Using Browser Data to Understand Desires to Spend Time Online

There is growing recognition that many people feel the need to reduce and/or manage their use of the internet and other digital communications technologies in support of their wellbeing. To understand the role played by various usage factors in desires to regulate time spent online we used Mozilla Firefox browser telemetry to investigate how six metrics pertaining to time spent on the internet and the diversity and intensity of use predict participants’ (n = 8,094) desires to spend more or less time online. Across all six metrics, we did not find evidence for a relationship between browser usage metrics and participants wanting to spend more or less time online, and this finding was robust across various analytical pathways. The study highlights a number of considerations and concerns that need to be addressed in future industry-academia collaborations that draw on trace data or usage telemetry.

Measuring migration 2.0: a review of digital data sources

The interest in human migration is at its all-time high, yet data to measure migration is notoriously limited. “Big data” or “digital trace data” have emerged as new sources of migration measurement complementing ‘traditional’ census, administrative and survey data. This paper reviews the strengths and weaknesses of eight novel, digital data sources along five domains: reliability, validity, scope, access and ethics. The review highlights the opportunities for migration scholars but also stresses the ethical and empirical challenges. This review intends to be of service to researchers and policy analysts alike and help them navigate this new and increasingly complex field.

Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis

Root cause identification of performance degradation within distributed systems is often a difficult and time-consuming task, yet it is crucial for maintaining high performance. In this paper, we present an execution trace-driven solution that reduces the efforts required to investigate, debug, and solve performance problems found in multinode distributed systems. The proposed approach employs a unified analysis method to represent trace data collected from the user-space level to the hardware level of involved nodes, allowing for efficient and effective root cause analysis. This solution works by extracting performance metrics and state information from trace data collected at user-space, kernel, and network levels. The multisource trace data is then synchronized and structured in a multidimensional data store, which is designed specifically for this kind of data. A posteriori analysis using a top-down approach is then used to investigate performance problems and detect their root causes. In this paper, we apply this generic framework to analyze trace data collected from the execution of the web server, database server, and application servers in a distributed LAMP (Linux, Apache, MySQL, and PHP) Stack. Using industrial level use cases, we show that the proposed approach is capable of investigating the root cause of performance issues, addressing unusual latency, and improving base latency by 70%. This is achieved with minimal tracing overhead that does not significantly impact performance, as well as O log n query response times for efficient analysis.

WHAT DEAD-AND-DYING PLATFORMS DO FOR INTERNET STUDIES: SITUATING TECHNOLOGICAL FAILURE, DIGITAL AFTERLIFE, AND THE WEB THAT WAS

This panel explores internet histories through the lens of “platform death” as a way of understanding how digital communities grapple with technological failure, power dynamics, and the divergent notions of the digital afterlife. Collectively, the contributions address the cultural, geopolitical, economic, and socio-legal repercussions of what happens when various platforms fail, decline, or expire. We bring together five presentations that draw on different methods—including document analysis, semi-structured interviews, participant observation—to explore the frailty of platforms, their underlying infrastructures, and their trace data. Together, by examining and theoretically situating the histories of five different platforms (TroopTube, Fanfou, MySpace, YikYak, and Couchsurfing), we consider and complicate how the concept of “platform death” as a metaphor can help reveal the Web’s rhythmic temporality, digital media’s constant reinvention of forms, and the collision of hegemonic and fragile infrastructures in divergent cultural contexts. We ask: What are the theoretical implications of situating platforms as killable, ephemeral, precarious, or transient technologies? What—and who—kills platforms, and in what ways can they have uncertain digital afterlives and even resurrections? What can conceptualizations of dead and dying technologies tell us about the Internet’s growth and stagnation, its present and futures? What is (un)knowable about platforms that once were, and how can this knowledge inform our predictions of future technological failure? We aim to build community, collective imaginings, and future collaborations around a research agenda that centers mnemonic experimentation, comparative platform studies, and archival contestations.

Dispositional Learning Analytics for Supporting Individualized Learning Feedback

An important goal of learning analytics (LA) is to improve learning by providing students with meaningful feedback. Feedback is often generated by prediction models of student success using data about students and their learning processes based on digital traces of learning activities. However, early in the learning process, when feedback is most fruitful, trace-data-based prediction models often have limited information about the initial ability of students, making it difficult to produce accurate prediction and personalized feedback to individual students. Furthermore, feedback generated from trace data without appropriate consideration of learners’ dispositions might hamper effective interventions. By providing an example of the role of learning dispositions in an LA application directed at predictive modeling in an introductory mathematics and statistics module, we make a plea for applying dispositional learning analytics (DLA) to make LA precise and actionable. DLA combines learning data with learners’ disposition data measured through for example self-report surveys. The advantage of DLA is twofold: first, to improve the accuracy of early predictions; and second, to link LA predictions with meaningful learning interventions that focus on addressing less developed learning dispositions. Dispositions in our DLA example include students’ mindsets, operationalized as entity and incremental theories of intelligence, and corresponding effort beliefs. These dispositions were inputs for a cluster analysis generating different learning profiles. These profiles were compared for other dispositions and module performance. The finding of profile differences suggests that the inclusion of disposition data and mindset data, in particular, adds predictive power to LA applications.

Trace data visualisation enquiry: a methodological coupling for studying information practices in relation to information systems

PurposeThe purpose of this paper is to examine whether and how a methodological coupling of visualisations of trace data and interview methods can be utilised for information practices studies.Design/methodology/approachTrace data visualisation enquiry is suggested as the coupling of visualising exported data from an information system and using these visualisations as basis for interview guides and elicitation in information practices research. The methodology is illustrated and applied through a small-scale empirical study of a citizen science project.FindingsThe study found that trace data visualisation enquiry enabled fine-grained investigations of temporal aspects of information practices and to compare and explore temporal and geographical aspects of practices. Moreover, the methodology made possible inquiries for understanding information practices through trace data that were discussed through elicitation with participants. The study also found that it can aid a researcher of gaining a simultaneous overarching and close picture of information practices, which can lead to theoretical and methodological implications for information practices research.Originality/valueTrace data visualisation enquiry extends current methods for investigating information practices as it enables focus to be placed on the traces of practices as recorded through interactions with information systems and study participants' accounts of activities.