Modeling the successes and failures of content-based platforms

Online platforms, such as Quora, Reddit, and Stack Exchange, provide substantial value to society through their original content. Content from these platforms informs many spheres of life—software development, finance, and academic research, among many others. Motivated by their content's powerful applications, we refer to these platforms as content-based platforms and study their successes and failures. The most common avenue of studying online platforms' successes and failures is to examine user growth. However, growth can be misleading. While many platforms initially attract a massive user base, a large fraction later exhibit post-growth failures. For example, despite their enormous growth, content-based platforms like Stack Exchange and Reddit have struggled with retaining users and generating high-quality content. Motivated by these post-growth failures, we ask: when are content-based platforms sustainable? This thesis aims to develop explanatory models that can shed light on the long-term successes and failures of content-based platforms. To this end, we conduct a series of large-scale empirical studies by developing explanatory and causal models. In the first study, we analyze the community question answering websites in Stack Exchange through the economic lens of a "market". We discover a curious phenomenon: in many Stack Exchange sites, platform success measures, such as the percentage of the answered questions, decline with an increase in the number of users. In the second study, we identify the causal factors that contribute to this decline. Specifically, we show that impression signals such as contributing user's reputation, aggregate vote thus far, and position of content significantly affect the votes on content in Stack Exchange sites. These unintended effects are known as voter biases, which in turn affect the future participation of users. In the third study, we develop a methodology for reasoning about alternative voting norms, specifically how they impact user retention. We show that if the Stack Exchange community members had voted based upon content-based criteria, such as length, readability, objectivity, and polarity, the platform would have attained higher user retention. In the fourth study, we examine the effect of user roles on the health of content-based platforms. We reveal that the composition of Stack Exchange communities (based on user roles) varies across topical categories. Further, these communities exhibit statistically significant differences in health metrics. Altogether, this thesis offers some fresh insights into understanding the successes and failures of content-based platforms

Towards high quality, scalable education: Techniques in automated assessment and probabilistic user behavior modeling

There are two primary challenges for instructors in offering a high-quality course at large scale. The first is scaling educational experiences to such a large audience. The second major challenge encountered is that of enabling adaptivity of the educational experience. This thesis addresses both major challenges in the way of high-quality scalable education by developing new techniques for large-scale automated assessment (for addressing scalability) and developing new models for interpretable user behavior analysis in educational environments for improving the quality of interaction via personalized education. Specifically, I perform a study of automated assessment of complex assignments where I explore the effectiveness of different types of features in a feasibility study. I argue for re-framing automated assessment techniques in these more complex contexts as a ranking problem, and provide a systematic approach for integrating expert, peer, and automated assessment techniques via an active-learning-to-rank formulation that outperforms a traditional randomized training solution. I also present the design and implementation of CLaDS---a Cloud-based Lab for Data Science---to enable students to engage with real-world data science problems at-scale with minimal cost ($7.40/student). I discuss our experience with deploying seven major text data assignments for students in both on-campus and online courses and show that the general infrastructure of CLaDS can be used to efficiently deliver a wide range of hands-on data science assignments. Understanding student behavior is necessary for improving the quality of scalable education through adaptivity. To this end, I present two general user behavior models for analyzing student interaction log data to understand student behavior. The first focuses on the discovery and analysis of action-based roles in community question answering (CQA) platforms using a generative model called the MDMM behavior model. I show interesting distinctions within CQA communities in question-asking behavior (where two distinct types of askers can be identified) and answering behavior (where two distinct roles surrounding answers emerge). Second, I find that where there are statistically significant differences in health metrics across topical groups on StackExchange, there are also statistically significant differences in behavior compositions, suggesting a relationship between behavior composition and health. Third, I show that the MDMM behavior model can be used to demonstrate similar but distinct evolutionary patterns between topical groups. The second model focuses on discovering temporal action patterns of learners in Coursera MOOCs. I present a two-layer hidden Markov model (2L-HMM) to extract a multi-resolution summary of user behavior patterns and their evolution, and show that these patterns can be used to extract latent features that correlate with educational outcomes. Finally, I develop the Piazza Educational Role Mining (PERM) system to close the gap between theory and practice by providing an easy-to-use web-based interface for leveraging probabilistic user behavior models on Piazza CQA interaction data. PERM allows instructors to easily crawl their courses and run subsequent MDMM behavior analyses on them. Analyses provide instructors with insight into the common user behavior patterns (roles) uncovered by plotting their action distributions in a browser. PERM enables instructors to perform deep-dives into an individual role by viewing the concrete sessions that have been assigned a specific role by the model, along with each session's individual actions and associated content. This allows instructors to flexibly combine data-driven statistical inference (through the MDMM behavior model) with a qualitative understanding of the behavior within a role. Finally, PERM develops a model of individual users as mixtures over the discovered roles, which instructors can also deep-dive into to explore exactly what individual users were doing on the platform

Rethinking drug design in the artificial intelligence era

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them

ICS Materials. Towards a re-Interpretation of material qualities through interactive, connected, and smart materials.

The domain of materials for design is changing under the influence of an increased technological advancement, miniaturization and democratization. Materials are becoming connected, augmented, computational, interactive, active, responsive, and dynamic. These are ICS Materials, an acronym that stands for Interactive, Connected and Smart. While labs around the world are experimenting with these new materials, there is the need to reflect on their potentials and impact on design. This paper is a first step in this direction: to interpret and describe the qualities of ICS materials, considering their experiential pattern, their expressive sensorial dimension, and their aesthetic of interaction. Through case studies, we analyse and classify these emerging ICS Materials and identified common characteristics, and challenges, e.g. the ability to change over time or their programmability by the designers and users. On that basis, we argue there is the need to reframe and redesign existing models to describe ICS materials, making their qualities emerge

Negotiation and Design for the Self-Organizing City

An understanding of cities as open systems whose agents act on them simultaneously from below and above, influencing urban processes by their interaction with them and with each other, is replacing the simplistic debate on urban participation which asks whether cities should be organized bottom-up or top-down. This conceptualization of cities as complex systems calls for new collaborative city-making methods: a combination of collaborative planning (which already embraces various agencies and derives decision-making from negotiations between them) and collaborative design (existing methods rely on rule-based iterative processes which control spatial outcomes). While current collaborative planning methods are open and interactive, they fail to simulate realistic power negotiations in the evolution of the physical environments they plan; collaborative design methods fall short in modelling the decision-making mechanisms of the physical environments they control. This research is dedicated to building an open negotiation and design method for cities as self-organizing systems that bridges this gap. Gaming as a tool for knowledge creation and negotiation serves as an interface between the more abstract decision-making and material city-making. Rarely involved in the creation of our environment, it has the unexplored potential of combining the socio-spatial dimensions of self-organizing urban processes. Diverse agents, the collaborations and conflicts within and between interest groups, and the parameters provided by topological data can all be combined in an operational form in gaming: potentially a great unifier of multiple stakeholder negotiations and individual design aspirations through which to generate popularly informed policies or design. The simple language and rules of games will allow jargon-free communication between stakeholders, experts and non-experts alike. The interactive and iterative nature of city gaming encourages the development of collective intelligence, derived from the real lives of players to be redeployed in their real urban futures. Vitally, city gaming enables the negotiation of this future, as players with conflicting interests are given an opportunity to develop compatible, even shared, visions. By transforming serious issues into a playful and engaging (although no less serious) experience, city gaming unlocks difficult conversations and helps to build communities in the long term. The urban design, policy and action plans generated collaboratively through gaming will increase social coherence and local agency, as well as cutting costs and time in urban development processes. This thesis proposes Generative City Gaming as an innovative urban planning and design method built on the tradition of serious gaming. Going beyond the educational scope of other serious games, the ultimate aim of city gaming is to become operational in urban processes – a goal in the process of making a reality since 2008, when Generative City Gaming was first applied to a real urban questions in the Netherlands, later expanding to Istanbul, Tirana, Brussels, and Cape Town. “Negotiation and Design for the Self-Organizing City” reports on six of the twelve city games played to date which were instrumental in the evolution of the method: Play Almere Haven tested whether a game based on self-organizing mechanisms could provide an urban order; Play Rotterdam questioned whether game-derived design could be implemented in urban renewal of a central Rotterdam neighborhood; Yap-Yaşa was played with real urban stakeholders for transforming Istanbul’s self-built neighbourhoods; Play Noord investigated a masterplan on hold could be fixed by unconventional stakeholders; Play Oosterwold jumped up a scale to test the rules of a flexible urban expansion plan for 4500 hectares; Play Van Gendthallen, was the first to enable stakeholders to make the leap from design to reality within the game process. The Generative City Gaming method evolves continuously. Every new case tests and proves the applicability of city gaming to a specific urban complexity, while challenging the method to adapt itself and develop new features tailored to tackle each unique urban question. Through use, this gaming method is finding its place within existing city-making procedures in a number of countries. The next big question is whether cyclical and open-ended city gaming can move beyond being a consultancy and research tool to become the principal medium of processing and executing city planning

Negotiation and Design for the Self-Organizing City:

An understanding of cities as open systems whose agents act on them simultaneously from below and above, influencing urban processes by their interaction with them and with each other, is replacing the simplistic debate on urban participation which asks whether cities should be organized bottom-up or top-down. This conceptualization of cities as complex systems calls for new collaborative city-making methods: a combination of collaborative planning (which already embraces various agencies and derives decision-making from negotiations between them) and collaborative design (existing methods rely on rule-based iterative processes which control spatial outcomes). While current collaborative planning methods are open and interactive, they fail to simulate realistic power negotiations in the evolution of the physical environments they plan; collaborative design methods fall short in modelling the decision-making mechanisms of the physical environments they control. This research is dedicated to building an open negotiation and design method for cities as self-organizing systems that bridges this gap. Gaming as a tool for knowledge creation and negotiation serves as an interface between the more abstract decision-making and material city-making. Rarely involved in the creation of our environment, it has the unexplored potential of combining the socio-spatial dimensions of self-organizing urban processes. Diverse agents, the collaborations and conflicts within and between interest groups, and the parameters provided by topological data can all be combined in an operational form in gaming: potentially a great unifier of multiple stakeholder negotiations and individual design aspirations through which to generate popularly informed policies or design. The simple language and rules of games will allow jargon-free communication between stakeholders, experts and non-experts alike. The interactive and iterative nature of city gaming encourages the development of collective intelligence, derived from the real lives of players to be redeployed in their real urban futures. Vitally, city gaming enables the negotiation of this future, as players with conflicting interests are given an opportunity to develop compatible, even shared, visions. By transforming serious issues into a playful and engaging (although no less serious) experience, city gaming unlocks difficult conversations and helps to build communities in the long term. The urban design, policy and action plans generated collaboratively through gaming will increase social coherence and local agency, as well as cutting costs and time in urban development processes. This thesis proposes Generative City Gaming as an innovative urban planning and design method built on the tradition of serious gaming. Going beyond the educational scope of other serious games, the ultimate aim of city gaming is to become operational in urban processes – a goal in the process of making a reality since 2008, when Generative City Gaming was first applied to a real urban questions in the Netherlands, later expanding to Istanbul, Tirana, Brussels, and Cape Town. “Negotiation and Design for the Self-Organizing City” reports on six of the twelve city games played to date which were instrumental in the evolution of the method: Play Almere Haven tested whether a game based on self-organizing mechanisms could provide an urban order; Play Rotterdam questioned whether game-derived design could be implemented in urban renewal of a central Rotterdam neighborhood; Yap-Yaşa was played with real urban stakeholders for transforming Istanbul’s self-built neighbourhoods; Play Noord investigated a masterplan on hold could be fixed by unconventional stakeholders; Play Oosterwold jumped up a scale to test the rules of a flexible urban expansion plan for 4500 hectares; Play Van Gendthallen, was the first to enable stakeholders to make the leap from design to reality within the game process. The Generative City Gaming method evolves continuously. Every new case tests and proves the applicability of city gaming to a specific urban complexity, while challenging the method to adapt itself and develop new features tailored to tackle each unique urban question. Through use, this gaming method is finding its place within existing city-making procedures in a number of countries. The next big question is whether cyclical and open-ended city gaming can move beyond being a consultancy and research tool to become the principal medium of processing and executing city planning

Inventing Network Composition: Mobilizing Rhetorical Invention and Social Media for Digital Pedagogy

Inventing Network Composition: Mobilizing Rhetorical Invention and Social Media for Digital Pedagogy investigates how students learn through writing and invention in digital social networks. Pursuing a primary research question of How do student composers invent within networked social media environments?, the dissertation examines how social media and digital writing tools can help students to learn, connect, and share generatively. The core theoretical contribution that this dissertation offers is a theory of network composition, which is a mode of invention that composers engage in social media environments that is intensely social, that is structured by a digital interface, that is interactive and participatory, and that incorporates linguistic, visual, sonic, and other multimodal communication forms. Network composition manifests most notably in network composition pedagogy, which organically locates the work of composing, as well as the disciplinary work of rhetoric and composition, within networked social media environments. This dissertation revisits and updates disciplinary exigencies related to rhetorical invention in digital networks, social media use in the writing classroom, and digital participation as a mode for learning. The dissertation offers an updated approach to invention called network-emergent rhetorical invention that approaches invention as a distributed emergence arising from a network of actants that includes humans, hardware, technologies, interfaces, communities, cultures, software, and infrastructures. It also features an IRB-approved qualitative case study that finds social media to support learning ecology formation, distributed expertise, rhetorical invention, digital and social media literacy development, rhetoric and writing skills formation, and digital citizenship activities. The dissertation additionally examines challenges for social media use in the writing classroom, considering how accessibility, digital aggression, digital discrimination, and data/privacy challenges can and should be navigated. The dissertation closes by speculating about futures for network composition and considering what is at stake for the future of learning, interaction, and participation in digital networks