Paper Presenation Conference Management in Django

It is difficult to generalize and accumulate experiences of system development as methodologies for building meta-learning support systems because the meaning of “meta-cognition” is vague. Therefore, the importance of a model oriented system development approach has been recognized. It contributes to systematic refinement of each learning system by iterating a loop that building a model that can clarify design rationale of the system, developing and evaluating each learning system according to the model, and revising the model based on it. Moreover we can accumulate knowledge on meta-learning system development based on it. Thus, we adopt a model-oriented approach: (i) we adopt Kayashima's computational model as a basis to build a meta-learning task model and we add two factors of difficulties in performing meta-learning activities, (ii) we conceptualize five concepts for building meta-learning scheme that clarifies means to remove/ eliminate the factors of difficulties; then (iii) we embed support functions to facilitate meta-learning processes based on the model. This constitutes a promising approach not only for building learning support systems but also for accumulating/ revising knowledge on the system development. In this paper, we firstly describe the philosophy of our research to elucidate our model-oriented approach. Secondly, we present a meta-learning process model as a basis for understanding meta-learning tasks and what factors of difficulty exist in performing meta-learning activities. Thirdly, we explain our conceptualizations as a basis to design sophisticated meta-learning scheme to prompt learners' meta-learning processes. Fourthly, we integrate a meta-learning process model and conceptualizations so that we design our meta-learning scheme based on the deep understanding of meta-learning processes. Then, we present our presentation-based meta-learning scheme designed based on the model and clarify the design rationale of our system based on the model. Finally, we describe the usefulness of the model by characterizing other meta-cognition support schemes.