The goal of San Bernardino County Medical Information Resource is to develop, coordinate, and implement a centralized information and referral database that contains information about health and human services provided by public and private entities throughtout the county of San Bernardino and is accessible to the public via the Internet.

The primary purpose of this project is to develop an online forum to facilitate communication among educators, parents, and students resulting in an open environment for more informed decisions by all those involved in the educational process. The second purpose is the personal development of an electronic learning application using online tools needed for an effective online learning environment that will cost the school district little or nothing.

This project will address the issues concerning the benefits of distance learning, a growing area in our education system today. Distance learning takes place when the teacher and students are separated, and technology is used to bridge the instructional gap.

Presents an educational multimedia development project created to teach novice learners about the various elements of the film making process. The multimedia web site designed for this project was created using Macromedia Flash 8. The process involved surveying people to determine learner needs and alpha and beta testing the final project to gather data regarding functionality and learner satisfaction.

Thesis (Ph. D. (Media Studies)) -- University of Limpopo, 2021 / This study focuses on the use of the internet for students’ academic performance at institutions of higher learning. Various internet applications including search engines, online library resources and social media are evaluated to explore how best they can be used to enhance students’ performance at institutions of higher learning. This is critical to ensure that students maximise the use of technology specifically for academic purposes. Generally, students are constant users of the internet at institutions of higher learning, hence it is cardinal to examine their internet use patterns for academic purposes. Institutions of higher learning have invested in advanced technology through the internet to enhance student academic purposes. Therefore, one would expect students to use various internet applications effectively for the betterment of their studies. In this study, the researcher used both qualitative and quantitative research methodologies. The researcher also employed exploratory and descriptive designs. These methodologies and designs are appropriate for this study because they allow data to be collected through focus group interviews and questionnaires. Focus group interviews were employed to collect qualitative data and questionnaires were used to collect quantitative data. The researcher conducted six focus groups from three institutions of higher learning, namely the University of Limpopo, University of Venda and Tshwane University of Technology, Polokwane campus. Three hundred and forty-three (343) questionnaires were analysed for this study. These data collection tools were pertinent for this study since they assist to determine factors that influence the attitudes, opinions and behavior of the participants. Online library resources play a cardinal role in enhancing the learning process for students by providing online content which could have been difficult to access without the internet. Social media improve and enhance students’ academic performance, but students do not know how best to use it for academic purposes. Proper integration of social media into education is needed. In contrast, students prefer to use social media purely for socialising and entertainment. Despite their ability to assist students in enhancing and improving learning process, social media are, mostly not used for academic purposes. xiv Additionally, students’ use of internet search engines exposes them to an array of information which require critical online information literacy in order to choose the best information. Lastly, the study contributes to the existing body of knowledge by creating a model which will enhance and assist students to easily access academic information through the use of a mobile application. The study also provides significant information which could be used to amend and draft new ICT policies within institutions of higher learning, taking into consideration the adoption of online learning through the use various technologies including social media. / The National Institute for the Humanities and Social Sciences (NIHSS)

This dissertation explores applicant reactions to Internet-based selection procedures in order to advance theory and practice related to the use modern employee selection tools. Previous authors have explored this topic area (e.g., Harris et al., 2003). However, this dissertation goes beyond previous research by proposing and testing a model that incorporates the measurement of multiple constructs that are highly relevant to organizations when utilizing Internet-based selection procedures. Such constructs include privacy concerns, explanations, control, fairness perceptions, litigation intentions, organizational intentions, and test-taking motivation. Current organizational justice theory, previous findings from studies on applicant reactions to selection procedures, and research on Internet privacy concerns provided the foundation on which this research is based. This dissertation also pulls from theory in the legal, information sciences, and psychology literatures. A model of applicant reactions that included privacy concerns and multiple outcomes relevant to organizations was proposed. Hypotheses examining this model were tested via a high-fidelity laboratory study with student participants. One-third of the participants in this study were seeking jobs at the time of participation. Findings indicated that privacy concerns are an important predictor of both proximal (i.e., fairness perceptions) and distal (i.e., organizational intentions, test-taking motivation) applicant reaction outcomes. Results also demonstrated support for a mediating role of fairness perceptions in the relationships between privacy concerns and organizational intentions as well as between privacy concerns and test-taking motivation. Providing applicants with control and explanations were found to have no moderating effect on the relationship between privacy concerns and fairness perceptions. However, post-hoc analyses indicated that excuse explanations moderated the effect of privacy concerns on test-taking motivation. Theoretical implications of this dissertation include support for a one-factor model of organizational justice as well as a call for more integration of research from outside of industrial-organizational psychology. Additionally, areas for future research, including opportunities for improvement of study design involving timing of measures, are presented. Finally, implications for practice are discussed in regard to the possible impact of privacy concerns to large numbers of applicants participating in Internet-based selection processes, including a discussion on the importance of applicant privacy concerns to organizations and the use of multiple, inexpensive methods that may aid organizations in increasing fairness perceptions among applicants.

In this dissertation, I present three theoretical results with real-world applications related to scheduling and distributionally-robust games, important fields in discrete optimization, and computer science. The first chapter provides simple, technology-free interventions to manage elevator queues in high-rise buildings when passenger demand far exceeds the capacity of the elevator system. The problem was motivated by the need to manage passengers safely in light of reduced elevator capacities during the COVID-19 pandemic. We use mathematical modeling, epidemiological expertise, and simulation to design and evaluate our algorithmic solutions. The key idea is to explicitly or implicitly group passengers that are going to the same floor into the same elevator as much as possible, substantiated theoretically using a technique from queuing theory known as stability analysis. This chapter is joint work with Charles Branas, Adam Elmachtoub, Clifford Stein, and Yeqing Zhou, directly in collaboration with the New York City Mayor’s Office of the Chief Technology Officer and the Department of Citywide Administrative Services. The second chapter proposes new algorithms for recomputing passenger itineraries for airlines during major disruptions when carefully planned schedules are thrown into disarray. An airline network is a massive temporal graph, often with tight regulatory and operational constraints. When disruptions propagate through an airline network, the objective is to \textit{recover} within a given time frame from a disruption, meaning we replan schedules affected by the disruption such that the new schedules have to match the originally planned schedules after the time frame. We aim to solve the large-scale airline recovery problem with quick, user-independent, consistent, and near-optimal algorithms. We provide new algorithms to solve the passenger recovery problem, given recovered flight and crew solutions. We build a preprocessing step and construct an Integer Program as well as a network-based approach based on solving multiple-label shortest path problems. Experiments show the tractability of our proposed algorithms on airline data sets with heavy flight disruptions. This chapter is joint work with Clifford Stein, stemming from an internship and collaboration with the Machine Learning team (Artificial Intelligence organization) of GE Global Research, Niskayuna, New York. The third chapter is about computing distributionally-robust strategies for a popular game theory model called Stackelberg games, where one player, called the leader, is able to commit to a strategy first, assuming the other player(s), called follower(s) would best respond to the strategy. In many of the real-world applications of Stackelberg games, parameters such as payoffs of the follower(s) are not known with certainty. Distributionally-robust optimization allows a distribution over possible model parameters, where this distribution comes from a set of possible distributions. The goal for the leader is to maximize their expected utility with respect to the worst-case distribution from the set. We initiate the study of distributionally-robust models for Stackelberg games, show that a distributionally-robust Stackelberg equilibrium always exists across a wide array of uncertainty models, and provide tractable algorithms for some general settings with experimental results. This chapter is joint work with Christian Kroer.

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