Success and Satisfaction with Virtual Hematology Curriculum Implemented in Response to COVID-19

Introduction: In response to the COVID-19 pandemic, the Oregon Health and Science University Blood & Host Defense medical school pre-clinical block was reformatted to a completely online curriculum. In previous years, the curriculum consisted of traditional 1-hour lectures from Monday to Wednesday, with small group review sessions on Thursday prior to weekly assessments on Friday. Changes for the virtual curriculum included shortened, pre-recorded lectures divided into modules by topic, with follow-up questions to test comprehension in real-time. These were followed by live, 1-hour Q&A sessions each day. Weekly, 2-3-hour case-based review sessions were also held virtually in real-time. We aimed to study student performance in this new curriculum, and to learn about the satisfaction of both students and instructors with these changes. Methods: To measure performance, class testing averages across graded components were compared to previous years. To measure satisfaction, first-year medical students and course instructors were polled via anonymous, voluntary Qualtrics® surveys after course completion. Answers were given on a 5-point Likert scale. Students were also asked to answer four free-response questions. Results: Class testing averages were similar to previous years across all graded components of the curriculum. Following remediation, the pass rate for the course was 100%. Fifty eight out of 150 students completed the satisfaction survey, a response rate of 39%. Most students found pre-recorded lectures and weekly live review sessions "useful" or "very useful," but responses were more varied for daily Q&A sessions. Most students either "somewhat preferred" or "greatly preferred" the module-based format over hour-long lectures and indicated they would like a similar format in future virtual blocks. Themes from qualitative questions included a preference for virtual curriculum for its increased flexibility. A small subset of students described a preference for in-person lecture due to increased engagement. Thirteen out of 31 instructors completed the survey, for a response rate of 42%. Six of the respondents indicated that they would prefer the traditional version of the curriculum for the following year, while 5 selected the new virtual-only format. Twelve instructors completed Likert-scale questions comparing the two curriculums. There was no statistically significant difference in satisfaction with lecture format, time and effort to prepare lectures, amount of interaction with students, and overall teaching experience. However, there was a significant increase in dissatisfaction with the quality of student interaction and student engagement with the new virtual curriculum. Conclusions: Students successfully learned in the new, virtual curriculum as demonstrated by summative assessments. Trends that emerged from student feedback included a preference for module-based format over hour-long lectures, and pre-recorded lectures over live sessions. Most respondents enjoyed the weekly live review sessions, but were mixed regarding daily live Q&A sessions. We suspect this mixed feedback for the daily reviews was due to constraints on the schedule and the necessity of viewing all modules each morning prior to the session. From the perspective of instructors, there was perhaps unsurprisingly decreased satisfaction with student engagement in the virtual setting. However, overall, there was no meaningful difference in preference regarding lecture format. When combining this with the diverse needs and preferences of medical students, future versions of the course should consider incorporating more virtual elements. Disclosures No relevant conflicts of interest to declare.

Entanglement-Based Feature Extraction by Tensor Network Machine Learning

It is a hot topic how entanglement, a quantity from quantum information theory, can assist machine learning. In this work, we implement numerical experiments to classify patterns/images by representing the classifiers as matrix product states (MPS). We show how entanglement can interpret machine learning by characterizing the importance of data and propose a feature extraction algorithm. We show on the MNIST dataset that when reducing the number of the retained pixels to 1/10 of the original number, the decrease of the ten-class testing accuracy is only O (10–3), which significantly improves the efficiency of the MPS machine learning. Our work improves machine learning’s interpretability and efficiency under the MPS representation by using the properties of MPS representing entanglement.

IpDFT-Tuned Estimation Algorithms for PMUs: Overview and Performance Comparison

The Interpolated Discrete Fourier Transform (IpDFT) is one of the most popular algorithms for Phasor Measurement Units (PMUs), due to its quite low computational complexity and its good accuracy in various operating conditions. However, the basic IpDFT algorithm can be used also as a preliminary estimator of the amplitude, phase, frequency and rate of change of frequency of voltage or current AC waveforms at times synchronized to the Universal Coordinated Time (UTC). Indeed, another cascaded algorithm can be used to refine the waveform parameters estimation. In this context, the main novelty of this work is a fair and extensive performance comparison of three different state-of-the-art IpDFT-tuned estimation algorithms for PMUs. The three algorithms are: (i) the so-called corrected IpDFT (IpDFTc), which is conceived to compensate for the effect of both the image of the fundamental tone and second-order harmonic; (ii) a frequency-tuned version of the Taylor Weighted Least-Squares (TWLS) algorithm, and (iii) the frequency Down-Conversion and low-pass Filtering (DCF) technique described also in the IEEE/IEC Standard 60255-118-1:2018. The simulation results obtained in the P Class and M Class testing conditions specified in the same Standard show that the IpDFTc algorithm is generally preferable under the effect of steady-state disturbances. On the contrary, the tuned TWLS estimator is usually the best solution when dynamic changes of amplitude, phase or frequency occur. In transient conditions (i.e., under the effect of amplitude or phase steps), the IpDFTc and the tuned TWLS algorithms do not clearly outperform one another. The DCF approach generally returns the worst results. However, its actual performances heavily depend on the adopted low-pass filter.

Pengembangan Strategi Belajar Flipped Classroom Untuk Meningkatkan Hasil Belajar Kognitif Siswa Pada Pelajaran Biologi Kelas XI MIPA SMAK Santo Fransiskus Assisi Samarinda

The study was intended for: 1).to know the results of validation of Flipped Classroom learning strategy to improve the results of students' cognitive learning on XI grade in SMAK Santo Fransiskus Assisi Samarinda. 2). to know the effectiveness of the development of Flipped Classroom learning strategy to improve the results of students' cognitive learning on XI grade in SMAK Santo Fransiskus Assisi Samarinda. This development research refers to development measures with the model Thiagarajan & Semmel. The design of the development is grouped over 3 comprehensive development procedures: (a) define stages (definition) (b) design stages (design) and (c) development stages (development). The product's test consists of validation tests conducted by both study and linguistics specialists and product assessments conducted through the 2 stages the control class test to 31 students of XI grade and experimental class testing to 31 students of XI grade in SMAK Santo Fransiskus Assisi Samarinda. Data collection uses interview,observation,questionnaire for linguists and test text instruments. The study (1) resulted in Flipped Classroom learning device (2) the product produced effectively improves the cognitive value of students of XI grade in SMAK Santo Fransiskus Assisi Samarinda as a study result,shown that the pretest average of 52.48 increased in a post test of 56.38 to the control class while 53.83 increased at the post test level of 87.37. And testing t independent t test of 0.467 > 0.05,this proves that there is no significant difference between control class and experiment class during learning using Flipped Classroom to improve students' cognitive learning on Biology of XI grade in SMAK Santo Fransiskus Assisi Samarinda.

Zoning of agroecological potential areas for agricultural land assessment of the Saratov region

The standard yield model recommended for land assessment and certification of land according to the degree of suitability for agricultural use is based on the assessment of agro-ecological potential (AP) as characteristics of the territory 's water and thermal resources. The quality check of the model according to the data of the class testing areas for the period 2007-2018 showed an overestimation of the standard grain yield for the conditions of the Saratov region by 1.2-1.5 times. The error is due to the use of the most available agro climatic parameters in the model, which establish their connection with productivity at the interregional level, but do not meet the conditions of adequate differentiation of conditions of moisture supply of crops at the regional level. Correction of the AP model, when using the parameters of water and thermal resources provision by the coefficients of annual moistening and the sum of biologically active temperatures, is achieved by reducing the AP level by 1.2 times and fixing the standard value of the climate continental index - 187. A new version of estimated zoning is proposed based on the corrected model of AP, at which the number of agro climatic subzones is reduced from 9 to 6. The adequacy of the assessment of land productivity is ensured by the use of AP parameters that are individual for municipal areas rather than medium-sized ones.

TeaTime4App – Raising awareness about the role of soils with the educational “Tea Bag Index App”

<p>With the Tea Bag Index (TBI) App, we aim to foster awareness of the importance of soils and their ecosystem services to students above the age of 10. The TBI app consists of three categories of hands-on activities: Basic soil attributes, Soil observations, and Tea Bag Index. Basic soil attributes include land use, soil colour and soil life, whereas soil observations go further to Texture by Feel, Spade Test and observation of soil pollution. The Tea Bag Index (Keuskamp et al., 2013) provides an easy and scientifically recognized way to measure decomposition rates and stabilisation of organic matter in soils. The method consists of burying tea bags and measuring the degradation of organic material after three months’ time. Each of the methods includes clear instructions and extra information in the app. Data gathered are interactively shown on a map in the App as well as online. Hence, students are encouraged to gain hands-on science experience and to witness how science connects across regions, countries and cultures. By using playful tools such as rewards, badges and a point system, we attract and maintain the interest of students. Social media channels are used to exchange and share their results as well as to reach teachers and citizen scientists in order to inspire them to use the educational App.</p><p>Having this awareness on soil and its functions, citizen scientists can make valuable contributions to the sustainable use of soils. They also have the opportunity to participate in a global scientific initiative, acquire skills in conducting a scientific experiment and gain knowledge on soil functions. The science community, on the other hand, increases its understanding of factors influencing decomposition (and associated soil functions) at different times and in different places globally.</p><p>Moreover, the TBI App can be used for „Content Language Integrated Lessons“ (CLIL), which is the use of a foreign language for the integrative teaching of content and language competence outside of language teaching in agricultural schools in Austria. Individual learning outcomes (ILOs) of an agricultural school class testing the TBI App were evaluated in an online questionnaire. Results showed high appreciation of activities offered by the TBI App and high motivation of students to contribute to science.</p><p> </p><p> </p><p>Keuskamp, J.A., Dingemans, B.J.J., Lehtinen, T., Sarneel, J.M. and Hefting, M.M. (2013), Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Methods Ecol Evol, 4: 1070-1075. doi:10.1111/2041-210X.12097</p>

Exploiting Class Learnability in Noisy Data

In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged Web data. Noisy labels occur frequently in data sets harvested via these means, sometimes resulting in entire classes of data on which learned classifiers generalize poorly. For real world applications, we argue that it can be beneficial to avoid training on such classes entirely. In this work, we aim to explore the classes in a given data set, and guide supervised training to spend time on a class proportional to its learnability. By focusing the training process, we aim to improve model generalization on classes with a strong signal. To that end, we develop an online algorithm that works in conjunction with classifier and training algorithm, iteratively selecting training data for the classifier based on how well it appears to generalize on each class. Testing our approach on a variety of data sets, we show our algorithm learns to focus on classes for which the model has low generalization error relative to strong baselines, yielding a classifier with good performance on learnable classes.