The learning process concepts discussed here are abstracted from Nonaka and Takeuchi´s SECI cycle, given in their book “The knowledge creating company” (Nonaka, Takeuchi, 1995). According to these authors, there are two different kinds of human knowledge: tacit knowledge and explicit knowledge. Tacit knowledge - a term introduced by Michael Polanyi in 1967 - is the personal and hidden knowledge which resides within the mind. Examples of tacit knowledge are know how, expertise, understandings, experiences and skills resulting from previous activities. Tacit knowledge is personal and hard to formalize, codify or communicate. Unlike tacit knowledge, explicit knowledge is codified, systematic knowledge that can be transmitted in formal language. It can easily be captured, accessed and shared. Similar to the knowledge creation process, the learning process is knowledge in action, a cyclic conversion of tacit knowledge and explicit knowledge. This spiraling, highly dynamic and complex process is modeled in the figure below. It consists of four modes: socialization, externalization, combination, and internalization. These modes occur when tacit and explicit knowledge interacts with each other. In the following four sections, we examine each of these modes.
- Socialization: Socialization is the first mode in the learning process and the primary source of learning. As Polanyi (1967, p. 4) mentioned “We know more than we can tell”. There is a huge mass of high-quality tacit knowledge embedded in people, which is not easily expressible. This knowledge can, however, be made available to others through socialization. In this mode, learning occurs implicitly, within a social context through observation, imitation, participation, interaction and practice, rather than through written or verbal communication (e.g. on the job training). The process of acquiring tacit knowledge can be supported by joint activities, personal connections, social networking, and community of practice (CoP) building. CoP “are focused on a domain of knowledge and over time accumulate expertise in this domain. They develop their shared practice by interacting around problems, solutions, and insights, and building a common store of knowledge” (Wenger, 1998). Therefore, a learning system should include an effective collaborative learning environment that can encourage tacit knowledge sharing and facilitate socialization.
- Externalization: Through externalization, tacit knowledge is made explicit, i.e., expressed in language or symbols, in a form which can be accessed, understood, shared, adapted, and reused. The conversion of tacit into explicit knowledge involves techniques that help to express one’s ideas or images as words, concepts, figurative language (such as metaphors, analogies or narratives) and visuals (Nonaka, Konno, 1998). Externalization is a complex process aiming at creating high-quality and valuable learning objects. In the externalization process, software engineering concepts and principles should be applied. The first step in this process is knowledge de-contextualization. That is, extract knowledge from its context such that it is not bound to the situation from which it stems, thus enabling the reusability of this knowledge in different learning situations. The next step is planning. That is, define a set of goals and requirements that need to be achieved. Good planning will leverage the created learning object (i.e. knowledge component) to its best use. Parts of the planning process are on the one hand to determine the target user’s needs, preferences, interests, skills, learning goals, background and on the other hand to fix the knowledge level that he/she will have acquired at the end. Modeling and modularity are the cornerstones of the externalization process. It is crucial to disaggregate a learning resource into tiny learning objects and identify how these objects relate to each other. Those modular learning objects can then be reused by different user communities for diverse purposes. Once the objectives of the new learning resource are defined and modular learning objects are identified, it is possible to move to the development step using all software and hardware means that are able to reduce the time to develop valuable learning content such as simulations and experiments. The result of the application of software engineering concepts in the knowledge capturing process will be granular, organized and reusable learning objects.
Successful knowledge capturing also requires the use of metadata for describing learning objects as well as adopted, common, open and accredited standards (Hodgins, 2000). According to Hodgins, metadata is the full and rich set of information needed in order to find, filter, select, and combine the information. Metadata becomes increasingly important and is required to annotate learning resources in order to support indexing, storage, search, and retrieval of appropriate learning resources or learning paths relevant for a specific learner or a group of similar learners (Chatti et al., 2005). It is also crucial to use standards for metadata and learning objects to provide fixed data structures and communication protocols for learning objects and cross-system workflows (Ellis, 2005) and assure accessibility, interoperability, adaptability, reusability, durability, and affordability of learning (Hodgins, 2000). Furthermore, since knowledge must be current in order to be of value, attention should be paid to the development of up-to-date and dynamic learning resources. A possible way to achieve this is, instead of inserting an existing learning object into a learning resource, just to point directly to the community which is currently working on the development of this object. To achieve best results from the externalization process, a learning system should include a standard-based, collaborative and effective knowledge capture system (can also be called a knowledge representation system or an authoring system) that supports learning communities in designing, creating, reviewing, modifying, and posting up-to-date and valuable learning objects in a short time. This system should particularly include an intelligent component for automatic learning object annotation, which is based on powerful data mining algorithms and advanced pattern recognition techniques.
- Combination: As discussed in the previous section, the output of the externalization process is granular, annotated, classified, context free, standard-based, and up-to-date learning objects (i.e. explicit knowledge). These quality learning objects can now be shared, disseminated, stored, reused, analyzed, re-categorized, re-contextualized, reconfigured, reorganized, combined, and delivered. The manipulation of existing learning objects leads to new, possibly more complex learning objects. This process is referred to as combination. The combination process is supported by learning repositories to store and manage learning objects and their associated metadata, as well as learning paths and activities. In case these repositories are based on standards for interoperability and reusability, they can be accessed and managed so that they are available as a virtual single pool of learning objects and metadata (Hodgins, 2000). In a learning repository, new modular learning objects can be added and existing ones can be analyzed, compared, sorted, restructured and associated. This results in new learning object configurations and combinations or new learning paths that can be applied to address different learner needs and solve new problems.
In addition to learning repositories, the combination process is most efficiently supported in collaborative environments utilizing information technology (Nonaka, Konno, 1998). Stacey mentioned that active and alive learning environments are more like learning communities than learning repositories. They focus on bringing people to people not just people to content (Stacey, 2003). According to this, learning has to occur within a social context which supports listening, viewing, reading, writing, speaking, commenting, suggesting, asking, discussing, disseminating, and sharing of learning objects and best practices among community members (i.e. academic and professional novice/mature learners, customers in an organizational context, peers, learning facilitators, coordinators, mentors, experts). To help building the required personal connections in an online social network, the use of synchronous and asynchronous communication tools is crucial (e.g. e-mail, instant messaging, video conferencing, Voice-Over-IP, group scheduling, announcements, news, events, calendar, weblogs, wikis, webfeeds). In addition to learning repositories and learning communities, powerful access and search capabilities across content, metadata and people are required. A learner should be able to query the learning system to quickly locate appropriate learning resources, as well as persons who share his/her interests or experts who can help achieving better results.
- Internalization: Internalization is the conversion of explicit knowledge into new tacit knowledge (Nonaka, Konno, 1998). In the learning process, internalization refers to the embodying of knowledge through reflection and application of the gained explicit knowledge in a given context. It is closely related to learning by doing, performing, and working. In the internalization process personalization is the key. Personalization is the ability to get just the right stuff to just the right person at just the right time and place in just the right way and with just the right context on just the right device and through just the right medium (Hodgins, 2000). The learning system should include an intelligent personalization/adaptation engine, able to deliver quality learning resources that are tailored to the learner’s needs, preferences, interests, skills, learning goals, cultural background.
M. A. Chatti, R. Klamma, M. Jarke, V. Kamtsiou, D. Pappa, M. Kravcik, A. Naeve:
TECHOLOGY ENHANCED PROFESSIONAL LEARNING: Process, Challenges and Requirements. Proceedings of the second International Conference on Web Information Systems and Technologies (WEBIST 2006), April 11-13, Setubal, Portugal (Paper as PDF)
- Chatti, M. A., Klamma R., Quix C. , Kensche, D., 2005. LM-DTM: An Environment for XML-Based, LIP/PAPI-Compliant Deployment, Transformation and Matching of Learner Models. Proceedings ICALT 2005, July 5-8, Kaohsiung, Taiwan, 567-569.
- Ellis, R. K., 2005. E-Learning Standards Update. Learning Circuits, Article. Retrieved October 26, 2005, from http://www.learningcircuits.org/2005/jul2005/ellis.htm.
- Hodgins, H. W., (2000, February). Into the Future. Learnativity, Vision Paper. Retrieved October 25, 2005, from http://www.learnativity.com/download/MP7.PDF.
- Nonaka, I., Konno, N., 1998. The concept of “Ba”: Building foundation for Knowledge Creation. California Management Review, Vol. 40, No. 3.
- Nonaka, I., Takeuchi, H., 1995. The Knowledge-Creating Company. Oxford University Press, Oxford.
- Stacey, P., (2003, February). People to People not just People to Content. E-Learning for the BC Tech Industry, Article. Retrieved November 2, 2005, from http://www.bctechnology.com/statics/pstacey-feb1403.html.
- Wenger, E., 1998. Communities of practice: Learning, meaning and identity. Cambridge University Press. Cambridge, UK.