Modeling computer-based distance learning platforms

Abstract

Pragmatic problems in computer-based distance learning platforms concern generally the correct use of technological tools. These problems have technical solutions and do not represent the most important preoccupation in a platform design phase: even a repetitive utilization of the platform could become their naïf users very farniliar with its functionalities. Theoretical problems, in contrast, are more complex because they involve learner's cognitive gains and requirements of dynamic and independent decisions. The dichotomy cognitive gain x punctual leaner's Socratic reasoning is a good example of this complexity. So, it is irnportant to represent the dynamical knowledge and reasoning involved in learner's cognitive parameters (i.e., learner's abilities and features such as mental representations, knowledge acquisitions, use of inferences, heuristics, deductive or inductive reasoning etc). The diagnosis of these parameters (that are naturally fluctuating in a platform during a learning activity) is crucial to provide dynamic inference processes and interpretation of information in order to generate data and metadata to the platform. This paper aims theoretical supports for computer-based distance learning platforms. We discuss about the design of this kind of platform, which uses uniform and diagnosis processes in order to generate dynamical data and metadata. These data are used to extract new multi-sensorial knowledge to be integrated or then to perform an interoperational search of common knowledge resident on online learning platforms: the status from this knowledge should to shift from a platonic methodology -where the objects are independent from the discuss- to an intuitionist methodology -where the objects are represented as the result of a mental construction-. We affirm that a platform based on these theoretical supports alIows a safer learning process.