The Relation Between ‘Student Loyalty’ and ‘Student Satisfaction’ (A case of College/Intermediate Students at Forman Christian College)

Due to the increase in the number of educational institutions in the past few years, the competition has significantly increased. This change in the environment has shown the declining trends in enrollments and also in low quality students. It is becoming extremely important that policy makers of educational institutions find ways to increase the loyalty of their students. Such type of student loyalty can help in marketing the institutions by spreading a good word of mouth. In order to do so, institutions should find the areas which contribute more in student loyalty towards their institution. This would also help to identify ways of attracting prospective students with tailored and aggressive marketing programs. The study was conducted with College/Intermediate students of Forman Christian College (FCC), a twoyear data has been considered to find the relationship between “student satisfaction” and “student loyalty” (spreading a good word of mouth and recommending their institution to others). 2,309, FCC students were surveyed, and correlation and regression analysis was performed to establish a model to predict “student loyalty” as a dependent, using “student satisfaction” as an independent variable. After performing the data analysis it was discovered that some of the satisfaction areas contribute in student loyalty as compared to others. Based on the results some suggestions and recommendation were made by the author that can help in creating a positive word of mouth among their alumni students, which can help in attracting good students for FCC

The Relation Between ‘Student Loyalty’ and ‘Student Satisfaction’ (A case of College/Intermediate Students at Forman Christian College)

Due to the increase in the number of educational institutions in the past few years, the competition has significantly increased. This change in the environment has shown the declining trends in enrollments and also in low quality students. It is becoming extremely important that policy makers of educational institutions find ways to increase the loyalty of their students. Such type of student loyalty can help in marketing the institutions by spreading a good word of mouth. In order to do so, institutions should find the areas which contribute more in student loyalty towards their institution. This would also help to identify ways of attracting prospective students with tailored and aggressive marketing programs. The study was conducted with College/Intermediate students of Forman Christian College (FCC), a twoyear data has been considered to find the relationship between “student satisfaction” and “student loyalty” (spreading a good word of mouth and recommending their institution to others). 2,309, FCC students were surveyed, and correlation and regression analysis was performed to establish a model to predict “student loyalty” as a dependent, using “student satisfaction” as an independent variable. After performing the data analysis it was discovered that some of the satisfaction areas contribute in student loyalty as compared to others. Based on the results some suggestions and recommendation were made by the author that can help in creating a positive word of mouth among their alumni students, which can help in attracting good students for FCC

Public bookmarks and private benefits: An analysis of incentives in social computing

Users of social computing websites are both producers and consumers of the information found on the site. This creates a novel problem for web-based software applications: how can website designers induce users to produce information that is useful for others? We study this question by interviewing users of the social bookmarking website del.icio.us. We find that for the users in our sample, metadata reflecting who bookmarked a webpage better supports information seeking than free-form keyword metadata (tags). We explain this finding by describing differences in the way that the design of del.icio.us motivates users to contribute by providing personal benefits for bookmarking and tagging.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61317/1/1450440240_ftp.pd

Collaborative Filtering Based Recommendation System: A survey

Abstract—the most common technique used for recommendations is collaborative filtering. Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships from a group of user who share the same preferences and taste. In this paper we have explored various aspects of collaborative filtering recommendation system. We have categorized collaborative filtering recommendation system and shown how the similarity is computed. The desired criteria for selection of data set are also listed. The measures used for evaluating the performance of collaborative filtering recommendation system are discussed along with the challenges faced by the recommendation system. Types of rating that can be collected from the user to rate items are also discussed along with the uses of collaborative filtering recommendation system

Modeling User Preferences in Recommender Systems: A Classification Framework for Explicit and Implicit User Feedback

Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users' preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, UserModel, Scale of Measurement, and Domain Relevance.We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback. © 2014 ACM

Incentive-Centered Design for User-Contributed Content

We review incentive-centered design for user-contributed content (UCC) on the Internet. UCC systems, produced (in part) through voluntary contributions made by non-employees, face fundamental incentives problems. In particular, to succeed, users need to be motivated to contribute in the first place ("getting stuff in"). Further, given heterogeneity in content quality and variety, the degree of success will depend on incentives to contribute a desirable mix of quality and variety ("getting \emph{good} stuff in"). Third, because UCC systems generally function as open-access publishing platforms, there is a need to prevent or reduce the amount of negative value (polluting or manipulating) content. The work to date on incentives problems facing UCC is limited and uneven in coverage. Much of the empirical research concerns specific settings and does not provide readily generalizable results. And, although there are well-developed theoretical literatures on, for example, the private provision of public goods (the "getting stuff in" problem), this literature is only applicable to UCC in a limited way because it focuses on contributions of (homogeneous) money, and thus does not address the many problems associated with heterogeneous information content contributions (the "getting \emph{good} stuff in" problem). We believe that our review of the literature has identified more open questions for research than it has pointed to known results.http://deepblue.lib.umich.edu/bitstream/2027.42/100229/1/icd4ucc.pdf7

Tom Sawyer production on the Internet: Getting the good stuff in, keeping the bad stuff out

User-contributed content as an input to the production of information services is not new, but it is growing rapidly in significance and prevalence. Open-source software, Wikipedia, and Flickr are but a few examples of the variety of information products and services relying on user-contributed content. I propose a characterization of user-contributed content, and identify contributor behavior issues critical for success. From the perspective of an information service provider, or the economy as a whole, these issues predict underprovision of content, inefficient mixes of quality and variety, and undesirable levels of content pollution. How might we design information services or systems to ameliorate these problems? Given the centrality of autonomous, motivated human behavior in user-contributed content problems, I argue this is a problem for \emph{incentive-centered design}: how to configure economic, social and psychological incentives to induce contribution, discourage pollution, and motivate sufficient effort to generate quality? To illustrate, for a content pollution problem loosely based on a popular Web site's experience, I offer a stylized mechanism that relies on user-contributed (meta)content to screen out polluting contributions.http://deepblue.lib.umich.edu/bitstream/2027.42/78183/1/sea-icd4ucc.pd

WikiSensing: A collaborative sensor management system with trust assessment for big data

Big Data for sensor networks and collaborative systems have become ever more important in the digital economy and is a focal point of technological interest while posing many noteworthy challenges. This research addresses some of the challenges in the areas of online collaboration and Big Data for sensor networks. This research demonstrates WikiSensing (www.wikisensing.org), a high performance, heterogeneous, collaborative data cloud for managing and analysis of real-time sensor data. The system is based on the Big Data architecture with comprehensive functionalities for smart city sensor data integration and analysis. The system is fully functional and served as the main data management platform for the 2013 UPLondon Hackathon. This system is unique as it introduced a novel methodology that incorporates online collaboration with sensor data. While there are other platforms available for sensor data management WikiSensing is one of the first platforms that enable online collaboration by providing services to store and query dynamic sensor information without any restriction of the type and format of sensor data. An emerging challenge of collaborative sensor systems is modelling and assessing the trustworthiness of sensors and their measurements. This is with direct relevance to WikiSensing as an open collaborative sensor data management system. Thus if the trustworthiness of the sensor data can be accurately assessed, WikiSensing will be more than just a collaborative data management system for sensor but also a platform that provides information to the users on the validity of its data. Hence this research presents a new generic framework for capturing and analysing sensor trustworthiness considering the different forms of evidence available to the user. It uses an extensible set of metrics that can represent such evidence and use Bayesian analysis to develop a trust classification model. Based on this work there are several publications and others are at the final stage of submission. Further improvement is also planned to make the platform serve as a cloud service accessible to any online user to build up a community of collaborators for smart city research.Open Acces

An analysis of popularity biases in recommender system evaluation and algorithms

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 03-10-2019Las tecnologías de recomendación han ido progresivamente extendiendo su presencia en las aplicaciones y servicios de uso diario. Los sistemas de recomendación buscan realizar sugerencias individualizadas de productos u opciones que los usuarios puedan encontrar interesantes o útiles. Implícita en el concepto de recomendación está la idea de que las sugerencias más satisfactorias para cada usuario son aquellas que tienen en cuenta sus gustos particulares, por lo que cabría esperar que los algoritmos de recomendación más eficaces sean los más personalizados. Sin embargo, se ha observado recientemente que recomendar simplemente los productos más populares no resulta una estrategia mucho peor que los mejores y más sofisticados algoritmos personalizados, y más aún, que estos tienden a sesgar sus recomendaciones hacia opciones mayoritarias. Por todo ello, es rele-vante entender en qué medida y bajo qué circunstancias es la popularidad una señal real-mente efectiva a la hora de recomendar, y si su aparente efectividad se debe a la existencia de ciertos sesgos en las metodologías de evaluación offline actuales, como todo parece indicar, o no. En esta tesis abordamos esta cuestión desde un punto de vista plenamente formal, identificando los factores que pueden determinar la respuesta y modelizándolos en térmi-nos de dependencias probabilísticas entre variables aleatorias, tales como la votación, el descubrimiento y la relevancia. De esta forma, caracterizamos situaciones concretas que garantizan que la popularidad sea efectiva o que no lo sea, y establecemos las condiciones bajo las cuales pueden existir contradicciones entre el acierto observado y el real. Las principales conclusiones hacen referencia a escenarios simplificados prototípicos, más allá de los cuales el análisis formal concluye que cualquier resultado es posible. Para profun-dizar en el escenario general sin suposiciones tan simplificadas, estudiamos un caso parti-cular donde el descubrimiento de ítems es consecuencia de la interacción entre usuarios en una red social. Además, en esta tesis proporcionamos una explicación formal del sesgo de populari-dad que presentan los algoritmos de filtrado colaborativo. Para ello, desarrollamos una versión probabilística del algoritmo de vecinos próximos kNN. Dicha versión evidencia además la condición fundamental que hace que kNN produzca recomendaciones perso-nalizadas y se diferencie de la popularidad pura