Saromporn Charoenpit’s research while affiliated with Thai-Nichi Institute of Technology and other places

Affective values are critical factors for manufacturing in Japan. Kawaii, which is a positive adjective that denotes such positive meanings as cute or lovable, becomes even more important as an affective value (Chap. 1). Some research has evaluated kawaii feelings by various biological signals. However, since no detailed eye tracking has been conducted yet, we employed it to identify the relationship between kawaii feelings and eye movements. We previously performed an experiment on preferences in which participants chose their favorite kawaii illustrations from six choices. However, we could not perform detailed analysis due to the complexity of the eye movements and the calibration-free data. Therefore, we improved our experiment method by randomly showing only two illustrations at a time from the six choices. From our analyzed results, we clarified the relationship between kawaii feelings and eye movements and identified two new indexes.

Social Networking Sites (SNSs) have exploded as a type of popular communication, suggesting exponential appeal. Unfortunately, one reason for their rise is the potential of excessive usage, which leads to negative consequences that are associated with addiction. In this research, we assessed the symptoms of excessive SNS usage by studying user behavior in SNSs. We employed the modified Internet Addiction Test (IAT) and the modified Bergen Facebook Addiction Scale (BFAS) to reflect addictive behaviors. We previously developed a data collection application and experimentally collected data from undergraduates in Thailand. In this article, we clarify the factors associated with addiction components (e.g., salience, mood modification, tolerance, withdrawal, conflict, and relapse), which are reflected by the questions of IAT and BFAS. We analyzed questionnaire and Facebook data by various methods. Our analytic results identified the effective factors associated with addiction components. Then we employed the Support Vector Regression (SVR) for evaluation. The outcome of our research can be applied for developing prevention strategies to increase the awareness of excessive SNS usage.

The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Such excessive and compulsive use has been categorized as a behavioral addiction. We assessed the symptoms of excessive SNS usage by studying user behavior and emotion in SNSs. In previous studies, we developed a data collection application as a tool for collecting data from questionnaires and SNSs by APIs. We experimentally collected data from undergraduate students at the Thai-Nichi Institute of Technology (TNI), Thailand. To improve our data analysis, we employed web log data and analyzed, including the combination with questionnaires data to clarify SNS usage behaviors and the factors associated with SNS addiction. Our analytical results identified the variables that distinguish excessive users from normal users.

The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Excessive and compulsive use of them has been categorized as a behavioral addiction. This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior and emotion in SNSs. We designed a data collection application and developed a tool for collecting data from questionnaires and SNSs by APIs. The data were collected at the Thai-Nichi Institute of Technology (TNI), Thailand from 177 volunteers. We introduce our analysis of data obtained by SNS APIs by focusing on Facebook and Twitter. We used modified IAT and BFAS to measure SNS addiction. The Facebook and Twitter results, including a combination with questionnaires, were analyzed to identify the factors associated with SNS addiction. Our analytic results identified potential candidates of the key components of SNS addiction.

Kansei values are important factors in manufacturing in Japan. Kawaii, which is a positive adjective that denotes such positive meanings as cute or lovable, becomes even more important as a kansei value. Some research has evaluated kawaii feelings by various biological signals. However, since no detailed eye tracking has been conducted yet, we employed it to identify the relationship between kawaii feelings and eye movements. We previously performed an experiment on preferences in which participants chose their favorite kawaii illustrations from six choices. However, we could not perform detailed analysis due to the complexity of the eye movements and the calibration-free data. Therefore, we improved our experiment method by randomly showing only two illustrations at a time from the six choices. From our analyzed results, we clarified the relationship between kawaii feelings and eye movements and identified two new indexes.

The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Such often excessive and compulsive use has been categorized as a behavioral addiction. This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior in SNSs. We designed and developed a data collection application as a tool for gathering data from questionnaires and SNSs by APIs from 177 volunteers at the Thai-Nichi Institute of Technology (TNI), Thailand. This paper introduces the analysis results of questionnaire data to find the factors associated with SNS addiction. Our analytic results identified potential candidates for the key components of SNS addiction.

Kansei value has been considered as one of the important factors in the manufacturing in Japan. Kawaii, which is a positive adjective that has such positive meaning as cute or lovable, becomes more important as a kansei value. Currently, there are researches that evaluate kawaii feeling by employing various biological signals. However, the eye tracking has not been conducted in detail yet. Therefore, we employed it in our research to find the relationship between kawaii feeling and eye movement. Previously, we performed an experiment on the preferences of kawaii illustrations by selecting one of the six kawaii illustrations. However, we could not perform detailed analysis due to the complexity of eye movement and the calibration-free data. Therefore, we improved the experiment method by randomly showing only two illustrations at a time selected from the six. From the analyzed result, we clarify the relationship between kawaii feeling and eye movement.

E-learning is the computer and network-enabled transfer of skills and knowledge. In e-learning systems, emotions are critical for learners to create positive contexts for optimal learning. In particular, increasing learner motivation by stimulating their interest is crucial. To data, however, few e-learning systems have derived emotions from eye tracking data. With eye tracking equipment, we recorded the eye movements of learners and calculated their eye metric indexes, which we focused on to explore their relationship to two learners' emotions groups: interest and boredom groups. We designed and implemented a prototype and experimentally evaluated it.