Kirk Plangger

Bio: Kirk Plangger is an academic researcher from King's College London. The author has contributed to research in topics: Social media & Business. The author has an hindex of 15, co-authored 55 publications receiving 2524 citations. Previous affiliations of Kirk Plangger include Simon Fraser University & University of London.

Is It All a Game? Understanding the Principles of Gamification

Abstract: There is growing interest in how gamification – defined as the application of game design principles in non-gaming contexts – can be used in business. However, academic research and management practice have paid little attention to the challenges of how best to design, implement, manage, and optimize gamification strategies. To advance understanding of gamification, this article defines what it is and explains how it prompts managers to think about business practice in new and innovative ways. Drawing upon the game design literature, we present a framework of three gamification principles – mechanics, dynamics, and emotions (MDE) – to explain how gamified experiences can be created. We then provide an extended illustration of gamification and conclude with ideas for future research and application opportunities.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Outsiders Studies in the Sociology of Deviance.

Abstract: This 1966 paperback edition of a publication which first appeared in 1963 has by now been widely reviewed as a worthy contribution to the sociological study of deviant behavior. Its current appearance as a paperback is a testimonial both to the quality of the work and to the prominence of deviant behavior in this generation. In general the author places deviance in perspective, identifies types of deviant behavior, considers the role of rule makers and enforcers, and some of the problems in studying deviance. In addition, he develops a sequential model of deviance relying on the concept of career, a concept originally developed in studies of occupations. In his study of a particular kind of deviance, the use of marihuana, the author posits and tests systematically an hypothesis about the genesis of marihuana use for pleasure. The hypothesis traces the sequence of changes in individual attitude

A scoping review of scoping reviews: advancing the approach and enhancing the consistency

Abstract: Background The scoping review has become an increasingly popular approach for synthesizing research evidence. It is a relatively new approach for which a universal study definition or definitive procedure has not been established. The purpose of this scoping review was to provide an overview of scoping reviews in the literature. Methods A scoping review was conducted using the Arksey and O'Malley framework. A search was conducted in four bibliographic databases and the gray literature to identify scoping review studies. Review selection and characterization were performed by two independent reviewers using pretested forms. Results The search identified 344 scoping reviews published from 1999 to October 2012. The reviews varied in terms of purpose, methodology, and detail of reporting. Nearly three-quarter of reviews (74.1%) addressed a health topic. Study completion times varied from 2 weeks to 20 months, and 51% utilized a published methodological framework. Quality assessment of included studies was infrequently performed (22.38%). Conclusions Scoping reviews are a relatively new but increasingly common approach for mapping broad topics. Because of variability in their conduct, there is a need for their methodological standardization to ensure the utility and strength of evidence. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.