Authors have been performing the educational practice with a text communication tool "iConversation" since fiscal year 2012. iConversation is a web application program that a student can send the text messages addressed to a teacher just before the end of a lesson. The teacher can confirm the each student's recognition concerning state of his/her understanding and willingness to learn, the question about lesson content, and the demand for lesson progress from each student's text messages. Thus, the teacher can add the explanation and the encouragement tailor for each student's understanding and willingness, answer to the each questions, and improve the teaching material and lesson design. However, although the teacher's workload of text communication with students should be made a minimum achieving the effect of teacher's responses, it is not clarified the cases of the content that should be paid attention, the teacher's responses, and the their effects. In this study, the cases are guessed based on the text message data of iConversation.

Recently, the research in intelligent educational systems has much interest in exploring data from academic settings to understand learners behavior and mental states. We have been developing the Intelligent Mentoring System which performs automatic mentoring by using an Intelligent Tutoring System to scaffold learning activities and an ontology to provide a specification of learner models. To provide learner models in mentoring, the ontology describes qualities and quality values which represent scale of learners emotions in academic settings. To specify relationships among the qualities, this study organized action to appraise the emotions on the basis of Control-value theory and proposed their ontological descriptions.

Recently, using tablet PCs in various school are conducted. Especially, it is remarked that all students can use own tablet PC. There are some advantage that the students can study own speed by using own device. In this paper, we propose the learning support system of pulley learning using a tablet PC and a portable haptic device for giving all students to own experiment environment. The user can experience the virtual experiment of pulley with the force feedback. The force feedback is corresponding to a pulley combination constructed by the user, and presented to the user's finger by touching the display. The pulley combination is constructed by arranging the pulleys displayed on the display. The system decides the pulley combination by the pulley's position to the user can construct the pulley combination only arranging the pulleys. Additionally, the user can arrange pulleys with the trajectory of real experiment by using touch panel. In the verification, it confirmed that the learner can conduct the virtual experiment with the trajectory like the real experiment.

Designing the agents extraordinarily humanlike sometimes makes humans elicit a negative feeling against it. It is well known as the uncanny valley. We proposed the model generating how the feeling occurs based on human brain function related to emotions. However, the model was defective in its emotional information processing for the inputs, the features of the agents' eye. In this study we propose the advanced model to express the human information processing toward them. The results of simulation indicated that a little detection of abnormality of the eye in the early but imprecise information processing caused the agent to be low-evaluated, and that agent should be negatively evaluated if human kept on gazing at an abnormality point.

Problem posing by which learners create problems by themselves has been identified as an important activity in mathematics education. However, problem posing is a heavy task for both learners and teachers because it is a divergent task that has various possible answers. To develop problem posing skill of learners, it is indispensable to evaluate posed problems, particularly when they include errors in mathematical structures. To provide a basis in designing computational support for addressing errors to improve problem posing skill, this study empirically investigated errors of mathematical word problems posed by novices. Undergraduates were engaged in a problem-posing task where they were asked to pose many, diverse and unique problems from a problem initially given. Posed problems that included errors were analyzed, with the result indicating that the undergraduates failed to pose problems due to failures in setting constraints or inconsistencies between texts and solutions. A computational system can detect constraint errors because they are represented in formal structures of solutions. On the other hand, it cannot detect inconsistent errors because semantic analysis of problem texts is required. We then discussed how to approach errors in problem posing by computational systems.

We have already developed several interactive environments for learning by problem-posing in arithmetical word problems and use them practically in several elementary schools. Currently, we have formalized this learning activity as "objective action" as a step to promote a learner to externalize a target model. In this paper, we have also explained triplet structure model of arithmetical word problem as the target model.