Computer Literacy Challenges for Adult Returning Students, Lost in a Different Generation of Computer?

In recent years, many adult returning students go back to school for college degree and retooling. Many of them are struggling with computer literacy skills. In this study, we try to understand the computer related challenges that adult returning students are facing. We identify the difficulties they encounter through direct observations in classroom. We also use informal conversations with students to help us understand why they have the challenges. Next, we conduct critical analysis by taking the conceptual lens of empathetic thinking and disruptive information technology (IT) innovation. We discover several factors that may contribute to adult returning students’ struggles with computer literacy. Furthermore, we suggest several ways that may help adult returning students overcome the adversity. This research will contribute to our general understanding of the computer literacy challenges faced by adult returning students. The research findings also have important implications for Information Systems (IS) educators and college administrators

Text Classification with Imperfect Hierarchical Structure Knowledge

Many real world classification problems involve classes organized in a hierarchical tree-like structure. However in many cases the hierarchical structure is ignored and each class is treated in isolation or in other words the class structure is flattened (Dumais and Chen, 2000). In this paper, we propose a new approach of incorporating hierarchical structure knowledge by cascading it as an additional feature for Child level classifier. We posit that our cascading model will outperform the baseline “flat” model. Our empirical experiment provides strong evidences supporting our proposal. Interestingly, even imperfect hierarchical structure knowledge would also improve classification performance

STRESS FROM INTERNET FRAUD AND ONLINE SOCIAL SUPPORT

This exploratory research attempts to fill the literature gap by investigating ordinary user’s response to Internet transaction fraud and examining the types of online social support. We adapt the stress process model from the psychology literature and apply it to the context of Internet fraud. Moreover, we propose a conceptual online social support model for Internet fraud. We argue that there are two types of support mechanisms – factual information exchange and emotional support. In addition to proposing the above two conceptual models, we conduct two preliminary qualitative case studies. First, we report a case of eBay fraud and perform in-depth analysis of the victims’ belief, emotion and behavior response to a fraud in light of the stress process model. Second, we present a threaded discussion on eBay Community Answer Center, which is about money wire transfer fraud. We show that the reply sentences convey either information exchange or emotional support

Faculty Informal Self-learning and Ways to Engage Students

Many junior Information Systems (IS) faculty members are facing the challenge of how to find time to learn and bring in fresh subject content to classroom teaching beyond textbooks, given the tight constraint of time consumed by multiple teaching preparations, research activities, and various service commitments. We propose the idea of faculty informal self-learning and then sharing the newly acquired knowledge with students in classroom discussions. We describe the initiative of collaborating with our college IT services department for this purpose. More specifically, we invite college IT services staff to give students a campus network tour and a talk on network administration in our telecommunication class, and speak on cloud service and disaster recovery in our MIS club meetings. We also intentionally engage in informal conversations with the IT services staff to learn new practical knowledge with the aim to share with students and thus motivate their desire to learn

Novice User’s Trust of Data Mining Technology

Although data mining has been widely and successfully used in the domain of business operations, research into how novice end users might make an adoption decision is sparse and much needed. This paper employs a view that trust is generally assumed to be an important precondition for the adoption of any information technology and identifies the antecedents of trust in data mining systems synthesizing empirical studies on people’s trust in computer-mediated services as well as multiple anecdotal evidences documented in the literature

MOTIVATING BUSINESS MAJOR STUDENTS TO LEARN COMPUTER PROGRAMMING – A CASE STUDY

Learning to program is viewed as difficult by many students. How to motivate and engage business major students to learn programming is challenging. In this paper, we report our attempt to teach business students programming. In our teaching method, first we orient students with the business and managerial aspect of programming by posing six original questions. By discussing these six questions, students gain appreciation of the high level responsibility of participating, contributing, assessing, and managing Information Systems (IS) as business professionals. Second, we adopt a Let Us Do It Together approach to deliver hands on labs to teach the technical aspect of programming. Inspired by the constructivism learning theory and the learning by doing and experimentation idea, our Let Us Do It Together approach mitigates students’ anxiety and fear of programming. Overall, our teaching approach seems to enhance students’ interest in programming

Teaching Students Mainframe Skills for the Niche Market: An Exploratory Proposal

Contrary to the common belief, mainframe is here to stay in corporate Information Technology (IT) infrastructure. Many organizations find it more advantageous in leveraging the mainframe and are adding more workloads and exploiting new technologies. They increase the mainframe usage and leverage it to drive business value and become more successful. However, the mainframe industry faces the challenge of talent shortage because many older mainframe professionals are retiring. On the other hand, very few universities teach students mainframe skills. The high demand of mainframe jobs and the low supply of college graduates with mainframe skills present a unique opportunity for Information Systems (IS) programs to train students mainframe skills for job placement in the niche market. Therefore, we propose the idea of exploring teaching students mainframe skills in IS curriculum. Several excellent programs and resources to prepare students with mainframe skills are also reported in this paper

E-COMMERCE EXPERIENCES AND IMPACT ON THE DEVELOPMENT OF PROFESSIONALISM

ABSTRACT Nowaday

The Concise Guide to PHARMACOLOGY 2023/24: G protein-coupled receptors.

The Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and about 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (https://www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/bph.16177. G protein-coupled receptors are one of the six major pharmacological targets into which the Guide is divided, with the others being: ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages