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With increasingly technology-driven workplaces and high data volumes, instructors across STEM+C disciplines are integrating more data science topics into their course learning objectives. However, instructors face significant challenges in integrating additional data science concepts into their already full course schedules. Streamlined instructional modules that are integrated with course content, and cover relevant data science topics, such as data collection, uncertainty in data, visualization, and analysis using statistical and machine learning methods can benefit instructors across multiple disciplines. As part of a cross-university research program, we designed a systematic structural approach–based on shared instructional and assessment principles–to construct modules that are tailored to meet the needs of multiple instructional disciplines, academic levels, and pedagogies. Adopting a research-practice partnership approach, we have collectively developed twelve modules working closely with instructors and their teaching assistants for six undergraduate courses. We identified and coded primary data science concepts in the modules into five common themes: 1) data acquisition, 2) data quality issues, 3) data use and visualization, 4) advanced machine learning techniques, and 5) miscellaneous topics that may be unique to a particular discipline (e.g., how to analyze data streams collected by a special sensor). These themes were further subdivided to make it easier for instructors to contextualize the data science concepts in discipline-specific work. In this paper, we present as a case study the design and analysis of four of the modules, primarily so we can compare and contrast pairs of similar courses that were taught at different levels or at different universities. Preliminary analyses show the wide distribution of data science topics that are common among a number of environmental science and engineering courses. We identified commonalities and differences in the integration of data science instruction (through modules) into these courses. This analysis informs the development of a set of key considerations for integrating data science concepts into a variety of STEM + C courses. 

With increasingly technology-driven workplaces and high data volumes, instructors across STEM+C disciplines are integrating more data science topics into their course learning objectives. However, instructors face significant challenges in integrating additional data science concepts into their already full course schedules. Streamlined instructional modules that are integrated with course content, and cover relevant data science topics, such as data collection, uncertainty in data, visualization, and analysis using statistical and machine learning methods can benefit instructors across multiple disciplines. As part of a cross-university research program, we designed a systematic structural approach–based on shared instructional and assessment principles–to construct modules that are tailored to meet the needs of multiple instructional disciplines, academic levels, and pedagogies. Adopting a research-practice partnership approach, we have collectively developed twelve modules working closely with instructors and their teaching assistants for six undergraduate courses. We identified and coded primary data science concepts in the modules into five common themes: 1) data acquisition, 2) data quality issues, 3) data use and visualization, 4) advanced machine learning techniques, and 5) miscellaneous topics that may be unique to a particular discipline (e.g., how to analyze data streams collected by a special sensor). These themes were further subdivided to make it easier for instructors to contextualize the data science concepts in discipline-specific work. In this paper, we present as a case study the design and analysis of four of the modules, primarily so we can compare and contrast pairs of similar courses that were taught at different levels or at different universities. Preliminary analyses show the wide distribution of data science topics that are common among a number of environmental science and engineering courses. We identified commonalities and differences in the integration of data science instruction (through modules) into these courses. This analysis informs the development of a set of key considerations for integrating data science concepts into a variety of STEM + C courses. 

As technology advances, data driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings has improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses. 

A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb⁻¹of proton-proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple signal regions are defined, targeting several SUSY simplified models yielding the desired final states. A single control region is used to constrain the normalisation of theWZ+ jets background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of several supersymmetric models featuring R-parity conservation or R-parity violation, yielding exclusion limits surpassing those from previous searches. In models considering gluino (squark) pair production, gluino (squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level.

 

A search for supersymmetry targeting the direct production of winos and higgsinos is conducted in final states with either two leptons (eorμ) with the same electric charge, or three leptons. The analysis uses 139 fb⁻¹ofppcollision data at$$ \sqrt{s} $$$\sqrt{s}$= 13 TeV collected with the ATLAS detector during Run 2 of the Large Hadron Collider. No significant excess over the Standard Model expectation is observed. Simplified and complete models with and withoutR-parity conservation are considered. In topologies with intermediate states including eitherWhorWZpairs, wino masses up to 525 GeV and 250 GeV are excluded, respectively, for a bino of vanishing mass. Higgsino masses smaller than 440 GeV are excluded in a naturalR-parity-violating model with bilinear terms. Upper limits on the production cross section of generic events beyond the Standard Model as low as 40 ab are obtained in signal regions optimised for these models and also for anR-parity-violating scenario with baryon-number-violating higgsino decays into top quarks and jets. The analysis significantly improves sensitivity to supersymmetric models and other processes beyond the Standard Model that may contribute to the considered final states.

 

Search for a new pseudoscalar a-boson decaying to muons in events with additional top quark pairs. 

A search for dark matter produced in association with a Higgs boson in final states with two hadronically decayingτ-leptons and missing transverse momentum is presented. The analysis uses 139 fb⁻¹of proton-proton collision data at$$ \sqrt{s} $$$\sqrt{s}$= 13 TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. No evidence of physics beyond the Standard Model is found. The results are interpreted in terms of a 2HDM+amodel featuring two scalar Higgs doublets and a pseudoscalar singlet field. Exclusion limits on the parameters of the model in selected benchmark scenarios are derived at 95% confidence level. Model-independent limits are also set on the visible cross-section for processes beyond the Standard Model producing missing transverse momentum in association with a Higgs boson decaying intoτ-leptons.