MODELS OF THE GROWTH OF KNOWLEDGE IN DYNAMIC SETTINGS

The concept of 'growth of knowledge' has stimulated scientific interest in various disciplines from cognitive psychology to the learning sciences. In the intersection of these approaches one finds the thesis that knowledge does grow as a result of information exchange with the environment and with others. In certain cases the benefits of such exchanges (e.g., on the Internet) and the evidence for the growth of knowledge as their result may not be obvious. Simple counterexamples to the thesis underline the need for new research methodologies for the growth of knowledge as a result of information exchange.

Conceptions of ‘growth’
Several different uses of the term of ‘growth’ can be identified with respect to knowledge, including individual level as well as collective level notions. The main alternatives are critically reviewed to assess their strengths and weaknesses with respect to the description of the dynamics of factual knowledge acquisition and communication. “Learning in activity” usually assumes both, however recent distinctions of soft and hard information are due also in the learning context. The analysis confirms that communication protocols and our knowledge about them may determine not only the efficiency of information exchange but the very nature of the knowledge that it produces.

Dynamics of Learning Situations
Logic frameworks for common knowledge are capable of a compositional analysis of complex communication scenarios such as announcements to subgroups, or private and secret massaging. In dynamic settings one can analyze multi-agent scenarios, such as revealing individuals’ answers to a group in order to promote discussion, peer assessment, forming (sub)groups of differing levels of ability, or giving particular roles to different performers in a group. The process of seeking and interpreting evidence when learners, or researchers are to tell how they need to go further, naturally introduce exchange of information between peers about their knowledge. As a result, the assessment of their knowledge raise the problem of the comparison of the efficiency of networking scenarios based on sharing and representing information in multi-agent frameworks.

Distinctions
Several different uses of ‘group knowledge’ and ‘common knowledge’ can be identified in current conceptions of “growth of knowledge” that can be applied to describe dynamic changes in knowledge states of learners (elimination of possibilities, belief change, updates and upgrades, problem solving, etc.) in order to provide logic-models for the assessment of growth in collaborative environments. Simple examples prove that their logical analysis removes ambiguity and improves the understanding the dynamics of learning in multi-agents situations.

In conclusion
Analysis of conceptions of growth in a composite framework of dynamic epistemic logic and the learning sciences provides new insights for the development of knowledge in interactive situations. Setting the protocols of information exchange dependent on the assessment of the knowledge states of the competitive or collaborative learners can be shown to have tremendous effects to the performance of the group that is sharing information according to different models of learning scenarios. The research methodology in porgress promotes the development of competitive, cooperative and game based models of learning and may provide new assessment paradigms for empirical research.