

on IOS devices, detailed instruction manual can be

found in the APP. They are originally designed to

record a multi-party meeting, with the function to

specify a meeting’s issue, participants, and

decisions. In this research, the interface of this APP

was arranged according to the decision-making

structure showed in Figure 1. The interface of this

APP is shown in Figure 2, as follows:

Figure 2: The user interface of MMrecord.

On the left side of the screen, a customizable list

of meeting issues is presented, on the right side are 5

interactive tags: argument, counter argument,

decision, off subject and proposition. The APP

enables the user to give one’s speech intention by

tapping on one of the 5 buttons while recording the

user’s speech. In MMreport, a meeting recording can

be generated, as well as a log file containing all the

tap events given by the users. Therefore, by using

this APP, the user is given the possibility to explain

intuitively their rationale in the form of

argumentation with a simple gesture of tapping on

the screen, and meeting data will be segmented, and

classified by these tapping events, resulting in a

more comprehensible form of meeting report that

can be shared with other users.

3 EXPERIMENT DESIGN

In order to study the influence of this technical

support on user’s meeting experience, a series of

comparative experiments were undertaken. 20

students, with an average age of 20 years (sd =1,01),

majoring in psychology from the university of

Twente were recruited for this study. They were

randomly divided into 5 groups of 4 people. Each

group was asked to go through two sessions of 30

minutes’ project meeting. In one session the

participants are required to use the APP during their

discussion, they were required to tap on the screen

button to indicate the intention of their speech in the

form of argumentation as showed above in the figure

2, in the other session the participants went through

their meeting without the APP. In order to rule out

the learning factor on the discussion topic, two

different meeting topics were assigned to the two

sessions. The first one is “design a university student

online forum for the university of Twente”, the

second one is “design a bike parking system for the

university of Twente”. Four different experiment

conditions can be identified: “with APP” and

“without APP” in terms of APP usage, “online

forum” and “bike parking” in terms of meeting

topic. In order to include all the four possible

conditions, the experiment was arranged as shown in

table 1.

3.1 Meeting Data Collection

The two meeting sessions of each group were hold at

two consecutive days, and took approximately 90

minutes each, including interviews and

questionnaires after each meeting. Each participant

signed an informed consent form before the

experiment, allowing their meeting to be registered

and used for research purposes. Then the

participants were given 5 minutes to read the project

specification, as for the APP session, a short

demonstration of the APP usage was given. The

maximum meeting time is limited to 30 minutes.

Each participant is required to wear a lavalier

microphone connected to an iPad to record his or her

speech, and two video cameras were set up in two

diagonal corners in the meeting room to record the

meeting’s audio and video in a holistic manner.

After the meeting, the participants were asked to fill

in a questionnaire, then being interviewed

individually in a random order, the interviews were

recorded with an ambient voice recorder.

Table 1: Experiment design of group meetings.

Group

Participant Session 1 Session 2

1,2,3,4 No APP

Bike

APP

Website

5,6,7,8 APP

Bike

No APP

Website

9,10,11,12 No APP

Bike

APP

Website

13,14,15,16 APP

Website

No APP

Bike

17,18,19,20

No APP

Website

APP

Bike

3.2 Interview Design

The goal of the APP is to capture the collaborative

problem solving process without hindering it.

Although literature has shown that argumentation-

based design rationale is aligned with the conceptual

collaborative problem solving process, the problem

solver may still experience difficulties in explaining

it within this process, due to the fact that interaction

with the APP may increase user’s cognitive load,

resulting in poor conversation flow. The interviews

aim to investigate the influence of the APP usage on

participant’s meeting experience. The interview

examines generally three aspects: structure of the

discussion, group communication and individual

task focus. The detailed interview question list can

be found in appendix 1.

Table 2: interview results on user’s meeting experience (concept "discussion").

Communication Content Depth Flow Focus General Structure

Group

With

app

Positive 3 0 1 0 3 0 4

Negative 2 0 3 6 9 1 11

Without

app

Positive 4 2 1 6 7 3 1

Negative 0 0 1 0 3 1 7

Group

With

app

Positive 4 1 1 3 12 4 12

Negative 1 0 0 1 1 1 0

Without

app

Positive 0 0 0 0 3 5 1

Negative 1 0 0 0 5 0 3

Group

With

app

Positive 6 0 0 2 8 5 10

Negative 1 1 0 5 2 0 5

Without

app

Positive 1 0 0 3 2 3 3

Negative 1 3 1 1 3 0 8

Group

With

app

Positive 2 1 2 2 3 2 9

Negative 0 0 1 1 3 0 2

Without

app

Positive 0 1 0 0 3 3 4

Negative 0 0 1 0 1 0 4

4 RESULTS

Due to the absence of one participant in the first

group, the two sessions of group 1 was used as a

pilot study. First, the interview recording was

transcribed into text through a speech recognizer. In

this secion, results are based on the analysis of the

interview transcription. The analysis of the interview

transcription is done according to (Baarda et al.,

2005). The analysis is based on the concept of

“grounded theory”, which means that the data is

analysed in an iterative process, to extract new

theories. First, the interview transritpion was

labelled under the main topics of this study, namely

“discussion”, “communication”, “decision” and

“cognitive load”. These concepts were used as the

first-round classification by annotators. After they

finished labelling the transcription for the first

round, the labelled transcription was refined and

modified in a second-round classification, with more

specific sub-concepts identified under each first-

round concepts. In the third round, a variation code,

ranging from negative, neutral to positive, is

attached to the labelled data. The variation code

indicates the interviewee’s opition on a specific

concept. Each time a similar opinion is found about

a specific concept, score of the related variation code

will increase by 1. Finally, a total score was

calculated for each concept by adding up the score

of variation code. Two annotators were involved in

analysing the interview data, in order to validate the

rating reliability. The Cohen’s Kappa is between

0.84 and 0.96, which represents an almost perfect

agreement between annotators (Landis and Koch,

1977).

Table 2 shows the result on the concept

“discussion”, which is further specified into 7 sub-

concepts: group communication, content of

discussion, depth of discussion, conversation flow,

user’s focus on the primary task, general impression

and structure of the discussion. Table 3 shows the

average score of each concept of the two sessions.

Both sessions of discussions are perceived as

positive in general. The general impression of

discussion without APP (positive average = 3.50) is

rated slightly higher than that with APP (positive

Table 3: comparison of the two sessions.

Communication Content Depth Flow Focus General Structure

Condition

With

app

Positive 3.75 0.5 1 1.75 6.5 2.75 8.75

Negative 1 0.25 1 3.25 3.75 1 4.5

Without

app

Positive 1.25 0.75 0.25 2.25 3.75 3.5 2.25

Negative 0.5 0.75 0.75 1 3 1 5.5

average = 2.75), whereas the discussion structure,

the task focus and group communication are

perceived better in the discussion with the APP.

The structure of the discussion was perceived

positive for the discussion with APP with an average

of 8.75 positive ratings (median = 9.5, range = 8, sd

= 3.403) and 4.5 negative ratings (median = 3.5,

range = 11, sd = 4.796) per group, whereas the

structure of the discussion without APP was rated

negative with an average of only 2.25 positive

ratings (median = 2, range = 3, sd = 1.5) and an

average of 5.5 negative ratings (median = 5.5, range

= 5, sd = 2.380) per group. The focus on the

discussion and the communication were rated

positive for both sessions, with and without APP.

However, the sessions with APP were rated more

positive for both factors. The focus on the discussion

with APP was rated with an average of 6.5 positive

ratings (median = 5.5, range = 9, sd = 4.359) and an

average of 3.75 negative ratings (median = 2.5,

range = 8; sd = 3.594) per group, whereas the focus

on the discussion without APP was rated with an

average of 3.75 positive ratings (median = 3, range =

5, sd = 2.217) and an average of 3 negative ratings

(median = 3, range = 4, sd = 1.633) per group.

The communication was rated positive for the

discussion with APP with an average of 3.75

positive ratings (median = 3.5, range = 4, sd =

1.708) and an average of 1 negative rating (median =

1, range = 2, sd = 0.816) per group. Compared to

that the communication in the discussion without

APP was rated less positive with an average of only

1.25 positive ratings (median = 0.5, range = 4, sd =

1.893) and an average of 0.5 negative ratings

(median = 0.5, range = 1, sd = 0.577) per group.

In contrast to that, the conversation flow was

rated positive for the discussion without APP with

an average of 2.25 positive ratings (median = 1.5,

range = 6, sd = 2.872) per group and only one

negative statement (average = 0.25, median = 0,

range = 1, sd = 0.5). The flow on the discussion with

APP on the other hand was rated negative with an

average of 3.25 negative ratings (median: 3; range:

5; sd = 2.630) and only 1.75 positive ratings

(median: 2; range: 3; sd = 1.258) per group.

5 GAIN AND LOST

The results above have shown that the APP usage in

a group discussion has both positive and negative

influences on participants’ meeting experience. The

problem-solving structure, group communication

and task focus were reported to be improved by the

APP, while conversation flow was reported to be

hindered by the APP.

One of the challenges of small group decision

making lies in the fact that they don’t know where to

start. The APP guides the collaborative problem-

solving process by allowing the participants to

follow a systematic discussion structure through a

intuitive tactile interface, which improves the

efficiency of decision making (Antunes et al., 2014).

The visualization of topics and issues increases the

awareness of participants, which leads to better task

focus. The participants’ perception of communica-

tion is also improved by the APP. Communication in

a project meeting is very decision oriented, the

function of communication is to share information,

coordinate conflicts towards consensus, and

argumentation offers the participants a common

framework of communication, which makes them

aware of their speech intention, leading to a more

explicit communication. One participant said in his

interview that his discussion in the second session

without the APP was improved by his experience

from the first session with the APP, he felt that he

was better at structuring the group discussion, and

more mindful of the topics that he needed to discuss.

The major negative effect of this APP is to over

burden user’s cognitive load, making them distracted

from their primary task, resulting in poor

conversation flow. According to (Watson et al.,

1988), a 20 minutes training is needed for the users

to get used to a new computer system, which is

missing in this study. Therefore, the negative effect

on conversation flow can be potentially mitigated

with training.

6 CONCLUSION

Knowledge management is a research topic that

encompasses several disciplines, from management

science to computer science, from economy to

anthropology. Although the conceptual framework

of KM has been developed extensively in the last

two decades, relatively few empirical

implementations of KM were discussed. In this

paper, KM is placed in the context of higher

education. It is argued that graduate students should

be regarded as knowledge workers, and their student

project experience harbours valuable knowledge, for

both pedagogic and entrepreneurial purposes. A

knowledge capturing tool is introduced to capture

project meeting knowledge during the meeting

process, and a series of comparative studies were

undertaken to investigate its influence on user’s

meeting experience. Results based on interviews

with the participants showed that the tool usage

improved the structure of problem solving, group

communication and task focus, but hindered

conversation flow. Participants also reported that the

tool usage trained them at better structuring their

problem solving.

This study is limited by its small number of

subjects, and subjects’ prior knowledge on the

discussion topic may also influence their meeting

experience. In the following research, more meeting

data will be collected. More subjects are needed to

mitigate the influence of their prior knowledge, and

a long term experiment is needed to examine the

learning effect of the tool usage.

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APPENDIX 1

Interview 1 (meeting with APP)

What do you think about your discussion?

• What do you think about the structure of

your discussion?

• What do you think about the structure of

your communication?

• How would you describe the focus on the

defined issue in the group?

• What do you think about your decision(s)?

What do you think about the communication?

• Do you think the app influenced the

communication? How?

• How did the app influence your focus on the

discussion?

• How did the app influence your focus on the

communicational aspects that are pointed

out by the app (buttons)?

Do you have any other comments or questions?

Interview 2 (meeting without APP)

What do you think about your discussion?

• What do you think about the structure of

your discussion?

• What do you think about the structure of

your communication?

• How would you describe the focus on the

defined issue in the group?

• What do you think about your decision (s)?

Do you have any other comments or questions?

Interview 3 (comparing the two sessions)

What are the differences you perceived,

comparing the decision making without the app to

the decision making with the app, regarding to the

discussion/structure/workflow/decision?

Do you have any other comments or questions?