Presentation Based Meta-learning Environment by Facilitating Thinking between Lines: A Model Based Approach

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 based 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-based approach: (i) we extend Kayashima’s computational model as a basis to build a meta-learning task model that clarifies factors of difficulties in performing meta-cognitive activities for learning processes, (ii) we specify design concepts for meta-learning scheme as a 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 building human-centric systems in general. 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. Fifthly, 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. Then, we present experimental results that suggest that users tightened their criteria to evaluate their own learning processes and understanding states. Furthermore, our support system deepens learners understanding states by prompting their thinking between lines. Finally, we describe the usefulness of the model by characterizing other meta-cognition support schemes.