S. R. S. Iyengar

Maneet Singh, Rishemjit Kaur, Akiko Matsuo et al.

Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup, and Purity, whereas English tweets expressed more positive emotions for all moral dimensions. Considering moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority and Purity for English and Ingroup, Authority and Purity for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English- and Japanese-speaking users.

Anamika Chhabra, S. R. S. Iyengar, Jaspal Singh Saini

Crowdsourcing has revolutionized the process of knowledge building on the web. Wikipedia and StackOverflow are witness to this uprising development. However, the dynamics behind the process of crowdsourcing in the domain of knowledge building is an area relatively unexplored. It has been observed that an ecosystem exists in the collaborative knowledge building environments (KBE), which puts users of a KBE into various categories based on their expertise. Classical cognitive theories indicate triggering among the knowledge units to be one of the most important reasons behind accelerated knowledge building in collaborative KBEs. We use the concept of ecosystem and the triggering phenomenon to highlight the necessity for the right mix of users in a KBE. We provide a hill climbing based algorithm which gives the ideal mixture of users in a KBE, given the amount of triggering that takes place among the users of various categories. The study will help the portal designers to accordingly build suitable crowdsourced environments.

Anamika Chhabra, S. R. S. Iyengar, Poonam Saini et al.

Despite a significant improvement in the educational aids in terms of effective teaching-learning process, most of the educational content available to the students is less than optimal in the context of being up-to-date, exhaustive and easy-to-understand. There is a need to iteratively improve the educational material based on the feedback collected from the students' learning experience. This can be achieved by observing the students' interactions with the content, and then having the authors modify it based on this feedback. Hence, we aim to facilitate and promote communication between the communities of authors, instructors and students in order to gradually improve the educational material. Such a system will also help in students' learning process by encouraging student-to-student teaching. Underpinning these objectives, we provide the framework of a platform named Crowdsourced Annotation System (CAS) where the people from these communities can collaborate and benefit from each other. We use the concept of in-context annotations, through which, the students can add their comments about the given text while learning it. An experiment was conducted on 60 students who try to learn an article of a textbook by annotating it for four days. According to the result of the experiment, most of the students were highly satisfied with the use of CAS. They stated that the system is extremely useful for learning and they would like to use it for learning other concepts in future.