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1. Machine learning for the educational sciences

   https://doi.org/10.1002/rev3.3310  

   Hilbert, Sven ; Coors, Stefan ; Kraus, Elisabeth ; Bischl, Bernd ; Lindl, Alfred ; Frei, Mario ; Wild, Johannes ; Krauss, Stefan ; Goretzko, David ; Stachl, Clemens ( November 2021 , Review of Education)

   Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education.

   In 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials.

   The process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application.

   The progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences.

   Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.

    

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2. Scalable multiple changepoint detection for functional data sequences

   https://doi.org/10.1002/env.2710  

   Harris, Trevor ; Li, Bo ; Tucker, J. Derek ( November 2021 , Environmetrics)

   We propose the multiple changepoint isolation (MCI) method for detecting multiple changes in the mean and covariance of a functional process. We first introduce a pair of projections to represent the variability “between” and “within” the functional observations. We then present an augmented fused lasso procedure to split the projections into multiple regions robustly. These regions act to isolate each changepoint away from the others so that the powerful univariate CUSUM statistic can be applied region‐wise to identify the changepoints. Simulations show that our method accurately detects the number and locations of changepoints under many different scenarios. These include light and heavy tailed data, data with symmetric and skewed distributions, sparsely and densely sampled changepoints, and mean and covariance changes. We show that our method outperforms a recent multiple functional changepoint detector and several univariate changepoint detectors applied to our proposed projections. We also show that MCI is more robust than existing approaches and scales linearly with sample size. Finally, we demonstrate our method on a large time series of water vapor mixing ratio profiles from atmospheric emitted radiance interferometer measurements.

    

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3. Climate change and infectious disease: a review of evidence and research trends

   https://doi.org/10.1186/s40249-023-01102-2  

   Van de Vuurst, Paige ; Escobar, Luis E. ( May 2023 , Infectious Diseases of Poverty)

   Climate change presents an imminent threat to almost all biological systems across the globe. In recent years there have been a series of studies showing how changes in climate can impact infectious disease transmission. Many of these publications focus on simulations based on in silico data, shadowing empirical research based on field and laboratory data. A synthesis work of empirical climate change and infectious disease research is still lacking.

   We conducted a systemic review of research from 2015 to 2020 period on climate change and infectious diseases to identify major trends and current gaps of research. Literature was sourced from Web of Science and PubMed literary repositories using a key word search, and was reviewed using a delineated inclusion criteria by a team of reviewers.

   Our review revealed that both taxonomic and geographic biases are present in climate and infectious disease research, specifically with regard to types of disease transmission and localities studied. Empirical investigations on vector-borne diseases associated with mosquitoes comprised the majority of research on the climate change and infectious disease literature. Furthermore, demographic trends in the institutions and individuals published revealed research bias towards research conducted across temperate, high-income countries. We also identified key trends in funding sources for most resent literature and a discrepancy in the gender identities of publishing authors which may reflect current systemic inequities in the scientific field.

   Future research lines on climate change and infectious diseases should considered diseases of direct transmission (non-vector-borne) and more research effort in the tropics. Inclusion of local research in low- and middle-income countries was generally neglected. Research on climate change and infectious disease has failed to be socially inclusive, geographically balanced, and broad in terms of the disease systems studied, limiting our capacities to better understand the actual effects of climate change on health.

    

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4. A Permutation-Based Changepoint Technique for Monitoring Effect Sizes

   https://doi.org/10.1017/pan.2020.44  

   Kent, Daniel ; Wilson, James D. ; Cranmer, Skyler J. ( January 2021 , Political Analysis)

   null (Ed.)

   Abstract Across the social sciences, scholars regularly pool effects over substantial periods of time, a practice that produces faulty inferences if the underlying data generating process is dynamic. To help researchers better perform principled analyses of time-varying processes, we develop a two-stage procedure based upon techniques for permutation testing and statistical process monitoring. Given time series cross-sectional data, we break the role of time through permutation inference and produce a null distribution that reflects a time-invariant data generating process. The null distribution then serves as a stable reference point, enabling the detection of effect changepoints. In Monte Carlo simulations, our randomization technique outperforms alternatives for changepoint analysis. A particular benefit of our method is that, by establishing the bounds for time-invariant effects before interacting with actual estimates, it is able to differentiate stochastic fluctuations from genuine changes. We demonstrate the method’s utility by applying it to a popular study on the relationship between alliances and the initiation of militarized interstate disputes. The example illustrates how the technique can help researchers make inferences about where changes occur in dynamic relationships and ask important questions about such changes. 

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5. The Centrality of Black Identity for Black Students in Engineering

   Mobley, Catherine ; Brawner, Catherine E. ; Brent, Rebecca ; Orr, Marisa K. ( January 2021 , Collaborative Network for Engineering and Computing Diversity (CoNECD) Conference)

   Introduction and Theoretical Frameworks Our study draws upon several theoretical foundations to investigate and explain the educational experiences of Black students majoring in ME, CpE, and EE: intersectionality, critical race theory, and community cultural wealth theory. Intersectionality explains how gender operates together with race, not independently, to produce multiple, overlapping forms of discrimination and social inequality (Crenshaw, 1989; Collins, 2013). Critical race theory recognizes the unique experiences of marginalized groups and strives to identify the micro- and macro-institutional sources of discrimination and prejudice (Delgado & Stefancic, 2001). Community cultural wealth integrates an asset-based perspective to our analysis of engineering education to assist in the identification of factors that contribute to the success of engineering students (Yosso, 2005). These three theoretical frameworks are buttressed by our use of Racial Identity Theory, which expands understanding about the significance and meaning associated with students’ sense of group membership. Sellers and colleagues (1997) introduced the Multidimensional Model of Racial Identity (MMRI), in which they indicated that racial identity refers to the “significance and meaning that African Americans place on race in defining themselves” (p. 19). The development of this model was based on the reality that individuals vary greatly in the extent to which they attach meaning to being a member of the Black racial group. Sellers et al. (1997) posited that there are four components of racial identity: 1. Racial salience: “the extent to which one’s race is a relevant part of one’s self-concept at a particular moment or in a particular situation” (p. 24). 2. Racial centrality: “the extent to which a person normatively defines himself or herself with regard to race” (p. 25). 3. Racial regard: “a person’s affective or evaluative judgment of his or her race in terms of positive-negative valence” (p. 26). This element consists of public regard and private regard. 4. Racial ideology: “composed of the individual’s beliefs, opinions and attitudes with respect to the way he or she feels that the members of the race should act” (p. 27). The resulting 56-item inventory, the Multidimensional Inventory of Black Identity (MIBI), provides a robust measure of Black identity that can be used across multiple contexts. Research Questions Our 3-year, mixed-method study of Black students in computer (CpE), electrical (EE) and mechanical engineering (ME) aims to identify institutional policies and practices that contribute to the retention and attrition of Black students in electrical, computer, and mechanical engineering. Our four study institutions include historically Black institutions as well as predominantly white institutions, all of which are in the top 15 nationally in the number of Black engineering graduates. We are using a transformative mixed-methods design to answer the following overarching research questions: 1. Why do Black men and women choose and persist in, or leave, EE, CpE, and ME? 2. What are the academic trajectories of Black men and women in EE, CpE, and ME? 3. In what way do these pathways vary by gender or institution? 4. What institutional policies and practices promote greater retention of Black engineering students? Methods This study of Black students in CpE, EE, and ME reports initial results from in-depth interviews at one HBCU and one PWI. We asked students about a variety of topics, including their sense of belonging on campus and in the major, experiences with discrimination, the impact of race on their experiences, and experiences with microaggressions. For this paper, we draw on two methodological approaches that allowed us to move beyond a traditional, linear approach to in-depth interviews, allowing for more diverse experiences and narratives to emerge. First, we used an identity circle to gain a better understanding of the relative importance to the participants of racial identity, as compared to other identities. The identity circle is a series of three concentric circles, surrounding an “inner core” representing one’s “core self.” Participants were asked to place various identities from a provided list that included demographic, family-related, and school-related identities on the identity circle to reflect the relative importance of the different identities to participants’ current engineering education experiences. Second, participants were asked to complete an 8-item survey which measured the “centrality” of racial identity as defined by Sellers et al. (1997). Following Enders’ (2018) reflection on the MMRI and Nigrescence Theory, we chose to use the measure of racial centrality as it is generally more stable across situations and best “describes the place race holds in the hierarchy of identities an individual possesses and answers the question ‘How important is race to me in my life?’” (p. 518). Participants completed the MIBI items at the end of the interview to allow us to learn more about the participants’ identification with their racial group, to avoid biasing their responses to the Identity Circle, and to avoid potentially creating a stereotype threat at the beginning of the interview. This paper focuses on the results of the MIBI survey and the identity circles to investigate whether these measures were correlated. Recognizing that Blackness (race) is not monolithic, we were interested in knowing the extent to which the participants considered their Black identity as central to their engineering education experiences. Combined with discussion about the identity circles, this approach allowed us to learn more about how other elements of identity may shape the participants’ educational experiences and outcomes and revealed possible differences in how participants may enact various points of their identity. Findings For this paper, we focus on the results for five HBCU students and 27 PWI students who completed the MIBI and identity circle. The overall MIBI average for HBCU students was 43 (out of a possible 56) and the overall MIBI scores ranged from 36-51; the overall MIBI average for the PWI students was 40; the overall MIBI scores for the PWI students ranged from 24-51. Twenty-one students placed race in the inner circle, indicating that race was central to their identity. Five placed race on the second, middle circle; three placed race on the third, outer circle. Three students did not place race on their identity circle. For our cross-case qualitative analysis, we will choose cases across the two institutions that represent low, medium and high MIBI scores and different ranges of centrality of race to identity, as expressed in the identity circles. Our final analysis will include descriptive quotes from these in-depth interviews to further elucidate the significance of race to the participants’ identities and engineering education experiences. The results will provide context for our larger study of a total of 60 Black students in engineering at our four study institutions. Theoretically, our study represents a new application of Racial Identity Theory and will provide a unique opportunity to apply the theories of intersectionality, critical race theory, and community cultural wealth theory. Methodologically, our findings provide insights into the utility of combining our two qualitative research tools, the MIBI centrality scale and the identity circle, to better understand the influence of race on the education experiences of Black students in engineering. 

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