M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Technology) Vaal University of Technology. / The speedy growth of Internet based information and communication tools produced a new field of prospects for educational organizations to reach their aims. One of the options is crowdsourcing. Crowdsourcing was recently the answer to the growth for providing different applications in areas such as education, financing, and entrepreneurship. South African schools are considerably failing in education. A big challenge is when it comes to the mathematics delivery method which ends up affecting the learners’ performance. When compared to other middle income nations, South Africa is ranked third from the bottom in terms of its performance when it comes to mathematics. This study designed a conceptual crowdsourcing tutoring framework. The framework defines the use of how crowdsourcing can contribute to tutoring grade 11 and 12 mathematics in order to improve the learners’ performance. A prototype was developed to illustrate the crowdsourcing tutoring framework. The simpleKmeans algorithm was used in the prototype. The algorithm was used to select learners, tutors and appropriate textbooks for the virtual class. The prototype system proved to be effective as it was able to cluster students according to their performance and tutors according to their student pass rate. Through the usage of a clustering simpleKmeans algorithm, this study was able to create a virtual class that illustrated how all the components come together for the proposed crowdsourcing tutoring virtual class. The use of the prototype system was able to fill the virtual class with students who obtained low average marks and educators with the pupils who had the highest pass rate. This study was able to build a virtual class with the following components: learners, tutors and textbooks. Objectives and research questions of this study were fulfilled. In future studies the researcher will endeavor to make the system recommend textbooks without using the textbooks used by the teachers who produced the best results.

Expert investigators leverage their advanced skills and deep experience to solve complex investigations, but they face limits on their time and attention. In contrast, crowds of novices can be highly scalable and parallelizable, but lack expertise and may engage in vigilante behavior. In this dissertation, I introduce and evaluate the framework of expert-led crowdsourcing through three studies across two domains, journalism and law enforcement. First, through an ethnographic study of two law enforcement murder investigations, I uncover tensions in a real-world crowdsourced investigation and introduce the expert-led crowdsourcing framework. Second, I instantiate expert-led crowdsourcing in two collaboration systems: GroundTruth and CuriOSINTy. GroundTruth is focused on one specific investigative task, image geolocation. CuriOSINTy expands the flexibility and scope of expert-led crowdsourcing to handle more complex and multiple investigative tasks: identifying and debunking misinformation. Third, I introduce a framework for understanding how expert-led crowdsourced investigations work and how to better support them. Finally, I conclude with a discussion of how expert-led crowdsourcing enables experts and crowds to do more than either could alone, as well as how it can be generalized to other domains. / Doctor of Philosophy / Expert investigators leverage their advanced skills and deep experience to solve complex investigations, but they face limits on their time and attention. In contrast, there is growing interest among non-professional members of the public to participate in investigations, but they lack the expertise or may engage in harmful behavior. In this dissertation, I introduce a new concept called, expert-led crowdsourcing, that allows professionals and non-professionals to work together on a high-stakes investigations in two domains: journalism and law enforcement. First, I explored how expert-led crowdsourcing played out in CrowdSolve, a real-world investigation of two decades-old murder cases. At CrowdSolve, over 250 amateur sleuths supported eight law enforcement experts to uncover new leads two for the two cases. Second, I build two software applications, GroundTruth and CuriOSINTy, to better support expert-led crowdsourced investigations. GroundTruth helps investigators work with a crowd to find the exact geographic location where a photo was taken. CuriOSINTy extends GroundTruth's capabilities to help investigators with more complex and multiple investigative tasks involved in identifying and debunking misinformation on social media. Third, I compared and contrasted the three prior studies to develop a more detailed understanding of expert-led crowdsourced investigations and how to better support them. Finally, I conclude with a discussion of how expert-led crowdsourcing enables experts and crowds to do more than either could alone, as well as how it can be used in other professions.

In many online communities, users reveal innovative and potentially valuable intellectual property (IP) under conditions that entail the risk of theft and imitation. Where there is rivalry and formal IP law is not effective, this would lead to underinvestment or withholding of IP, unless user-organized norms compensate for these shortcomings. This study is the first to explore the characteristics and functioning of such a norms-based IP system in the setting of anonymous, large-scale, and loose-knit online communities. In order to do so, we use data on the Threadless crowdsourcing community obtained through netnography, a survey, and a field experiment. On this basis, we identify an integrated system of well-established norms that regulate the use of IP within this community. We analyze the system's characteristics and functioning, and we find that the "legal certainty" it provides is conducive to cooperation, cumulative effects, and innovation. We generalize our findings from the case by developing propositions aimed to spark further research. These propositions focus on similarities and differences between norms-based IP systems in online and offline settings, and the conditions that determine the existence of norms-based IP systems as well as their form and effectiveness in online communities. In this way, we contribute to the literatures on norms-based IP systems and online communities and offer advice for the management of crowdsourcing communities.

Decision-making is a fundamental process of human thinking and behavior. In engineering design, decision-making is studied from two different points of view: users and designers. User focused design studies tend to investigate ways to better inform the design process through the elicitation of preferences or information. Designer studies are broad in nature, but usually attempt to illustrate and understand some aspect of designer behavior, such as ideation, fixation, or collaboration. Despite their power, both qualitative and quantitative research methods are ultimately limited by the fact that they rely on direct input from the research participants themselves. This can be problematic, as individuals may not be able to accurately represent what they are truly thinking, feeling, or desiring at the time of the decision. A fundamental goal in both user- and designer-focused studies is to understand how the mind works while individuals are making decisions. This dissertation addresses these issues through the use of complementary behavioral and neuroimaging experiments, uncovering insights into how the mind processes design-related decision-making and the implications of those processes. To examine user decision-making, a visual conjoint analysis (preference modeling approach) was utilized for sustainable preference judgments. Here, a novel preference-modeling framework was employed, allowing for the real time calculation of dependent environmental impact metrics during individual choice decisions. However, in difficult moral and emotional decision-making scenarios, such as those involving sustainability, traditional methods of uncovering user preferences have proven to be inconclusive. To overcome these shortcomings, a neuroimaging approach was used. Specifically, study participants completed preference judgments for sustainable products inside of a functional magnetic resonance imaging (fMRI) scanner. Results indicated that theory of mind and moral reasoning processes occur during product evaluations involving sustainability. Designer decision-making was explored using an analogical reasoning and concept development experiment. First, a crowdsourcing method was used to obtain meaningful analogical stimuli, which were validated using a behavioral experiment. Following this, fMRI was used to uncover the neural mechanisms associated with analogical reasoning in design. Results demonstrated that analogies generally benefit designers; particularly after significant time on idea generation has taken place. Neuroimaging data helped to show two distinct brain activation networks based upon reasoning with and without analogies. We term these fixation driven external search and analogically driven internal search.. Fixation driven external search shows designers during impasse, as increased activation in brain regions associated with visual processing causes them to direct attention outward in search of inspiration. Conversely, during analogically driven internal search, significant areas of activation are observed in bilateral temporal and left parietal regions of the brain. These brain regions are significant, as prior research has linked them to semantic word-processing, directing attention to memory retrieval, and insight during problem solving. It is during analogically driven internal search that brain activity shows the most effective periods of ideation by participants.

The purpose of this thesis is to find out whether there is a significant opportunity for the use of innovator and early adopter segments in a marketing strategy, with particular emphasis on ambassador and crowdsourcing programs.Recent changes on the consumer market which lead to changing roles of both consumers and brands are described in the first chapter. This chapter also includes a description of both innovator and early adopter segments which is taken from segmentation based on innovation adoption.The second chapter includes information about the two segments obtained from other surveys available on the Czech market.Specific ways of using the two segments within a marketing strategy are described in the third chapter. The practical part of this thesis consists mainly of the analysis of data obtained by my own research. Special emphasis is put on behaviour of innovators and early adopters on social networks and their motivation to participate in ambassador and crowdsourcing programs. This part is followed by a proposal of a specific ambassador program for a technology brand.

Crowdsourcing has gained favor among many social scientists as a method for collecting data because this method is both time- and resource-efficient. The present study uses a within-subject test-retest design to evaluate the psychometric characteristics of crowdsource samples for developing and field testing measurement instruments. As evidenced by similar patterns of psychometric characteristics across time, strong test-retest reliability, and low failure rates of attention check items, the results of this study provide evidence that Amazon Mechanical Turk might represent a fruitful platform for field testing to support the development of a variety of measures. These findings, in turn, have significant implications for resource efficiency in the fields of educational and organizational measurement.

In current Android architecture, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to avoid unnecessary private data breach. However, the majority of regular users are not technically capable or do not care to consider privacy implications to make safe decisions. To assist the technically incapable crowd, we propose a permission control framework based on crowdsourcing. At its core, our framework runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or not the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using a transitional Bayesian inference model. The recommendation is based on aggregated expert responses and their confidence level. As a complete framework design of the system, this thesis also includes a solution for Android app risks estimation based on behaviour analysis. To eliminate the negative impact from dishonest app owners, we also proposed a bot user detection to make it harder to utilize false recommendations through bot users to impact the overall recommendations. This work also covers a multi-view permission notification design to customize the app safety notification interface based on users' need and an app recommendation method to suggest safe and usable alternative apps to users.

Mobile and web application security, particularly concerning the area of data privacy, has received much attention from the public in recent years. Most applications are installed without disclosing full information to users and clearly stating what they have access to. This often raises concerns when users become aware of unnecessary information being collected or stored. Unfortunately, most users have little to no technical knowledge in regard to what permissions should be granted and can only rely on their intuition and past experiences to make relatively uninformed decisions. DroidNet, a crowdsource based Android recommendation tool and framework, is a proposed avenue for the technically incapable. DroidNet alleviates privacy concerns and presents users with permission recommendations of high confidence based on the decisions from expert users on the network who are using the same applications. The framework combines an interactive user interface, used for data collection and presenting permission recommendations to users, with a transitional Bayesian inference model and multiple algorithms used for rating users based on their respective expertise levels. As a result, the recommendations that are provided to users are based on aggregated expert responses and their confidence levels. This work presents the completed DroidNet project in its entirety, including the implementation of the application, algorithms, and user interface itself. Additionally, this thesis presents and utilizes a unique collection of real-world data from actual Android users. The primary goal of this work is to evaluate the effectiveness and accuracy of DroidNet's recommendations and to show that regular mobile device users can benefit from crowdsourcing.

Text-to-scene generation systems take input in the form of a natural language text and output a 3D scene illustrating the meaning of that text. A major benefit of text-to-scene generation is that it allows users to create custom 3D scenes without requiring them to have a background in 3D graphics or knowledge of specialized software packages. This contributes to making text-to-scene useful in scenarios from creative applications to education. The primary goal of this thesis is to explore how we can use text-to-scene generation in a new way: as a tool to facilitate the elicitation and formal documentation of language. In particular, we use text-to-scene generation (a) to assist field linguists studying endangered languages; (b) to provide a cross-linguistic framework for formally modeling spatial language; and (c) to collect language data using crowdsourcing. As a side effect of these goals, we also explore the problem of multilingual text-to-scene generation, that is, systems for generating 3D scenes from languages other than English. The contributions of this thesis are the following. First, we develop a novel tool suite (the WordsEye Linguistics Tools, or WELT) that uses the WordsEye text-to-scene system to assist field linguists with eliciting and documenting endangered languages. WELT allows linguists to create custom elicitation materials and to document semantics in a formal way. We test WELT with two endangered languages, Nahuatl and Arrernte. Second, we explore the question of how to learn a syntactic parser for WELT. We show that an incremental learning method using a small number of annotated dependency structures can produce reasonably accurate results. We demonstrate that using a parser trained in this way can significantly decrease the time it takes an annotator to label a new sentence with dependency information. Third, we develop a framework that generates 3D scenes from spatial and graphical semantic primitives. We incorporate this system into the WELT tools for creating custom elicitation materials, allowing users to directly manipulate the underlying semantics of a generated scene. Fourth, we introduce a deep semantic representation of spatial relations and use this to create a new resource, SpatialNet, which formally declares the lexical semantics of spatial relations for a language. We demonstrate how SpatialNet can be used to support multilingual text-to-scene generation. Finally, we show how WordsEye and the semantic resources it provides can be used to facilitate elicitation of language using crowdsourcing.

No description available.