**4. Research studies supported by KnowCat**

KnowCat has been tested for more than ten years in several research studies with student communities at Universidad Autónoma de Madrid (UAM, Spain), Universitat de Lleida (UdL, Spain) and Universidad Pontificia Bolivariana (UPB, Colombia), among others. Table 1 shows a summary of the participants'communites of these research studies.

Most of these research studies have corroborated these design hypotheses of KnowCat (Alamán & Cobos, 1999; Cobos & Alamán, 2002; Cobos, 2003; Cobos & Pifarré, 2008; Diez & Cobos, 2007; Gómez, Gutiérrez, Cobos & Alaman, 2001):


These research studies took the form of longitudinal case studies conducted in authentic university environments. In order to illustrate the research methodology of these studies, an example about how the system could be used for any community is exposed:

	- a. The students were distributed into the topics that were established by the instructor in the knowledge tree. Normally, there were between five to ten students

Knowledge Crystallisation Supported by the KnowCat System 197

#PARTICIPANTS PER YEAR

DEPARTMENT, UNIVERSITY

UAM

UAM

UAM

Pedagogy and Psychology, UdL

Pedagogy and Psychology, UdL

UAM

UAM

UAM

UAM

Pedagogy and Psychology, UdL

UAM

UAM

UPB

UPB

<sup>250</sup>Computer Engineeting,

<sup>15</sup>Computer Engineeting,

<sup>40</sup>Theory of Education,

<sup>250</sup>Computer Engineeting,

<sup>90</sup>Computer Engineeting,

<sup>200</sup>Computer Engineeting,

<sup>35</sup>Chemical Engineering ,

<sup>10</sup>Computer Engineeting,

<sup>15</sup>Computer Engineeting,

<sup>40</sup>Biochemistry,

COURSE ACADEMIC

Operating Systems

Uncertain Reasoning

Mathematics for Children's Training

Psychopedagogy

Artificial Intelligence

Automata Theory and Formal Languages

Computers Systems II

Psychopedagogy Intervention in children development

Technical Office

Technology Management

Collaborative Systems

disorders

Biology for Development YEARS

1998/1999 - 2006/2007 (nine years)

1999/2000 - 2002/2003 (four years)

2000/2001 – 2004/2005 (five years)

2004/2005 - 2007/2008 (four years)

2004/2005 - 2006/2007 (three years)

2004/2005 - 2006/2007 (three years)

2005/2006 – 2006/2007 (two years)

2007/2008 – 2009/2010 (three years)

2008/2009 - 2010/2011 (two years)

2009/2010 – 2010/2011 (two years)

Table 1. Courses and participants in the realised research experiences.

Informatics I 2009/2010 20 Law,

2006/2007 26

Learning Strategies 2002/2003 31

Intervention 2002/2003 18

working in the same topic. Individually, students read some information about a specific topic course.


Some specific research studies carried out with communities at UdL have contributed the following corroborated hypotheses (Pifarré & Cobos, 2009; Pifarré & Cobos, 2010):


specific topic course.

version.

efficiently.

monitoring skills.

transcribed word by word.

study (see some examples below).

working in the same topic. Individually, students read some information about a

b. The students wrote an individual report (document) about the topic and entered it into KnowCat. These reports contained a personal reflection on the content of the

d. The document's author read the notes concerning her/his report, taking into account her/his classmates scaffolds, re-wrote the report and entered it back into

e. Finally the students voted for the best document on a topic. Moreover, they gave their opinions about the ''continuity'' and ''improvement'' characteristics of the new document versions (see Section 3.2) in order to facilitate the system the decisions over which documents had to be replaced with their new document

3. Students answered to a questionnaire about the work realised with KnowCat. In some cases, they were interviewed. To enable coding and analysing, the interviews were

4. The data analysis was generated by the instructors. Both a qualitative content analysis and a quantitative analysis were made about the students' contributions in the system. These analyses were useful in order to corroborate both the common mentioned system design hypotheses as specific ones formulated in the context of each specific research

Some specific research studies carried out with communities at UdL have contributed the

 The pedagogical application of the KnowCat system may favour and improve the development of the students' metacognitive learning processes. The content analysis of the students' interviews revealed the existence of metacognitive knowledge regarding the learning processes that students develop while interacting with KnowCat knowledge elements. Students showed high levels of consciousness about learning new strategies and about the conditional use of these strategies to solve specific tasks

 The instructional application of the KnowCat system may favour and improve the development of students' self-regulated skills. Small group interaction patterns appear while their members are working together throughout the instructional process supported by KnowCat. These interaction patters were related with an increasing number of self-regulated processes, specially planning, asking for clarification and

following corroborated hypotheses (Pifarré & Cobos, 2009; Pifarré & Cobos, 2010):

read information, or suggested a personal solution to a specific problem. c. The students read some peers' report and annotated them –i.e. by giving assistance– in order to help the fellow classmates improve on it. For each individual topic, the students were asked to annotate a minimum number of classmate's report (e.g. to write at least three notes and to write at least five assessments, these notes and assessments could be done on one or more documents). During the study, the students were strongly encouraged to annotate the reports of different classmates. In most of the research studies, the students' documents received a different number of interactions, for instance, none of the students' documents received less than three notes. In some studies, the students voted for the best document on a topic. In this way, the knowledge crystallisation

mechanisms could be started to generate an initial classification.

the system again, as a new document version.


Table 1. Courses and participants in the realised research experiences.

Knowledge Crystallisation Supported by the KnowCat System 199

votes of each type and determinates its social acceptation. A social accepted annotation can

Documents' versions receive votes about the content continuity (i.e. if the new document version deals the content of the previous one in a similar way) and the content improvement (if the new document is an improvement of the previous one). When a document version has social acceptation about the content continuity then it replaces the previous one, i.e., the new document version "consolidate". The social acceptation about the content improvement is used to calculate an specific part of the social acceptation degree of the corresponding

KnowCat has been tested for twelve years in several research studies with student communities at several universities. These research studies have corroborated the KnowCat design hypotheses and the details of these studies and their results are a great contribution of this research work (Alamán & Cobos, 1999; Cobos & Alamán, 2002; Cobos, 2003; Cobos & Pifarré, 2008; Diez & Cobos, 2007; Gómez, Gutiérrez, Cobos & Alaman, 2001; Pifarré &

The system was extended with: i) awareness services, which provide users useful information about how they are interacting with the system and supports users to be aware of their collaborative interactions with the system (Cobos, Claros & Moreno-Llorena, 2009) and ii) a motivation booster service, which provide users feedback information about its work progress in KnowCat and supports to maintain user motivation to interact with the

An interesting open research issue is the integration of KnowCat with other Web platforms or Knowledge Management systems. A first effort in this directions is an initial version of the integration of KnowCat and another Knowledge Management system called Sofia (Cobos, et.al., 2010). Sofia system provides the ability to externalize tacit knowledge, through the group storytelling approach and it has been developed at Federal University of Rio de Janeiro (Luz, et.al., 2008). This integration proposal supports both tacit and explicit knowledge management thanks to the characteristics and functionalities of both systems. Finally, the KnowCat system is in evolution, furthermore more research studies are planned, both with new user communities and with some of the communities that have used it in

This research was partly funded by the Spanish National Plan of R+D, project numbers: TIN2008-2081/TIN and TIN2011-24139; by the CAM (Autonomous Community of Madrid),

Alamán, X., Cobos, R. (1999). KnowCat: a Web Application for Knowledge Organization. In: *LNCS 1727*, Chen, P.P., et al. (Eds.), pp. 348-359, Springer, ISSN 0302-9743. Berners-Lee, T. (1996). *The World Wide Web: Past, Present and Future*. September, 2011. Available from: <http://www.w3.org/People/Berners-Lee/1996/ppf.html>. Cobos, R. Alamán, X. (2002). Creating e-books in a distributed and collaborative way. *Journal* 

*of Electronic Library on Electronic book for Education*, Vol. 20, No. 4, (May 2002), pp.

document: the part related with its evolution through several document versions.

"stay" in the knowledge area.

Cobos, 2009; Pifarré & Cobos, 2010).

system (Echeverria & Cobos, 2010).

previous academic years.

**6. Acknowledgment** 

**7. References** 

project number: S2009/TIC-1650.

288-295, ISSN 0264-0473.

On the one hand, KnowCat system has been involved since the first system version in 1999 until nowadays, due to the results obtained and the users' opinions from all the research studies. Moreover, the system was extended with new services.

Firstly, a console with awareness services was added to KnowCat (Cobos, Claros & Moreno-Llorena, 2009). These services are: brief information about registered users (what have these users done?), brief information about connected users, a radar view (where and what are the connected users doing?), participation-meter (How many times have the registered users done each task?), a fish eye view (when, where and what has each registered user done?) and a map of interaction among users in the annotating task (who has annotated the document of whom?).

These new services provide users useful information about how KnowCat users are interacting with the system. This console is shown in the bottom part ("Group Information") of the KnowCat screen. In Figure 2, we can see the participation-meter service.

Secondly, in this mentioned console a motivation booster service was added, which provide users feedback information about its work progress in KnowCat (Echeverria & Cobos, 2010).

These extensions to the system have corroborated these hypothesis related to user community feelings:


On the other hand, a prototype called Semantic KnowCat (SKC) was developed on KnowCat to investigate solutions to information overload in ICT-based systems, using knowledge management systems as a model (Moreno-Llorena & Alamán, 2006; Moreno-Llorena 2008). SKC uses for this purpose some hidden aspects of such systems, as the residual energy of their activity, and properties of both the elements and the activities involved (see the chapter about this prototype in the book).

### **5. Conclusion and future work**

In this paper, the KnowCat (acronym for "Knowledge Catalyser") system is presented. KnowCat deals with knowledge in evolution and its main contribution is a Knowledge Crystallisation mechanism, which is supported by virtual communities of experts. This mechanism maintains in the KnowCat knowledge areas the collective accepted knowledge by its user community.

There is a crystallisation process for each knowledge element that can receive user interactions in the community knowledge areas: documents, annotations, version documents and topics. In the case of documents, each document has a social acceptation degree (a value between 0-1, 1 is the maximum value), which takes into account: i) explicit received opinions (i.e. the received votes and how theses votes have been received; and the received assessments, notes and their types); ii) implicit received opinions (i.e. accesses to the document) and iii) its evolution through several document versions. A document, which have enough social acceptation during a determinate period of time, may "crystallise". Therefore, its author wil become and expert in the topic where the document is located.

The annotations receive votes, which can be "in favour" or "against" the annotation. The knowledge crystallisation mechanism calculates per annotation the number of the received

On the one hand, KnowCat system has been involved since the first system version in 1999 until nowadays, due to the results obtained and the users' opinions from all the research

Firstly, a console with awareness services was added to KnowCat (Cobos, Claros & Moreno-Llorena, 2009). These services are: brief information about registered users (what have these users done?), brief information about connected users, a radar view (where and what are the connected users doing?), participation-meter (How many times have the registered users done each task?), a fish eye view (when, where and what has each registered user done?) and a map of interaction among users in the annotating task (who has annotated the

These new services provide users useful information about how KnowCat users are interacting with the system. This console is shown in the bottom part ("Group Information")

Secondly, in this mentioned console a motivation booster service was added, which provide users feedback information about its work progress in KnowCat (Echeverria & Cobos, 2010). These extensions to the system have corroborated these hypothesis related to user

The users are aware of their participation in a collaborative work, in other words, they

 When users receive feedback information about their activities progress, then they increase their interactions with the system performing their activities in a better

On the other hand, a prototype called Semantic KnowCat (SKC) was developed on KnowCat to investigate solutions to information overload in ICT-based systems, using knowledge management systems as a model (Moreno-Llorena & Alamán, 2006; Moreno-Llorena 2008). SKC uses for this purpose some hidden aspects of such systems, as the residual energy of their activity, and properties of both the elements and the activities

In this paper, the KnowCat (acronym for "Knowledge Catalyser") system is presented. KnowCat deals with knowledge in evolution and its main contribution is a Knowledge Crystallisation mechanism, which is supported by virtual communities of experts. This mechanism maintains in the KnowCat knowledge areas the collective accepted knowledge

There is a crystallisation process for each knowledge element that can receive user interactions in the community knowledge areas: documents, annotations, version documents and topics. In the case of documents, each document has a social acceptation degree (a value between 0-1, 1 is the maximum value), which takes into account: i) explicit received opinions (i.e. the received votes and how theses votes have been received; and the received assessments, notes and their types); ii) implicit received opinions (i.e. accesses to the document) and iii) its evolution through several document versions. A document, which have enough social acceptation during a determinate period of time, may "crystallise". Therefore, its author wil become and expert in the topic where the document is located. The annotations receive votes, which can be "in favour" or "against" the annotation. The knowledge crystallisation mechanism calculates per annotation the number of the received

of the KnowCat screen. In Figure 2, we can see the participation-meter service.

manner, and they become more motivated to interact with the system.

studies. Moreover, the system was extended with new services.

feel that are working in a collaborative way.

involved (see the chapter about this prototype in the book).

**5. Conclusion and future work** 

by its user community.

document of whom?).

community feelings:

votes of each type and determinates its social acceptation. A social accepted annotation can "stay" in the knowledge area.

Documents' versions receive votes about the content continuity (i.e. if the new document version deals the content of the previous one in a similar way) and the content improvement (if the new document is an improvement of the previous one). When a document version has social acceptation about the content continuity then it replaces the previous one, i.e., the new document version "consolidate". The social acceptation about the content improvement is used to calculate an specific part of the social acceptation degree of the corresponding document: the part related with its evolution through several document versions.

KnowCat has been tested for twelve years in several research studies with student communities at several universities. These research studies have corroborated the KnowCat design hypotheses and the details of these studies and their results are a great contribution of this research work (Alamán & Cobos, 1999; Cobos & Alamán, 2002; Cobos, 2003; Cobos & Pifarré, 2008; Diez & Cobos, 2007; Gómez, Gutiérrez, Cobos & Alaman, 2001; Pifarré & Cobos, 2009; Pifarré & Cobos, 2010).

The system was extended with: i) awareness services, which provide users useful information about how they are interacting with the system and supports users to be aware of their collaborative interactions with the system (Cobos, Claros & Moreno-Llorena, 2009) and ii) a motivation booster service, which provide users feedback information about its work progress in KnowCat and supports to maintain user motivation to interact with the system (Echeverria & Cobos, 2010).

An interesting open research issue is the integration of KnowCat with other Web platforms or Knowledge Management systems. A first effort in this directions is an initial version of the integration of KnowCat and another Knowledge Management system called Sofia (Cobos, et.al., 2010). Sofia system provides the ability to externalize tacit knowledge, through the group storytelling approach and it has been developed at Federal University of Rio de Janeiro (Luz, et.al., 2008). This integration proposal supports both tacit and explicit knowledge management thanks to the characteristics and functionalities of both systems.

Finally, the KnowCat system is in evolution, furthermore more research studies are planned, both with new user communities and with some of the communities that have used it in previous academic years.

#### **6. Acknowledgment**

This research was partly funded by the Spanish National Plan of R+D, project numbers: TIN2008-2081/TIN and TIN2011-24139; by the CAM (Autonomous Community of Madrid), project number: S2009/TIC-1650.

#### **7. References**

Alamán, X., Cobos, R. (1999). KnowCat: a Web Application for Knowledge Organization. In: *LNCS 1727*, Chen, P.P., et al. (Eds.), pp. 348-359, Springer, ISSN 0302-9743.


**12** 

*Taiwan* 

**Enhancing Knowledge Management for** 

*2National Taipei University of Technology, Department of Civil Engineering* 

Chun-Sung Chen1 and Yu-Cheng Lin2

*1Ching Yun University, Department of Applied Geomatics* 

**Engineers Using Mind Mapping in Construction** 

Knowledge management (KM) is the collection of processes governing the creation, storage, reuse, maintenance, dissemination and reuse of knowledge. KM refers to the collection of processes controlling the creation, storage and usage of experience in a particular situation or problem solving context. To transfer knowledge between experienced engineers, construction professionals have traditionally used techniques ranging from formal annual meetings to face-to-face interviews. Construction KM focuses on the acquisition and

To enhance the quality of KM gained by engineers involved in construction projects, this study proposes a knowledge flow approach integrated with mind mapping to achieving KM solutions in the construction industry. Combined with web-based technology and mind mapping, this study proposes a Construction Web Topic-based Knowledge Management (CWTKM) system enabling engineers to reuse domain knowledge and experience by dynamically exchanging and managing knowledge during the construction phase of a project. In the proposed CWTKM system, the topic-based experience exchange environment in the mind map enables engineers to illustrate and share their experience with other engineers effectively. Engineers are, thus, invited to exchange and share their knowledge

By integrating web-based technology and mind mapping, engineers can obtain problem solutions and experience directly from senior engineers, decreasing the time and reducing the cost of on-the-job training. By exchanging and sharing previous experiences among engineers, similar and related experiences used to execute similar projects can clarify domain knowledge and enable the exchange of knowledge through web KM. The CWTKM system provides a web-based platform for users who can request assistance from selected or all engineers in the enterprise who have relevant experience. The user can also submit knowledge description using mind mapping in CWTKM system. Moreover, senior and junior engineers can effectively and easily exchange knowledge and experience regarding a specific aspect of their current construction project. In this study of a Taiwan construction building project, the survey (questionnaire) results indicated that the CWTKM system, integrated with mind mapping approach is effective for construction knowledge exchange

management of important issues and experience from participating engineers.

**1. Introduction** 

based on their previous experience.

and management.

