Ryuichi Nakaike

Learning through intermediate problems in creating cognitive models

Cognitive modelling is one of the representative research methods in cognitive science. It is believed that creating cognitive models promotes learners’ meta-cognitive activities such as self-monitoring and reflecting on their own cognitive processing. Preceding studies have confirmed that such meta-cognitive activities actually promote learning effects. However, there are some difficulties in bringing about learning by creating cognitive models in an educational context. To overcome the difficulties, we propose an innovative learning design, ‘learning through intermediate problems’ and also developed a web-based production system called DoCoPro that can be used anywhere and anytime in an environment connected to the Internet. We performed three introductory cognitive science classes in which the participants learned cognitive modelling and constructed running computer models using our system. In the first and second classes, the participants were required to construct production system models that solve pulley problems. They also posed their original pulley problems that their own models were subsequently able to solve. These generated problems were distributed to the other members. The participants were able to find incompleteness in their cognitive models, revise them to remove the incompleteness, and improve their models while solving the given problems. The participants, by successfully creating sophisticated models, acquired a deeper knowledge of the learning domain. The class practices confirmed the utility of ‘learning through intermediate problems’ when constructing an educational environment for learning creating cognitive models. In the third class, the participants constructed cognitive models solving addition and subtraction problems using DoCoPro. The cognitive processing underlying such problem solving is automated, therefore it may be difficult to verbalize and externalize such cognitive processes. The post-questionnaire showed evidence that the participants actually performed meta-cognitive activities while monitoring their own internal information processing.

Learning Mental Models of Human Cognitive Processing by Creating Cognitive Models

We investigated how creating cognitive models enhances learners’ construction of mental models on human cognitive information processing. Two class practices for undergraduates and graduates were performed, in which participants were required to construct a computational running model of solving subtraction problems and then develop a bug model that simulated students’ arithmetic errors. Analyses showed that by creating cognitive models, participants learned to identify buggy procedures that produce systematic errors and predict expected erroneous answers by mentally simulating the mental model. The limitation is that this benefit of creating cognitive models was observed only in participants who successfully programmed a computational model.

Use of a Cognitive Simulator to Enhance Students' Mental Simulation Activities

We developed a cognitive simulator of the dual storage model of the human memory system that simulates the serial position effect of a traditional memory recall experiment. In a cognitive science class, participants learned cognitive information processing while observing the memory processes visualized by the simulator. Through the practice, we confirmed that participants learned to predict experimental results in assumed situations implying that participants successfully constructed a mental model and performed mental simulations while running the mental model in various settings. We discuss the possibility that a cognitive model can be used as a learning tool and, more specifically, as a mediator tool connecting theory and empirical data.

A Learning Environment That Combines Problem-Posing and Problem-Solving Activities

We developed a learning environment to combine problem-posing and problem-solving activities. The participants learned a formal logic system, natural deduction, by alternating between the problem-posing and problem-solving phases. In the problem posing-phase, the participants posed original problems and presented them on a shared problem database called “Forum,” which was accessible to other group members. During the problem-solving phase, the participants solved the problems presented on Forum. This first round of problem posing and solving was followed by a second round of problem posing. We performed two practices for evaluation. The results showed that the participants successfully posed more advanced problems in the second round of problem posing as compared to the first. The empirical data gathered from the two practices indicated a significant relationship between problem-solving and problem-posing abilities.

Empirical investigation on self fading as adaptive behavior of hint seeking

We investigated whether students behave adaptively in hint-seeking from the viewpoint of self-fading. To let students effectively learn, scaffolding should be eliminated gradually with the progress of learning. We define self-fading as fading behavior lowing the levels of support by students themselves. We investigated the relation between such metacognitive behavior and learning effects through two experiments in a laboratory setting and in actual class activities. The results showed that our participants successfully faded help supports, and also confirmed that those who lowered the levels of support and learned with their own efforts gained larger learning effects.