Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLearning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness
44
0
0.0
(
0
)
Added by
Ruoyan Kong
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Bulk email is a primary communication channel within organizations, with all-company emails and regular newsletters serving as a mechanism for making employees aware of policies and events. Ineffective communication could result in wasted employee time and a lack of compliance or awareness. Previous studies on organizational emails focused mostly on recipients. However, organizational bulk email system is a multi-stakeholder problem including recipients, communicators, and the organization itself. We studied the effectiveness, practice, and assessments of the organizational bulk email system of a large university from multi-stakeholders perspectives. We conducted a qualitative study with the universitys communicators, recipients, and managers. We delved into the organizational bulk emails distributing mechanisms of the communicators, the reading behaviors of recipients, and the perspectives on emails values of communicators, managers, and recipients. We found that the organizational bulk email system as a whole was strained, and communicators are caught in the middle of this multi-stakeholder problem. First, though the communicators had an interest in preserving the effectiveness of channels in reaching employees, they had high-level clients whose interests might outweigh judgment about whether a message deserves widespread circulation. Second, though communicators thought they were sending important information, recipients viewed most of the organizational bulk emails as not relevant to them. Third, this disagreement was amplified by the success metric used by communicators. They viewed their bulk emails as successful if they had a high open rate. But recipients often opened and then rapidly discarded emails without reading the details. Last, while the communicators in general understood the challenge, they had a limited set of targeting and feedback tools to support their task.
rate research
Read More
Computing devices such as laptops, tablets and mobile phones have become part of our daily lives. End users increasingly know more and more information about these devices. Further, more technically savvy end users know how such devices are being built and know how to choose one over the others. However, we cannot say the same about the Internet of Things (IoT) products. Due to its infancy nature of the marketplace, end users have very little idea about IoT products. To address this issue, we developed a method, a crowdsourced peer learning activity, supported by an online platform (OLYMPUS) to enable a group of learners to learn IoT products space better. We conducted two different user studies to validate that our tool enables better IoT education. Our method guide learners to think more deeply about IoT products and their design decisions. The learning platform we developed is open source and available for the community.
Differential privacy is a promising framework for addressing the privacy concerns in sharing sensitive datasets for others to analyze. However differential privacy is a highly technical area and current deployments often require experts to write code, tune parameters, and optimize the trade-off between the privacy and accuracy of statistical releases. For differential privacy to achieve its potential for wide impact, it is important to design usable systems that enable differential privacy to be used by ordinary data owners and analysts. PSI is a tool that was designed for this purpose, allowing researchers to release useful differentially private statistical information about their datasets without being experts in computer science, statistics, or privacy. We conducted a thorough usability study of PSI to test whether it accomplishes its goal of usability by non-experts. The usability test illuminated which features of PSI are most user-friendly and prompted us to improve aspects of the tool that caused confusion. The test also highlighted some general principles and lessons for designing usable systems for differential privacy, which we discuss in depth.
Crowdsourcing information constitutes an important aspect of human-in-the-loop learning for researchers across multiple disciplines such as AI, HCI, and social science. While using crowdsourced data for subjective tasks is not new, eliciting useful insights from such data remains challenging due to a variety of factors such as difficulty of the task, personal prejudices of the human evaluators, lack of question clarity, etc. In this paper, we consider one such subjective evaluation task, namely that of estimating experienced emotions of distressed individuals who are conversing with a human listener in an online coaching platform. We explore strategies to aggregate the evaluators choices, and show that a simple voting consensus is as effective as an optimum aggregation method for the task considered. Intrigued by how an objective assessment would compare to the subjective evaluation of evaluators, we also designed a machine learning algorithm to perform the same task. Interestingly, we observed a machine learning algorithm that is not explicitly modeled to characterize evaluators subjectivity is as reliable as the human evaluation in terms of assessing the most dominant experienced emotions.
Lower computer system input-to-output latency substantially reduces many task completion times. In fact, literature shows that reduction in targeting task completion time from decreased latency often exceeds the decrease in latency alone. However, for aiming in first person shooter (FPS) games, some prior work has demonstrated diminishing returns below 40 ms of local input-to-output computer system latency. In this paper, we review this prior art and provide an additional case study with data demonstrating the importance of local system latency improvement, even at latency values below 20 ms. Though other factors may determine victory in a particular esports challenge, ensuring balanced local computer latency among competitors is essential to fair competition.
Teaching that uses projected presentation media such as slide-shows lacks support for dynamic content whose form and behaviors require live changes during a lecture. Recent software alternatives such as the Chalktalk software platform allow the creation of interactive simulations in arbitrary sequences and combinations within presentations. These more dynamic solutions, however, do not optimize for face-to-face interactions: eye-contact, gaze direction, and concurrent awareness of another persons movements together with the presented content. To explore the extent to which these face-to-face interactions may improve learning and engagement during a lecture, we propose a Mixed Reality (MR) platform that places Chalktalks behaviors and simulations within a mirrored virtual world environment designed for face-to-face, one-on-one interactions. We compare our system with projected Chalktalk to evaluate its relative effectiveness for learning, retention, and level of engagement.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
