Data-driven Inquiry Research Articles

A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills

Abstract Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates.

HiRegEx: Interactive Visual Query and Exploration of Multivariate Hierarchical Data.

When using exploratory visual analysis to examine multivariate hierarchical data, users often need to query data to narrow down the scope of analysis. However, formulating effective query expressions remains a challenge for multivariate hierarchical data, particularly when datasets become very large. To address this issue, we develop a declarative grammar, HiRegEx (Hierarchical data Regular Expression), for querying and exploring multivariate hierarchical data. Rooted in the extended multi-level task topology framework for tree visualizations (e-MLTT), HiRegEx delineates three query targets (node, path, and subtree) and two aspects for querying these targets (features and positions), and uses operators developed based on classical regular expressions for query construction. Based on the HiRegEx grammar, we develop an exploratory framework for querying and exploring multivariate hierarchical data and integrate it into the TreeQueryER prototype system. The exploratory framework includes three major components: top-down pattern specification, bottom-up data-driven inquiry, and context-creation data overview. We validate the expressiveness of HiRegEx with the tasks from the e-MLTT framework and showcase the utility and effectiveness of TreeQueryER system through a case study involving expert users in the analysis of a citation tree dataset.

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.

Reconciling big data and thick data to advance the new urban science and smart city governance

ABSTRACT Amid growing enthusiasm for a ”new urban science” and ”smart city” approaches to urban management, ”big data” is expected to create radical new opportunities for urban research and practice. Meanwhile, anthropologists, sociologists, and human geographers, among others, generate highly contextualized and nuanced data, sometimes referred to as ‘thick data,’ that can potentially complement, refine and calibrate big data analytics while generating new interpretations of the city through diverse forms of reasoning. While researchers in a range of fields have begun to consider such questions, scholars of urban affairs have not yet engaged in these discussions. The article explores how ethnographic research could be reconciled with big data-driven inquiry into urban phenomena. We orient our critical reflections around an illustrative example: road safety in Mexico City. We argue that big and thick data can be reconciled in and through three stages of the research process: research formulation, data collection and analysis, and research output and knowledge representation.

Macrosystems EDDIE Teaching Modules Increase Students’ Ability to Define, Interpret, and Apply Concepts in Macrosystems Ecology

Ecologists are increasingly using macrosystems approaches to understand population, community, and ecosystem dynamics across interconnected spatial and temporal scales. Consequently, integrating macrosystems skills, including simulation modeling and sensor data analysis, into undergraduate and graduate curricula is needed to train future environmental biologists. Through the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration) program, we developed four teaching modules to introduce macrosystems ecology to ecology and biology students. Modules combine high-frequency sensor data from GLEON (Global Lake Ecological Observatory Network) and NEON (National Ecological Observatory Network) sites with ecosystem simulation models. Pre- and post-module assessments of 319 students across 24 classrooms indicate that hands-on, inquiry-based modules increase students’ understanding of macrosystems ecology, including complex processes that occur across multiple spatial and temporal scales. Following module use, students were more likely to correctly define macrosystems concepts, interpret complex data visualizations and apply macrosystems approaches in new contexts. In addition, there was an increase in student’s self-perceived proficiency and confidence using both long-term and high-frequency data; key macrosystems ecology techniques. Our results suggest that integrating short (1–3 h) macrosystems activities into ecology courses can improve students’ ability to interpret complex and non-linear ecological processes. In addition, our study serves as one of the first documented instances for directly incorporating concepts in macrosystems ecology into undergraduate and graduate ecology and biology curricula.

Collaboration for Literacy Improvement: The Experiences of Teacher-Researchers in the First Stages of an Inquiry Process

This study describes the experiences of teachers during a data-driven inquiry process to improve literacy-related practices. This action research was initiated at an international English immersion school for native Chinese speakers. Two 9th grade professional learning communities (PLC) from the English and Social Studies departments collaboratively studied student and instructional data to form an action plan. Four team members reflected upon their experiences during the process, in light of their own identities, cultural backgrounds, and previous experiences. These reflections, as well as follow-up qualitative interviews, were qualitatively analyzed based on action research. The data showed that while the process was perceived as beneficial to most participants, perceptions varied based on teacher background.

Interleaving Computational and Inferential Thinking in an Undergraduate Data Science Curriculum

The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and the perspective gained by working on real-world problems. We believe that interleaving these elements within our core courses is essential to preparing students to engage in data-driven inquiry at the scale that contemporary scientific and industrial applications demand. This new curriculum is already reshaping the undergraduate experience at Berkeley, where these courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.

Macrosystems EDDIE teaching modules significantly increase ecology students' proficiency and confidence working with ecosystem models and use of systems thinking.

Simulation models are increasingly used by ecologists to study complex, ecosystem‐scale phenomena, but integrating ecosystem simulation modeling into ecology undergraduate and graduate curricula remains rare. Engaging ecology students with ecosystem simulation models may enable students to conduct hypothesis‐driven scientific inquiry while also promoting their use of systems thinking, but it remains unknown how using hands‐on modeling activities in the classroom affects student learning. Here, we developed short (3‐hr) teaching modules as part of the Macrosystems EDDIE (Environmental Data‐Driven Inquiry & Exploration) program that engage students with hands‐on ecosystem modeling in the R statistical environment. We embedded the modules into in‐person ecology courses at 17 colleges and universities and assessed student perceptions of their proficiency and confidence before and after working with models. Across all 277 undergraduate and graduate students who participated in our study, completing one Macrosystems EDDIE teaching module significantly increased students' self‐reported proficiency, confidence, and likely future use of simulation models, as well as their perceived knowledge of ecosystem simulation models. Further, students were significantly more likely to describe that an important benefit of ecosystem models was their “ease of use” after completing a module. Interestingly, students were significantly more likely to provide evidence of systems thinking in their assessment responses about the benefits of ecosystem models after completing a module, suggesting that these hands‐on ecosystem modeling activities may increase students’ awareness of how individual components interact to affect system‐level dynamics. Overall, Macrosystems EDDIE modules help students gain confidence in their ability to use ecosystem models and provide a useful method for ecology educators to introduce undergraduate and graduate students to ecosystem simulation modeling using in‐person, hybrid, or virtual modes of instruction.

Leveraging public data to offer online inquiry opportunities.

Inquiry activities have become increasingly common in Ecology and Evolution courses, but the rapid shift to remote instruction for many faculty members in response to the COVID‐19 pandemic has created new challenges for maintaining these student‐centered activities in a distance learning format. Moving forward, many instructors will be asked to create flexible course structures that allow for a mix of different teaching modalities and will be looking for resources to support student inquiry in both online and in‐person settings. Here, we propose the use of data‐driven inquiry activities as a flexible option for offering students experiences to build career‐relevant skills and learn fundamental ecological concepts. We share lessons learned from our experiences teaching a two‐semester course‐based research experience in global change ecology that leverages publicly available datasets to engage students in broadly relevant scientific inquiry.

Why caregivers discontinue applied behavior analysis (ABA) and choose communication-based autism interventions

Why caregivers discontinue applied behavior analysis (ABA) and choose communication-based autism interventions

Ancient genetics to ancient genomics: celebrity and credibility in data-driven practice

“Ancient DNA Research” is the practice of extracting, sequencing, and analyzing degraded DNA from dead organisms that are hundreds to thousands of years old. Today, many researchers are interested in adapting state-of-the-art molecular biological techniques and high-throughput sequencing technologies to optimize the recovery of DNA from fossils, then use it for studying evolutionary history. However, the recovery of DNA from fossils has also fueled the idea of resurrecting extinct species, especially as its emergence corresponded with the book and movie Jurassic Park in the 1990s. In this paper, I use historical material, interviews with scientists, and philosophical literature to argue that the search for DNA from fossils can be characterized as a data-driven and celebrity-driven practice. Philosophers have recently argued the need to seriously consider the role of data-driven inquiry in the sciences, and likewise, this history highlights the need to seriously consider the role of celebrity in shaping the kind of research that gets pursued, funded, and ultimately completed. On this point, this history highlights that the traditional philosophical and scientific distinctions between data-driven and hypothesis-driven research are not always useful for understanding the process and practice of science. Consequently, I argue that the celebrity status of a particular research practice can be considered as a “serious epistemic strategy” that researchers, as well as editors and funders, employ when making choices about their research and publication processes. This interplay between celebrity and methodology matters for the epistemology of science.

Capturing information on technology convergence, international collaboration, and knowledge flow from patent documents: A case of information and communication technology

In addressing persistent gaps in existing theories, recent advances in data-driven research approaches offer novel perspectives and exciting insights across a spectrum of scientific fields concerned with technological change and the socio-economic impact thereof. The present investigation suggests a novel approach to identify and analyze the evolution of technology sectors, in this case, information and communications technology (ICT), considering international collaboration patterns and knowledge flows and spillovers via information inputs derived from patent documents.The objective is to utilize and explore information regarding inventors’ geo-location, technology sector classifications, and patent citation records to construct various types of networks. This, in turn, will open up avenues to discover the nature of evolutionary pathways in ICT trajectories and will also provide evidence of how the overall ICT knowledge space, as well as directional knowledge flows within the ICT space, have evolved differently. It is expected that this data-driven inquiry will deliver intuitive results for decision makers seeking evidence for future resource allocation and who are interested in identifying well-suited collaborators for the development of potential next-generation technologies. Further, it will equip researchers in technology management, economic geography, or similar fields with a systematic approach to analyze evolutionary pathways of technological advancements and further enable exploitation and development of new theories regarding technological change and its socio-economic consequences.

Data-Driven Inquiry in the PBL Classroom

Data-Driven Inquiry in the PBL Classroom

EDDIE modules are effective learning tools for developing quantitative literacy and seismological understanding

ABSTRACTEnvironmental Data-Driven Inquiry and Exploration (EDDIE) modules engage students in analysis of data collected by networks of environmental sensors, which are used to study various natural phenomena, such as nutrient loading, climate change, and stream discharge. We compared two approaches to EDDIE module implementation in an undergraduate time-series analysis course. Course goals were to use high-frequency and long-term environmental datasets to improve quantitative literacy, develop data manipulation and analysis skills, construct scientific knowledge about natural phenomena, highlight the inherent variability in real data, and develop informed views about the nature of science (NOS). In both instructional treatments, students explored data and developed skills through a scaffolded in-class analysis and then solved more complex problems in homework assignments. In Treatment 1, engage and explore lesson phases involved discussion of instructor-prepared plots using the think–pair–share method. Conversely, in Treatment 2's engage and explore lesson phases, students prepared graphs and completed activities in a computer lab, which required more guidance in data manipulation and thus contained less structured discussion of data analysis and interpretation. We administered a pre/postquestionnaire to compare learning gains between the two treatments in quantitative literacy, statistical reasoning, nature-of-science (NOS) understanding, and understanding of seismological concepts. Results indicate that EDDIE modules are sufficiently flexible to be effective in both learning environments. Our results indicate that students reacted similarly to both instructional treatments, suggesting that EDDIE modules are flexible enough platforms to achieve measurable learning gains in a variety of pedagogical environments.

Algorithms under the Reign of Probability

Algorithms under the Reign of Probability Theodora J. Dryer (bio) In a 1930 issue of Scientific Monthly, US mathematician Warren Weaver proclaimed the twentieth century as the Reign of Probability.1 He argued that "it is nearly impossible to escape from this mathematical goddess of chance,"1 as probability tools were then being designed to quantify uncertainty across all areas of statistical research, even in slippery calculations of quantum particles. Weaver's proclamation was a reflection of a growing embrace of new "probability tools" in statistical research and governance throughout interwar Europe, India, and the US. But it was also a future-looking declaration of an ascendant epistemological power over modern society. It was a promise that wide-ranging forms of knowledge would be subject to probability—its dominion stretching from the seemingly banal instruments of life insurance to the highest theoretical sciences. Probability would command all domains of data-driven inquiry. The interwar turn toward uncertainty management reverberated throughout science, industry, and government. It was a movement that produced powerful and long-lasting social infrastructures toward a world dictated by uncertainty. And we are currently experiencing this movement in revival under its big data iteration: algorithmic uncertainty. MAPPING UNCERTAINTY To understand the shifting powers of uncertainty management, we must map out its epistemological, technological, and political meanings. As a base definition, probability describes likelihoods of propositions and events, usually expressed as a percentage, where perfect certainty is 1 and uncertainty is < 1. Beyond this, probabilistic knowing is a commitment to greater analytic (laws, axioms, and definitions) and technological (computers and data systems) architectures needed to express limited information in terms of likelihoods. Under conditions of limited information, elaborate mathematical and social infrastructures are needed to sustain the probabilistic worldview. My dissertation examines the significance of a dramatic inflection point in 1920s and 1930s-era uncertainty management, when new probability tools and infrastructures were designed to manage uncertainty in statistical research and governance. Studies of data uncertainty then persisted with drastically different applications throughout Cold War proxy wars. After 1980, computer scientists redesigned uncertainty models to work for big data, and since 2008, this research has ballooned, with over 1.3 million publications on algorithmic-driven uncertainty management. Algorithmic uncertainty constitutes a very different set of technologies and social politics from the interwar years, but there is nonetheless a clear lineage. Significantly, algorithmic uncertainty processes involve layered computer-implemented runs of probability tests and statistical estimations on computer-implemented sampling bodies. [End Page 93] A growing commitment to uncertainty management is resurfacing today, a newly reconfigured language of both truth and objectivity. It has overwritten earlier social doctrines—such as the mechanistic worldview—and stands at the exclusion of alternative possibilities. Today we test the validity of science through computer-simulated assessments of the statistical significance of its findings.2 The Intergovernmental Panel on Climate Change (IPCC) assesses the past, present, and future of climate knowledge in terms of uncertainty logics.3 And uncertainty-modeling practices are increasingly gaining power in finance innovation cultures.4 Through merging uncertainty practices with midcentury capital risk logics, "market makers" now promise to offload market risk accountability entirely to algorithms.5 PREDIGITAL UNCERTAINTY The 1920s and 1930s were a massive inflection point in uncertainty work that established a modernizing conception of data technologies and data-driven governance. What is significant here and what should be asked about algorithmic uncertainty is how uncertainty epistemology came to power and who benefits in a world purportedly dictated by probability. The popular adoption of interwar uncertainty tools was a response to an apparent loss of technocratic control over nineteenth-century modes of statistical governance. The utility of statistical data was put to question as practitioners described both an overabundance and incompleteness to statistical information. Statistics was by then not a science of counting but of estimation, and error was an inherent component of this work. Anxious about a loss of public confidence in data-driven institutions, technocrats sought to command error in statistical estimation. In Weaver's description: all knowledge was undergirded by an "insufficiently accurate and insufficiently extensive body of data" and needed probability tools to render that data into "probability data."1 New probability tools were...

Implications of Big Data for Knowledge Organization

KO KNOWLEDGE ORGANIZATION , Seite 187 - 198

Test–Retest Reliability of the Adaptive Chemistry Assessment Survey for Teachers: Measurement Error and Alternatives to Correlation

Determining the error of measurement is a necessity for researchers engaged in bench chemistry, chemistry education research (CER), and a multitude of other fields. Discussions regarding what constructs measurement error entails and how to best measure them have occurred, but the critiques about traditional measures have yielded few alternatives. We present a brief review of psychometric reliability from its beginning in the early 1900s as well as a summary of critiques of the test–retest reliability coefficient. Then, we posit our own novel measurement, the zeta-range estimator, to assist in quantifying and accounting for measurement error that those interested in educational researchers will find beneficial. We provide a proof-of-concept using simulated data and then analyze the reliability of items on the Adaptive Chemistry Assessment Survey for Teachers, a survey designed to characterize data-driven inquiry. While the focus is for CER, the zeta-range estimator also holds significant value for those ou...

A Model for using Environmental Data‐Driven Inquiry and Exploration to Teach Limnology to Undergraduates

A Model for using Environmental Data‐Driven Inquiry and Exploration to Teach Limnology to Undergraduates

Guiding teaching with assessments: high school chemistry teachers’ use of data-driven inquiry

Data-driven inquiry (DDI) is the process by which teachers design and implement classroom assessments and use student results to inform/adjust teaching. Although much has been written about DDI, few details exist about how teachers conduct this process on a day-to-day basis in any specific subject area, let alone chemistry. Nineteen high school chemistry teachers participated in semi-structured interviews regarding how they used assessments to inform teaching. Results are reported for each step in the DDI process. Goals – there was a lack in specificity of learning and instructional goals and goals stated were not conducive to informing instruction. Evidence – at least once, all teachers determined student learning based solely on scores and/or subscores, suggesting an over-reliance on these measures. Conclusions – many teachers claimed that students either did or did not “understand” a topic, but the teachers did not adequately describe what this means. Actions – very few teachers listed a specific action as a result of analyzing student data, while the majority gave multiple broad pedagogical strategies to address content deficiencies. The results of this study show the limitations of teachers' DDI practices and inform how professional development on DDI should have a finer grain size and be based in disciplinary content.

The structure of the EU mediasphere.

BackgroundA trend towards automation of scientific research has recently resulted in what has been termed “data-driven inquiry” in various disciplines, including physics and biology. The automation of many tasks has been identified as a possible future also for the humanities and the social sciences, particularly in those disciplines concerned with the analysis of text, due to the recent availability of millions of books and news articles in digital format. In the social sciences, the analysis of news media is done largely by hand and in a hypothesis-driven fashion: the scholar needs to formulate a very specific assumption about the patterns that might be in the data, and then set out to verify if they are present or not.Methodology/Principal FindingsIn this study, we report what we think is the first large scale content-analysis of cross-linguistic text in the social sciences, by using various artificial intelligence techniques. We analyse 1.3 M news articles in 22 languages detecting a clear structure in the choice of stories covered by the various outlets. This is significantly affected by objective national, geographic, economic and cultural relations among outlets and countries, e.g., outlets from countries sharing strong economic ties are more likely to cover the same stories. We also show that the deviation from average content is significantly correlated with membership to the eurozone, as well as with the year of accession to the EU.Conclusions/SignificanceWhile independently making a multitude of small editorial decisions, the leading media of the 27 EU countries, over a period of six months, shaped the contents of the EU mediasphere in a way that reflects its deep geographic, economic and cultural relations. Detecting these subtle signals in a statistically rigorous way would be out of the reach of traditional methods. This analysis demonstrates the power of the available methods for significant automation of media content analysis.