A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach

Selection of a suitable Ph.D. supervisor is a very important step in a student’s career. This paper presents a multi-criteria decision support system to assist students in making this choice. The system employs a hybrid method that first utilizes a fuzzy analytic hierarchy process to extract the relative importance of the identified criteria and sub-criteria to consider when selecting a supervisor. Then, it applies an information retrieval-based similarity algorithm (TF/IDF or Okapi BM25) to retrieve relevant candidate supervisor profiles based on the student’s research interest. The selected profiles are then re-ranked based on other relevant factors chosen by the user, such as publication record, research grant record, and collaboration record. The ranking method evaluates the potential supervisors objectively based on various metrics that are defined in terms of detailed domain-specific knowledge, making part of the decision making automatic. In contrast with other existing works, this system does not require the professor’s involvement and no subjective measures are employed

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Big Data Ethics in Research

The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (Data Protection Directive - General Data Protection Regulation, "GDPR"). The Ethics Issues section details the specific aspects of Big Data. After a brief section of Big Data Research, I finalize my work with the presentation of Conclusions on research ethics in working with Big Data. CONTENTS: Abstract 1. Introduction - 1.1 Definitions - 1.2 Big Data dimensions 2. Technology - 2.1 Applications - - 2.1.1 In research 3. Philosophical aspects 4. Legal aspects - 4.1 GDPR - - Stages of processing of personal data - - Principles of data processing - - Privacy policy and transparency - - Purposes of data processing - - Design and implicit confidentiality - - The (legal) paradox of Big Data 5. Ethical issues - Ethics in research - Awareness - Consent - Control - Transparency - Trust - Ownership - Surveillance and security - Digital identity - Tailored reality - De-identification - Digital inequality - Privacy 6. Big Data research Conclusions Bibliography DOI: 10.13140/RG.2.2.11054.4640

Evidence-Based Selection of a Fall Risk Assessment Tool: A Program Evaluation Review

Fall prevention strategies are a consistent topic of discussion for healthcare regarding patient safety, as patient falls are costly to the patient and the organization. This project uses the CDC Framework for Program Evaluation to assess the fall prevention policy of a local hospital system, with particular emphasis on the fall risk assessment tool, Hester Davis. This project also explores the risks and benefits of adopting an alternative fall risk assessment tool, predictive analytics. Predictive analytics uses electronic health record (EHR) data analysis to provide a highly individualized patient fall risk score based on a large variety of patient and environmental factors. Comparative analysis of the two tools was performed in 104 chart reviews, which provided evidence for the use of predictive analytics. Recommendations are provided for a development of a new fall prevention policy that includes predictive analytics as the primary fall risk assessment tool. Based on these recommendations, this project also includes a competency-based orientation toolkit, which can be put into place should the organization choose to transition the policy to utilize predictive analytics as the primary fall risk assessment

Annual Report 2017-2018

LETTER FROM THE DEAN I am pleased to share with you the College of Computing and Digital Media’s (CDM) 2017-18 annual report, highlighting the many achievements across our community. It was a big year. We began offering five new programs (two bachelor’s, two master’s, and one PhD) across our three schools, in addition to several new certificate programs through our Institute for Professional Development. We built new, cutting-edge spaces to support these and other programs— most notably a 4,500 square-foot makerspace, a robotics and medical engineering lab, an augmented and virtual reality lab, and plans for a cyber-physical systems project lab. Our faculty continued to pursue their research and creative agendas, offering collaborative opportunities with students and partners. CDM students and alumni were celebrated for their many achievements— everything from leading the winning teams at the U.S. Cyber Challenge and Campus 1871 to showcasing their games at juried festivals and winning national screenwriting competitions. We encouraged greater research and teaching collaboration, both between our own schools and with units outside CDM. Design and Computing faculty are working together on an NSA grant for smart home devices that considers both software and interface/design, as well as a new grant-funded game lab. One Project Bluelight film team collaborated with The Theatre School and the School of Music while CDM and College of Science and Health faculty joined forces to research the links between traumatic brain injury, domestic violence, and deep games. It has been exciting and inspiring to witness the accomplishments of our innovative and dedicated community. We are proud to provide the space and resources for them to do their exceptional work. David MillerDean, College of Computing and Digital Mediahttps://via.library.depaul.edu/cdmannual/1001/thumbnail.jp

A proposal to improve marketing efectiveness in a business to business model

Um projecto de apresentação de uma proposta de melhoria da eficácia e eficiência de marketing e retorno de investimentos em marketing (ROMI), numa empresa multinacional de embalagens para alimentos líquidos. Iniciando por uma análise da abordagem actual da empresa na maximização e garantia do retorno dos seus investimentos indirectos com os seus clientes da industria alimentar. Procurando respostas às questões que normalmente rodeiam a temática de eficiência e eficácia de investimentos em marketing como: estão os nossos investimentos indirectos com os nossos clientes da indústria alimentar realmente a beneficiar a nossa quota de mercado e o nosso volume de vendas a logo prazo? O projecto assenta no pressuposto base do quadro conceptual utilizado que a modelação adequada do ROMI contribui significativamente para o aumento deste. A recomendação de melhoria assenta na implementação uma abordagem de modelação proposta pelo Boston Consulting Group (BCG), que dê cobertura à maioria dos investimentos que a companhia faz indirectamente com a indústria alimentar. Os resultados esperados são uma significativa melhoria no ROMI e uma linha no orçamento anual da empresa especialmente para suportar os três elementos fundamentais de uma cultura de marketing effectiveness: medição de desempenho, modelação e simulação, e gestão e monitorização. Se desempenha funções em marketing e está pressionado para demonstrar a sua contribuição para os resultados no longo prazo, se está preocupado com o impacto publicitário dos seus investimentos, ou se está preocupado em valorizar marcas ou em como atribuir orçamento por categorias de produtos, então este documento merece definitivamente uma leitura.A project presenting a proposal for marketing effectiveness and return on marketing investments (ROMI) improvement in a business to business company that operates in food packaging industry, starting from the analysis of the company’s actual approach to maximize effectiveness. Answers to questions surrounding the effectiveness of marketing investments like: are our investments together with our partners (customers) really benefiting our market shares and derived long term sales volume? The project’s basic framework assumption is that proper ROMI modelling tends to contribute significantly to increase ROMI. The key recommendation is to implement marketing effectiveness modelling based on the Boston Consulting Group (BCG) approach covering the majority of the company’s marketing investment with their business partners (customers). The expected results will be a significant improved ROMI and a line item in the budget specifically designated to support the three elements of the marketing effectiveness culture: measurement and metrics, modelling and simulation, and management and monitoring. If you work in marketing in a business to business environment and you are pressurized to demonstrate the contribution of marketing in the long term, worried about your advertising’s effects, want to value your partner’s brands and wondering how to allocate marketing budget across partners and categories, so this document should definitely read on

A decision-making framework for aligning business analytics with business objectives

Throughout this thesis, we discuss the impact of Business Analytics on the organizational decision-making process with the objective of designing a framework that provides the organization with extra-knowledge on how to implement and sustain their analytics. First, we develop the concept of capability using the resource-based view and the IT literature to define what is a Business Analytics capability. We then define the key capabilities that provide the organization with a competitive advantage. Moreover, we investigate the role of governance and alignment as well as the impact of the concepts on the decision making effectiveness. To provide an insight on the adjustment to be made in order to increase the organization Business Analytics performance, we emphasise the role of alignment between Information Technology governance, corporate governance, data governance and Business Analytics governance. Thereafter we create the framework based on academic and empirical research and apply this framework throughout a case study. Based on this case study we provide an academic recommendation to the investigated organization. This thesis highlights the importance of the creation of a Business Analytics governance. Also, the research provides a framework linking Business Analytics with decision making successfulness

Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be crucial to optimize utilities not just for the end user, but also for other actors such as item sellers or producers who desire a fair representation of their items. Existing solutions do not properly address various aspects of multi-sided fairness in recommendations as they may either solely have one-sided view (i.e. improving the fairness only for one side), or do not appropriately measure the fairness for each actor involved in the system. In this thesis, I aim at first investigating the impact of unfair recommendations on the system and how these unfair recommendations can negatively affect major actors in the system. Then, I seek to propose solutions to tackle the unfairness of recommendations. I propose a rating transformation technique that works as a pre-processing step before building the recommendation model to alleviate the inherent popularity bias in the input data and consequently to mitigate the exposure unfairness for items and suppliers in the recommendation lists. Also, as another solution, I propose a general graph-based solution that works as a post-processing approach after recommendation generation for mitigating the multi-sided exposure bias in the recommendation results. For evaluation, I introduce several metrics for measuring the exposure fairness for items and suppliers, and show that these metrics better capture the fairness properties in the recommendation results. I perform extensive experiments to evaluate the effectiveness of the proposed solutions. The experiments on different publicly-available datasets and comparison with various baselines confirm the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi

Model-Driven Skills Assessment in Knowledge Management Systems

Organizations need employees who perform satisfactorily in generic skills, such as teamwork, leadership, problem solving or interpersonal abilities, among others. In organizational environments, employees perform work that is not always visible for supervisors and, thus, they can hardly assess their performance in generic skills. By using a knowledge management system, the users are able to leave a trace of their activity in the system’s records. This research aims to address a computer supported assessment of the user’s generic skills from the perspective of Model-Driven engineering. First, a systematic mapping study is carried out to understand the state of the art. Second, a proposal based on Model-Driven engineering is presented and is then validated through an organizational learning process model. Our results are promising and we are able to conduct a scalable assessment based on objective indicators of the employee’s planning, time management and problem solving skills