A technique for expediting comprehensive written feedback on assignments

Providing detailed feedback in large classes is challenging. We describe how we develop an archive of comments while marking – noting good points, what needs improvement, and how to correct shortcomings. Comments are recorded in a single document with codes. Relevant codes are marked on students’ work where issues arise. Each student’s annotated assignment is returned with a copy of the comments for the class. Thus, they receive specific feedback on their own work, plus all comments given to the class. Instructors save on marking time because comments are written once on the master list, and only codes and a personalized summary statement are written on the assignment. Markers may collaborate in preparing comments to assist in moderation; some generic comments (e.g., presentation and grammar) are portable across different assignments and years; and comments from past years may form a rubric for sharing with students before they start an assignment

Editor\u27s Note

It is once again that time of year when I have the distinct honor and pleasure of sharing a few comments related to the publication of the most recent volume of the Journal of the Indiana Academy of the Social Sciences. The current issue, Volume 18 (2015), represents the fifth and final year of my first term as senior editor in chief. I have been fortunate during this time period to have been able to work with a talented and dedicated staff of coeditors, referee-reviewers and, of course, authors. I am extremely pleased to report that the current volume presents another outstanding collection of high-quality cutting-edge research articles that reflect the rich diversity of social science disciplines, topics, and methods. Our current issue represents a balance between research dealing with national and local issues, and includes papers on international topics as well

Exploiting User Comments for Audio-Visual Content Indexing and Retrieval

State-of-the-art content sharing platforms often require users to assign tags to pieces of media in order to make them easily retrievable. Since this task is sometimes perceived as tedious or boring, annotations can be sparse. Commenting on the other hand is a frequently used means of expressing user opinion towards shared media items. This work makes use of time series analyses in order to infer potential tags and indexing terms for audio-visual content from user comments. In this way, we mitigate the vocabulary gap between queries and document descriptors. Additionally, we show how large-scale encyclopaedias such as Wikipedia can aid the task of tag prediction by serving as surrogates for high-coverage natural language vocabulary lists. Our evaluation is conducted on a corpus of several million real-world user comments from the popular video sharing platform YouTube, and demonstrates signicant improvements in retrieval performance

Profile of the Social Network in Photo Sharing Systems

People, who interact, cooperate or share common activities within the photo sharing system can be seen as a multirelational social network. The results of their activities, i.e. tags, comments, references to favourites and others that semantically connect users through multimedia objects, i.e. pictures are the crucial component of the semantic web concept. Every online sharing system provides data that can be used for extraction of different kinds of relations grouped in layers in the multirelational social network. Layers and their profiles were identified and studied on two, spanned in time, snapshots of Flickr population for better understanding of social network structure complexity. Additionally, for each of the identified layers, a separate strength measure was proposed in the paper. The experiments on the Flickr photo sharing system revealed that users are inspired by both the semantic relationships between objects they operate on and social links they have to other users. Moreover, the density and affluence of the social network grows over course of time

Social Network Analysis for Online Knowledge Exchange Platform: Evidence from Zhihu

The knowledge exchange platform is an innovative way that empowers online learning for the Internet users to utilize their spare time slots for knowledge sharing and seeking. Many researchers have conducted research on the user interaction and content of the knowledge payment platform. This paper analyzes the user interaction and user comments by analyzing the data of Zhihu live, a major online knowledge exchange platform in China. We employ social network analysis and deep learning method to explore the users’ interaction structure in Zhihu live platform and their emotional tendency for knowledge exchange. Particularly, we use social network analysis theory supplemented by social analysis tools Gephi and neural network algorithm, LSTM to achieve our goals. We propose a set of hypotheses from the perspective of a small world phenomenon and users’ social engagement in the platform. Our results show that there is a small world phenomenon on core topics and the more frequent users interaction is, the more positive the users’ comments are. Theoretically, this study explores the users’ knowledge seeking and sharing behavior from the perspective of user interaction and user emotion. Also, our research offers implications to practice that enhancing sociality can be an effective strategy to motivate the desirable users’ paid knowledge sharing behaviors in the platform

How Can We Make Language Learning Effective and Sustainable Outside the ClassroomUsing Self-accessCALL withBlogging?

This paper investigates how and to what extent learners utilize the computer assisted language learning (CALL) system outside the classroom. A pretest and post-test design was carried out and the test scores evaluating learners’ English proficiency were logged for the quantitative analysis. Throughout the six-month course, the participants were given three types of assignment every week: Assignment (1) to complete compulsory tasks, Assignment (2) to complete optional tasks, and Assignment (3) to report the optional tasks completed and to post some comments concerning their learning on CALL. Their protocols within Assignment (3) were analyzed in terms of cognitive process of learning. For Assignment (3), there were two conditions: for Condition (a) students had to send reports and comments to the instructor via e-mail individually, and for Condition (b) students posted them on the blog launched for the participants. The result of the quantitative analysis showed that both learning time and the number the tasks increased throughout the training period on Condition (b), sharing students’ comments on the blog, whereas for Condition (a), few students completed many more tasks on CALL than the weekly obligatory assignment. There were also some differences in the variety of tasks completed; the students in Condition (b) tackled a much greater variety of tasks, and made a greater variety of comments than students in Condition (a). As a result of analysis for report comments, comments from Condition (b) showed the progress of the students’ cognitive stages whereas most of the comments from Condition (a) were superficial, just their impression to tasks. The results derived through these comparisons suggest that creation of a learning community outside the classroom would be a key for the effective and sustainable use of self-study-fashioned CALL materials, and would be enhanced by the implementation of a social networking service such as a blog