Felix Jimenez

An Emotional Expression Model for Educational-Support Robots

Abstract With the growth of robot technology, robots that assist learning have attracted increasing attention. However, users tend to lose interest in educational-support robots. To solve this problem, we propose a model of emotional expression based on human-agent interaction studies. This model in which the agent autonomously expresses the user’s emotions establishes effective interactions between agents and humans. This paper examines the psychological effect of a robot that is operated by the model of emotional expressions and the role of this effect in prompting collaborative learning.

Robot That Can Promote Learning by Observing in Collaborative Learning

This study sought to examine how behavior of a robot can prompt learning by observing in collaborative learning. We designed the robot to learn in the same way as a human learner at the when learning begin. Moreover, the robot changes the way of learning to a more effective one as the learning progresses. Ten college students with low level English learned using an English vocabulary learning system with robot for two months and were videoed during that time to see how they learned. We found that learners came to imitate the learning method of the robot and change their way of learning to the more effective one. This suggests that the robot that changes the learning method as the learning progresses prompts learners to learn by observing in collaborative learning.

Psychological Effects of a Synchronously Reliant Agent on Human Beings

The ability of human symbiosis robots to communicate is indispensable for their coexistence with humans, so studies on the interaction between humans and robots are important. In this paper, we propose a model robot self-sufficiency system that empathizes with human emotions, a model in which we apply the urge system to an autonomous system of emotions. We carry out simulation experiments on this model and verify the psychological interaction between the software robot and its users.

Effect of collaborative learning with robot that prompts constructive interaction

In this study, we sought to examine how the behavior of a robot can prompt collaborative learning with a human. We focus on constructive interaction that has been regarded as a foundation of collaborative learning and occurs when two learners alternately solve a question. For this, the robot is designed to alternately perform speaker and listener motions for constructive interaction with a human. With the speaker motion, the robot explains a solving method to the partner and solves a question. Moreover, the robot improves its accuracy rate as learning progresses. With the listener motion, the robot does not solve a question and instead pays attention to the partner who is solving the question. The robot learns while solving a question issued by a learning system with a human student. College students recruited as volunteers learned with learning system with the robot for one month and were videoed during that time to see how they learned. The results of examination suggest that the robot prompts learners to learn by constructive interaction in collaborative learning and possibly gains the same learning effect as collaborative learning between two humans.

Learning Effect of Collaborative Learning between Human and Robot Having Emotion Expression Model

Recently, more educational-support robots, which support learning, are paid attention to. However, the problem of these robots is that a user loses his/her interest in them. To solve this problem, some studies which focus on emotional expression models have been reported in Human-Agent-Interaction. The model of emotional expressions is defined as the agent expressing its emotions autonomously. Although these models have been shown to be beneficial for effective interaction between an agent and a human, no reports have addressed the educational-support robots using these models. Thus, this paper studies how much learning effect with a robot which expresses the emotion by using the model of emotional expression can be prompted for learners in a collaborative learning.

Psychological effects of educational-support robots using an emotional expression model

With the growth of robot technology, robots that assist learning have attracted increasing attention. However, users tend to lose their interest in these educational-support robots. To solve this problem, human-agent interaction studies have proposed a model of emotional expressions. Nevertheless, this model, in which the agent expresses his/her emotions as autonomous emotions, has proved to be effective for interactions between agents and humans. Thus, this paper examines how psychological effect of robot which expresses the emotion by using the model of emotional expressions can prompt learners in a collaborative learning.

Effect of robot utterances using onomatopoeia on collaborative learning

We investigated the effect of robots utterances using onomatopoeia on collaborative learning. The robot was designed to praise or comfort by using onomatopoeia when learners are given problem to solve through a learning system. When learners can correctly solve a problem, the robot praises the learners success. When learners cannot solve it, the robot comforts the learners to keep working at it. Eight college students learns mathematics by using a learning system with a robot for three weeks and took exams. We found that a robot could comfort learners that used onomatopoeia more than a robot that did not use onomatopoeia. This suggests that the robot that praises or comforts by using onomatopoeia helps learners maintain their motivation in collaborative learning.

Change in learning ability using scaffolding in EFL vocabulary learning system

This study clarified change of learning ability of English as a Foreign Language (EFL) in order to propose a more adaptive learning system. We developed an English vocabulary learning system for Japanese students. The system presents each word in an example sentence and the Japanese translation of the sentence except for the target word upon user request. We sought to examine how learners use this system as a scaffold. Five college students each of lower and intermediate ability used the system for six weeks and then took exams. We found that the intermediate-ability group learned more vocabulary than the lower-ability group depended on the help given by the scaffold all though the learning period, whereas the intermediate-ability group reduced their use of it gradually as a way of checking their learning period. This suggests that proposed system with scaffolding is effective for intermediate learner. Moreover the proposed system prompts intermediate learner fading out the scaffolding for intermediate.

A study on document classification using multiple distributed representations

Document classification is an essential task in digital society. In document classification, it is important how to represent a document. Topical document classification methods represent a document as Bag-of-Words (BOW). It uses only the number of occurrences of each word, so it ignores the semantic meaning of words. Recent years, document classification methods using Word2Vec are proposed and got much attention. Word2Vec is a tool for learning semantic-syntactic relationship among words as word vectors. The word vectors are called distributed representation. The document classification methods using Word2Vec represent a document as the centroid of word vectors in a document. It uses only semantic meaning of each word, so it ignores the number of occurrences of words. In this paper, we propose a new document classification method combining BOW and multiple distributed representations. Different corpus has different words and phrases, so each distributed representation learned from each corpus is expected to have different semantic meaning.

Learning effect of robotic encouragement-based collaborative learning

With the growth of robot technology, more robots are being designed to support learning. Most studies in the field have focused on robot behavior, with only a few studies focusing on robot utterances. Correspondingly, the manner in which such utterances affect learning is poorly understood. This study investigates the effects of collaborative learning wherein a robot encourages a learner. We conducted an experiment to compare the learning effect in three groups. In the first group, learning was conducted with a robot that supplied praises using onomatopoeia, i.e., the representation of an object or state based on the sounds associated with it. In the second group, a robot supplied praises using adjectives or adverbs. In the third group, a robot supplied praise without using onomatopoeia, adjectives, or adverbs. The results of this study suggest that collaborative learning using the first or third method is more effective than that involving adjective/adverb communication.