Computers in Human Behavior Computers in Human Behavior 21 (2005) 29–44 www.elsevier.com/locate/comphumbeh
Developing a Web assisted knowledge construction system based on the approach of constructivist knowledge analysis of tasks Shu-Sheng Liaw
*
General Education Center, China Medical University, 91, Shiuesh Road, Taichung 404, Taiwan, ROC Available online 24 July 2004
Abstract The purpose of this study is to develop a Web assisted knowledge construction (WAKC) system as an individual knowledge construction tool for Internet users. The system is based on the theory of constructivist knowledge analysis of tasks (CKAT). The CKAT integrates constructivist reflection cycle and knowledge analysis of tasks. The conceptual model of CKAT includes four different stages: knowledge objective, knowledge gathering, knowledge analysis, and task knowledge structure. In order to match these four stages, this research designs an assisted knowledge construction system that includes four systematic sub-functions: the keyword function, the URL resource function, the analysis function, and the construction function. After understanding usersÕ perceptions toward the WAKC system, users have highly positive behavioral intention to use the system as a Web-based assisted knowledge construction tool. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Knowledge analysis of tasks; Constructivist reflection cycle; Search engines; Knowledge construction
1. Introduction The idea that knowledge is the most valuable source of competitive advantage has been widely considered for years. Although no clear consensus has yet emerged on the most appropriate definition of knowledge, it can be seen as the capacity, em*
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bodied in the brains of people and embedded in social practice, which interprets and transforms information (Davenport & Prusak, 1998). In other words, knowledge is not only context-specific and relational, but also connected to human action as it interprets, transfers, and constructs information into knowledge (Nonaka & Takeuchi, 1995). Kang and Byun (2001) stated that knowledge is the product of a learning activity in which a learner, based on experience acquired through cognitive activities (such as perception, interpretation, and analysis), assimilates and accommodates information into his/her cognitive structure in accordance with the environment as he/she understands it and in collaboration with other people. Under the cognitive perspective, knowledge is the advanced stage of information. It means that information represents the fundamental basis of knowledge and is directly associated with the facts of the real world. Information needs to be interpreted, processed, and constructed to form humanÕs knowledge. Nonaka (1994) divides knowledge into two parts: explicit knowledge that can be easily expressed in words and numbers; and tacit knowledge, such as bodily skills and mental models, that is not articulated. Although Web applications are popular today, the primary use of the Internet, other than e-mail, is to use Web search engines as a knowledge retrieval tool. Various search engines have been developed for helping users to look for online information, but there is just too much information on the Web. From the Google (http:// www.google.com/) search engine, there are over three billion Web pages on the Internet. Though it is fair to say that Web information retrieval would collapse if search engines were not available on the Internet, the issues of helping users to find their needed information and assisting them to construct their knowledge from the Internet remain critical. Based on the approach of constructivist knowledge analysis of tasks (CKAT), the current study is to develop a Web assisted knowledge construction (WAKC) system to help individual Internet users when they search for knowledge with search engines. In this paper, the types of search engines will be introduced first. Then the approach of CKAT will be modulated. The third section presents a WAKC model that is based on CKAT. And the last section analyzes usersÕ attitudes toward the WAKC system.
2. Types of search engines Search engines have three major functions: First, they gather a set of Web pages that form the universe from which a searcher could retrieve information; second, they represent the pages in this universe in a fashion that attempted to capture their content; and third, they allow searchers to issue queries by employing information or knowledge retrieval algorithms that facilitate the search for the most relevant pages from this universe (Gordon & Pathak, 1999). Although various search engines have similar search functions, each of them has its own unique database and search methods. Regarding search types, search engines adopt from three basic paradigms, directory-based services, query-based, and filter-based search engines. Directory-based services, such as Yahoo! (http://www.yahoo.com) for the general purpose or MedWeb (http://www.medweb.emory.edu/MedWeb), Medengine (http://
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www.themedengine.com), and Medical Matrix (http://www.medmatrix.org) for medical use, provide a hierarchical organization of resources, most often developed by human cataloguers who select, index, and annotate links (Callery & TracyProulx, 1997). Careful organization of resources present directory in services enables rapid discovery and browsing of resources by topic or category a more intuitive mode of access than keyword selection and query refinement for users (Dempsey, Vreeland, Sumner, & Yang, 2000). In contrast to directory-based services, querybased search engines, such as Excite (http://www.excite.com), Altavista (http:// www.altavista.com), and Google for general purpose or med411 (http:// www.med411.com/), MedExplorer (http://www.medexplorer.com), Medscape (http://www.medscape.com), and HON (http://www.hon.ch) for medical needs, provide broad coverage of the Web through intensive automation of the indexing and retrieval processes. These services construct databases from robotic collection of remote Web pages and rely primarily on textural input from the user to match a request with a set of Web links. The filter-based search engine is a kind of agents that integrated various search engines. In essence, filter-based search engines usually are built for specific purposes, such as for medical or business application. Filter-based search engines, such as MediAgent (Bin & Lun, 2001), MARVIN (Multi-Agent Retrieval Vagabond on Information Network) (Baujard, Baujard, Aurel, Boyer, & Appel, 1998), OMNI (http://omni.ac.uk), MEDBOT (http://medworld.stanford.edu/ medbot), and HONselect (http://www.hon.ch/HONselect) (Boyer, Baujard, Griesser, & Schrrer, 2001) for the medical purpose, potentially lead to better retrieval and outcomes. With the growth of the Internet, there are continued changes in the types, features, and functions of search tools, capturing our attention and interests.
3. Knowledge construction Regarding knowledge, it has become the preeminent economic resource that is more important than automobiles, oil, steel, or any of the products of the Industrial Age; thus, the emergence of knowledge-intensive products and services increases amount of information and knowledge retrieval in the Internet Age. The proliferation of the Internet and the emergence of knowledge-based society are accelerating the need for a flexible and generative knowledge construction system. The methodology of knowledge construction has changed from its traditional methodology of non-digital accumulation to a new and popular finding method of digital accumulation that takes place via the Internet and the World Wide Web (WWW/Web). Knowledge can be divided into two kinds of formats: explicit knowledge and tacit knowledge. Explicit knowledge can be expressed in words and numbers, as well as distributed as data, scientific formulae, product description, basic principles, and so on. In other words, explicit knowledge is easy to transmit in definite and organized forms. It can be easily managed on a computer, communicated via a network, and stored in a database (Trentin, 2001). In contract, tacit knowledge is highly personal as well as hard to define and express. In addition, it is also difficult to communicate and share tacit knowledge with others because it embraces individual perception,
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intuition, and foresight. Tacit knowledge is firmly rooted in personal experience, as are oneÕs ideals, values, and emotions. 3.1. Task knowledge The term ‘‘task’’ can be defined as a specified objective, undertaken as part of an educational course or at work (Long & Crookes, 1992). Long and Crookes (1992) further distinguish between target tasks and pedagogical tasks. Target tasks are realworld tasks for which learners should acquire both linguistic and sociolinguistic competences. They focus on authentic language use for a specific purpose and are derived from a need analysis. In general, reading a technical manual, solving a math problem, reporting a chemistry experiment, taking lecture notes, or even buying a train ticket are examples of target tasks. Pedagogical tasks are derived from target tasks. In other words, they consist of one or more skills or knowledge components represented by a target task (Wenden, 1995). For instance, taking lecture notes requires learners to recognize different discourse types, that is they should be able to identify relevant cues and use techniques for recording, analyzing, and organizing information. Pedagogical tasks refer to problem posing activities; at the heart of a task, there is a learning problem or a communication problem. Pedagogical tasks can vary from the simple to the complex; they can also have a learning goal as well as a communicative goal. Task knowledge refers to what teachers know and what learners need to understand about the purpose of a task, its demands, and a determination of the kind of task it is (Wenden, 1995). Additionally, task purpose refers to the outcome of a pedagogical task. It is what the teacher expects students to learn. From the viewpoint of learners, knowledge of task purpose is their perceptions of the learning needs which the task intends to meet, and a basis for determining its relevance. Knowledge of task demands is commonly known as domain-specific knowledge and it refers to the knowledge (or skills) that is necessary to do the task and the ways of doing it. Determining the kind of task means that learners classify a learning activity. When classifying a pedagogical task, learners seek connections between prior learning tasks and a current one. They try to identify the nature of the problem posed by the task by comparing it with others that they are familiar. 3.2. Task knowledge construction Developing an effective knowledge construct methodology is a crucial issue for using search engines as a knowledge construction tool. This research develops a knowledge construction conceptual model that integrates knowledge analysis of tasks (KAT) (Johnson & Johnson, 1992) and constructivist reflection cycle (CRC) (Oliver, 2000). Based on theoretical research, the conceptual model, called CKAT, is expected to be an effective methodology for constructing knowledge for Internet users. Constructivism is a theory of learning that describes how individual minds create knowledge or how individual knowledge structures their deeper conceptual understanding. A CRC (Fig. 1) is included: individuals express their conceptions of ideas
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Expr ess
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Revise
Reflect
Fig. 1. Constructivist reflection cycle.
or their mental models, reflect on feedbacks about their ideas, and revise initial conceptions to account for new expressions (Oliver, 2000). Essentially, reflection allows individuals to have opportunities to modify misconceptions or improve inadequate understanding. KAT develops structures to represent the knowledge that individuals require to perform a particular task. KAT can be divided into four stages: knowledge objective, knowledge gathering, knowledge analysis, and task knowledge structure. The stage of knowledge objective is to create domain boundaries and helps individuals to ensure that attention is concentrated on relevant activities. As for the stage of knowledge gathering, it is to find relevant information or knowledge. The stage of knowledge analysis is based on individual experience that decides on what the demanded knowledge is. The results of knowledge analysis are used to produce a model of tasks in terms of task knowledge structure, which represent the knowledge that individuals possess about the tasks they perform (Uden & Brandt, 2001). The stage of task knowledge structure is acquired through learning and previous task performances, and are dynamically structured into meaningful units in memory. In CKAT (Fig. 2), the first stage is knowledge objective for establishing a taskÕs domain boundaries and for helping to ensure that attention is concentrated on the most critical and relevant activities. Once the first stage has been accomplished, knowledge gathering can begin. The principal inputs to the second stage are knowledge objective itself and the knowledge gathered through search tools. The major outputs of the second stage are a preliminary picture of the domain knowledge expressed in terms of URLs (Universal Resource Locators), titles, percentages of relevance, brief descriptions, and keywords all intended to describe the Web pageÕs contents. The third stage, knowledge analysis, takes the outputs of the second stage and analyzes each in terms of its correlation with knowledge objective (the first stage). The final stage is to construct the task knowledge structure. In CKAT, the task knowledge structure includes three components: the task category, the aspect of task knowledge, and the URL. Users may input task categories to systematically construct knowledge. In addition, they may input abstract to note aspects of knowledge. Furthermore, the URL is a subordinate level that links users to a particular instance of the objective. In essence, the human mind is similar to a computer process which explains psychological events in terms of input, storage, and output. Based on the
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S.-S. Liaw / Computers in Human Behavior 21 (2005) 29–44 Reflect Knowledge objective Revise
Express
Reflect Knowledge Revise
gathering
Express
Reflect Knowledge Revise
analysis Express
Reflect Task Revise
Knowledge
Express
structure
Fig. 2. Constructivist knowledge analysis of tasks (CKAT).
information processing point of view, individual knowledge construction includes four different stages: information definition, information acquisition, information transformation, and knowledge construction (Gagne, Yekovich, & Yekovich, 1993). The information definition stage is similar to the stage of knowledge objective. In this stage, the purpose of knowledge construction is to define and clarify the needed knowledge. In other words, the needed knowledge should be defined and confirmed at first before an individual establishes new knowledge. The information acquisition stage is equal to the stage of knowledge gathering, in which an individual expresses
S.-S. Liaw / Computers in Human Behavior 21 (2005) 29–44 The CKAT conceptual model
Knowledge objective
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The Web assisted knowledge system The keyword function: Includes the Internet search, database search, and integrated search sub-functions.
Knowledge gathering
Knowledge analysis
Task Knowledge Structure
The URL resource function: Includes the URL and Web-page-title sub-functions.
The analysis function: Includes the connection and bookmark sub-functions.
The construction function: Includes the category, abstract, and IP address sub-functions.
Fig. 3. The CKAT and major functions of the Web assisted knowledge construction (WAKC) system.
his/her interests in finding useful information and attempts to explore and transform external stimuli by reviewing his/her own knowledge structures. The information transformation stage can be viewed as the stage of knowledge analysis whereby an individual selects appropriate information, organizes and integrates it with existing knowledge. The knowledge construction stage is similar to the stage of task knowledge structure. Here, an individual constructs his/her knowledge that is not limited to the results of rote memorization, but also a kind of new knowledge that could be applied in unknown circumstances and used to solve problems. Fig. 3 presents the CKAT conceptual model and major functions of the system.
4. System development 4.1. System architecture The system is a kind of filter-based search system that combines three major Webbased medical search tools including MEDWEB, med411, and MedExplorer. Two reasons for using these three medical search tools: first, med411 and MedExplorer are query-based search engines and the MEDWEB is directory-based service;
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therefore, using these three search engines may include general types of Web search engines. Second, these three search engines are all popular Web search tools for medical purposes. 4.2. System implementation In order to implement CKAT to assist Internet users to construct their own knowledge, this research establishes the WAKC system. This research designs a prototype for users to retrieval knowledge through the Internet and to enhance their knowledge construction capability. This system is a kind of filter-based search system that uses integrated, personalized, and server-based prototypes. Based on CKAT (Fig. 2), this research creates four different functions – the keyword function, the URL resource function, the analysis function, and the construction function, for assisting individuals to establish their own knowledge from Internet resources. The major platform is Citrix system and the major system developing tools include Visual Basic 6.0, Access database 2000, and ASPHTTP. This research builds the keyword function for the stage of knowledge objective. In the keyword function, three major sub-functions for searching desired information are: Internet search, database search, and integrated search. The sub-function of Internet search is to retrieve URLs from MEDWEB, MED411, and MEDExplorer. The subfunction of Internet search is to search from the Internet but not search from database. The sub-function of database search is based on historical retrieval from the database. When users request for a database search, this system does not send any request to the Internet. The sub-function of integrated search combines Internet and database search sub-functions. The URL resource function is established for the second stage (knowledge gathering). The URL resource function offers two resources that include the URL and the Web-page-title sub-functions. The analysis function is created for the stage of knowledge analysis. The analysis function includes two major sub-functions: connection and bookmark sub-functions. The connection sub-function is to connect Web pages that users are interested and bookmark sub-function is to mark the URLs into the database (users believe those URL addresses are all useful knowledge for them). The construction function is to build the Task Knowledge Structure. The construction function includes three sub-functions: category, abstract, and URL sub-functions. The category sub-function is to produce task categories, the abstract sub-function is to note aspects of knowledge, and the URL address sub-function, also a sub-function of the URL resource function, is to link knowledge resources (Web pages). Fig. 4 is the main screen of the WAKC system. For instance, if a user wants to find ‘‘medical informatics’’ knowledge from this WAKC system, then he/she inputs ‘‘medical informatics’’ as the keyword into the keyword function. This is the stage of knowledge objective. After that, based on Internet search and/or serverÕs database search, the results will be presented in the URL function that includes URLs and titles of Web pages. This is the stage of knowledge gathering. Then the student will connect to those Web pages that he/she regards as useful knowledge for him/her. This is the stage of knowledge analysis. At
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Fig. 4. Main screen of the WAKC system.
the last stage, the user needs to make a decision on which Web pages are useful and then bookmark those pages. In addition, the user has two optional sub-functions for helping him/her to construct his/her own knowledge. First, he/she can categorize each Web page according to his/her standard. Based on the individual point of view, every Web page can be classified at most three different categories. And second, the user can input abstract for each Web page to enhance individual knowledge construction. The sub-functions of the bookmark, category, and abstract are all assist users to create their own knowledge. Here, knowledge construction is posited as a subset of learning. Nonaka (1994) identifies knowledge construction as serendipitous and learning would qualify for that description as well. The learning, rather than the capture of knowledge (i.e., browsing content of Web pages) would focus on facilitating individual ideas, filtering knowledge classification, and codifying personalized knowledge into explicit knowledge. Indeed, as knowledge is filtered along with this assisted knowledge construction system, individual knowledge gradually becomes increasingly classified, codified and documented, thus evolving from tacit knowledge into explicit knowledge. As knowledge is captured and processed from tacit to explicit, individuals may begin to apply it as they see appropriate (Malone, 2002).
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5. System evaluation This WAKC system is evaluated by the viewpoint of usersÕ perceptions. In general, when usersÕ have more positive attitudes toward an information system, then the system is more highly used and accepted. Therefore, the current research uses the technology acceptance model (TAM) (Davis, Bagozzi, & Warshaw, 1989) for evaluating individual use and acceptance of the WAKC system. 5.1. Perception model The purposes of TAM and motivation are to understand individual perceptions toward Web technology (Davis et al., 1989). Based on TAM, perceived usefulness is a key factor to determine individual behavioral intention to use information systems and it leads to actual system use. In addition, motivation perspective is usually divided into two components: intrinsic motivation (perceived enjoyment) and extrinsic motivation (perceived usefulness). Intrinsic motivation and extrinsic motivation both are key drivers on behavioral intention to use information systems. Therefore, in order to understand usersÕ intention to adopt the WAKC system, TAM and motivation are appropriate models to detect their attitudes toward the system. The two hypotheses are defined in Table 1. 5.2. Research participants The study was conducted in a medical college in central Taiwan with a sample of 86 students. All participants were asked to answer a questionnaire after three weeks of using the WAKC system. In these three weeks, students used the system to learn more about medical related knowledge. The questionnaire included demographic information and two different components (computer and Internet experience, attitude scales toward the WAKC system). Three weeks later, the questionnaire was distributed by the researchers to participants in class. All participants were asked to respond to the questionnaire immediately and their responses were guaranteed confidentiality. Questionnaires with missing responses were eliminated for statistical analyses in order to avoid confounding variables (two missing responses). A total of 84 responses were collected in which 45 were male students and 39 were female students. Table 1 Hypotheses of research Hypothesis
Relative supported references
H1. When users have higher perceived usefulness, then they have higher behavioral intention to use the WAKC system H2. When users have higher perceived Enjoyment, then they have higher behavioral intention to use the WAKC system
Gefen and Straub (1997), Liaw (2002), Liaw and Huang (2003), Szajna (1996), Taylor and Todd (1995), Vankatesh (1999), Vankatesh and Davis (1996) Gefen and Straub (1997), Liaw (2002), Szajna (1996), Taylor and Todd (1995), Vankatesh (1999), Vankatesh and Davis (1996)
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5.3. Instruments The data for this study was gathered by a questionnaire survey. The questionnaire survey included three major components: (a) demographic information, (b) computer and Internet experience, (c) attitude scales toward the WAKC system. The questionnaire was described as follows. Demographic information: The demographic component of the questionnaire covered gender and the field of study. Computer and Internet experience: In this component, subjects were asked to indicate whether they had experience with operating systems, the Internet/WWW, engines, word processing packages, and programming languages. These questionnaires are all 7-point likert scales (from ‘‘1’’ which means ‘‘no experience’’ to ‘‘7’’, ‘‘highly experienced’’). Attitude scales toward the WAKC system: in these three components, subjects were asked to indicate their perceived enjoyment, usefulness, and behavioral intention to use the system. These questionnaires are all 7-point likert scales (from ‘‘1’’ which means ‘‘strongly disagree’’ to ‘‘7’’, ‘‘strongly agree’’). 5.4. Results The item-total correlations and descriptive statistics were presented in Table 2 and the descriptive statistics of computer experience were shown in Table 3. The Pearson correlation coefficients among the variables were presented in Table 4. For examining H1 and H2, a regression analysis was conducted to check the effect of the perceived enjoyment and the perceived usefulness on behavioral intention to use the WAKC system. The result showed that perceived usefulness was only one predictor (F ¼ 128:41, p ¼ 0:000, R2 ¼ 0:61) and perceived enjoyment was not a predictor although there is a high correlation between perceived enjoyment and behavioral intention to use the WAKC system. 5.5. Discussions Based on the system evaluation by usersÕ perceptions of TAM and motivation, the WAKC system is highly accepted (the mean of behavioral intention to use the system is 6.06). Thus, most users have positive behavioral intention to use the WAKC system. In addition, usersÕ attitudes of perceived usefulness (mean is 5.26) and perceived enjoyment (mean is 5.43) are all highly accepted, from the evidence that users have confidence to use this WAKC system as a tool for helping them with their knowledge construction. Based on regression analysis, perceived usefulness is a key factor that affects usersÕ behavioral intention to use the system. It means that when users feel the WAKC system is more useful, then they have more behavioral intention to use the system. Therefore, on a micro level, our findings provide the evidence that the WAKC system is available for practical use. On a macro level, present results corroborate previous research that usersÕ perceptions toward the Internet affect their behaviors to
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Table 2 Mean (M), standard deviation (S.D.), and corrected item-total correlations No. 1 2 3 4 5 6 7 8
9 10 11 12
Item
M
S.D.
r
Perceived enjoyment I like to use the WAKC system I am satisfied with the functions of the WAKC system I am satisfied with the quality of information generated from the WAKC system I like to use the Internet
5.43 5.64 4.95 4.64
0.91 1.14 1.79 1.62
0.47 0.60 0.79
5.79
1.45
0.66
Perceived usefulness I believe using the WAKC system is a good way to find Internet knowledge I believe using the WAKC system is an efficient way to find useful knowledge I believe using the WAKC system can help me find useful knowledge I believe using the WAKC system can allow me to find high quality knowledge
5.26 5.77
0.96 1.27
0.80
5.57
1.13
0.72
5.49
1.20
0.58
4.87
1.37
0.71
Behavioral intention to use the system I believe it is worthwhile to use the WAKC system to find medical knowledge I intend to use the WAKC system to find information and knowledge in the future It is necessary to use the WAKC system to find medical knowledge I intend to use the Internet to find information and knowledge in the future
6.06 5.85
0.93 1.14
0.73
6.08
1.24
0.68
6.06
1.02
0.74
6.24
1.31
0.64
r means item-total correlation. Table 3 Descriptive statistics of computer experience Variables
M
S.D.
Experience with operating systems Experience using the Internet Experience using search engines Experience with word processing packages Experience with programming languages (e.g. Visual Basic, HTML)
4.96 5.39 5.60 5.13 2.16
1.74 1.38 1.24 1.67 1.13
use the Web. Although perceived enjoyment is not a key predictor for behavioral intention to use the WAKC system, the correlation between perceived enjoyment and behavioral intention to use the WAKC system is highly accepted (r ¼ 0:78). From perceived enjoyment and perceived usefulness, the usersÕ major concern is the knowledge quality of the system (from Table 3, the mean of item three is 4.64 and the mean of item eight is 4.87). In other words, knowledge quality of search engines is the usersÕ major concern. Based on integrating three different medical-based search engines, the WAKC system is a filter-based assisted knowledge construction tool and it is difficult to control the knowledge quality because the resources all come from different Internet search engines.
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Table 4 Correlation analysis Variables 1. 2. 3. 4. 5. 6. *
Experience using the Internet Experience using search engines Perceived self-efficacy Perceived usefulness Perceived enjoyment Behavioral intention to use the WAKC system
1 1
2
3
0.80 1
4
0.57 0.57 1
5
0.31 0.43 0.63 1
6
0.27 0.33 0.54 0.79 1
0.29 0.36 0.50 0.62 0.78 1
p < 0:01.
6. Conclusions In summary, this research offers a direction of how search engines can facilitate and assist usersÕ knowledge construction. In general, knowledge can be viewed as information in context, together with an understanding of how to find it and how to use it. Knowledge construction from search engines should be investigated based on self-directed searching, hyperlink retrieval and interdisciplinary tasks. The purpose of this research is to develop a WAKC system to help users when they seek information from the Internet for constructing their own knowledge. Based on the approach of constructivism and knowledge analysis of tasks, this research develops CKAT model and implements a WAKC system. In general, with each stage of CKAT, the WAKC systemsÕ users have more opportunities to revise their concepts and enhance their understanding while using the WAKC system as a knowledge retrieval tool for individual knowledge construction. 6.1. An assisted tool for individual knowledge construction From Table 3, users like to use the WAKC system (mean is 5.64). In addition, they believe the WAKC system is a good way to find Internet knowledge (mean is 5.77). Furthermore, they intend to use the WAKC system in the future (mean is 6.08). Based on the evidence, the WAKC system is an available tool to help individuals to find Internet knowledge. In general, within the boundaries of information systems, users tend to focus their efforts on knowledge which is explicit, or mechanistic in nature. As knowledge is filtered along through the WAKC system, it gradually becomes classified, codified, and documented. As a result, knowledge is captured by individuals in the explicit format. Although tacit knowledge is difficult to formalize and translate (Nonaka & Takeuchi, 1995), it can be transferred through personal mental concepts, technical skills, and experience (Choi & Lee, 2002). Based on the approach of CKAT, the WAKC system offers a strategy that enables individuals to employ and sharpen their tacit knowledge into explicit formats. Therefore, the functions of the WAKC system provide opportunities for users to create their own knowledge based on their mental concepts and prior experience.
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6.2. The advantages of the WAKC system The WAKC system is a kind of filter-based search systems. Filter-based search systems may change search interfaces, index polices, and even retrieval algorithm (Boyer et al., 2001). Thus, filter-based search systems are created for speeding up indexes and improving retrieval effectiveness. Indeed, the filter-based search systems are a hybrid solution that maximizes recall, improves precision, and structure categories of search results (Bin & Lun, 2001). These changes may assist individual information retrieval and knowledge construction. The main advantages of the WAKC system can be categorized into five advantages when using the WAKC system as a knowledge construction tool: self-directed searching methodologies, non-linear retrieval network, multimedia information formats, integrated domains of tasks, and reducing information overload. Table 5 presents the advantages of the WAKC system. Users of the WAKC system need to have more autonomous and self-directed attitudes to find needed information or knowledge. Indeed, non self-directed users may encounter the phenomena of disorientation (Liaw & Huang, 2002). Because the Internet offers hyperlink environments, the retrieval network of the WAKC system is by hyperlink retrieval or by nonlinear network. From the characteristics of the Web, the type of retrieval knowledge is multiple media. Various search engines, such as Google, offer both Web-page-based and image-based searching methodologies. Generally speaking, when using the WAKC system as a knowledge construction tool, the task domain has multiple subjects because search engines integrate various kinds and topics of information databases. The WAKC system offers bookmark, category, and abstract sub-functions, those may reduce the phenomenon of information overload (Liaw & Huang, 2003). Although this research integrates three different medical search engines for medical students, it is available to extend the system to include general purpose search engines (such as Yahoo, Google, or Excite), thereby developing another type of a WAKC system.
Table 5 The WAKC system as a knowledge construction tool Category
Component
Purpose
Self-directed searching methodology Non-linear retrieval network Multimedia information format
Based on individual autonomous searching Based on hyperlink retrieval Based on multiple media with ill-structured or well-structured formats Based on interdisciplinary tasks
To set the knowledge objective
Integrated tasks domains Reducing information overload
Based on the bookmark, category, and abstract sub-functions
To gather knowledge To analyze and construct knowledge To analyze and construct knowledge To analyze and construct knowledge
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6.3. The limitations of the WAKC system From statistical analysis, users are not very satisfied with the quality of search results. Indeed, developing individual-based search tools may be a solution for improving the quality of search results. Kang and Byun (2001) state that knowledge is the product of a learning activity in which a learner assimilates and accommodates as he/she understands it. Individual knowledge construction is based on experience acquired through cognitive activities, such as perception, interpretation, and analysis. Therefore, how to collect and analyze individual previous search records and establish an individual historical database are crucial issues for users. Based on analyzing or investigating individual historical searching records, it is easy to monitor individual cognitive activities. Essentially, when a system keeps more records of usersÕ searching activities, it could better assist users to find individual needed information for constructing their own knowledge. In other words, when more valuable knowledge and user-friendly functions are built for users, then usersÕ knowledge construction activities could be more easily completed.
Acknowledgements The author wishes to thank I-Hsien Ting for his help of system implementation. This research was supported in part by a grant from the Ministry of Education and the National Science Council of Republic of China, project numbers: H045 and NSC92-2520-S-039-001.
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