Examining the relationship between student performance and video interactions

In this work, we attempted to predict student performance on a suite of laboratory assessments using students' interactions with associated instructional videos. The students' performance is measured by a graded presentation for each of four laboratory presentations in an introductory mechanics course. Each lab assessment was associated with between one and three videos of instructional content. Using video clickstream data, we define summary features (number of pauses, seeks) and contextual information (fraction of time played, in-semester order). These features serve as inputs to a logistic regression (LR) model that aims to predict student performance on the laboratory assessments. Our findings show that LR models are unable to predict student performance. Adding contextual information did not change the model performance. We compare our findings to findings from other studies and explore caveats to the null-result such as representation of the features, the possibility of underfitting, and the complexity of the assessment.Comment: 4 pages, 1 figure, submitted to the PERC 2018 proceeding

The chronology of reindeer hunting on Norway's highest ice patches.

The melting of perennial ice patches globally is uncovering a fragile record of alpine activity, especially hunting and the use of mountain passes. When rescued by systematic fieldwork (glacial archaeology), this evidence opens an unprecedented window on the chronology of high-elevation activity. Recent research in Jotunheimen and surrounding mountain areas of Norway has recovered over 2000 finds-many associated with reindeer hunting (e.g. arrows). We report the radiocarbon dates of 153 objects and use a kernel density estimation (KDE) method to determine the distribution of dated events from ca 4000 BCE to the present. Interpreted in light of shifting environmental, preservation and socio-economic factors, these new data show counterintuitive trends in the intensity of reindeer hunting and other high-elevation activity. Cold temperatures may sometimes have kept humans from Norway's highest elevations, as expected based on accessibility, exposure and reindeer distributions. In times of increasing demand for mountain resources, however, activity probably continued in the face of adverse or variable climatic conditions. The use of KDE modelling makes it possible to observe this patterning without the spurious effects of noise introduced by the discrete nature of the finds and the radiocarbon calibration process

How can we inspire nations of learners? Investigating growth mindset and challenge-seeking in two countries

© American Psychological Association, 2020. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: https://doi.org/10.1037/amp0000647Here we evaluate the potential for growth mindset interventions (which teach students that intellectual abilities can be developed) to inspire adolescents to be “learners”—that is, to seek out challenging learning experiences. In a previous analysis, the U.S. National Study of Learning Mindsets (NSLM) showed that a growth mindset could improve the grades of lower-achieving adolescents, and, in an exploratory analysis, increase enrollment in advanced math courses across achievement levels. Yet the importance of being a “learner” in today’s global economy requires clarification and replication of potential challenge-seeking effects, as well as an investigation of the school affordances that make intervention effects on challenge-seeking possible. To this end, the present paper presents new analyses of the U.S. NSLM (N = 14,472) to (a) validate a standardized, behavioral measure of challenge-seeking (the “make-a-math worksheet” task), and (b) show that the growth mindset treatment increased challenge-seeking on this task. Second, a new experiment conducted with nearly all schools in two counties in Norway, the U-say experiment (N = 6,541), replicated the effects of the growth mindset intervention on the behavioral challenge-seeking task and on increased advanced math course-enrollment rates. Treated students took (and subsequently passed) advanced math at a higher rate. Critically, the U-say experiment provided the first direct evidence that a structural factor—school policies governing when and how students opt in to advanced math—can afford students the possibility of profiting from a growth mindset intervention or not. These results highlight the importance of motivational research that goes beyond grades or performance alone and focuses on challenge-seeking. The findings also call attention to the affordances of school contexts that interact with student motivation to promote better achievement and economic trajectories.acceptedVersio

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Latent Variable Machine Learning Algorithms: Applications in a Nuclear Physics Experiment

In this thesis, we introduce the application of convolutional autoencoder neural networks to the analysis of two-dimensional projections of particle tracks from a resonant proton scattering experiment on 46Ar. We also build on recent works applying pre-trained models from the image analysis community to this type of data. The data we analyze in this thesis was recorded by an active target time- projection chamber (AT-TPC). Machine learning presents an interesting avenue for researchers operating an AT-TPC, as traditional analysis methods of AT-TPC data are both computationally expensive and fit all particle tracks against the event type of interest. The latter presents a considerable challenge when the space of reactions is not known prior to the analysis. We explore the performance of the autoencoder neural networks and a pre-trained VGG16 [1] convolutional neural network on two tasks: a semi-supervised classification task and the unsupervised clustering of particle tracks. On the semi-supervised task, we find that a logistic regression classifier trained on small labelled subsets of the latent space of these models perform very well. On simulated data these classifiers achieve an f 1 score [2] of f1 > 0.95. The VGG16 latent classifier achieves this result with as few as N = 100 samples, as does the convolutional autoencoder when trained on the VGG16 representations of the particle tracks. On real data, pre-processed with noise filtering, the same models achieve an f1 > 0.7. For unfiltered real data the models achieve an f 1 > 0.6. Both of the previous results were found with the classifiers trained on N = 100 samples. Furthermore, we found that the autoencoder model reduces the variability in the identification of proton events by 64% from the benchmark logistic regression classifier trained on the VGG16 latent space on real experimental data. On the clustering task, we found that a K-means algorithm applied to the simulated data in the VGG16 latent space forms almost perfect clusters, with an adjusted rand index [3] (ARI) > 0.8. Additionally, the VGG16+K- means approach finds high purity clusters of proton events for real experimental data. We also explore the application of neural networks to cluster- ing by implementing a mixture of autoencoders algorithm. With this model we improved clustering performance on the real experimental data from an ARI = 0.17 to an ARI = 0.40. However, the neural network clustering suffers from stability issues necessitating further investigations into this approach

Brukbar klimakunnskap? Kommunalt ansattes forhold til forskning og annen kunnskap om klimaendringer og klimatilpasning

This paper is an analysis of how local government employees domesticate climate science for the purpose of climate adaptation. Employees in Norwegian municipalities perceive the consequences of climate change as a serious challenge, but while placing trust in climate science they consider it too difficult to use. The paper discusses how these employees perceive the challenges of appropriating climate science knowledge and putting it to use. It is found that technologies of bureaucracy, such as new and updated standards and regulations, are in demand as they are considered vital in enabling practical knowledge as well as political authorization of its credibility