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Students' informal conversations on social media (e.g., Twitter, Facebook) shed light into their educational experiencesopinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students' experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students' Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students' college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students' problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students' experiences.

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Purpose – The purpose of this research is to examine why and when restaurant consumers use and contribute user-generated reviews. This research is needed to determine the relevance of user-generated restaurant reviews in the current marketplace. Design/methodology/approach – The research methodology is based on a quantitative approach, and focused on current Yelp.com users as its population. Questions focused on the amount of usage, motives for usage, level of trust, users’ tendencies to seek novelty in restaurants and motives for contribution. Findings – Users tend to trust the reviews on Yelp.com and engage in the community aspects of the platform. Yelp.com users also are altruistic in their motivation for contributing reviews to Yelp.com. Yelp.com users who access it tend to act on the information found within the reviews. Originality/value – Research articles have focused on user-generated reviews in the past; however, few have examined motivations of using and posting restaurant reviews. The value of conducting research comes from being able to understand the importance of user-generated restaurant reviews for customers in a comprehensive manner.

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With the advent and accelerated development of augmented reality (AR), an increasing number of studies have been conducted to test the effectiveness of this technique in education. Few, however, have investigated how AR might influence students' motivation toward learning of a second language. To address this gap in the literature, we used a combination of convenience sampling and criterion sampling to select five Chinese college students to evaluate an English vocabulary learning application built upon augmented reality technology. To assess student motivation, the ARCS motivational model was adopted. A semi-structured interview with openended questions was used to collect data. Participants indicated that though they were attracted by this tool at the beginning, their motivation level decreased toward the end of the study session. An interpretation of our observations in the context of the ARCS model suggests three motivational issues. First, predefined AR materials failed to establish relevance to subjects' personal interests and previous experiences. Secondly, subjects' confidence seemed to have been negatively influenced due to their difficulty in achieving the stated learning objectives. Lastly, technical issues delayed the computer quickly identifying the triggering image and thus resulted in a noticeable lack of system responsiveness. It seems this delay decreased subjects' satisfaction and distracted their attention from the learning task. These factors seemed most determinative in compromising AR's effectiveness as a tool to increase student motivation toward English vocabulary learning. It must be stressed that this study is a low subject N exploratory pilot not intended to produce binding generalizations. Nonetheless, these findings should provide useful insights toward the successful application of AR in the educational realm and identify potential causal factors that could form the foundation of future experimental research. The authors recommend further study with a larger number of subjects with a wider range of vocabulary sample and a more powerful viewing device capable of more quickly identifying the trigger images.

Abstract-This paper analyzes students' experience with Cogent, a virtual economy system used throughout the 4 years of a B.S. degree in a Technology major. The case study explains the rules of the Cogent system and investigates its effectiveness to motivate students to learn. Using focus groups and interviews, we collected qualitative data from students about their experience and perceptions of Cogent. The results indicate that Cogent played an encouraging and motivational role for these students and suggest potential for the successful design and implementation of meaningful gamification systems to promote student motivation and engagement within an educational context.

We present a design study of the Deep Insights Anywhere, Anytime (DIA2) platform, a web-based visual analytics system that allows program managers and academic staff at the U.S. National Science Foundation to search, view, and analyze their research funding portfolio. The goal of this system is to facilitate users' understanding of both past and currently active research awards in order to make more informed decisions of their future funding. This user group is characterized by high domain expertise yet not necessarily high literacy in visualization and visual analytics-they are essentially casual experts-and thus require careful visual and information design, including adhering to user experience standards, providing a self-instructive interface, and progressively refining visualizations to minimize complexity. We discuss the challenges of designing a system for casual experts and highlight how we addressed this issue by modeling the organizational structure and workflows of the NSF within our system. We discuss each stage of the design process, starting with formative interviews, prototypes, and finally live deployments and evaluation with stakeholders.